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2023,32(11):3-10, DOI: 10.15888/j.cnki.csa.009330
Abstract:
The system emulator creates a virtual environment by emulating hardware resources such as processor, memory, and peripherals, which can support software running and debugging of different architectures and greatly shorten the cross-architecture software development cycle. The emulator usually supports instruction tracing and can be employed for analysis by recording the instruction sequence of program running, such as running time evaluation and behavior pattern analysis related to the program, and joint emulation of software and hardware. As the mainstream emulators supporting RISC-V architecture, both QEMU and Spike support instruction tracing. However, they are time- and space-expensive and inefficient when dealing with large-scale applications. Thus, this study proposes an instruction tracing technology with QEMU. When instructions are traced without distortion, static information such as basic blocks and control flow charts in the program is decoupled from branch selection and other dynamic information. Compared with the native instruction tracing implemented by QEMU, the proposed technology reduces the time overhead by more than 80% and the space overhead by more than 95%. Additionally, based on RISC-V architecture, this study realizes off-line analysis of instruction sequences in various scenarios, such as instruction classification statistics, program hotspot marking, and program behavior analysis.
The system emulator creates a virtual environment by emulating hardware resources such as processor, memory, and peripherals, which can support software running and debugging of different architectures and greatly shorten the cross-architecture software development cycle. The emulator usually supports instruction tracing and can be employed for analysis by recording the instruction sequence of program running, such as running time evaluation and behavior pattern analysis related to the program, and joint emulation of software and hardware. As the mainstream emulators supporting RISC-V architecture, both QEMU and Spike support instruction tracing. However, they are time- and space-expensive and inefficient when dealing with large-scale applications. Thus, this study proposes an instruction tracing technology with QEMU. When instructions are traced without distortion, static information such as basic blocks and control flow charts in the program is decoupled from branch selection and other dynamic information. Compared with the native instruction tracing implemented by QEMU, the proposed technology reduces the time overhead by more than 80% and the space overhead by more than 95%. Additionally, based on RISC-V architecture, this study realizes off-line analysis of instruction sequences in various scenarios, such as instruction classification statistics, program hotspot marking, and program behavior analysis.
2023,32(11):11-20, DOI: 10.15888/j.cnki.csa.009331
Abstract:
Traditional x86-based and software-based user-mode memory safety defenses can hardly be deployed in a production-ready environment due to significant runtime overheads. In recent years, as mainstream commercial processors begin to provide hardware security extensions and open-source architectures like RISC-V rise, hardware-assisted memory safety protections have become popular, and their implementations are based on various architectures, such as x86-64, ARM, and RISC-V. This study discusses user-mode memory safety defenses on the RISC-V architecture and compares the features of x86-64, ARM, and RISC-V in the context of security defense design. RISC-V has some advantages over other architectures due to its opening ecosystem, making the implementation of some low-cost and promising defense techniques possible.
Traditional x86-based and software-based user-mode memory safety defenses can hardly be deployed in a production-ready environment due to significant runtime overheads. In recent years, as mainstream commercial processors begin to provide hardware security extensions and open-source architectures like RISC-V rise, hardware-assisted memory safety protections have become popular, and their implementations are based on various architectures, such as x86-64, ARM, and RISC-V. This study discusses user-mode memory safety defenses on the RISC-V architecture and compares the features of x86-64, ARM, and RISC-V in the context of security defense design. RISC-V has some advantages over other architectures due to its opening ecosystem, making the implementation of some low-cost and promising defense techniques possible.
2023,32(11):21-28, DOI: 10.15888/j.cnki.csa.009333
Abstract:
RISC-V instruction set architecture (ISA) has promoted the rapid development of the RISC-V hardware platforms, leading to growing demands for efficient and easy-to-use operating systems running on RISC-V architecture. As a distributed open-source mobile operating system, OpenHarmony continues to evolve with constant prosperous ecology. However, adapting OpenHarmony to RISC-V ISA platforms poses new challenges, including software stack and chip porting. This study presents an approach and methodology for porting the OpenHarmony standard system to the RISC-V QEMU platform. Based on adapting critical software stack components and porting the graphics display driver on the QEMU RISC-V virtualization hardware platform, the OpenHarmony standard system successfully starts on the QEMU RISC-V virtualization hardware platform and enters the system desktop. This achievement provides developers with a platform to test and apply the OpenHarmony standard system on RISC-V platforms and serves as a reference for porting the OpenHarmony standard system to new RISC-V hardware platforms.
RISC-V instruction set architecture (ISA) has promoted the rapid development of the RISC-V hardware platforms, leading to growing demands for efficient and easy-to-use operating systems running on RISC-V architecture. As a distributed open-source mobile operating system, OpenHarmony continues to evolve with constant prosperous ecology. However, adapting OpenHarmony to RISC-V ISA platforms poses new challenges, including software stack and chip porting. This study presents an approach and methodology for porting the OpenHarmony standard system to the RISC-V QEMU platform. Based on adapting critical software stack components and porting the graphics display driver on the QEMU RISC-V virtualization hardware platform, the OpenHarmony standard system successfully starts on the QEMU RISC-V virtualization hardware platform and enters the system desktop. This achievement provides developers with a platform to test and apply the OpenHarmony standard system on RISC-V platforms and serves as a reference for porting the OpenHarmony standard system to new RISC-V hardware platforms.
2023,32(11):29-35, DOI: 10.15888/j.cnki.csa.009332
Abstract:
As a lightweight standard C library, musl libc features a small code base providing comprehensive POSIX interface support, and high portability and support for various architectures and operating systems. It is widely employed in embedded systems, Web servers, containers, and other fields. RISC-V instruction set is an open source instruction set that has released the relatively stable SIMD instruction set at present. Meanwhile, the RISC-V ecological software environment has ushered in a new optimization boom, but the RVV extension optimization of the musl libc library is still a research gap. Based on the collaborative research of the musl libc basic library and RISC-V RVV extended instruction set, this study proposes an implementation scheme compatible with the basic instruction set and vector extended instruction set. The common C library functions strlen and memset are optimized by the vector extended instruction set, and comparative analysis is carried out on gem5 simulator. The experimental results show that compared with the implementation of C language, the performance of strlen function optimized by RVV is improved by 83%–703% on average, and that of memset function is improved by 85%–334% on average.
As a lightweight standard C library, musl libc features a small code base providing comprehensive POSIX interface support, and high portability and support for various architectures and operating systems. It is widely employed in embedded systems, Web servers, containers, and other fields. RISC-V instruction set is an open source instruction set that has released the relatively stable SIMD instruction set at present. Meanwhile, the RISC-V ecological software environment has ushered in a new optimization boom, but the RVV extension optimization of the musl libc library is still a research gap. Based on the collaborative research of the musl libc basic library and RISC-V RVV extended instruction set, this study proposes an implementation scheme compatible with the basic instruction set and vector extended instruction set. The common C library functions strlen and memset are optimized by the vector extended instruction set, and comparative analysis is carried out on gem5 simulator. The experimental results show that compared with the implementation of C language, the performance of strlen function optimized by RVV is improved by 83%–703% on average, and that of memset function is improved by 85%–334% on average.
2023,32(11):36-47, DOI: 10.15888/j.cnki.csa.009269
Abstract:
Deep learning models require certain interpretability in practical applications in certain scenarios, and vision is a basic tool for humans to understand the surrounding world. Visualization technology can transform the model training process from an invisible black box to an interactive and analyzable visual process, effectively improving the credibility and interpretability of the model. At present, there is a lack of review on deep learning model visualization tools in related fields, as well as a lack of research on the actual needs of different users and the evaluation of user experience. Therefore, this study summarizes the current situation of the application of visualization tools in different fields by investigating the literature related to interpretability and visualization in recent years. It proposes a classification method and basis for target user-oriented visualization tools and introduces and compares each type of tool from the aspects of visualization content, computational cost, etc., so that different users can select and deploy suitable tools. Finally, on this basis, the problems in the field of visualization are discussed and its prospects are provided.
Deep learning models require certain interpretability in practical applications in certain scenarios, and vision is a basic tool for humans to understand the surrounding world. Visualization technology can transform the model training process from an invisible black box to an interactive and analyzable visual process, effectively improving the credibility and interpretability of the model. At present, there is a lack of review on deep learning model visualization tools in related fields, as well as a lack of research on the actual needs of different users and the evaluation of user experience. Therefore, this study summarizes the current situation of the application of visualization tools in different fields by investigating the literature related to interpretability and visualization in recent years. It proposes a classification method and basis for target user-oriented visualization tools and introduces and compares each type of tool from the aspects of visualization content, computational cost, etc., so that different users can select and deploy suitable tools. Finally, on this basis, the problems in the field of visualization are discussed and its prospects are provided.
2023,32(11):48-61, DOI: 10.15888/j.cnki.csa.009306
Abstract:
At present, the yak breeding method in the Qinghai-Tibet Plateau region of China is mainly based on traditional manual grazing. To solve the problem that human breeding methods cannot quickly track and count the number of yaks, an improved YOLOv5 and Bytetrack yak tracking method is proposed in this study to achieve the fast detection and tracking of yaks under video input. The YOLOv5 object detection network based on deep learning, combined with optimization methods such as coordinate attention, cross-scale feature fusion, and atrous spatial pyramid pooling pyramid, is adopted to reduce the difficulty of detection and misdetection caused by occlusion in yak detection, so as to accurately detect yak targets in videos. The Bytetrack tracker is used to implement the inter-frame object association through Kalman filtering and Hungarian algorithm, and the IDs are matched to the targets. The model is trained by using part of the yak data in ImageNet Dataset and yak sample images collected from the Yushu region of Qinghai. The experimental results show that the average detection accuracy of the improved model proposed in this study is 98.7%, which is 1.1, 1.89, 8.33, and 0.4 percentage points higher than the original YOLOv5s, SSD, YOLOX, and Faster RCNN models, respectively. It can converge quickly and has the best detection performance. The improved YOLOv5s and Bytetrack tracking results are the best, with MOTA increased by7.1646 %. The improved model developed in this study can detect and track yaks more quickly and accurately, providing technical support for the intelligent development of animal husbandry in the Qinghai region.
At present, the yak breeding method in the Qinghai-Tibet Plateau region of China is mainly based on traditional manual grazing. To solve the problem that human breeding methods cannot quickly track and count the number of yaks, an improved YOLOv5 and Bytetrack yak tracking method is proposed in this study to achieve the fast detection and tracking of yaks under video input. The YOLOv5 object detection network based on deep learning, combined with optimization methods such as coordinate attention, cross-scale feature fusion, and atrous spatial pyramid pooling pyramid, is adopted to reduce the difficulty of detection and misdetection caused by occlusion in yak detection, so as to accurately detect yak targets in videos. The Bytetrack tracker is used to implement the inter-frame object association through Kalman filtering and Hungarian algorithm, and the IDs are matched to the targets. The model is trained by using part of the yak data in ImageNet Dataset and yak sample images collected from the Yushu region of Qinghai. The experimental results show that the average detection accuracy of the improved model proposed in this study is 98.7%, which is 1.1, 1.89, 8.33, and 0.4 percentage points higher than the original YOLOv5s, SSD, YOLOX, and Faster RCNN models, respectively. It can converge quickly and has the best detection performance. The improved YOLOv5s and Bytetrack tracking results are the best, with MOTA increased by
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Available online: November 28, 2023 , DOI: 10.15888/j.cnki.csa.009341
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Due to the disorder and lack of topological information, the classification and segmentation of 3D point clouds is still challenging. To this end, this study designs a 3D point cloud classification algorithm based on the self-attention mechanism to learn point cloud feature information for object classification and segmentation. Firstly, a self-attention module suitable for point clouds is designed for feature extraction. A neighborhood graph is constructed to enhance the input embedding, and the local features are extracted and aggregated by utilizing the self-attention mechanism. Finally, the local features are combined via multi-layer perceptron and encoder-decoder approaches to achieve 3D point cloud classification and segmentation. This method considers the local context information of individual points in the point cloud during input embedding, constructs a network structure under local long distances, and ultimately yields more distinctive results. Experiments on datasets such as ShapeNetPart and RoofN3D demonstrate that the proposed method performs better in classification and segmentation.
Due to the disorder and lack of topological information, the classification and segmentation of 3D point clouds is still challenging. To this end, this study designs a 3D point cloud classification algorithm based on the self-attention mechanism to learn point cloud feature information for object classification and segmentation. Firstly, a self-attention module suitable for point clouds is designed for feature extraction. A neighborhood graph is constructed to enhance the input embedding, and the local features are extracted and aggregated by utilizing the self-attention mechanism. Finally, the local features are combined via multi-layer perceptron and encoder-decoder approaches to achieve 3D point cloud classification and segmentation. This method considers the local context information of individual points in the point cloud during input embedding, constructs a network structure under local long distances, and ultimately yields more distinctive results. Experiments on datasets such as ShapeNetPart and RoofN3D demonstrate that the proposed method performs better in classification and segmentation.
Available online: November 28, 2023 , DOI: 10.15888/j.cnki.csa.009357
Abstract:
In the context of complex structures and blurred cell boundaries in microscopic breast cancer histopathological images, traditional threshold-based segmentation faces challenges in accurately separating lesion areas of breast cancer images. To address this issue, this study proposes a multi-threshold segmentation method for breast cancer images based on the improved dandelion optimization algorithm (IDO). This method introduces the IDO to calculate the maximum inter-class variance (Otsu) as the objective function for finding the optimal thresholds. The IDO incorporates a defensive strategy to address the issue of unbounded search in the traditional dandelion optimization algorithm (DO) that extends beyond pixel ranges. Additionally, opposition-based learning (OBL) is introduced to prevent the algorithm from getting trapped in local optima. The experimental results indicate that compared with the Harris Hawks optimization (HHO), gorilla troop optimization (GTO), traditional DO, and marine predators algorithm (MPA), the IDO algorithm achieves the highest fitness value and fastest convergence under the same number of threshold levels. Moreover, it outperforms other comparative algorithms in terms of peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) , and feature similarity index (FSIM).
In the context of complex structures and blurred cell boundaries in microscopic breast cancer histopathological images, traditional threshold-based segmentation faces challenges in accurately separating lesion areas of breast cancer images. To address this issue, this study proposes a multi-threshold segmentation method for breast cancer images based on the improved dandelion optimization algorithm (IDO). This method introduces the IDO to calculate the maximum inter-class variance (Otsu) as the objective function for finding the optimal thresholds. The IDO incorporates a defensive strategy to address the issue of unbounded search in the traditional dandelion optimization algorithm (DO) that extends beyond pixel ranges. Additionally, opposition-based learning (OBL) is introduced to prevent the algorithm from getting trapped in local optima. The experimental results indicate that compared with the Harris Hawks optimization (HHO), gorilla troop optimization (GTO), traditional DO, and marine predators algorithm (MPA), the IDO algorithm achieves the highest fitness value and fastest convergence under the same number of threshold levels. Moreover, it outperforms other comparative algorithms in terms of peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) , and feature similarity index (FSIM).
Available online: November 28, 2023 , DOI: 10.15888/j.cnki.csa.009366
Abstract:
Traffic data loss is common in network systems and is usually caused by sensor failure, transmission errors, and storage loss. The existing data repair methods cannot learn the multi-dimensional characteristics of traffic data. Therefore, this study proposes a dual-channel parallel architecture that combines bidirectional long short-term memory (LSTM) networks with multi-scale convolutional networks (ST-MFCN) for filling the missing values in traffic data. Meanwhile, a novel adversarial loss function is designed to further improve the prediction accuracy, which allows the model to effectively learn the temporal and dynamic spatial features of traffic data. Additionally, the model is tested on the Web traffic time series dataset and compared with the existing repair methods. Experimental results demonstrate that ST-MFCN can reduce data recovery errors and improve data repair accuracy, providing a robust and efficient solution for traffic data repair in network systems.
Traffic data loss is common in network systems and is usually caused by sensor failure, transmission errors, and storage loss. The existing data repair methods cannot learn the multi-dimensional characteristics of traffic data. Therefore, this study proposes a dual-channel parallel architecture that combines bidirectional long short-term memory (LSTM) networks with multi-scale convolutional networks (ST-MFCN) for filling the missing values in traffic data. Meanwhile, a novel adversarial loss function is designed to further improve the prediction accuracy, which allows the model to effectively learn the temporal and dynamic spatial features of traffic data. Additionally, the model is tested on the Web traffic time series dataset and compared with the existing repair methods. Experimental results demonstrate that ST-MFCN can reduce data recovery errors and improve data repair accuracy, providing a robust and efficient solution for traffic data repair in network systems.
Available online: November 28, 2023 , DOI: 10.15888/j.cnki.csa.009373
Abstract:
Aiming at insufficient user fairness in UAV-assisted mobile edge computing systems, this study proposes a user fairness-oriented 3D deployment and unloading optimization algorithm. The algorithm comprehensively considers the effects of user matching, 3D UAV deployment, computing resource allocation, and unloading factors on the total system delay and user fairness. Meanwhile, a multivariate optimization problem is established to minimize the total system delay, and a two-stage joint optimization algorithm is put forward for this problem. In the first stage, a clustering algorithm with balanced constraints is adopted to solve the problem of user matching and horizontal UAV deployment. In the second stage, the convex optimization algorithm is utilized to iteratively solve the UAV altitude deployment, resource allocation, and optimization problems of unloading factors. The experimental results show that the proposed algorithm has better performance than the four benchmark algorithms in both total system latency and user fairness.
Aiming at insufficient user fairness in UAV-assisted mobile edge computing systems, this study proposes a user fairness-oriented 3D deployment and unloading optimization algorithm. The algorithm comprehensively considers the effects of user matching, 3D UAV deployment, computing resource allocation, and unloading factors on the total system delay and user fairness. Meanwhile, a multivariate optimization problem is established to minimize the total system delay, and a two-stage joint optimization algorithm is put forward for this problem. In the first stage, a clustering algorithm with balanced constraints is adopted to solve the problem of user matching and horizontal UAV deployment. In the second stage, the convex optimization algorithm is utilized to iteratively solve the UAV altitude deployment, resource allocation, and optimization problems of unloading factors. The experimental results show that the proposed algorithm has better performance than the four benchmark algorithms in both total system latency and user fairness.
Available online: November 28, 2023 , DOI: 10.15888/j.cnki.csa.009374
Abstract:
This study designs the dynamic fully connected layer (DyFC) to enhance the feature fusion, which redefines the weights and biases by adopting base vectors to represent the new weights and biases. The coefficients of the base vectors are learned based on each input feature, and the weights and biases are no longer shared but unique, which provides more directional expressiveness for each feature. In this study, a dual-stream mapping architecture model IUINet is proposed. IUINet combines the 3DShift operation and spatial separable convolution to achieve medical image segmentation tasks and maintain a balance between accuracy and efficiency. The proposed IUINet follows an encoder-decoder structure, where the encoder consists of two parts. One part includes the Shift operation and pointwise Conv1×1 operation, and the other part incorporates spatial separable convolution operation. IUINet utilizes multi-scale inputs and multi-scale feature mapping layers to improve the backpropagation speed and reduce the average backpropagation distance. Finally, this enhances the model accuracy, improves generalization ability, and reduces overfitting.
This study designs the dynamic fully connected layer (DyFC) to enhance the feature fusion, which redefines the weights and biases by adopting base vectors to represent the new weights and biases. The coefficients of the base vectors are learned based on each input feature, and the weights and biases are no longer shared but unique, which provides more directional expressiveness for each feature. In this study, a dual-stream mapping architecture model IUINet is proposed. IUINet combines the 3DShift operation and spatial separable convolution to achieve medical image segmentation tasks and maintain a balance between accuracy and efficiency. The proposed IUINet follows an encoder-decoder structure, where the encoder consists of two parts. One part includes the Shift operation and pointwise Conv1×1 operation, and the other part incorporates spatial separable convolution operation. IUINet utilizes multi-scale inputs and multi-scale feature mapping layers to improve the backpropagation speed and reduce the average backpropagation distance. Finally, this enhances the model accuracy, improves generalization ability, and reduces overfitting.
Available online: November 28, 2023 , DOI: 10.15888/j.cnki.csa.009375
Abstract:
During petroleum exploration, core particles are effective data for studying geological sequence, evaluating oil and gas contents, and understanding geological structures. The extraction of core particle images is conducive to the further analysis of geological researchers. The core particle images usually have blurred particle edges, and complex backgrounds and particle colors. To improve the extraction effect of core particles, this study designs a core image particle extraction algorithm based on the improved UNet3+. This algorithm adds the receptive field module (RFB) after each coding layer of UNet3+ to expand the receptive field of the network, thus solving the low segmentation accuracy caused by the limited receptive field of the network. Meanwhile, the convolutional block attention module (CBAM) is embedded after the RFB module to make the network focus on the target region more accurately and improve the feature weight of the target region. The experimental results show that compared with the original UNet3+ network, the improved algorithm yields a good segmentation effect on the core particle images, improving mIoU, mPA, and FWIoU by 5.43%, 2.99%, and 5.34%, respectively.
During petroleum exploration, core particles are effective data for studying geological sequence, evaluating oil and gas contents, and understanding geological structures. The extraction of core particle images is conducive to the further analysis of geological researchers. The core particle images usually have blurred particle edges, and complex backgrounds and particle colors. To improve the extraction effect of core particles, this study designs a core image particle extraction algorithm based on the improved UNet3+. This algorithm adds the receptive field module (RFB) after each coding layer of UNet3+ to expand the receptive field of the network, thus solving the low segmentation accuracy caused by the limited receptive field of the network. Meanwhile, the convolutional block attention module (CBAM) is embedded after the RFB module to make the network focus on the target region more accurately and improve the feature weight of the target region. The experimental results show that compared with the original UNet3+ network, the improved algorithm yields a good segmentation effect on the core particle images, improving mIoU, mPA, and FWIoU by 5.43%, 2.99%, and 5.34%, respectively.
Available online: November 28, 2023 , DOI: 10.15888/j.cnki.csa.009381
Abstract:
Water pollution seriously affects the water landscape and water ecology. In this study, a deep-wise convolution and cross attention (DCCA) algorithm module is proposed to address the issues of complex water surface scenes and difficulty in extracting features of small target pollutants in the process of identifying water surface pollution. The use of deep-wise convolution reduces the parameters and computational complexity of the model, and establishes relationships between feature maps at different scales using cross attention, enabling the model to better understand contextual information and improve its ability to recognize complex scenes and small targets. The experimental results show that the average accuracy has been improved by 1.8% after adding the DCCA module, reaching 88.7%. The detection effect of water surface pollution has been improved by using less memory occupation.
Water pollution seriously affects the water landscape and water ecology. In this study, a deep-wise convolution and cross attention (DCCA) algorithm module is proposed to address the issues of complex water surface scenes and difficulty in extracting features of small target pollutants in the process of identifying water surface pollution. The use of deep-wise convolution reduces the parameters and computational complexity of the model, and establishes relationships between feature maps at different scales using cross attention, enabling the model to better understand contextual information and improve its ability to recognize complex scenes and small targets. The experimental results show that the average accuracy has been improved by 1.8% after adding the DCCA module, reaching 88.7%. The detection effect of water surface pollution has been improved by using less memory occupation.
Available online: November 28, 2023 , DOI: 10.15888/j.cnki.csa.009382
Abstract:
High-speed rail (HSR) has gradually become a popular travel option, and passengers have high demand for streaming media services during HSR travel. However, in high-speed mobile scenarios, user bandwidth jitter is severe, and user media experience cannot be guaranteed. To this end, a cross-layer optimization method for adaptive cloud collaborative transmission of streaming media, based on DASH protocol, is proposed in this study. Firstly, a cross-layer architecture for adaptive cloud cooperative transmission of streaming media, based on DASH protocol, is proposed, and a QoE model for users in high-speed rail environment is suggested. Next, on this basis, a cross-layer optimization model for adaptive cloud collaborative transmission of streaming media, based on DASH protocol, is constructed, and a cross-layer adaptive bitrate selection algorithm for cloud collaborative transmission of streaming media, based on DASH protocol, is proposed to improve the user’s media experience. Finally, the simulation experiment results show that the method proposed in this study can greatly improve the media experience of HSR passengers, and is helpful for the optimization study of the transmission of streaming media in high-speed mobile scenarios.
High-speed rail (HSR) has gradually become a popular travel option, and passengers have high demand for streaming media services during HSR travel. However, in high-speed mobile scenarios, user bandwidth jitter is severe, and user media experience cannot be guaranteed. To this end, a cross-layer optimization method for adaptive cloud collaborative transmission of streaming media, based on DASH protocol, is proposed in this study. Firstly, a cross-layer architecture for adaptive cloud cooperative transmission of streaming media, based on DASH protocol, is proposed, and a QoE model for users in high-speed rail environment is suggested. Next, on this basis, a cross-layer optimization model for adaptive cloud collaborative transmission of streaming media, based on DASH protocol, is constructed, and a cross-layer adaptive bitrate selection algorithm for cloud collaborative transmission of streaming media, based on DASH protocol, is proposed to improve the user’s media experience. Finally, the simulation experiment results show that the method proposed in this study can greatly improve the media experience of HSR passengers, and is helpful for the optimization study of the transmission of streaming media in high-speed mobile scenarios.
Available online: November 28, 2023 , DOI: 10.15888/j.cnki.csa.009383
Abstract:
To address the problem that existing knowledge graph-based recommendation models only perform feature extraction from one end of users or items, missing the feature extraction from the other end, a bipartite knowledge-aware graph convolution recommendation model based on knowledge graph is proposed. First, the initial feature representation is obtained by random initialization characterization of users, items and entities in the knowledge graph; then, a user and item-based knowledge-aware attention mechanism is used to simultaneously extract features from both users and items in the knowledge graph; next, a graph convolutional network is used to aggregate feature information in the knowledge graph propagation process using different aggregation methods and predict the click-through rate; finally, the effectiveness of the model is verified by comparing it with four baseline models on two publicly available datasets, Last.FM and Book-Crossing. On the Last.FM dataset, AUC and F1 improve by 4.4% and 3.8% respectively, and ACC improves by 1.1%, compared with the optimal baseline model. On the Book-Crossing dataset, AUC and F1 improve by 1.5% and 2.2% respectively, and ACC improves by 1.4% . The experimental results show that the model in this study has better robustness than other baseline models in AUC, F1 and ACC metrics.
To address the problem that existing knowledge graph-based recommendation models only perform feature extraction from one end of users or items, missing the feature extraction from the other end, a bipartite knowledge-aware graph convolution recommendation model based on knowledge graph is proposed. First, the initial feature representation is obtained by random initialization characterization of users, items and entities in the knowledge graph; then, a user and item-based knowledge-aware attention mechanism is used to simultaneously extract features from both users and items in the knowledge graph; next, a graph convolutional network is used to aggregate feature information in the knowledge graph propagation process using different aggregation methods and predict the click-through rate; finally, the effectiveness of the model is verified by comparing it with four baseline models on two publicly available datasets, Last.FM and Book-Crossing. On the Last.FM dataset, AUC and F1 improve by 4.4% and 3.8% respectively, and ACC improves by 1.1%, compared with the optimal baseline model. On the Book-Crossing dataset, AUC and F1 improve by 1.5% and 2.2% respectively, and ACC improves by 1.4% . The experimental results show that the model in this study has better robustness than other baseline models in AUC, F1 and ACC metrics.
Available online: November 28, 2023 , DOI: 10.15888/j.cnki.csa.009371
Abstract:
In the Transformer model, the convolutional vision Transformer (CvT) has caught attention for its ability to extract both local and global features from images simultaneously. However, for abdominal organ segmentation tasks, the blurry object boundaries in CNN models should be addressed. Thus, this study proposes a novel dual-branch closed-loop segmentation model DBLNet based on CvT and CNN. The model employs explicit supervision of segmented contours using shape priors and predicted results to guide the network learning. The DBLNet model includes contour extraction encoding module (CEE), boundary shape segmentation network (BSSN), and closed-loop structure. The CEE module first utilizes modified 3D CvT and 3D gated convolutional layers (GCL) to capture multi-level contour features and assist in BSSN training. The BSSN module contains a shape feature fusion (SFF) module that captures both the object region and contour features to promote CEE training convergence. The closed-loop structure allows mutual feedback of segmentation results between the dual branches, assisting each other’s training. Experimental evaluations on the BTCV benchmark show that DBLNet achieves an average Dice score of 0.878, ranking 13th. Application tests on clinical hospital data demonstrate the strong performance of the proposed model.
In the Transformer model, the convolutional vision Transformer (CvT) has caught attention for its ability to extract both local and global features from images simultaneously. However, for abdominal organ segmentation tasks, the blurry object boundaries in CNN models should be addressed. Thus, this study proposes a novel dual-branch closed-loop segmentation model DBLNet based on CvT and CNN. The model employs explicit supervision of segmented contours using shape priors and predicted results to guide the network learning. The DBLNet model includes contour extraction encoding module (CEE), boundary shape segmentation network (BSSN), and closed-loop structure. The CEE module first utilizes modified 3D CvT and 3D gated convolutional layers (GCL) to capture multi-level contour features and assist in BSSN training. The BSSN module contains a shape feature fusion (SFF) module that captures both the object region and contour features to promote CEE training convergence. The closed-loop structure allows mutual feedback of segmentation results between the dual branches, assisting each other’s training. Experimental evaluations on the BTCV benchmark show that DBLNet achieves an average Dice score of 0.878, ranking 13th. Application tests on clinical hospital data demonstrate the strong performance of the proposed model.
Available online: November 28, 2023 , DOI: 10.15888/j.cnki.csa.009377
Abstract:
With the continuous evolution of computer technology, process simulation is becoming increasingly widely employed in various industries and utilizes simulation models to mimic business process behavior. Additionally, it can be adopted to predict and optimize system performance, assess the impact of decisions, provide a decision-making basis for managers, and reduce the experimental cost and time. Currently, how to efficiently develop a simulation model that can be trusted has caught widespread attention. This study traces, summarizes, and analyzes the relevant references on methods for building business process simulation models. Meanwhile, the processes, advantages, disadvantages, and progress of process model-based, system dynamics-based, and deep learning-based simulation modeling approaches are presented. Finally, the challenges and future directions of process simulation are discussed to provide references for future research in this field.
With the continuous evolution of computer technology, process simulation is becoming increasingly widely employed in various industries and utilizes simulation models to mimic business process behavior. Additionally, it can be adopted to predict and optimize system performance, assess the impact of decisions, provide a decision-making basis for managers, and reduce the experimental cost and time. Currently, how to efficiently develop a simulation model that can be trusted has caught widespread attention. This study traces, summarizes, and analyzes the relevant references on methods for building business process simulation models. Meanwhile, the processes, advantages, disadvantages, and progress of process model-based, system dynamics-based, and deep learning-based simulation modeling approaches are presented. Finally, the challenges and future directions of process simulation are discussed to provide references for future research in this field.
LUO Min, GAO Jun-Tao, YAN Ting, CHEN Qing-Yu, ZHANG Yan-Yan, ZHAO Wei-Yu, REN Yu-Tao, CHENG Yuan-Zhi, LIN Cheng-Zhang, WU Tao, ZHOU Qi-Zhao, CHEN Xi, MENG Fan-Lin, HE Xiao-Xi, LIU Ying-Hu, LI Jia-Ru, ZHU Qun, WANG Hao, XIONG Shu-Hua, HE Hai-Bo, WU Xiao-Hong, TENG Qi-Zhi, JIANG Kun, MA Han-Da, HU Zhi-Peng, WANG Ning, YANG Zhi-Bin, ZHU Geng-Xin, LIU Hao, WANG Zheng-Hong, WANG Dan, HU Rong-Jun
Available online: November 28, 2023 , DOI: 10.15888/j.cnki.csa.009377
Abstract:
With the continuous evolution of computer technology, process simulation is becoming increasingly widely employed in various industries and utilizes simulation models to mimic business process behavior. Additionally, it can be adopted to predict and optimize system performance, assess the impact of decisions, provide a decision-making basis for managers, and reduce the experimental cost and time. Currently, how to efficiently develop a simulation model that can be trusted has caught widespread attention. This study traces, summarizes, and analyzes the relevant references on methods for building business process simulation models. Meanwhile, the processes, advantages, disadvantages, and progress of process model-based, system dynamics-based, and deep learning-based simulation modeling approaches are presented. Finally, the challenges and future directions of process simulation are discussed to provide references for future research in this field.
With the continuous evolution of computer technology, process simulation is becoming increasingly widely employed in various industries and utilizes simulation models to mimic business process behavior. Additionally, it can be adopted to predict and optimize system performance, assess the impact of decisions, provide a decision-making basis for managers, and reduce the experimental cost and time. Currently, how to efficiently develop a simulation model that can be trusted has caught widespread attention. This study traces, summarizes, and analyzes the relevant references on methods for building business process simulation models. Meanwhile, the processes, advantages, disadvantages, and progress of process model-based, system dynamics-based, and deep learning-based simulation modeling approaches are presented. Finally, the challenges and future directions of process simulation are discussed to provide references for future research in this field.
Available online: November 24, 2023 , DOI: 10.15888/j.cnki.csa.009367
Abstract:
Instance segmentation of 3D point clouds is a critical preprocessing step in industrial automation. However, there are often many occlusions in industrial grasping scenarios, which makes it difficult for instance segmentation networks of 3D point clouds to distinguish between similar objects. To this end, this study proposes an improved algorithm based on FPCC. This algorithm has two branches, including a center point branch for inferring the center points of instances and an embedded feature branch for describing point features. The segmentation results are obtained by clustering algorithms. The feature enhancement (FEH) module plays a crucial role in improving the accuracy of center point prediction. This module employs FEH methods to improve the prediction accuracy and further modifies the loss function for center point prediction. Experimental results show that compared with the FPCC algorithm, the improved algorithm increases the Precision and Recall values by 10% and 15% respectively.
Instance segmentation of 3D point clouds is a critical preprocessing step in industrial automation. However, there are often many occlusions in industrial grasping scenarios, which makes it difficult for instance segmentation networks of 3D point clouds to distinguish between similar objects. To this end, this study proposes an improved algorithm based on FPCC. This algorithm has two branches, including a center point branch for inferring the center points of instances and an embedded feature branch for describing point features. The segmentation results are obtained by clustering algorithms. The feature enhancement (FEH) module plays a crucial role in improving the accuracy of center point prediction. This module employs FEH methods to improve the prediction accuracy and further modifies the loss function for center point prediction. Experimental results show that compared with the FPCC algorithm, the improved algorithm increases the Precision and Recall values by 10% and 15% respectively.
Available online: November 24, 2023 , DOI: 10.15888/j.cnki.csa.009372
Abstract:
This study aims to meet the requirements of member working hours and efficiency analysis, and reasonable task allocation assessment in scientific research management of labs. It studies a multi-mode analysis system of research efficiency in labs named MASRE based on camera videos, attendance machines, and Web systems. Meanwhile, the system can motivate researchers to invest more time in academic studies by comparing and presenting actual work time, invalid work hours caused by phone abuse, and the research efficiency of researchers. Additionally, according to the research efficiency trends calculated by the system, the lab leaders can analyze whether the research tasks are allocated reasonably or not, and the researchers can explore the factors influencing their efficiency. The MASRE system comprises two core modules of the Web system module and the AI analysis module. The Web system module is responsible for work hours and efficiency statistics, and the AI analysis module supports the automatic identification of invalid work hours. The system is implemented by PyTorch, VUE 3, and MySQL. The work hour and efficiency analysis developed by this system and written by its research report are taken as an example to conduct experimental analysis. The results show that the MASRE system can identify invalid work hours and perform work hour statistics and efficiency analysis. Meanwhile, the system MASRE is now available at https://icnc-fskd.fzu.edu.cn/htower/, and research labs can apply for free use.
This study aims to meet the requirements of member working hours and efficiency analysis, and reasonable task allocation assessment in scientific research management of labs. It studies a multi-mode analysis system of research efficiency in labs named MASRE based on camera videos, attendance machines, and Web systems. Meanwhile, the system can motivate researchers to invest more time in academic studies by comparing and presenting actual work time, invalid work hours caused by phone abuse, and the research efficiency of researchers. Additionally, according to the research efficiency trends calculated by the system, the lab leaders can analyze whether the research tasks are allocated reasonably or not, and the researchers can explore the factors influencing their efficiency. The MASRE system comprises two core modules of the Web system module and the AI analysis module. The Web system module is responsible for work hours and efficiency statistics, and the AI analysis module supports the automatic identification of invalid work hours. The system is implemented by PyTorch, VUE 3, and MySQL. The work hour and efficiency analysis developed by this system and written by its research report are taken as an example to conduct experimental analysis. The results show that the MASRE system can identify invalid work hours and perform work hour statistics and efficiency analysis. Meanwhile, the system MASRE is now available at https://icnc-fskd.fzu.edu.cn/htower/, and research labs can apply for free use.
Available online: November 24, 2023 , DOI: 10.15888/j.cnki.csa.009358
Abstract:
This study proposes a cross feature fusion and RASPP-driven scene segmentation method to address the edge segmentation errors and feature discontinuity caused by target diversity and scale inconsistency in the scenes. This method combines the multi-scale features output by the encoder in the way of cross feature fusion and employs the compound convolution attention module to process high-level semantic information fusion. As a result, this avoids the feature information loss caused by the upsampling operation and the influence of noise and refines the segmentation effect of target edges. Meanwhile, this study proposes a depthwise separable convolution combining residual connections. Based on this, a pyramid pooling module RASPP combining residuals is designed and implemented to process the features after cross fusion, obtain contextual information at different scales, and enhance feature semantic expression. Finally, the features processed by the RASPP module are merged to improve the segmentation effect. The experimental results on the Cityscapes and CamVid datasets show that the proposed method outperforms existing methods and has better segmentation performance on target edges in the scenes.
This study proposes a cross feature fusion and RASPP-driven scene segmentation method to address the edge segmentation errors and feature discontinuity caused by target diversity and scale inconsistency in the scenes. This method combines the multi-scale features output by the encoder in the way of cross feature fusion and employs the compound convolution attention module to process high-level semantic information fusion. As a result, this avoids the feature information loss caused by the upsampling operation and the influence of noise and refines the segmentation effect of target edges. Meanwhile, this study proposes a depthwise separable convolution combining residual connections. Based on this, a pyramid pooling module RASPP combining residuals is designed and implemented to process the features after cross fusion, obtain contextual information at different scales, and enhance feature semantic expression. Finally, the features processed by the RASPP module are merged to improve the segmentation effect. The experimental results on the Cityscapes and CamVid datasets show that the proposed method outperforms existing methods and has better segmentation performance on target edges in the scenes.
Available online: November 24, 2023 , DOI: 10.15888/j.cnki.csa.009360
Abstract:
Community detection for directed networks is an important topic in network science. Thus, this study proposes a semi-supervised community detection algorithm for directed networks based on non-negative matrix factorization (NMF). First, prior information is adopted to reconstruct the adjacency matrix and then penalize the community membership of nodes. Meanwhile, the influence of node degree heterogeneity is eliminated by row normalization, and finally, the objective function is solved using alternating iterative updates. Comparative experiments on real network datasets demonstrate the effectiveness of the proposed algorithm. Compared to existing NMF-based methods, this method can significantly improve community detection accuracy.
Community detection for directed networks is an important topic in network science. Thus, this study proposes a semi-supervised community detection algorithm for directed networks based on non-negative matrix factorization (NMF). First, prior information is adopted to reconstruct the adjacency matrix and then penalize the community membership of nodes. Meanwhile, the influence of node degree heterogeneity is eliminated by row normalization, and finally, the objective function is solved using alternating iterative updates. Comparative experiments on real network datasets demonstrate the effectiveness of the proposed algorithm. Compared to existing NMF-based methods, this method can significantly improve community detection accuracy.
Available online: November 24, 2023 , DOI: 10.15888/j.cnki.csa.009361
Abstract:
This study proposes a cross-modal fusion dual attention net (CFDA-Net) for brain tumor image segmentation to solve the insufficient multi-modal information fusion of brain tumors and detail loss of the tumor regions. Based on the encoder-decoder architecture, a new convolutional block with dense blocks and large kernel attention parallel is first adopted in the encoder branch, which can effectively fuse global and local information and prevent the gradient vanishing during backpropagation. Secondly, a multi-modal deep fusion module is added to the left sides of the second, third, and fourth layers of the encoder to effectively utilize the complementary information among different modalities. Then, in the decoder branch, Shuffle Attention is adopted to group the feature maps and aggregate them, and the subfeatures of the group are divided into two parts to obtain important attention features of space and channels. Finally, binary cross entropy (BCE), Dice Loss, and L2 Loss are employed to form a new hybrid loss function, which alleviates the category imbalance of brain tumor data and further improves the segmentation performance. The experimental results on the BraTS 2019 brain tumor dataset show that the average Dice coefficient values of the model in the whole tumor region, tumor core region, and tumor enhancement region are 0.887, 0.892, and 0.815 respectively. The proposed model has better segmentation performance in the core and enhanced regions of tumors than other advanced segmentation methods such as ADHDC-Net and SDS-MSA-Net.
This study proposes a cross-modal fusion dual attention net (CFDA-Net) for brain tumor image segmentation to solve the insufficient multi-modal information fusion of brain tumors and detail loss of the tumor regions. Based on the encoder-decoder architecture, a new convolutional block with dense blocks and large kernel attention parallel is first adopted in the encoder branch, which can effectively fuse global and local information and prevent the gradient vanishing during backpropagation. Secondly, a multi-modal deep fusion module is added to the left sides of the second, third, and fourth layers of the encoder to effectively utilize the complementary information among different modalities. Then, in the decoder branch, Shuffle Attention is adopted to group the feature maps and aggregate them, and the subfeatures of the group are divided into two parts to obtain important attention features of space and channels. Finally, binary cross entropy (BCE), Dice Loss, and L2 Loss are employed to form a new hybrid loss function, which alleviates the category imbalance of brain tumor data and further improves the segmentation performance. The experimental results on the BraTS 2019 brain tumor dataset show that the average Dice coefficient values of the model in the whole tumor region, tumor core region, and tumor enhancement region are 0.887, 0.892, and 0.815 respectively. The proposed model has better segmentation performance in the core and enhanced regions of tumors than other advanced segmentation methods such as ADHDC-Net and SDS-MSA-Net.
Available online: November 24, 2023 , DOI: 10.15888/j.cnki.csa.009364
Abstract:
To solve the limited resources and long time of detection equipment in detecting surface damage of steel cables, this study applies advanced technology of deep learning and convolutional neural networks (CNNs) to surface damage detection of the cables. On this basis, it proposes a YOLO-based defect detection network model to integrate GhostNet into the backbone network, and a new feature extraction module (ShuffleC3) based on ShuffleNet and attention mechanism, and then prunes and improves the Head part. Experimental results show that compared with the baseline YOLOv5s, the average accuracy of the improved network is increased by 1.1%. In addition, the number of parameters and calculations are reduced by 43.4% and 31% respectively, and the model size is reduced by 42.3%. Thus, the proposed model can reduce the network computing cost and maintain higher identification accuracy, which better meets the requirements for surface damage detection of steel cable materials.
To solve the limited resources and long time of detection equipment in detecting surface damage of steel cables, this study applies advanced technology of deep learning and convolutional neural networks (CNNs) to surface damage detection of the cables. On this basis, it proposes a YOLO-based defect detection network model to integrate GhostNet into the backbone network, and a new feature extraction module (ShuffleC3) based on ShuffleNet and attention mechanism, and then prunes and improves the Head part. Experimental results show that compared with the baseline YOLOv5s, the average accuracy of the improved network is increased by 1.1%. In addition, the number of parameters and calculations are reduced by 43.4% and 31% respectively, and the model size is reduced by 42.3%. Thus, the proposed model can reduce the network computing cost and maintain higher identification accuracy, which better meets the requirements for surface damage detection of steel cable materials.
Available online: November 24, 2023 , DOI: 10.15888/j.cnki.csa.009353
Abstract:
Predicting click-through rate (CTR) is a fundamental task in online advertising and recommendation systems. Mainstream models often enhance performance and generalization by modeling interactions between high-order and low-order features. However, many models only learn fixed representations of each feature, neglecting the importance of features in different contexts and having overly simplistic model structures. To address these issues, this study proposes the feature refinement convolutional neural network-fusion matrix factorization (FRCNN-F) model. Firstly, the study integrates the feature generation module of convolutional neural networks into the feature refinement network (FRNet), leveraging its ability to generate new features by recombining local patterns to enhance important feature selection. Secondly, the study designs the fusion matrix factorization mechanism to enable the model to perceive context and model displays through interactions across different scenarios, thereby enhancing the combination of submodels. Finally, through comparative experiments on the publicly available datasets Frappe and MovieLens, the results demonstrate that the FRCNN-F model outperforms the baseline FRNet, with improvements of 0.32% and 0.40% in AUC scores and reductions of 1.50% and 1.11% in cross-entropy loss (Logloss) respectively. This research has practical applications in achieving precise advertising and personalized recommendations.
Predicting click-through rate (CTR) is a fundamental task in online advertising and recommendation systems. Mainstream models often enhance performance and generalization by modeling interactions between high-order and low-order features. However, many models only learn fixed representations of each feature, neglecting the importance of features in different contexts and having overly simplistic model structures. To address these issues, this study proposes the feature refinement convolutional neural network-fusion matrix factorization (FRCNN-F) model. Firstly, the study integrates the feature generation module of convolutional neural networks into the feature refinement network (FRNet), leveraging its ability to generate new features by recombining local patterns to enhance important feature selection. Secondly, the study designs the fusion matrix factorization mechanism to enable the model to perceive context and model displays through interactions across different scenarios, thereby enhancing the combination of submodels. Finally, through comparative experiments on the publicly available datasets Frappe and MovieLens, the results demonstrate that the FRCNN-F model outperforms the baseline FRNet, with improvements of 0.32% and 0.40% in AUC scores and reductions of 1.50% and 1.11% in cross-entropy loss (Logloss) respectively. This research has practical applications in achieving precise advertising and personalized recommendations.
Available online: November 24, 2023 , DOI: 10.15888/j.cnki.csa.009346
Abstract:
Federated learning is a distributed machine learning approach that enables model delivery and aggregation without compromising the privacy and security of local data. However, federated learning faces a major challenge: the large size of the models and the parameters that need to be communicated multiple times between the client and the server, bringing difficulties for small devices with insufficient communication capability. Therefore, this study set up the client and server to communicate with each other only once. Another challenge in federated learning is the data imbalance among different clients. The model aggregation for servers becomes inefficient in data imbalance. To overcome these challenges, the paper proposes a lightweight federated learning framework that requires only one-shot communication between the client and the server. The framework also introduces an aggregation policy algorithm, FBL-LD. The algorithm selects the most reliable and dominant model from the client models in a one-shot communication and adjusts the weights of other models based on a validation set to achieve a generalized federated model. FBL-LD reduces the communication overhead and improves aggregation efficiency. Experimental results show that FBL-LD outperforms existing federated learning algorithms in terms of accuracy and robustness to data imbalance.
Federated learning is a distributed machine learning approach that enables model delivery and aggregation without compromising the privacy and security of local data. However, federated learning faces a major challenge: the large size of the models and the parameters that need to be communicated multiple times between the client and the server, bringing difficulties for small devices with insufficient communication capability. Therefore, this study set up the client and server to communicate with each other only once. Another challenge in federated learning is the data imbalance among different clients. The model aggregation for servers becomes inefficient in data imbalance. To overcome these challenges, the paper proposes a lightweight federated learning framework that requires only one-shot communication between the client and the server. The framework also introduces an aggregation policy algorithm, FBL-LD. The algorithm selects the most reliable and dominant model from the client models in a one-shot communication and adjusts the weights of other models based on a validation set to achieve a generalized federated model. FBL-LD reduces the communication overhead and improves aggregation efficiency. Experimental results show that FBL-LD outperforms existing federated learning algorithms in terms of accuracy and robustness to data imbalance.
Available online: November 24, 2023 , DOI: 10.15888/j.cnki.csa.009352
Abstract:
Facial expression recognition (FER) has widespread application significance in many fields, but it is difficult to extract effective FER features due to local occlusion during the recognition. FER with local occlusion may require expression features of multiple regions, and a single attention mechanism cannot focus on the features of multiple facial regions simultaneously. To this end, this study proposes a local occlusion FER model based on weighted multi-head parallel attention. The model extracts the expression features of multiple facial regions that are not occluded by multiple channels in parallel-spatial attention, alleviating the occlusion interference on expression recognition. A large number of experiments show that the proposed method yields the best performance compared with many advanced methods, and the accuracy on RAF-DB and FERPlus is 89.54% and 89.13%, respectively. On the occluded datasets Occlusion-RAF-DB and Occlusion-FERPlus, the accuracy is 87.47% and 86.28%, respectively. Therefore, this method has strong robustness.
Facial expression recognition (FER) has widespread application significance in many fields, but it is difficult to extract effective FER features due to local occlusion during the recognition. FER with local occlusion may require expression features of multiple regions, and a single attention mechanism cannot focus on the features of multiple facial regions simultaneously. To this end, this study proposes a local occlusion FER model based on weighted multi-head parallel attention. The model extracts the expression features of multiple facial regions that are not occluded by multiple channels in parallel-spatial attention, alleviating the occlusion interference on expression recognition. A large number of experiments show that the proposed method yields the best performance compared with many advanced methods, and the accuracy on RAF-DB and FERPlus is 89.54% and 89.13%, respectively. On the occluded datasets Occlusion-RAF-DB and Occlusion-FERPlus, the accuracy is 87.47% and 86.28%, respectively. Therefore, this method has strong robustness.
Available online: November 24, 2023 , DOI: 10.15888/j.cnki.csa.009362
Abstract:
Self-supervised learning on RGB-D datasets has attracted extensive attention. However, most methods focus on global-level representation learning, which tends to lose local details that are crucial for recognizing the objects. The geometric consistency between image and depth in RGB-D data can be used as a clue to guide self-supervised feature learning for the RGB-D data. In this study, ArbRot is proposed, which can not only rotate the angle without restriction and generate multiple pseudo-labels for pretext tasks, but also establish?the relationship between global and local context. The ArbRot can be jointly trained with contrastive learning methods for establishing a multi-modal, multiple pretext task self-supervised learning framework, so as to enforce feature consistency within image and depth views, thereby providing an effective initialization for RGB-D semantic segmentation. The experimental results on the datasets of SUN RGB-D and NYU Depth Dataset V2 show that the quality of feature representation obtained by multi-modal, arbitrary-orientation rotation self-supervised learning is better than the baseline models.
Self-supervised learning on RGB-D datasets has attracted extensive attention. However, most methods focus on global-level representation learning, which tends to lose local details that are crucial for recognizing the objects. The geometric consistency between image and depth in RGB-D data can be used as a clue to guide self-supervised feature learning for the RGB-D data. In this study, ArbRot is proposed, which can not only rotate the angle without restriction and generate multiple pseudo-labels for pretext tasks, but also establish?the relationship between global and local context. The ArbRot can be jointly trained with contrastive learning methods for establishing a multi-modal, multiple pretext task self-supervised learning framework, so as to enforce feature consistency within image and depth views, thereby providing an effective initialization for RGB-D semantic segmentation. The experimental results on the datasets of SUN RGB-D and NYU Depth Dataset V2 show that the quality of feature representation obtained by multi-modal, arbitrary-orientation rotation self-supervised learning is better than the baseline models.
Available online: November 24, 2023 , DOI: 10.15888/j.cnki.csa.009386
Abstract:
The bearing temperature of the blower is an important indicator to evaluate its stable operation. However, since bearings are usually installed in a relatively closed environment, it is difficult to achieve real-time and accurate detection of bearing temperature. To address this issue, a knowledge graph-based intelligent prediction of the bearing temperature of blowers is presented. First, a statistical method is applied to analyze the operational system of blowers, and the influencing factors related to bearing temperature are obtained. Second, a knowledge graph is constructed by combining mechanism and domain knowledge. In addition, the direct and indirect feature variables that affect the bearing temperature are extracted. Third, a dual modular fuzzy neural network is designed?to deduce the knowledge graph, and the real-time and accurate prediction of the bearing temperature of blowers is realized. Finally, the results show that the intelligent prediction method of bearing temperatures of blowers based on a knowledge graph can accurately model the blower system and has good temperature prediction ability. This research can provide support for real-time monitoring and change trend prediction of bearing temperatures.
The bearing temperature of the blower is an important indicator to evaluate its stable operation. However, since bearings are usually installed in a relatively closed environment, it is difficult to achieve real-time and accurate detection of bearing temperature. To address this issue, a knowledge graph-based intelligent prediction of the bearing temperature of blowers is presented. First, a statistical method is applied to analyze the operational system of blowers, and the influencing factors related to bearing temperature are obtained. Second, a knowledge graph is constructed by combining mechanism and domain knowledge. In addition, the direct and indirect feature variables that affect the bearing temperature are extracted. Third, a dual modular fuzzy neural network is designed?to deduce the knowledge graph, and the real-time and accurate prediction of the bearing temperature of blowers is realized. Finally, the results show that the intelligent prediction method of bearing temperatures of blowers based on a knowledge graph can accurately model the blower system and has good temperature prediction ability. This research can provide support for real-time monitoring and change trend prediction of bearing temperatures.
Available online: November 24, 2023 , DOI: 10.15888/j.cnki.csa.009393
Abstract:
The delay of the computing core access to the main memory of Shenwei heterogeneous many-core processors is very large, and thus the program should try to avoid the access of computing core code to main the memory as much as possible. The global offset table stores the addresses of global variables and functions in the program, which is not suitable to be saved in the rare local storage space of the computing core, and it is not suitable for cache prefetching because of its discrete access patterns. Therefore, accessing the main memory operation introduced by accessing the global offset table has a great influence on program performance. In view of the usage scenarios of static linking and dynamic linking of heterogeneous many-core programs, the usage limitations of linker relaxation optimization are analyzed, and a global symbol relocation optimization method is designed based on “gp address base+extended offset” to avoid accessing the main memory. Experimental results show that at the cost of adding a small amount of code, the relocation optimization method can effectively avoid the operation of accessing the main memory introduced by accessing the global offset table when the computing core code calls functions and accesses global variables, which improves the running performance of many-core programs.
The delay of the computing core access to the main memory of Shenwei heterogeneous many-core processors is very large, and thus the program should try to avoid the access of computing core code to main the memory as much as possible. The global offset table stores the addresses of global variables and functions in the program, which is not suitable to be saved in the rare local storage space of the computing core, and it is not suitable for cache prefetching because of its discrete access patterns. Therefore, accessing the main memory operation introduced by accessing the global offset table has a great influence on program performance. In view of the usage scenarios of static linking and dynamic linking of heterogeneous many-core programs, the usage limitations of linker relaxation optimization are analyzed, and a global symbol relocation optimization method is designed based on “gp address base+extended offset” to avoid accessing the main memory. Experimental results show that at the cost of adding a small amount of code, the relocation optimization method can effectively avoid the operation of accessing the main memory introduced by accessing the global offset table when the computing core code calls functions and accesses global variables, which improves the running performance of many-core programs.
Available online: November 24, 2023 , DOI: 10.15888/j.cnki.csa.009369
Abstract:
Wildlife monitoring is essential for wildlife conservation and ecosystem maintenance, and wildlife detection and identification is the core technology to achieve monitoring. In recent years, with the rapid development and widespread application of computer vision technology, image-based non-contact methods have attracted extensive attention in the field of wildlife monitoring, and researchers have proposed various methods to solve different problems in this field. However, the complexity of wild environment still poses challenges for accurate detection and identification of wildlife. In order to promote research in this field, the existing image-based wildlife monitoring methods are reviewed in this study, which mainly include three sections: wildlife image acquisition methods, wildlife image preprocessing methods, and wildlife detection and recognition algorithms. These methods are discussed and classified according to the different processing mechanisms of image datasets and wildlife detection and recognition algorithms. Finally, the research hotspots and existing problems of wildlife monitoring based on deep learning are analyzed and summarized, and the prospect for future research priorities is proposed in the study.
Wildlife monitoring is essential for wildlife conservation and ecosystem maintenance, and wildlife detection and identification is the core technology to achieve monitoring. In recent years, with the rapid development and widespread application of computer vision technology, image-based non-contact methods have attracted extensive attention in the field of wildlife monitoring, and researchers have proposed various methods to solve different problems in this field. However, the complexity of wild environment still poses challenges for accurate detection and identification of wildlife. In order to promote research in this field, the existing image-based wildlife monitoring methods are reviewed in this study, which mainly include three sections: wildlife image acquisition methods, wildlife image preprocessing methods, and wildlife detection and recognition algorithms. These methods are discussed and classified according to the different processing mechanisms of image datasets and wildlife detection and recognition algorithms. Finally, the research hotspots and existing problems of wildlife monitoring based on deep learning are analyzed and summarized, and the prospect for future research priorities is proposed in the study.
Available online: November 24, 2023 , DOI: 10.15888/j.cnki.csa.009370
Abstract:
Medical terminology standardization, as an important means to eliminate entity ambiguity, is widely used in the process of building knowledge graphs. Aiming at the problem that the medical field involves a large number of professional terminology and complex expressions, and the traditional matching models are often difficult to achieve a high accuracy rate, a two-stage model of semantic recall and precise sorting is proposed to improve the standardization effect of medical terminology. First, in the semantic recall stage, a semantic representation model CL-BERT is proposed based on the improved supervised contrastive learning and RoBERTa-wwm. The semantic representation vector of an entity is generated through CL-BERT, and recall is carried out according to the cosine similarity between the vectors, so as to obtain the standard word candidate set. Secondly, in the precise sorting stage, T5, combined with prompt tuning, is used to build a precise semantic matching model, and FGM confrontation training is applied to the model training; next, the precise matching model is used to precisely sort the original word and standard word candidate sets, so as to obtain the final standard words. The ccks2019 public data set is used for experiments, achieving an F1 value of 0.920 6. The experimental results show that the proposed two-stage model showcases high performance, and provides a new idea for medical terminology standardization.
Medical terminology standardization, as an important means to eliminate entity ambiguity, is widely used in the process of building knowledge graphs. Aiming at the problem that the medical field involves a large number of professional terminology and complex expressions, and the traditional matching models are often difficult to achieve a high accuracy rate, a two-stage model of semantic recall and precise sorting is proposed to improve the standardization effect of medical terminology. First, in the semantic recall stage, a semantic representation model CL-BERT is proposed based on the improved supervised contrastive learning and RoBERTa-wwm. The semantic representation vector of an entity is generated through CL-BERT, and recall is carried out according to the cosine similarity between the vectors, so as to obtain the standard word candidate set. Secondly, in the precise sorting stage, T5, combined with prompt tuning, is used to build a precise semantic matching model, and FGM confrontation training is applied to the model training; next, the precise matching model is used to precisely sort the original word and standard word candidate sets, so as to obtain the final standard words. The ccks2019 public data set is used for experiments, achieving an F1 value of 0.920 6. The experimental results show that the proposed two-stage model showcases high performance, and provides a new idea for medical terminology standardization.
Available online: November 24, 2023 , DOI: 10.15888/j.cnki.csa.009378
Abstract:
In order to address the problem that existing image dehazing algorithms cannot simultaneously consider both dehazing effects and real-time performance when processing road traffic images, a fast all-in-one dehazing network (AOD-Net) algorithm is improved in this study. Firstly, SE channel attention is added to the AOD-Net to adaptively allocate channel weights and focus on important features. Secondly, a pyramid pooling module is introduced to enlarge the receptive field of the network and fuse the features in different scales, so as to better capture image information. Finally, a composite loss function is used to simultaneously focus on image pixel information and structural texture information. Experimental results show that the improved AOD-Net algorithm increases the peak signal-to-noise ratio (SNR) of road traffic images by 2.52 dB after dehazing, and the structural similarity reaches 91.2%. The algorithm complexity and dehazing time are slightly increased, but still meet real-time requirements.
In order to address the problem that existing image dehazing algorithms cannot simultaneously consider both dehazing effects and real-time performance when processing road traffic images, a fast all-in-one dehazing network (AOD-Net) algorithm is improved in this study. Firstly, SE channel attention is added to the AOD-Net to adaptively allocate channel weights and focus on important features. Secondly, a pyramid pooling module is introduced to enlarge the receptive field of the network and fuse the features in different scales, so as to better capture image information. Finally, a composite loss function is used to simultaneously focus on image pixel information and structural texture information. Experimental results show that the improved AOD-Net algorithm increases the peak signal-to-noise ratio (SNR) of road traffic images by 2.52 dB after dehazing, and the structural similarity reaches 91.2%. The algorithm complexity and dehazing time are slightly increased, but still meet real-time requirements.
Available online: November 24, 2023 , DOI: 10.15888/j.cnki.csa.009354
Abstract:
This study presents a proposal to improve the delegated proof of stake consensus mechanism based on dynamic weighted election, so as to mitigate issues such as the lack of initiative in user nodes, collusion among nodes, difficulty in suppressing malicious node appearance, and increased centralization risk. Firstly, a system of rewards and penalties is established for user nodes to incentivize users’ participation in the election process. Moreover, an address clustering algorithm of user nodes is introduced to identify user nodes exhibiting similar voting behavior, effectively curbing undesirable voting actions of user nodes. The enhanced entropy weighting method is utilized to dynamically calculate the weights of each candidate node’s features during each round of the election process. The voting results of user nodes are combined with the performance distance algorithm to rank the candidate node, leading to more rational election results. Subsequently, in the block production process, the production order of production nodes is dynamically adjusted to avoid the centralization risk. Finally, the feasibility and effectiveness of the proposed scheme are validated through simulation. The results demonstrate that the proposed scheme can not only incentivize user nodes but also limit the bad behavior of nodes, effectively reducing the probability of malicious nodes and avoiding centralization risk.
This study presents a proposal to improve the delegated proof of stake consensus mechanism based on dynamic weighted election, so as to mitigate issues such as the lack of initiative in user nodes, collusion among nodes, difficulty in suppressing malicious node appearance, and increased centralization risk. Firstly, a system of rewards and penalties is established for user nodes to incentivize users’ participation in the election process. Moreover, an address clustering algorithm of user nodes is introduced to identify user nodes exhibiting similar voting behavior, effectively curbing undesirable voting actions of user nodes. The enhanced entropy weighting method is utilized to dynamically calculate the weights of each candidate node’s features during each round of the election process. The voting results of user nodes are combined with the performance distance algorithm to rank the candidate node, leading to more rational election results. Subsequently, in the block production process, the production order of production nodes is dynamically adjusted to avoid the centralization risk. Finally, the feasibility and effectiveness of the proposed scheme are validated through simulation. The results demonstrate that the proposed scheme can not only incentivize user nodes but also limit the bad behavior of nodes, effectively reducing the probability of malicious nodes and avoiding centralization risk.
Available online: November 24, 2023 , DOI: 10.15888/j.cnki.csa.009283
Abstract:
Graph neural network (GNN) has become an important method for handling graph data. Due to the complexity of calculation and large capacity of graph data, training GNNs on large-scale graphs relies on CPU-GPU cooperation and graph sampling, which stores graph structure and feature data in CPU memory and transfers sampled subgraphs and their features to GPU for training. However, this approach faces a serious bottleneck in graph feature data loading, leading to a significant decrease in end-to-end training performance and severely limiting graph scale that can be trained as graph features take up too much memory. To address these challenges, this study proposes a data loading approach based on input feature sparsification, which significantly reduces CPU memory usage and data transfer across the PCIe bus, significantly shortens data loading time, accelerates GNN training, and enables full utilization of GPU resources. In view of the graph features and GNN computational characteristics, the study proposes a sparsification method suitable for the graph feature data, which achieves a balance between compression ratio and model accuracy. The study also conducts experimental evaluations on three common GNN models and three datasets of different sizes, including MAG240M, one of the largest publicly available datasets. The results show that this method reduces the feature size by more than one order of magnitude and achieves 1.6–6.7 times end-to-end training acceleration, while the model accuracy is reduced by less than 1%. In addition, with only four GPUs, the GraphSAGE model can be trained on the MAG240M in just 40 minutes with expected accuracy.
Graph neural network (GNN) has become an important method for handling graph data. Due to the complexity of calculation and large capacity of graph data, training GNNs on large-scale graphs relies on CPU-GPU cooperation and graph sampling, which stores graph structure and feature data in CPU memory and transfers sampled subgraphs and their features to GPU for training. However, this approach faces a serious bottleneck in graph feature data loading, leading to a significant decrease in end-to-end training performance and severely limiting graph scale that can be trained as graph features take up too much memory. To address these challenges, this study proposes a data loading approach based on input feature sparsification, which significantly reduces CPU memory usage and data transfer across the PCIe bus, significantly shortens data loading time, accelerates GNN training, and enables full utilization of GPU resources. In view of the graph features and GNN computational characteristics, the study proposes a sparsification method suitable for the graph feature data, which achieves a balance between compression ratio and model accuracy. The study also conducts experimental evaluations on three common GNN models and three datasets of different sizes, including MAG240M, one of the largest publicly available datasets. The results show that this method reduces the feature size by more than one order of magnitude and achieves 1.6–6.7 times end-to-end training acceleration, while the model accuracy is reduced by less than 1%. In addition, with only four GPUs, the GraphSAGE model can be trained on the MAG240M in just 40 minutes with expected accuracy.
Available online: November 24, 2023 , DOI: 10.15888/j.cnki.csa.009310
Abstract:
In view of problems such as discreteness and sparsity in the massive data accumulated by “campus big data”, how to detect potential students with abnormal behavior from the campus student groups with a large base, wide activity ranges, and strong personality has become an urgent issue to be solved in the analysis of abnormal behavior of students.? This study proposes an early warning method for abnormal behavior of college students based on multi-modal fusion in big data environment (EWMAB). First of all, in view of the insufficient representation of student behavior portraits and the timeliness and dynamics of behavior labels, a cross-modal student behavior portrait model based on multi-modal feature deep learning is established; secondly, for the timeliness and post-alarm of the prediction and early warning of abnormal behavior of students, a multi-modal fusion-based early warning method for student abnormal behaviors is proposed based on the student behavior portrait and student behavior classification prediction. Through the long and short term memory network (LSTM), combined with student behavior multi-index data and text information, the problem of early warning of students’ abnormal behaviors is solved; finally, this study uses an example to verify the model and takes the early warning of abnormal academic performance of students as an example. Compared with other early warning algorithms, the EWMAB method can improve the accuracy of early warning and realize the timeliness and pre-alarm of abnormal behaviors of students so that the education of students is more targeted, personalized, and predictable.
In view of problems such as discreteness and sparsity in the massive data accumulated by “campus big data”, how to detect potential students with abnormal behavior from the campus student groups with a large base, wide activity ranges, and strong personality has become an urgent issue to be solved in the analysis of abnormal behavior of students.? This study proposes an early warning method for abnormal behavior of college students based on multi-modal fusion in big data environment (EWMAB). First of all, in view of the insufficient representation of student behavior portraits and the timeliness and dynamics of behavior labels, a cross-modal student behavior portrait model based on multi-modal feature deep learning is established; secondly, for the timeliness and post-alarm of the prediction and early warning of abnormal behavior of students, a multi-modal fusion-based early warning method for student abnormal behaviors is proposed based on the student behavior portrait and student behavior classification prediction. Through the long and short term memory network (LSTM), combined with student behavior multi-index data and text information, the problem of early warning of students’ abnormal behaviors is solved; finally, this study uses an example to verify the model and takes the early warning of abnormal academic performance of students as an example. Compared with other early warning algorithms, the EWMAB method can improve the accuracy of early warning and realize the timeliness and pre-alarm of abnormal behaviors of students so that the education of students is more targeted, personalized, and predictable.
Available online: November 17, 2023 , DOI: 10.15888/j.cnki.csa.009348
Abstract:
Most of the existing deep clustering algorithms adopt symmetric autoencoders to extract low-dimensional features of high-dimensional data. However, with the increasing training times of autoencoders, the low-dimensional feature space of the data is distorted to a certain extent, and then the obtained data low-dimensional feature space cannot reflect the potential clustering structure information in the original data space. To this end, this study proposes a new deep embedded K-means algorithm (SDEKC). First, during low-dimensional feature extraction, two skip connections are added with a certain weight between the corresponding encoder and decoder in the symmetric convolutional autoencoder. As a result, the encoding requirements of the decoder for the encoder are reduced, and the coding ability of the convolutional autoencoder is highlighted, which can better retain the clustering structure information in the original data space. Second, the low-dimensional data space is converted into a new space revealing clustering structure information by an orthogonal transformation matrix in the clustering stage. Finally, this study utilizes the greedy algorithm to iteratively optimize the low-dimensional representation of the data and its clustering in an end-to-end way and verifies the effectiveness of the proposed new algorithm on six real datasets.
Most of the existing deep clustering algorithms adopt symmetric autoencoders to extract low-dimensional features of high-dimensional data. However, with the increasing training times of autoencoders, the low-dimensional feature space of the data is distorted to a certain extent, and then the obtained data low-dimensional feature space cannot reflect the potential clustering structure information in the original data space. To this end, this study proposes a new deep embedded K-means algorithm (SDEKC). First, during low-dimensional feature extraction, two skip connections are added with a certain weight between the corresponding encoder and decoder in the symmetric convolutional autoencoder. As a result, the encoding requirements of the decoder for the encoder are reduced, and the coding ability of the convolutional autoencoder is highlighted, which can better retain the clustering structure information in the original data space. Second, the low-dimensional data space is converted into a new space revealing clustering structure information by an orthogonal transformation matrix in the clustering stage. Finally, this study utilizes the greedy algorithm to iteratively optimize the low-dimensional representation of the data and its clustering in an end-to-end way and verifies the effectiveness of the proposed new algorithm on six real datasets.
Available online: November 17, 2023 , DOI: 10.15888/j.cnki.csa.009363
Abstract:
Under a large data amount of sampling points, Delaunay triangulation can be adopted to establish a triangulation network and then employ local neighborhood sampling points for Kriging interpolation. However, this algorithm requires fitting a semi-variogram to each interpolation point, which incurs significant overhead in the condition of a large interpolation point scale. Therefore, this study proposes a Kriging interpolation method that fits the semi-variogram on a triangular basis. Additionally, it utilizes CPU-GPU load balancing to optimize some calculations and fully considers the influence of non-uniform samples on the Kriging interpolation effect. The results show that the proposed algorithm can ensure the interpolation effect of non-uniform sample sets, improve computational performance, and ensure high accuracy.
Under a large data amount of sampling points, Delaunay triangulation can be adopted to establish a triangulation network and then employ local neighborhood sampling points for Kriging interpolation. However, this algorithm requires fitting a semi-variogram to each interpolation point, which incurs significant overhead in the condition of a large interpolation point scale. Therefore, this study proposes a Kriging interpolation method that fits the semi-variogram on a triangular basis. Additionally, it utilizes CPU-GPU load balancing to optimize some calculations and fully considers the influence of non-uniform samples on the Kriging interpolation effect. The results show that the proposed algorithm can ensure the interpolation effect of non-uniform sample sets, improve computational performance, and ensure high accuracy.
Available online: November 17, 2023 , DOI: 10.15888/j.cnki.csa.009365
Abstract:
Gas load forecasting is an important task for cities to deploy gas safely and economically. At present, the Seq2Seq model based on the attention mechanism is increasingly utilized in gas data forecasting and is an effective method for gas load forecasting. However, the gas load data have such characteristics as high mutation frequency and large amplitude. The Seq2Seq model based on the general attention mechanism is difficult to extract the multivariate time pattern information in the data and deal with data random mutation. It is still necessary for improving gas load prediction with complex influencing factors. Therefore, this study proposes a multi-dimensional attention mechanism Seq2Seq model. On the one hand, a multi-level time attention module is designed and studied to integrate single-time step and multi-time step attention calculation to extract different time pattern information in the data. On the other hand, the design adds a local history attention module. By improving the model’s defect of distinguishing important historical information, the model tends to refer to more important historical information when making predictions. The improved model has better prediction performance for the unique gas load characteristics. The gas consumption data of an urban area in China and the electric load data of the 2016 electrical mathematical modeling competition are taken as examples. The experimental results show that the MAE of the improved model is reduced by 17% and 9% respectively compared with the general attention mechanism Seq2Seq model.
Gas load forecasting is an important task for cities to deploy gas safely and economically. At present, the Seq2Seq model based on the attention mechanism is increasingly utilized in gas data forecasting and is an effective method for gas load forecasting. However, the gas load data have such characteristics as high mutation frequency and large amplitude. The Seq2Seq model based on the general attention mechanism is difficult to extract the multivariate time pattern information in the data and deal with data random mutation. It is still necessary for improving gas load prediction with complex influencing factors. Therefore, this study proposes a multi-dimensional attention mechanism Seq2Seq model. On the one hand, a multi-level time attention module is designed and studied to integrate single-time step and multi-time step attention calculation to extract different time pattern information in the data. On the other hand, the design adds a local history attention module. By improving the model’s defect of distinguishing important historical information, the model tends to refer to more important historical information when making predictions. The improved model has better prediction performance for the unique gas load characteristics. The gas consumption data of an urban area in China and the electric load data of the 2016 electrical mathematical modeling competition are taken as examples. The experimental results show that the MAE of the improved model is reduced by 17% and 9% respectively compared with the general attention mechanism Seq2Seq model.
Available online: November 17, 2023 , DOI: 10.15888/j.cnki.csa.009368
Abstract:
In recent years, diabetic retinopathy (DR) has become the main reason for the global blind population increase. The early DR severity classification is particularly important to prevent vision loss in DR patients. As the number of diabetes patients grows year by year, the demand for DR grading is also rising. However, the traditional manual grading cannot meet the growing demands, and it is time-consuming and laborious. The development of deep learning technology provides a more efficient and reliable means for DR detection and grading. Although the current DR binary detection has yielded good results, DR severity grading is still challenging due to the slight differences between DR complexity and lesion degree. This work studies and summarizes DR grading methods in recent years. It introduces six deep learning classification methods based on VGG, InceptionNet, ResNet, EfficientNet, DenseNet, and CapsNet models. In addition, the study presents DR grading methods based on multi-network fusion. Finally, summary and prospect are provided for the research trends of DR grading methods based on deep learning.
In recent years, diabetic retinopathy (DR) has become the main reason for the global blind population increase. The early DR severity classification is particularly important to prevent vision loss in DR patients. As the number of diabetes patients grows year by year, the demand for DR grading is also rising. However, the traditional manual grading cannot meet the growing demands, and it is time-consuming and laborious. The development of deep learning technology provides a more efficient and reliable means for DR detection and grading. Although the current DR binary detection has yielded good results, DR severity grading is still challenging due to the slight differences between DR complexity and lesion degree. This work studies and summarizes DR grading methods in recent years. It introduces six deep learning classification methods based on VGG, InceptionNet, ResNet, EfficientNet, DenseNet, and CapsNet models. In addition, the study presents DR grading methods based on multi-network fusion. Finally, summary and prospect are provided for the research trends of DR grading methods based on deep learning.
Available online: November 17, 2023 , DOI: 10.15888/j.cnki.csa.009350
Abstract:
Many skin cancer diseases have obvious early symptoms. Currently, the diagnosis of skin cancer mainly relies on medical workers with professional knowledge, bringing the problems such as long time consumption and low reusability. In response to these problems, a lightweight skin disease recognition model based on improved MobileNetV3-Small is proposed in this study. Firstly, a CaCo attention module based on coordinate attention (CA) mechanism is proposed, Secondly, for the uneven distribution of the samples of skin-cancer datasets, a combination of multiple loss functions is proposed to enhance the learning ability of the model for cases with few samples. The improved MobileNetV3-CaCo model has an accuracy, balance accuracy, and model parameter quantity of 93.39%, 86.35%, and 2.29M, respectively, achieving ideal recognition results.
Many skin cancer diseases have obvious early symptoms. Currently, the diagnosis of skin cancer mainly relies on medical workers with professional knowledge, bringing the problems such as long time consumption and low reusability. In response to these problems, a lightweight skin disease recognition model based on improved MobileNetV3-Small is proposed in this study. Firstly, a CaCo attention module based on coordinate attention (CA) mechanism is proposed, Secondly, for the uneven distribution of the samples of skin-cancer datasets, a combination of multiple loss functions is proposed to enhance the learning ability of the model for cases with few samples. The improved MobileNetV3-CaCo model has an accuracy, balance accuracy, and model parameter quantity of 93.39%, 86.35%, and 2.29M, respectively, achieving ideal recognition results.
Available online: October 30, 2023 , DOI: 10.15888/j.cnki.csa.009340
Abstract:
Interactive image segmentation is an important tool for pixel-level annotation and image editing. Most existing methods adopt two-stage prediction: first predicting a rough result, and then refining the previously predicted results in the second stage to obtain more accurate predictions. To ensure the viability of the network model under limited hardware resources, the same network is shared across the two stages. To better propagate labeled information to unlabeled areas, a similarity constraint propagation module is designed. Meanwhile, a simple prototype extraction module is used during training to make forward click vectors highly cohesive, accelerate network convergence, and remove them during inference. At the inference stage, the implementation of intention perception modules to capture details further improves prediction performance. Numerous experiments show that the method is most comparable to the most advanced methods on all popular benchmark tests, demonstrating its effectiveness.
Interactive image segmentation is an important tool for pixel-level annotation and image editing. Most existing methods adopt two-stage prediction: first predicting a rough result, and then refining the previously predicted results in the second stage to obtain more accurate predictions. To ensure the viability of the network model under limited hardware resources, the same network is shared across the two stages. To better propagate labeled information to unlabeled areas, a similarity constraint propagation module is designed. Meanwhile, a simple prototype extraction module is used during training to make forward click vectors highly cohesive, accelerate network convergence, and remove them during inference. At the inference stage, the implementation of intention perception modules to capture details further improves prediction performance. Numerous experiments show that the method is most comparable to the most advanced methods on all popular benchmark tests, demonstrating its effectiveness.
Available online: October 27, 2023 , DOI: 10.15888/j.cnki.csa.009355
Abstract:
In traditional control systems, people rely on employing devices such as handles and joysticks to achieve human-machine interaction with external devices, which is a challenge for patients with movement disorders. Meanwhile, brain-computer interface (BCI) technology can convert EEG into control commands for external devices through the brain loop, allowing these patients to directly control external devices by their brain’s “consciousness”. This study proposes an autonomous driving system of intelligent car based on multimodal BCI to integrate the subjects’ EEG, electro-oculography, and gyroscope signals to control the car. EEG is used for controlling the car speed, electrooculography for controlling the start and stop of the car, and gyroscope signals for controlling the car steering. Additionally, computer vision technology is combined to add autonomous driving function for the intelligent car, making control more intelligent. The experiments show that the average accuracy rate of ten subjects utilizing the system to control the car is 92.47%, with an average response time of 1.55 s and an average information transmission rate of 55.94 bit/min, which indicates the effectiveness and efficiency of the control system. Meanwhile, multiple comparative experiments for verification are set up to verify the car’s autonomous driving function. The experimental results show that compared with manual driving, although the autonomous driving system has disadvantages in controlling the car speed, it has better performance advantages in accuracy and stability. This proves that this system can provide better control experience for the disabled, and has broad application prospects in brain control and autonomous driving.
In traditional control systems, people rely on employing devices such as handles and joysticks to achieve human-machine interaction with external devices, which is a challenge for patients with movement disorders. Meanwhile, brain-computer interface (BCI) technology can convert EEG into control commands for external devices through the brain loop, allowing these patients to directly control external devices by their brain’s “consciousness”. This study proposes an autonomous driving system of intelligent car based on multimodal BCI to integrate the subjects’ EEG, electro-oculography, and gyroscope signals to control the car. EEG is used for controlling the car speed, electrooculography for controlling the start and stop of the car, and gyroscope signals for controlling the car steering. Additionally, computer vision technology is combined to add autonomous driving function for the intelligent car, making control more intelligent. The experiments show that the average accuracy rate of ten subjects utilizing the system to control the car is 92.47%, with an average response time of 1.55 s and an average information transmission rate of 55.94 bit/min, which indicates the effectiveness and efficiency of the control system. Meanwhile, multiple comparative experiments for verification are set up to verify the car’s autonomous driving function. The experimental results show that compared with manual driving, although the autonomous driving system has disadvantages in controlling the car speed, it has better performance advantages in accuracy and stability. This proves that this system can provide better control experience for the disabled, and has broad application prospects in brain control and autonomous driving.
Available online: October 27, 2023 , DOI: 10.15888/j.cnki.csa.009347
Abstract:
Anomaly detection in multivariate time series is a challenging problem that requires models to learn information representations from complex temporal dynamics and derive a distinguishable criterion that can identify a small number of outliers from a large number of normal time points. However, in time series analysis, the complex temporal correlation and high dimensionality of multivariate time series will result in poor anomaly detection performance. To this end, this study proposes a model based on MLP (multi-layer perceptron) architecture (UMTS-Mixer). Since the linear structure of MLP is sensitive to order, it is employed to capture temporal correlation and cross-channel correlation. A large number of experiments show that UMTS-Mixer can detect time series anomalies and perform better on the four benchmark datasets. Meanwhile, the highest F1 is 91.35% and 92.93% on the MSL and PSM datasets, respectively.
Anomaly detection in multivariate time series is a challenging problem that requires models to learn information representations from complex temporal dynamics and derive a distinguishable criterion that can identify a small number of outliers from a large number of normal time points. However, in time series analysis, the complex temporal correlation and high dimensionality of multivariate time series will result in poor anomaly detection performance. To this end, this study proposes a model based on MLP (multi-layer perceptron) architecture (UMTS-Mixer). Since the linear structure of MLP is sensitive to order, it is employed to capture temporal correlation and cross-channel correlation. A large number of experiments show that UMTS-Mixer can detect time series anomalies and perform better on the four benchmark datasets. Meanwhile, the highest F1 is 91.35% and 92.93% on the MSL and PSM datasets, respectively.
Available online: October 27, 2023 , DOI: 10.15888/j.cnki.csa.009343
Abstract:
In response to the key information blur in images and poor adaptability in the gastrointestinal endoscopy diagnosis and treatment system, this study proposes a cycle generative adversarial network (CycleGAN) combining an improved attention mechanism to accurately estimate the depth information of the digestive tract. Based on CycleGAN, the network combines a dual attention mechanism and introduces a residual gate mechanism and a non-local module to comprehensively capture and understand the feature structure and global correlation of input data, thereby improving the quality and adaptation of depth image generation. Meanwhile, a dual-scale feature fusion network is employed as the discriminator to improve the discrimination ability and balance the working performance between the generator and the discriminator. Experimental results show that the proposed method yields good prediction performance in the gastrointestinal endoscopy scenes. Its average accuracy of the stomach, small intestine, and colon datasets is improved by 7.39%, 10.17%, and 10.27% respectively compared with other unsupervised methods. Additionally, it can accurately estimate the relative depth information and provide accurate boundary information in the laboratory human gastric organ model.
In response to the key information blur in images and poor adaptability in the gastrointestinal endoscopy diagnosis and treatment system, this study proposes a cycle generative adversarial network (CycleGAN) combining an improved attention mechanism to accurately estimate the depth information of the digestive tract. Based on CycleGAN, the network combines a dual attention mechanism and introduces a residual gate mechanism and a non-local module to comprehensively capture and understand the feature structure and global correlation of input data, thereby improving the quality and adaptation of depth image generation. Meanwhile, a dual-scale feature fusion network is employed as the discriminator to improve the discrimination ability and balance the working performance between the generator and the discriminator. Experimental results show that the proposed method yields good prediction performance in the gastrointestinal endoscopy scenes. Its average accuracy of the stomach, small intestine, and colon datasets is improved by 7.39%, 10.17%, and 10.27% respectively compared with other unsupervised methods. Additionally, it can accurately estimate the relative depth information and provide accurate boundary information in the laboratory human gastric organ model.
Available online: October 27, 2023 , DOI: 10.15888/j.cnki.csa.009344
Abstract:
The core of penetration testing is to discover penetration paths, but not all penetration paths can be successful. Therefore, the optimal penetration path needs to be chosen based on the current system environment. In this context, firstly, this study models the environment as a Markov decision process (MDP) graph based on the attack graph and uses a value iteration algorithm to find the optimal penetration path. Secondly, a new replanning algorithm is proposed to deal with the failure of penetration actions in the MDP graph and find the optimal penetration path again. Finally, in view of the existence of multiple attack targets in the penetration testing process, this study proposes a multi-objective global optimal penetration path algorithm for MDP graphs. Experimentally, the proposed algorithm shows higher efficiency and stability in replanning tasks and is effective in multi-objective tasks, which can prevent unnecessary penetration actions from being executed.
The core of penetration testing is to discover penetration paths, but not all penetration paths can be successful. Therefore, the optimal penetration path needs to be chosen based on the current system environment. In this context, firstly, this study models the environment as a Markov decision process (MDP) graph based on the attack graph and uses a value iteration algorithm to find the optimal penetration path. Secondly, a new replanning algorithm is proposed to deal with the failure of penetration actions in the MDP graph and find the optimal penetration path again. Finally, in view of the existence of multiple attack targets in the penetration testing process, this study proposes a multi-objective global optimal penetration path algorithm for MDP graphs. Experimentally, the proposed algorithm shows higher efficiency and stability in replanning tasks and is effective in multi-objective tasks, which can prevent unnecessary penetration actions from being executed.
Available online: October 27, 2023 , DOI: 10.15888/j.cnki.csa.009338
Abstract:
Prediction based on historical data has become essential in many fields, such as environmental management and urban transportation. Prediction accuracy plays a key role in practical production, scheduling, and other tasks. However, due to natural or human factors, some data exhibits high volatility and uncertainty, unable to fully achieve the potential of prediction models. Taking the sediment concentration prediction during the non-ice period as a case study, this study explores optimization methods for predicting high-volatility data. The results show that the feature selection optimization based on the Shapley additive explanations (SHAP), the data smoothing, and early-stage clustering can reduce prediction error of high-volatility data. The mean absolute error (MAE) decreases from 1.502 in the initial model to 0.194, and data smoothing shows the most significant optimization effect with a reduction of 76.51% in MAE. However, the increasing smoothing order results in poorer prediction results, which is because the subsequent rising exponentiation order correspondingly leads to an exponential increase in error. Additionally, employing clustering results as feature inputs can “guide” the parameter learning of multi-layer perceptron.
Prediction based on historical data has become essential in many fields, such as environmental management and urban transportation. Prediction accuracy plays a key role in practical production, scheduling, and other tasks. However, due to natural or human factors, some data exhibits high volatility and uncertainty, unable to fully achieve the potential of prediction models. Taking the sediment concentration prediction during the non-ice period as a case study, this study explores optimization methods for predicting high-volatility data. The results show that the feature selection optimization based on the Shapley additive explanations (SHAP), the data smoothing, and early-stage clustering can reduce prediction error of high-volatility data. The mean absolute error (MAE) decreases from 1.502 in the initial model to 0.194, and data smoothing shows the most significant optimization effect with a reduction of 76.51% in MAE. However, the increasing smoothing order results in poorer prediction results, which is because the subsequent rising exponentiation order correspondingly leads to an exponential increase in error. Additionally, employing clustering results as feature inputs can “guide” the parameter learning of multi-layer perceptron.
Available online: October 27, 2023 , DOI: 10.15888/j.cnki.csa.009335
Abstract:
In the booming autonomous driving technology, the results of pedestrian trajectory prediction often affect autonomous driving safety. Pedestrian trajectory prediction technology currently faces the problem of interaction with others when applied to practical scenarios, requiring consideration of social interaction and logical consistency during predicting trajectories. Therefore, this study proposes a pedestrian trajectory prediction method based on spatio-temporal graphs. This method employs graph attention networks to model pedestrian interactions in the scenarios and adopts a method of automatically generating positive and negative samples to reduce the collision rate of the output trajectory through contrastive learning, thus improving the safety and logical consistency of the output trajectory. Model training and testing are conducted on ETH and UCY datasets, and the results show that the proposed method reduces the collision rate and has better prediction accuracy than mainstream algorithms.
In the booming autonomous driving technology, the results of pedestrian trajectory prediction often affect autonomous driving safety. Pedestrian trajectory prediction technology currently faces the problem of interaction with others when applied to practical scenarios, requiring consideration of social interaction and logical consistency during predicting trajectories. Therefore, this study proposes a pedestrian trajectory prediction method based on spatio-temporal graphs. This method employs graph attention networks to model pedestrian interactions in the scenarios and adopts a method of automatically generating positive and negative samples to reduce the collision rate of the output trajectory through contrastive learning, thus improving the safety and logical consistency of the output trajectory. Model training and testing are conducted on ETH and UCY datasets, and the results show that the proposed method reduces the collision rate and has better prediction accuracy than mainstream algorithms.
Available online: October 25, 2023 , DOI: 10.15888/j.cnki.csa.009336
Abstract:
In multi-user and multi-task scenarios, using traditional decision algorithms to make computation offloading decisions for upcoming tasks in a short period can no longer meet users’ requirements for decision-making efficiency and resource utilization. Therefore, some studies have proposed deep reinforcement learning algorithms for offloading decisions to cater to various scenarios. However, most of these algorithms only consider the offloading first strategy, which leaves user equipment (UE) idle. This study improves the resource utilization of mobile edge computing (MEC) servers and UE and reduces the error rate of computation offloading. It proposes a decision offloading model combining local first and improved twin delayed deep deterministic policy gradient (TD3) algorithm and designs a simulation experiment. The experimental results show that the model can indeed improve the resource utilization of MEC servers and UE and reduce the error rate.
In multi-user and multi-task scenarios, using traditional decision algorithms to make computation offloading decisions for upcoming tasks in a short period can no longer meet users’ requirements for decision-making efficiency and resource utilization. Therefore, some studies have proposed deep reinforcement learning algorithms for offloading decisions to cater to various scenarios. However, most of these algorithms only consider the offloading first strategy, which leaves user equipment (UE) idle. This study improves the resource utilization of mobile edge computing (MEC) servers and UE and reduces the error rate of computation offloading. It proposes a decision offloading model combining local first and improved twin delayed deep deterministic policy gradient (TD3) algorithm and designs a simulation experiment. The experimental results show that the model can indeed improve the resource utilization of MEC servers and UE and reduce the error rate.
Available online: October 25, 2023 , DOI: 10.15888/j.cnki.csa.009351
Abstract:
At present, breast cancer, with the highest annual incidence, has replaced lung cancer, and the target detection technology based on deep learning can automatically detect lesions on non-invasive imaging such as mammography X-ray, breast ultrasound, and breast magnetic resonance imaging (MRI), and it has become the preferred way for adjuvant diagnosis of breast cancer. You only look once (YOLO) series algorithms are object detection algorithms based on deep learning, and classical YOLO algorithms have certain advantages in speed and accuracy and are widely used in computer vision fields. The latest YOLO algorithm is the state of the art (SOTA) model in the field of computer vision, and how to use YOLO series algorithms to improve the speed and accuracy of breast cancer detection has become one of the focus of researchers. On this basis, this study introduces the principle of the classical YOLO series algorithms, sorts out the application status of the classical YOLO series algorithms in breast cancer image detection, summarizes the existing problems, and looks forward to the further application of the YOLO series algorithms in breast cancer detection.
At present, breast cancer, with the highest annual incidence, has replaced lung cancer, and the target detection technology based on deep learning can automatically detect lesions on non-invasive imaging such as mammography X-ray, breast ultrasound, and breast magnetic resonance imaging (MRI), and it has become the preferred way for adjuvant diagnosis of breast cancer. You only look once (YOLO) series algorithms are object detection algorithms based on deep learning, and classical YOLO algorithms have certain advantages in speed and accuracy and are widely used in computer vision fields. The latest YOLO algorithm is the state of the art (SOTA) model in the field of computer vision, and how to use YOLO series algorithms to improve the speed and accuracy of breast cancer detection has become one of the focus of researchers. On this basis, this study introduces the principle of the classical YOLO series algorithms, sorts out the application status of the classical YOLO series algorithms in breast cancer image detection, summarizes the existing problems, and looks forward to the further application of the YOLO series algorithms in breast cancer detection.
Available online: October 25, 2023 , DOI: 10.15888/j.cnki.csa.009349
Abstract:
The distribution of remaining oil forms is of great significance for the deep development of oil fields. This study proposes a form classification method of remaining oil based on deep learning to address the problems of scarce remaining oil data and the limited ability of traditional morphological parameter classification. In the data preprocessing stage, the method uses the multi-class data generation characteristics of the generative adversarial network (ACGAN) to enhance the data of the remaining oil image. It employs the VGG19 model as the backbone network to extract deep features that cannot be described by traditional morphological parameters and introduces the SENet attention mechanism to improve the model’s feature expression ability, making the final classification results more accurate. To verify the effectiveness, the proposed method is compared with traditional classification methods based on morphological parameters and other deep learning models, and it is evaluated through subjective visual and objective indicators. The results showed that the proposed method provides a more accurate classification.
The distribution of remaining oil forms is of great significance for the deep development of oil fields. This study proposes a form classification method of remaining oil based on deep learning to address the problems of scarce remaining oil data and the limited ability of traditional morphological parameter classification. In the data preprocessing stage, the method uses the multi-class data generation characteristics of the generative adversarial network (ACGAN) to enhance the data of the remaining oil image. It employs the VGG19 model as the backbone network to extract deep features that cannot be described by traditional morphological parameters and introduces the SENet attention mechanism to improve the model’s feature expression ability, making the final classification results more accurate. To verify the effectiveness, the proposed method is compared with traditional classification methods based on morphological parameters and other deep learning models, and it is evaluated through subjective visual and objective indicators. The results showed that the proposed method provides a more accurate classification.
Available online: October 25, 2023 , DOI: 10.15888/j.cnki.csa.009318
Abstract:
Aiming at the difficulty in manually designing portrait paper-cuts, this study employs the generative adversarial network (GAN) to generate high-quality portrait paper-cuts for the first time. Based on the artistic characteristics of portrait paper-cuts, an improved network based on CycleGAN is proposed. 1) The CBAM attention module is introduced into the CycleGAN generator to enhance the feature extraction of the network. 2) The local discriminator for key facial regions such as nose, eyes, and lips is introduced to improve the generation effect of the above areas in generated portrait paper-cuts. 3) A new loss function is designed based on image edge information and SSIM, which will be adopted to replace the original forward cycle-consistency loss of CycleGAN and eliminate the shadows in the portrait paper-cuts. Compared with other automatic generation methods of portrait paper-cuts, the proposed method can quickly generate paper-cuts featuring high similarity to the original human face, continuous and smooth lines, and aesthetic beauty. Additionally, this study also puts forward a post-processing method of portrait paper-cut connectivity to make the obtained results more consistent with the overall connectivity of traditional Chinese paper-cuts.
Aiming at the difficulty in manually designing portrait paper-cuts, this study employs the generative adversarial network (GAN) to generate high-quality portrait paper-cuts for the first time. Based on the artistic characteristics of portrait paper-cuts, an improved network based on CycleGAN is proposed. 1) The CBAM attention module is introduced into the CycleGAN generator to enhance the feature extraction of the network. 2) The local discriminator for key facial regions such as nose, eyes, and lips is introduced to improve the generation effect of the above areas in generated portrait paper-cuts. 3) A new loss function is designed based on image edge information and SSIM, which will be adopted to replace the original forward cycle-consistency loss of CycleGAN and eliminate the shadows in the portrait paper-cuts. Compared with other automatic generation methods of portrait paper-cuts, the proposed method can quickly generate paper-cuts featuring high similarity to the original human face, continuous and smooth lines, and aesthetic beauty. Additionally, this study also puts forward a post-processing method of portrait paper-cut connectivity to make the obtained results more consistent with the overall connectivity of traditional Chinese paper-cuts.
Available online: October 20, 2023 , DOI: 10.15888/j.cnki.csa.009307
Abstract:
To address the problem that the solution accuracy of the sparrow search algorithm (SSA) depends on the population at the better location and is easily trapped in the local optimum, this study proposes an improved sparrow search algorithm (ISSA). The algorithm firstly proposes a normal shift strategy to shift the population with the center of gravity as the guide to achieve the decay of the normal distribution of the moving energy and effectively improve the exploration ability of the population for local search. Secondly, it introduces a dynamic sinusoidal perturbation strategy to achieve the two-way demands of the discoverer for the early search step and the late fast convergence through the scaling factor. Then, a backward learning mechanism is added for the poorly positioned early warners in the sparrow population to generate the backward solution of the perturbation with their current position, which is helpful to expand the search step and enable the algorithm to jump out of the local optimum. Finally, six test functions are randomly selected and compared with other similar algorithms, and the experimental results verify the effectiveness of the ISSA algorithm.
To address the problem that the solution accuracy of the sparrow search algorithm (SSA) depends on the population at the better location and is easily trapped in the local optimum, this study proposes an improved sparrow search algorithm (ISSA). The algorithm firstly proposes a normal shift strategy to shift the population with the center of gravity as the guide to achieve the decay of the normal distribution of the moving energy and effectively improve the exploration ability of the population for local search. Secondly, it introduces a dynamic sinusoidal perturbation strategy to achieve the two-way demands of the discoverer for the early search step and the late fast convergence through the scaling factor. Then, a backward learning mechanism is added for the poorly positioned early warners in the sparrow population to generate the backward solution of the perturbation with their current position, which is helpful to expand the search step and enable the algorithm to jump out of the local optimum. Finally, six test functions are randomly selected and compared with other similar algorithms, and the experimental results verify the effectiveness of the ISSA algorithm.
Available online: October 20, 2023 , DOI: 10.15888/j.cnki.csa.009328
Abstract:
Predicting the trend of inlet valve temperature changes provides significant references for the operating status of valve cooling systems. Since the traditional methods have problems such as a large time span of data collection and sensor deviation, this study proposes a Robust-InTemp prediction model for inlet valve temperature based on adversarial perturbation and local information enhancement. Specifically, Robust-InTemp enhances the model’s generalization ability and noise resistance robustness by adding rule-based Gaussian noise to the original data and employing projected gradient descent (PGD) for adversarial training. Meanwhile, relative positional encoding, one-dimensional convolution, and gated linear units (GLUs) are introduced to enhance the model’s ability to learn local features, thus improving prediction accuracy. Experimental results show that compared to various benchmark models, Robust-InTemp has clear advantages in predictive performance and anti-interference ability. Additionally, further ablation experiments validate the effectiveness of each component in the model.
Predicting the trend of inlet valve temperature changes provides significant references for the operating status of valve cooling systems. Since the traditional methods have problems such as a large time span of data collection and sensor deviation, this study proposes a Robust-InTemp prediction model for inlet valve temperature based on adversarial perturbation and local information enhancement. Specifically, Robust-InTemp enhances the model’s generalization ability and noise resistance robustness by adding rule-based Gaussian noise to the original data and employing projected gradient descent (PGD) for adversarial training. Meanwhile, relative positional encoding, one-dimensional convolution, and gated linear units (GLUs) are introduced to enhance the model’s ability to learn local features, thus improving prediction accuracy. Experimental results show that compared to various benchmark models, Robust-InTemp has clear advantages in predictive performance and anti-interference ability. Additionally, further ablation experiments validate the effectiveness of each component in the model.
Available online: October 20, 2023 , DOI: 10.15888/j.cnki.csa.009339
Abstract:
Label noise can greatly reduce the performance of deep network models. To address this problem, this study proposes a contrastive learning-based label noisy image classification method. The method includes an adaptive threshold, contrastive learning module, and class prototype-based label denoising module. Firstly, the robust features of the image are extracted by maximizing the similarity between two augmented views of the same image using contrastive learning. Then, a novel adaptive threshold filtering training sample is used to dynamically adjust the threshold based on the learning status of each class during model training. Finally, a class prototype-based label denoising module is introduced to update pseudo-labels by calculating the similarity between sample feature vectors and prototype vectors, thus avoiding the influence of label noise. Comparative experiments are conducted on the publicly available datasets CIFAR-10 and CIFAR-100 and the real dataset ANIMAL10. The experimental results show that under the condition of artificially synthesized noise, the proposed method outperforms conventional methods. By updating pseudo-labels based on the similarity between the robust feature vector of the image and various prototype vectors, the negative impact of noisy labels is reduced, and the anti-noise ability of the model is improved to some extent, verifying the effectiveness of the proposed model.
Label noise can greatly reduce the performance of deep network models. To address this problem, this study proposes a contrastive learning-based label noisy image classification method. The method includes an adaptive threshold, contrastive learning module, and class prototype-based label denoising module. Firstly, the robust features of the image are extracted by maximizing the similarity between two augmented views of the same image using contrastive learning. Then, a novel adaptive threshold filtering training sample is used to dynamically adjust the threshold based on the learning status of each class during model training. Finally, a class prototype-based label denoising module is introduced to update pseudo-labels by calculating the similarity between sample feature vectors and prototype vectors, thus avoiding the influence of label noise. Comparative experiments are conducted on the publicly available datasets CIFAR-10 and CIFAR-100 and the real dataset ANIMAL10. The experimental results show that under the condition of artificially synthesized noise, the proposed method outperforms conventional methods. By updating pseudo-labels based on the similarity between the robust feature vector of the image and various prototype vectors, the negative impact of noisy labels is reduced, and the anti-noise ability of the model is improved to some extent, verifying the effectiveness of the proposed model.
Available online: October 20, 2023 , DOI: 10.15888/j.cnki.csa.009342
Abstract:
Helpfulness prediction task of online reviews is significant in the contemporary e-commerce environment. It aims to evaluate the helpfulness of online reviews and then highlight the reviews more helpful to future consumers, thereby improving the consumers’ efficiency in obtaining information. This study concentrates on the new multidimensional scoring system emerging on various online platforms in recent years, and tries to study the influence of aspect ratings given by users in the system on the helpfulness of online reviews. To accomplish the helpfulness prediction task, it puts forward a multi-level neural network model HORA that considers all three components of review texts, overall ratings, and aspect ratings, as well as their interconnections. The experimental results on two real-world datasets show that HORA outperforms the present baseline models in terms of MAE and RMSE and exhibits good robustness. This indicates the significance of aspect ratings for the helpfulness awareness of users’ online reviews.
Helpfulness prediction task of online reviews is significant in the contemporary e-commerce environment. It aims to evaluate the helpfulness of online reviews and then highlight the reviews more helpful to future consumers, thereby improving the consumers’ efficiency in obtaining information. This study concentrates on the new multidimensional scoring system emerging on various online platforms in recent years, and tries to study the influence of aspect ratings given by users in the system on the helpfulness of online reviews. To accomplish the helpfulness prediction task, it puts forward a multi-level neural network model HORA that considers all three components of review texts, overall ratings, and aspect ratings, as well as their interconnections. The experimental results on two real-world datasets show that HORA outperforms the present baseline models in terms of MAE and RMSE and exhibits good robustness. This indicates the significance of aspect ratings for the helpfulness awareness of users’ online reviews.
Available online: October 20, 2023 , DOI: 10.15888/j.cnki.csa.009345
Abstract:
To improve the seal detection efficiency of threaded oil casing gas, this study proposes an automatic classification network, NAFENet, for threaded torque curves based on global attention feature fusion. Specifically, NAFENet extends the convolutional structure of EfficientNet-B0 to 11 layers to obtain EfficientNet-B11 and enhance the model expressiveness. Meanwhile, the modules based on non-local global attention and attentional feature fusion (AFF) are built in each MBConv convolutional layer to help the model acquire more global information in the curve images and improve the feature extraction ability. The experimental results show that compared with EfficientNet-B0, the parameter number of NAFENet is slightly increased with improved curve identification accuracy, and the model accuracy reaches 92.87% on the homemade UBT_Curve dataset.
To improve the seal detection efficiency of threaded oil casing gas, this study proposes an automatic classification network, NAFENet, for threaded torque curves based on global attention feature fusion. Specifically, NAFENet extends the convolutional structure of EfficientNet-B0 to 11 layers to obtain EfficientNet-B11 and enhance the model expressiveness. Meanwhile, the modules based on non-local global attention and attentional feature fusion (AFF) are built in each MBConv convolutional layer to help the model acquire more global information in the curve images and improve the feature extraction ability. The experimental results show that compared with EfficientNet-B0, the parameter number of NAFENet is slightly increased with improved curve identification accuracy, and the model accuracy reaches 92.87% on the homemade UBT_Curve dataset.
Available online: October 20, 2023 , DOI: 10.15888/j.cnki.csa.009356
Abstract:
The research on natural language to SQL (NL2SQL) has high application value. With the maturity of deep learning technology, increasingly more researchers have begun to apply deep learning technology to NL2SQL tasks. This study reviews the research status of NL2SQL in English and Chinese fields and summarizes the datasets and models published by year. Additionally, it compares the characteristics of the four major Chinese NL2SQL datasets and expounds on the basic framework of NL2SQL tasks based on deep learning and typical models for simple single-table problems and complex cross-table problems in Chinese NL2SQL fields. Finally, the commonly adopted model evaluation methods are introduced, and future research directions are put forward.
The research on natural language to SQL (NL2SQL) has high application value. With the maturity of deep learning technology, increasingly more researchers have begun to apply deep learning technology to NL2SQL tasks. This study reviews the research status of NL2SQL in English and Chinese fields and summarizes the datasets and models published by year. Additionally, it compares the characteristics of the four major Chinese NL2SQL datasets and expounds on the basic framework of NL2SQL tasks based on deep learning and typical models for simple single-table problems and complex cross-table problems in Chinese NL2SQL fields. Finally, the commonly adopted model evaluation methods are introduced, and future research directions are put forward.
Available online: October 19, 2023 , DOI: 10.15888/j.cnki.csa.009317
Abstract:
In recent years, remote sensing images have been widely employed in a series of work such as environmental monitoring. However, the images observed by satellite sensors often have low resolution, which is difficult to meet in-depth research needs. Super resolution (SR) aims to improve image resolution and provides finer spatial details, perfectly compensating for the weaknesses of satellite imagery. Therefore, a back-projection attention network (BPAN) is proposed for SR reconstruction of remote sensing images. The BPAN is composed of the back-projection network and the initial residual attention block. In the back projection network, the iterative error feedback mechanism is adopted to calculate the upper and lower projection errors to guide image reconstruction. In the initial residual attention block, the initial module is introduced to integrate local multilevel features to provide more information for reconstructing detailed textures to focus on the importance of the module to learn different spatial regions adaptively and promote high-frequency information recovery. To evaluate the effectiveness of this method, this study conducts a large number of experiments on AID datasets. The results show that the proposed network model improves the reconstruction performance of traditional deep networks and has significant improvements in visual effects and objective indicators.
In recent years, remote sensing images have been widely employed in a series of work such as environmental monitoring. However, the images observed by satellite sensors often have low resolution, which is difficult to meet in-depth research needs. Super resolution (SR) aims to improve image resolution and provides finer spatial details, perfectly compensating for the weaknesses of satellite imagery. Therefore, a back-projection attention network (BPAN) is proposed for SR reconstruction of remote sensing images. The BPAN is composed of the back-projection network and the initial residual attention block. In the back projection network, the iterative error feedback mechanism is adopted to calculate the upper and lower projection errors to guide image reconstruction. In the initial residual attention block, the initial module is introduced to integrate local multilevel features to provide more information for reconstructing detailed textures to focus on the importance of the module to learn different spatial regions adaptively and promote high-frequency information recovery. To evaluate the effectiveness of this method, this study conducts a large number of experiments on AID datasets. The results show that the proposed network model improves the reconstruction performance of traditional deep networks and has significant improvements in visual effects and objective indicators.
Available online: October 19, 2023 , DOI: 10.15888/j.cnki.csa.009324
Abstract:
Many studies apply Transformer to time series prediction tasks. However, compared with other time series, motion trajectory data has kinematic uncertainty without obvious periodicity. To reduce noise interference and enhance trend modeling, this study proposes a target trajectory prediction method based on time-frequency domain information fusion and multi-scale adversarial training based on Transformer architecture. The wavelet decomposition is embedded into the network model to realize the adaptive filtering in the time-frequency domain, and then time-domain attention is integrated to encode the long-term trend characteristics of the observed trajectory more effectively. Meanwhile, the study designs a full convolution discriminator to further improve the prediction accuracy by learning multi-scale short-term micro motion representation of the sequence through adversarial training. A trajectory prediction dataset DT including 2D ship trajectory and 3D aircraft trajectory is established as a benchmark, and comparative experiments with Transformer, LogTrans, Informer, and other models are conducted. Experiment results show that the proposed method is superior to other models in the tasks of medium and long-term trajectory prediction.
Many studies apply Transformer to time series prediction tasks. However, compared with other time series, motion trajectory data has kinematic uncertainty without obvious periodicity. To reduce noise interference and enhance trend modeling, this study proposes a target trajectory prediction method based on time-frequency domain information fusion and multi-scale adversarial training based on Transformer architecture. The wavelet decomposition is embedded into the network model to realize the adaptive filtering in the time-frequency domain, and then time-domain attention is integrated to encode the long-term trend characteristics of the observed trajectory more effectively. Meanwhile, the study designs a full convolution discriminator to further improve the prediction accuracy by learning multi-scale short-term micro motion representation of the sequence through adversarial training. A trajectory prediction dataset DT including 2D ship trajectory and 3D aircraft trajectory is established as a benchmark, and comparative experiments with Transformer, LogTrans, Informer, and other models are conducted. Experiment results show that the proposed method is superior to other models in the tasks of medium and long-term trajectory prediction.
Available online: October 19, 2023 , DOI: 10.15888/j.cnki.csa.009320
Abstract:
To solve the scarcity of text classification algorithms in finance and the inability of existing algorithms to adequately extract word-to-word relations, long-distance dependency, and deep feature information in texts, this study proposes a text depth relationship extraction algorithm based on improved convolutional self-attention model. The algorithm introduces self-attention in a modified deep pyramidal convolutional neural network (DPCNN) and builds a text classification model jointly with bi-directional gated neural network (BiGRU) module to solve the problem of extracting long-distance dependency feature information and word-to-word relationship feature information for long texts in finance. Then the joint extraction function of deep feature information and contextual semantic information in texts is realized. Experiments on THUCNews short text and long text datasets show that the proposed method has significant improvement in evaluation indexes compared with BERT and other methods. The comparison experiments on the dataset of homemade financial long texts show that the accuracy and F1 value of the algorithm model are higher compared with other models. A series of experiments demonstrate that the algorithmic model can perform the classification task against financial long texts more accurately.
To solve the scarcity of text classification algorithms in finance and the inability of existing algorithms to adequately extract word-to-word relations, long-distance dependency, and deep feature information in texts, this study proposes a text depth relationship extraction algorithm based on improved convolutional self-attention model. The algorithm introduces self-attention in a modified deep pyramidal convolutional neural network (DPCNN) and builds a text classification model jointly with bi-directional gated neural network (BiGRU) module to solve the problem of extracting long-distance dependency feature information and word-to-word relationship feature information for long texts in finance. Then the joint extraction function of deep feature information and contextual semantic information in texts is realized. Experiments on THUCNews short text and long text datasets show that the proposed method has significant improvement in evaluation indexes compared with BERT and other methods. The comparison experiments on the dataset of homemade financial long texts show that the accuracy and F1 value of the algorithm model are higher compared with other models. A series of experiments demonstrate that the algorithmic model can perform the classification task against financial long texts more accurately.
Available online: October 19, 2023 , DOI: 10.15888/j.cnki.csa.009311
Abstract:
Graph partitioning algorithm is part of distributed graph computing system. It divides a graph into several subgraphs to run in the distributed system and assigns the vertex and edge data and computing tasks on the subgraphs to each partition. Heterogeneous graph is a kind of graph widely existing in the real world. It is a graph with multiple vertex types or edge types. During the calculation of heterogeneous graphs, the existing graph partition algorithms do not consider the following problems. In graph calculation, different vertex and edge types may carry different data amounts. Meanwhile, different vertex and edge types may adopt different processing algorithms, with various calculation time. Aiming at the shortcomings of the existing graph partitioning methods, this study proposes an online graph partitioning algorithm for heterogeneous graphs, OGP-HG algorithm. Additionally, the existing GraphX graph computing engine is improved to implement the proposed algorithm in the improved graph computing engine. The proposed OGP-HG algorithm calculates the load balance score of vertices divided into different partitions and the data balance score of edges divided into different partitions, thus obtaining the division results of balancing the load and memory occupation of heterogeneous graphs. Experiments show that compared with traditional graph partitioning algorithms, this algorithm improves the computing efficiency of heterogeneous graphs by 1.05–1.4 times.
Graph partitioning algorithm is part of distributed graph computing system. It divides a graph into several subgraphs to run in the distributed system and assigns the vertex and edge data and computing tasks on the subgraphs to each partition. Heterogeneous graph is a kind of graph widely existing in the real world. It is a graph with multiple vertex types or edge types. During the calculation of heterogeneous graphs, the existing graph partition algorithms do not consider the following problems. In graph calculation, different vertex and edge types may carry different data amounts. Meanwhile, different vertex and edge types may adopt different processing algorithms, with various calculation time. Aiming at the shortcomings of the existing graph partitioning methods, this study proposes an online graph partitioning algorithm for heterogeneous graphs, OGP-HG algorithm. Additionally, the existing GraphX graph computing engine is improved to implement the proposed algorithm in the improved graph computing engine. The proposed OGP-HG algorithm calculates the load balance score of vertices divided into different partitions and the data balance score of edges divided into different partitions, thus obtaining the division results of balancing the load and memory occupation of heterogeneous graphs. Experiments show that compared with traditional graph partitioning algorithms, this algorithm improves the computing efficiency of heterogeneous graphs by 1.05–1.4 times.
Available online: September 22, 2023 , DOI: 10.15888/j.cnki.csa.009322
Abstract:
Regarding the challenge of handling nested medical entities in Chinese electronic medical records, this study proposes a knowledge-enhanced named entity recognition model for Chinese electronic medical records called ERBAEGP based on the RoBERTa-wwm-ext-large pre-trained model. The comprehensive word masking strategy employed by the RoBERTa-wwm-ext-large model can obtain semantic representations at the word level, which is more suitable for Chinese texts. First, the model learns a significant number of medical entity nouns by integrating knowledge graphs, further improving entity recognition accuracy in electronic medical records. Then, the contextual semantic information within the records can be better captured through BiLSTM encoding of the input sequence of medical records. Finally, the efficient GlobalPointer (EGP) model is adopted to simultaneously consider the features of both the head and tail of entities to predict nested entities, addressing the challenge of handling nested entities in named entity recognition tasks of Chinese electronic medical records. The effectiveness of the ERBAEGP model is demonstrated by yielding better recognition results on the four datasets within CBLUE.
Regarding the challenge of handling nested medical entities in Chinese electronic medical records, this study proposes a knowledge-enhanced named entity recognition model for Chinese electronic medical records called ERBAEGP based on the RoBERTa-wwm-ext-large pre-trained model. The comprehensive word masking strategy employed by the RoBERTa-wwm-ext-large model can obtain semantic representations at the word level, which is more suitable for Chinese texts. First, the model learns a significant number of medical entity nouns by integrating knowledge graphs, further improving entity recognition accuracy in electronic medical records. Then, the contextual semantic information within the records can be better captured through BiLSTM encoding of the input sequence of medical records. Finally, the efficient GlobalPointer (EGP) model is adopted to simultaneously consider the features of both the head and tail of entities to predict nested entities, addressing the challenge of handling nested entities in named entity recognition tasks of Chinese electronic medical records. The effectiveness of the ERBAEGP model is demonstrated by yielding better recognition results on the four datasets within CBLUE.
Available online: September 22, 2023 , DOI: 10.15888/j.cnki.csa.009337
Abstract:
With the development of intelligent transportation, a large amount of vehicle trajectory data is collected and stored. However, the trajectory data always has anomalous trajectory point data, seriously affecting the accuracy and effectiveness of subsequent trajectory data analysis. This study finds a class of implicit positional anomaly trajectory data that is difficult to be detected by traditional detection methods based on movement feature thresholds but plays a vital role in trajectory data analysis. To this end, this study proposes a method to detect the implicit anomalous trajectory data based on floating grid and clustering method. The parallelization method of data is realized by taking the trajectory data of some cabs in Xi’an as an example. The experimental results show that the data recall and accuracy of the proposed method to detect the hidden location anomaly could reach 0.90, and the F1-score is in the range of 0.88–0.91. The detection of such implicit anomalous trajectory data is beneficial to subsequent analysis and application of spatio-temporal trajectory data.
With the development of intelligent transportation, a large amount of vehicle trajectory data is collected and stored. However, the trajectory data always has anomalous trajectory point data, seriously affecting the accuracy and effectiveness of subsequent trajectory data analysis. This study finds a class of implicit positional anomaly trajectory data that is difficult to be detected by traditional detection methods based on movement feature thresholds but plays a vital role in trajectory data analysis. To this end, this study proposes a method to detect the implicit anomalous trajectory data based on floating grid and clustering method. The parallelization method of data is realized by taking the trajectory data of some cabs in Xi’an as an example. The experimental results show that the data recall and accuracy of the proposed method to detect the hidden location anomaly could reach 0.90, and the F1-score is in the range of 0.88–0.91. The detection of such implicit anomalous trajectory data is beneficial to subsequent analysis and application of spatio-temporal trajectory data.
Available online: September 21, 2023 , DOI: 10.15888/j.cnki.csa.009326
Abstract:
In gear train design, traditional algorithms exhibit drawbacks such as computational complexity and low accuracy. The seagull optimization algorithm (SOA) benefits from its simple algorithmic principle, strong universality, and few parameters, and is now commonly used in engineering design problems. However, the standard SOA is prone to problems such as low optimization accuracy and slow search speed. This study proposes a hybrid strategy improved seagull optimization algorithm (WLSOA). Firstly, it utilizes a nonlinear descent strategy to enhance the exploration and development capabilities of the SOA and improve optimization accuracy. Secondly, the adaptive weight balancing of global and local search capabilities and the addition of Levy flight steps to perturb the current optimal solution are introduced to improve the ability of the algorithm to jump out of the local optimal value. The performance of WLSOA is then explored through simulation experiments on 9 classic test functions, using WLSOA, golden sine algorithm, whale optimization algorithm, particle swarm optimization algorithm, traditional seagull optimization algorithm, and the newly proposed improved seagull optimization algorithm. The results show that WLSOA has higher optimization accuracy and faster convergence speed than the other six algorithms. Finally, in gear train design, a comparison with 13 other common swarm intelligence algorithms reveals that WLSOA has a better solving ability than other algorithms.
In gear train design, traditional algorithms exhibit drawbacks such as computational complexity and low accuracy. The seagull optimization algorithm (SOA) benefits from its simple algorithmic principle, strong universality, and few parameters, and is now commonly used in engineering design problems. However, the standard SOA is prone to problems such as low optimization accuracy and slow search speed. This study proposes a hybrid strategy improved seagull optimization algorithm (WLSOA). Firstly, it utilizes a nonlinear descent strategy to enhance the exploration and development capabilities of the SOA and improve optimization accuracy. Secondly, the adaptive weight balancing of global and local search capabilities and the addition of Levy flight steps to perturb the current optimal solution are introduced to improve the ability of the algorithm to jump out of the local optimal value. The performance of WLSOA is then explored through simulation experiments on 9 classic test functions, using WLSOA, golden sine algorithm, whale optimization algorithm, particle swarm optimization algorithm, traditional seagull optimization algorithm, and the newly proposed improved seagull optimization algorithm. The results show that WLSOA has higher optimization accuracy and faster convergence speed than the other six algorithms. Finally, in gear train design, a comparison with 13 other common swarm intelligence algorithms reveals that WLSOA has a better solving ability than other algorithms.
Available online: September 21, 2023 , DOI: 10.15888/j.cnki.csa.009312
Abstract:
Automatic program repair techniques can realize automatic repair of software defects and employ test suites to evaluate repair patches. However, because of inadequate test suites, the patches passing the test suites may not repair the defects correctly, or even introduce new defects with ripple effects, which results in a large number of overfitting patches generated by automatic program repair. To this end, an overfitting patch identification method based on data flow analysis is proposed. This method firstly decomposes the patch modifications to the program into operations on variables, then adopts data flow analysis to identify the patch influence domain, and selects targeted coverage criteria to identify target coverage elements according to the domain. Finally, test paths are selected and test cases are generated to fully test the repair program to avoid the impact of repairing side effects. This study conducts evaluations on two datasets, and the experimental results show that the overfitting patch identification method based on data flow analysis can improve the correctness of automatic program repair.
Automatic program repair techniques can realize automatic repair of software defects and employ test suites to evaluate repair patches. However, because of inadequate test suites, the patches passing the test suites may not repair the defects correctly, or even introduce new defects with ripple effects, which results in a large number of overfitting patches generated by automatic program repair. To this end, an overfitting patch identification method based on data flow analysis is proposed. This method firstly decomposes the patch modifications to the program into operations on variables, then adopts data flow analysis to identify the patch influence domain, and selects targeted coverage criteria to identify target coverage elements according to the domain. Finally, test paths are selected and test cases are generated to fully test the repair program to avoid the impact of repairing side effects. This study conducts evaluations on two datasets, and the experimental results show that the overfitting patch identification method based on data flow analysis can improve the correctness of automatic program repair.
Available online: September 21, 2023 , DOI: 10.15888/j.cnki.csa.009313
Abstract:
To solve the slow convergence, poor stability, and proneness to fall into local extremes of traditional path planning algorithms, this study proposes a vehicle path planning method based on a gradient statistical mutation quantum genetic algorithm. Firstly, based on the dynamic adjustment of the rotation angle step by the chromosome fitness value, the idea of gradient descent is introduced to improve the adjustment strategy of the quantum rotation gate. According to the statistical characteristics of chromosome variation trend, a mutation operator based on gradient statistics is designed to realize mutation operation, and an adaptive mutation strategy based on Qubit probability density is put forward. Then the vehicle path planning model is built with the shortest path as the index. Finally, the effectiveness of the improved algorithm in vehicle path planning is verified by simulation experiments. Compared with other optimization algorithms, the proposed algorithm has a shorter path and better search stability to avoid the algorithm from falling into the local optimum.
To solve the slow convergence, poor stability, and proneness to fall into local extremes of traditional path planning algorithms, this study proposes a vehicle path planning method based on a gradient statistical mutation quantum genetic algorithm. Firstly, based on the dynamic adjustment of the rotation angle step by the chromosome fitness value, the idea of gradient descent is introduced to improve the adjustment strategy of the quantum rotation gate. According to the statistical characteristics of chromosome variation trend, a mutation operator based on gradient statistics is designed to realize mutation operation, and an adaptive mutation strategy based on Qubit probability density is put forward. Then the vehicle path planning model is built with the shortest path as the index. Finally, the effectiveness of the improved algorithm in vehicle path planning is verified by simulation experiments. Compared with other optimization algorithms, the proposed algorithm has a shorter path and better search stability to avoid the algorithm from falling into the local optimum.
Available online: September 21, 2023 , DOI: 10.15888/j.cnki.csa.009303
Abstract:
Neural machine translation technology can translate the semantic information of multiple languages automatically. Therefore, it has been applied to binary code similarity detection of cross-instruction set architecture successfully. When the sequences of assembly instructions are treated as sequences of textual tokens, the order of instructions is important. When binary basic block-level similarity detection is performed, the neural networks model instruction positions with position embeddings, but it failed to reflect the ordered relationships (e.g., adjacency or precedence) between instructions. To address this problem, this paper uses a continuous function of instruction positions to model the global absolute positions and ordered relationships of assembly instructions, achieving the generalization of word order embeddings. Firstly, the source instruction set architecture (ISA) encoder is constructed by Transformer. Secondly, the target ISA encoder is trained by triplet loss, and the source ISA encoder is fine-tuned. Finally, the Euclidean distances between embedding vectors are mapped to [0,1], which are used as the similarity metrics between basic blocks. The experimental results on the public dataset MISA show that the evaluation metric P@1 of this paper is 69.5%, which is 4.6% higher than the baseline method MIRROR.
Neural machine translation technology can translate the semantic information of multiple languages automatically. Therefore, it has been applied to binary code similarity detection of cross-instruction set architecture successfully. When the sequences of assembly instructions are treated as sequences of textual tokens, the order of instructions is important. When binary basic block-level similarity detection is performed, the neural networks model instruction positions with position embeddings, but it failed to reflect the ordered relationships (e.g., adjacency or precedence) between instructions. To address this problem, this paper uses a continuous function of instruction positions to model the global absolute positions and ordered relationships of assembly instructions, achieving the generalization of word order embeddings. Firstly, the source instruction set architecture (ISA) encoder is constructed by Transformer. Secondly, the target ISA encoder is trained by triplet loss, and the source ISA encoder is fine-tuned. Finally, the Euclidean distances between embedding vectors are mapped to [0,1], which are used as the similarity metrics between basic blocks. The experimental results on the public dataset MISA show that the evaluation metric P@1 of this paper is 69.5%, which is 4.6% higher than the baseline method MIRROR.
Available online: September 21, 2023 , DOI: 10.15888/j.cnki.csa.009316
Abstract:
In the field of image compression perceptual reconstruction, high-quality images are reconstructed with high similarity to the original image, and details are retained to eliminate artifacts through effective image prior information reconstruction. Thus, aiming at the K-space data with insufficient sampling, based on the classic CNN algorithm CBDNet algorithm, this study adopts the method to combine the advantages of fusing deep learning prior information and traditional image restoration. Meanwhile, a hybrid reconstruction algorithm based on priori denoising of deep neural network and compressed sensing algorithm of BM3D block is studied. The algorithm employs an interactive method to train a multi-scale residual network to suppress noise levels and combines deep learning with the multi-scale matching of traditional blocks to extract image feature data at different scales through optimal selection, thus suppressing artifacts and quickly reconstructing high-quality MRI. The experimental results show that deep learning combined with BM3D can reduce artifacts and retain details in MR image reconstruction, enhancing the reconstruction effect. Additionally, the computational complexity of the algorithm is not much more than that of the single algorithm by the GPU accelerated operation. It can be seen that the hybrid MRI based on convolution blind denoising has a better effect.
In the field of image compression perceptual reconstruction, high-quality images are reconstructed with high similarity to the original image, and details are retained to eliminate artifacts through effective image prior information reconstruction. Thus, aiming at the K-space data with insufficient sampling, based on the classic CNN algorithm CBDNet algorithm, this study adopts the method to combine the advantages of fusing deep learning prior information and traditional image restoration. Meanwhile, a hybrid reconstruction algorithm based on priori denoising of deep neural network and compressed sensing algorithm of BM3D block is studied. The algorithm employs an interactive method to train a multi-scale residual network to suppress noise levels and combines deep learning with the multi-scale matching of traditional blocks to extract image feature data at different scales through optimal selection, thus suppressing artifacts and quickly reconstructing high-quality MRI. The experimental results show that deep learning combined with BM3D can reduce artifacts and retain details in MR image reconstruction, enhancing the reconstruction effect. Additionally, the computational complexity of the algorithm is not much more than that of the single algorithm by the GPU accelerated operation. It can be seen that the hybrid MRI based on convolution blind denoising has a better effect.
Available online: September 19, 2023 , DOI: 10.15888/j.cnki.csa.009319
Abstract:
Aiming at the small target scale and low detection accuracy in traffic signal detection, this paper proposes a traffic signal detection algorithm based on improved YOLOv5s. Firstly, a feature pyramid module RSN-BiFPN is constructed to fully integrate traffic signal features of different scales to reduce target missed detection and false detection. Secondly, a new feature fusion layer and prediction head are introduced to improve the perception performance of the network for small objects and enhance detection accuracy. Finally, the EIoU function is adopted to optimize the loss and accelerate network convergence. Experiments conducted on the public dataset S2TLD show that compared with the basic network, the precision rate of the proposed method is increased by 4.1% at 96.1%, the recall rate is 95.9% with an increase of 3%, and the average precision is increased by 1.9%, reaching 96.5%. Meanwhile, the improved algorithm achieves a faster detection speed of 22.7 frames per second. The proposed method can realize rapid and accurate detection of traffic lights and can be widely employed in the research on analyzing traffic lights.
Aiming at the small target scale and low detection accuracy in traffic signal detection, this paper proposes a traffic signal detection algorithm based on improved YOLOv5s. Firstly, a feature pyramid module RSN-BiFPN is constructed to fully integrate traffic signal features of different scales to reduce target missed detection and false detection. Secondly, a new feature fusion layer and prediction head are introduced to improve the perception performance of the network for small objects and enhance detection accuracy. Finally, the EIoU function is adopted to optimize the loss and accelerate network convergence. Experiments conducted on the public dataset S2TLD show that compared with the basic network, the precision rate of the proposed method is increased by 4.1% at 96.1%, the recall rate is 95.9% with an increase of 3%, and the average precision is increased by 1.9%, reaching 96.5%. Meanwhile, the improved algorithm achieves a faster detection speed of 22.7 frames per second. The proposed method can realize rapid and accurate detection of traffic lights and can be widely employed in the research on analyzing traffic lights.
Available online: September 19, 2023 , DOI: 10.15888/j.cnki.csa.009314
Abstract:
The quality of graph partitioning greatly affects the communication overhead and load balance among computers, which is crucial for the performance of large-scale parallel graph computation. However, as the scale of graph data continues to increase, the execution time and memory overhead of graph partitioning algorithms have become inevitable. Therefore, it is necessary to study how to optimize the execution efficiency of graph partitioning algorithms. This study proposes a heuristic graph partitioning method based on weighted graph generation by breadth-first traversal, which introduces only a small amount of preprocessing time overhead while achieving lower communication overhead and better load balance. Experimental results show that our partitioning method reduces replication factors, lowers communication overhead, and only introduces a small amount of time overhead.
The quality of graph partitioning greatly affects the communication overhead and load balance among computers, which is crucial for the performance of large-scale parallel graph computation. However, as the scale of graph data continues to increase, the execution time and memory overhead of graph partitioning algorithms have become inevitable. Therefore, it is necessary to study how to optimize the execution efficiency of graph partitioning algorithms. This study proposes a heuristic graph partitioning method based on weighted graph generation by breadth-first traversal, which introduces only a small amount of preprocessing time overhead while achieving lower communication overhead and better load balance. Experimental results show that our partitioning method reduces replication factors, lowers communication overhead, and only introduces a small amount of time overhead.
Available online: September 19, 2023 , DOI: 10.15888/j.cnki.csa.009315
Abstract:
To solve the low learning efficiency and slow convergence due to the complex relationship among intelligent agents in multi-agent reinforcement learning, this study proposes a two-level attention mechanism based on MADDPG-Attention. The mechanism adds soft and hard attention mechanisms to the Critic network of the MADDPG algorithm and learns the learnable experience among intelligent agents through the attention mechanism to improve the mutual learning efficiency of the agents. Since the single-level soft attention mechanism assigns learning weights to completely irrelevant intelligent agents, hard attention is employed to determine the necessity of learning between two intelligent agents, and the agents with irrelevant information are cut. Then soft attention is adopted to determine the importance of learning between two intelligent agents, and the learning weights are assigned according to the importance distribution to learn from the agents with available experience. Meanwhile, tests on a collaborative navigation environment with multi-agent particles show that the MADDPG-Attention algorithm has a clearer understanding of complex relationships and achieves a success rate of more than 90% in all three environments, which improves the learning efficiency and accelerates the convergence rate.
To solve the low learning efficiency and slow convergence due to the complex relationship among intelligent agents in multi-agent reinforcement learning, this study proposes a two-level attention mechanism based on MADDPG-Attention. The mechanism adds soft and hard attention mechanisms to the Critic network of the MADDPG algorithm and learns the learnable experience among intelligent agents through the attention mechanism to improve the mutual learning efficiency of the agents. Since the single-level soft attention mechanism assigns learning weights to completely irrelevant intelligent agents, hard attention is employed to determine the necessity of learning between two intelligent agents, and the agents with irrelevant information are cut. Then soft attention is adopted to determine the importance of learning between two intelligent agents, and the learning weights are assigned according to the importance distribution to learn from the agents with available experience. Meanwhile, tests on a collaborative navigation environment with multi-agent particles show that the MADDPG-Attention algorithm has a clearer understanding of complex relationships and achieves a success rate of more than 90% in all three environments, which improves the learning efficiency and accelerates the convergence rate.
Available online: September 15, 2023 , DOI: 10.15888/j.cnki.csa.009321
Abstract:
To solve the poor visual experience caused by the uncoupled ship-wave motion in existing navigation simulators, this study develops a set of visual simulation systems for real-time interaction between ship and wave motion. Firstly, the wave motion scene is built by wave spectrum modeling technology. Then, based on building the ship force model, the ship’s response to wave force is realized by collision detection between the ship and the water surface to calculate the real-time response attitude of the ship to wave motion. Meanwhile, the wave formula is adopted to calculate the water wave generated by the collision between the ship and the water body and its diffusion and enhance the realism of the simulation system. Compared with traditional navigation simulators, the navigation simulator coupled with ship-wave motion can provide more realistic visual and sports experience in high level sea conditions. The results indicate strong visual realism and sound real-time interaction between ship and wave, with a sound simulation effect on the navigation in bad navigation conditions.
To solve the poor visual experience caused by the uncoupled ship-wave motion in existing navigation simulators, this study develops a set of visual simulation systems for real-time interaction between ship and wave motion. Firstly, the wave motion scene is built by wave spectrum modeling technology. Then, based on building the ship force model, the ship’s response to wave force is realized by collision detection between the ship and the water surface to calculate the real-time response attitude of the ship to wave motion. Meanwhile, the wave formula is adopted to calculate the water wave generated by the collision between the ship and the water body and its diffusion and enhance the realism of the simulation system. Compared with traditional navigation simulators, the navigation simulator coupled with ship-wave motion can provide more realistic visual and sports experience in high level sea conditions. The results indicate strong visual realism and sound real-time interaction between ship and wave, with a sound simulation effect on the navigation in bad navigation conditions.
Available online: September 15, 2023 , DOI: 10.15888/j.cnki.csa.009308
Abstract:
To address the low feasibility of human pose estimation algorithms and low accuracy of jump rope counting based on pose estimation, this study proposes a jump rope counting algorithm based on a lightweight human pose estimation network. The algorithm first inputs a jump rope video, then extracts keyframe images by inter-frame difference method, and feeds them into the human pose estimation network for key joint point detection. To improve the detection accuracy of the lightweight network, the study builds an optimized LitePose detection model, which employs adaptive perception decoding to optimize the decoding part in the model and reduce quantization errors. Furthermore, a Kalman filter is adopted to smooth and denoise the coordinate data, reducing coordinate jitter errors. Finally, jump rope counting is determined based on the changes in key-point coordinates. Experimental results demonstrate that, in the same image resolution and environmental conditions, the proposed algorithm employing the optimized LitePose-S network model does not increase the parameter size and computational complexity of the model but improves network detection accuracy by 0.7% compared with other comparison networks. Meanwhile, the average error rate of this algorithm in jump rope counting can reach a minimum of 1.00%. The algorithm effectively determines the takeoff and landing of the human body by the results of human pose estimation and yields counting results.
To address the low feasibility of human pose estimation algorithms and low accuracy of jump rope counting based on pose estimation, this study proposes a jump rope counting algorithm based on a lightweight human pose estimation network. The algorithm first inputs a jump rope video, then extracts keyframe images by inter-frame difference method, and feeds them into the human pose estimation network for key joint point detection. To improve the detection accuracy of the lightweight network, the study builds an optimized LitePose detection model, which employs adaptive perception decoding to optimize the decoding part in the model and reduce quantization errors. Furthermore, a Kalman filter is adopted to smooth and denoise the coordinate data, reducing coordinate jitter errors. Finally, jump rope counting is determined based on the changes in key-point coordinates. Experimental results demonstrate that, in the same image resolution and environmental conditions, the proposed algorithm employing the optimized LitePose-S network model does not increase the parameter size and computational complexity of the model but improves network detection accuracy by 0.7% compared with other comparison networks. Meanwhile, the average error rate of this algorithm in jump rope counting can reach a minimum of 1.00%. The algorithm effectively determines the takeoff and landing of the human body by the results of human pose estimation and yields counting results.
Available online: September 15, 2023 , DOI: 10.15888/j.cnki.csa.009329
Abstract:
AIS data refers to the vessel’s motion trajectory information obtained through the AIS system. Mining AIS data can provide insights into the vessel’s motion patterns, navigation routes, docking locations, etc. However, outliers generated during the AIS data collection can have a negative effect on clustering and other tasks. Therefore, outlier detection on AIS data before mining is necessary. However, when there are a large number of outliers in AIS trajectory data, a significant decrease occurs in the accuracy of most outlier detection algorithms. To address this issue, this study proposes a trajectory outlier detection based on center shift (CSOD). The CSOD algorithm encourages data points to move towards the center of their K-nearest neighbor (KNN) set, making each data point closer to typical data and effectively eliminating the influence of outliers on clustering. To validate the effectiveness of the proposed algorithm, the study conducts comparative experiments between the CSOD algorithm and several classical outlier detection algorithms using the AIS fishing vessel trajectory dataset in the Zhejiang sea area. The experimental results demonstrate that the CSOD algorithm outperforms the other algorithms in terms of overall performance.
AIS data refers to the vessel’s motion trajectory information obtained through the AIS system. Mining AIS data can provide insights into the vessel’s motion patterns, navigation routes, docking locations, etc. However, outliers generated during the AIS data collection can have a negative effect on clustering and other tasks. Therefore, outlier detection on AIS data before mining is necessary. However, when there are a large number of outliers in AIS trajectory data, a significant decrease occurs in the accuracy of most outlier detection algorithms. To address this issue, this study proposes a trajectory outlier detection based on center shift (CSOD). The CSOD algorithm encourages data points to move towards the center of their K-nearest neighbor (KNN) set, making each data point closer to typical data and effectively eliminating the influence of outliers on clustering. To validate the effectiveness of the proposed algorithm, the study conducts comparative experiments between the CSOD algorithm and several classical outlier detection algorithms using the AIS fishing vessel trajectory dataset in the Zhejiang sea area. The experimental results demonstrate that the CSOD algorithm outperforms the other algorithms in terms of overall performance.
Available online: March 31, 2022 , DOI: 10.15888/j.cnki.csa.008603
Abstract:
The security of electric energy plays an important role in national security. With the development of power 5G communication, a large number of power terminals have positioning demand. The traditional global positioning system (GPS) is vulnerable to spoofing. How to improve the security of GPS effectively has become an urgent problem. This study proposes a GPS spoofing detection algorithm with base station assistance in power 5G terminals. It uses the base station positioning with high security to verify the GPS positioning that may be spoofed and introduces the consistency factor (CF) to measure the consistency between GPS positioning and base station positioning. If CF is greater than a threshold, the GPS positioning is classified as spoofed. Otherwise, it is judged as normal. The experimental results show that the accuracy of the algorithm is 99.98%, higher than that of traditional classification algorithms based on machine learning. In addition, our scheme is also faster than those algorithms.
The security of electric energy plays an important role in national security. With the development of power 5G communication, a large number of power terminals have positioning demand. The traditional global positioning system (GPS) is vulnerable to spoofing. How to improve the security of GPS effectively has become an urgent problem. This study proposes a GPS spoofing detection algorithm with base station assistance in power 5G terminals. It uses the base station positioning with high security to verify the GPS positioning that may be spoofed and introduces the consistency factor (CF) to measure the consistency between GPS positioning and base station positioning. If CF is greater than a threshold, the GPS positioning is classified as spoofed. Otherwise, it is judged as normal. The experimental results show that the accuracy of the algorithm is 99.98%, higher than that of traditional classification algorithms based on machine learning. In addition, our scheme is also faster than those algorithms.
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Abstract:
本文详细讨论了VRML技术与其他数据访问技术相结合 ,实现对数据库实时交互的技术实现方法 ,并简要阐述了相关技术规范的语法结构和技术要求。所用技术手段安全可靠 ,具有良好的实际应用表现 ,便于系统移植。
本文详细讨论了VRML技术与其他数据访问技术相结合 ,实现对数据库实时交互的技术实现方法 ,并简要阐述了相关技术规范的语法结构和技术要求。所用技术手段安全可靠 ,具有良好的实际应用表现 ,便于系统移植。
Abstract:
本文介绍了作者近年来应用工具软件NU清除磁盘引导区和硬盘主引导区病毒、修复引导区损坏磁盘的 经验，经实践检验，简便有效。
本文介绍了作者近年来应用工具软件NU清除磁盘引导区和硬盘主引导区病毒、修复引导区损坏磁盘的 经验，经实践检验，简便有效。
Abstract:
本文简要介绍了海关EDI自动化通关系统的定义概况及重要意义，对该EDI应用系统下的业务运作模式所涉及的法律问题，采用EDIFACT国际标准问题、网络与软件技术问题，以及工程管理问题进行了结合实际的分析。
本文简要介绍了海关EDI自动化通关系统的定义概况及重要意义，对该EDI应用系统下的业务运作模式所涉及的法律问题，采用EDIFACT国际标准问题、网络与软件技术问题，以及工程管理问题进行了结合实际的分析。
2016,25(8):1-7, DOI: 10.15888/j.cnki.csa.005283
Abstract:
从2006年开始，深度神经网络在图像/语音识别、自动驾驶等大数据处理和人工智能领域中都取得了巨大成功，其中无监督学习方法作为深度神经网络中的预训练方法为深度神经网络的成功起到了非常重要的作用. 为此，对深度学习中的无监督学习方法进行了介绍和分析，主要总结了两类常用的无监督学习方法，即确定型的自编码方法和基于概率型受限玻尔兹曼机的对比散度等学习方法，并介绍了这两类方法在深度学习系统中的应用，最后对无监督学习面临的问题和挑战进行了总结和展望.
从2006年开始，深度神经网络在图像/语音识别、自动驾驶等大数据处理和人工智能领域中都取得了巨大成功，其中无监督学习方法作为深度神经网络中的预训练方法为深度神经网络的成功起到了非常重要的作用. 为此，对深度学习中的无监督学习方法进行了介绍和分析，主要总结了两类常用的无监督学习方法，即确定型的自编码方法和基于概率型受限玻尔兹曼机的对比散度等学习方法，并介绍了这两类方法在深度学习系统中的应用，最后对无监督学习面临的问题和挑战进行了总结和展望.
Abstract:
在研究了目前主流的视频转码方案基础上,提出了一种分布式转码系统。系统采用HDFS(HadoopDistributed File System)进行视频存储,利用MapReduce 思想和FFMPEG 进行分布式转码。详细讨论了视频分布式存储时的分段策略,以及分段大小对存取时间的影响。同时,定义了视频存储和转换的元数据格式。提出了基于MapReduce 编程框架的分布式转码方案,即Mapper 端进行转码和Reducer 端进行视频合并。实验数据显示了转码时间随视频分段大小和转码机器数量不同而变化的趋势。结
在研究了目前主流的视频转码方案基础上,提出了一种分布式转码系统。系统采用HDFS(HadoopDistributed File System)进行视频存储,利用MapReduce 思想和FFMPEG 进行分布式转码。详细讨论了视频分布式存储时的分段策略,以及分段大小对存取时间的影响。同时,定义了视频存储和转换的元数据格式。提出了基于MapReduce 编程框架的分布式转码方案,即Mapper 端进行转码和Reducer 端进行视频合并。实验数据显示了转码时间随视频分段大小和转码机器数量不同而变化的趋势。结
Abstract:
随着Internet的迅速发展，网络资源越来越丰富，人们如何从网络上抽取信息也变得至关重要，尤其是占网络资源80%的Deep Web信息检索更是人们应该倍加关注的难点问题。为了更好的研究Deep Web爬虫技术，本文对有关Deep Web爬虫的内容进行了全面、详细地介绍。首先对Deep Web爬虫的定义及研究目标进行了阐述，接着介绍了近年来国内外关于Deep Web爬虫的研究进展，并对其加以分析。在此基础上展望了Deep Web爬虫的研究趋势，为下一步的研究奠定了基础。
随着Internet的迅速发展，网络资源越来越丰富，人们如何从网络上抽取信息也变得至关重要，尤其是占网络资源80%的Deep Web信息检索更是人们应该倍加关注的难点问题。为了更好的研究Deep Web爬虫技术，本文对有关Deep Web爬虫的内容进行了全面、详细地介绍。首先对Deep Web爬虫的定义及研究目标进行了阐述，接着介绍了近年来国内外关于Deep Web爬虫的研究进展，并对其加以分析。在此基础上展望了Deep Web爬虫的研究趋势，为下一步的研究奠定了基础。
Abstract:
用较少的颜色来表示较大的色彩空间一直是人们研究的课题，本文详细讨论了半色调技术和抖动技术，并将它们扩展到实用的真彩色空间来讨论，并给出了实现的算法。
用较少的颜色来表示较大的色彩空间一直是人们研究的课题，本文详细讨论了半色调技术和抖动技术，并将它们扩展到实用的真彩色空间来讨论，并给出了实现的算法。
Abstract:
本文结合物流遗留系统的实际安全状态,分析了面向对象的编程思想在横切关注点和核心关注点处理上的不足,指出面向方面的编程思想解决方案对系统进行分离关注点处理的优势,并对面向方面的编程的一种具体实现AspectJ进行分析,提出了一种依据AspectJ对遗留物流系统进行IC卡安全进化的方法.
本文结合物流遗留系统的实际安全状态,分析了面向对象的编程思想在横切关注点和核心关注点处理上的不足,指出面向方面的编程思想解决方案对系统进行分离关注点处理的优势,并对面向方面的编程的一种具体实现AspectJ进行分析,提出了一种依据AspectJ对遗留物流系统进行IC卡安全进化的方法.
Abstract:
开放平台的核心问题是用户验证和授权问题,OAuth 是目前国际通用的授权方式,它的特点是不需要用户在第三方应用输入用户名及密码,就可以申请访问该用户的受保护资源。OAuth 最新版本是OAuth2.0,其认证与授权的流程更简单、更安全。研究了OAuth2.0 的工作原理,分析了刷新访问令牌的工作流程,并给出了OAuth2.0 服务器端的设计方案和具体的应用实例。
开放平台的核心问题是用户验证和授权问题,OAuth 是目前国际通用的授权方式,它的特点是不需要用户在第三方应用输入用户名及密码,就可以申请访问该用户的受保护资源。OAuth 最新版本是OAuth2.0,其认证与授权的流程更简单、更安全。研究了OAuth2.0 的工作原理,分析了刷新访问令牌的工作流程,并给出了OAuth2.0 服务器端的设计方案和具体的应用实例。
Abstract:
针对智能家居、环境监测等的实际要求，设计了一种远距离通讯的无线传感器节点。该系统采用集射频与控制器于一体的第二代片上系统CC2530 为核心模块，外接CC2591 射频前端功放模块；软件上基于ZigBee2006 协议栈，在ZStack 通用模块基础上实现应用层各项功能。介绍了基于ZigBee 协议构建无线数据采集网络，给出了传感器节点、协调器节点的硬件设计原理图及软件流程图。实验证明节点性能良好、通讯可靠,通讯距离较TI 第一代产品有明显增大。
针对智能家居、环境监测等的实际要求，设计了一种远距离通讯的无线传感器节点。该系统采用集射频与控制器于一体的第二代片上系统CC2530 为核心模块，外接CC2591 射频前端功放模块；软件上基于ZigBee2006 协议栈，在ZStack 通用模块基础上实现应用层各项功能。介绍了基于ZigBee 协议构建无线数据采集网络，给出了传感器节点、协调器节点的硬件设计原理图及软件流程图。实验证明节点性能良好、通讯可靠,通讯距离较TI 第一代产品有明显增大。
Abstract:
Abstract:
本文介绍了车辆监控系统的组成,研究了如何应用Rockwell GPS OEM板和WISMOQUIKQ2406B模块进行移动单元的软硬件设计,以及监控中心 GIS软件的设计.重点介绍嵌入TCP/IP协议处理的Q2406B模块如何通过AT指令接入Internet以及如何和监控中心传输TCP数据.
本文介绍了车辆监控系统的组成,研究了如何应用Rockwell GPS OEM板和WISMOQUIKQ2406B模块进行移动单元的软硬件设计,以及监控中心 GIS软件的设计.重点介绍嵌入TCP/IP协议处理的Q2406B模块如何通过AT指令接入Internet以及如何和监控中心传输TCP数据.
Abstract:
排序是计算机程序设计中一种重要操作，本文论述了C语言中快速排序算法的改进，即快速排序与直接插入排序算法相结合的实现过程。在C语言程序设计中，实现大量的内部排序应用时，所寻求的目的就是找到一个简单、有效、快捷的算法。本文着重阐述快速排序的改进与提高过程，从基本的性能特征到基本的算法改进，通过不断的分析，实验，最后得出最佳的改进算法。
排序是计算机程序设计中一种重要操作，本文论述了C语言中快速排序算法的改进，即快速排序与直接插入排序算法相结合的实现过程。在C语言程序设计中，实现大量的内部排序应用时，所寻求的目的就是找到一个简单、有效、快捷的算法。本文着重阐述快速排序的改进与提高过程，从基本的性能特征到基本的算法改进，通过不断的分析，实验，最后得出最佳的改进算法。
Abstract:
随着面向对象软件开发技术的广泛应用和软件测试自动化的要求，基于模型的软件测试逐渐得到了软件开发人员和软件测试人员的认可和接受。基于模型的软件测试是软件编码阶段的主要测试方法之一，具有测试效率高、排除逻辑复杂故障测试效果好等特点。但是误报、漏报和故障机理有待进一步研究。对主要的测试模型进行了分析和分类，同时，对故障密度等参数进行了初步的分析；最后，提出了一种基于模型的软件测试流程。
随着面向对象软件开发技术的广泛应用和软件测试自动化的要求，基于模型的软件测试逐渐得到了软件开发人员和软件测试人员的认可和接受。基于模型的软件测试是软件编码阶段的主要测试方法之一，具有测试效率高、排除逻辑复杂故障测试效果好等特点。但是误报、漏报和故障机理有待进一步研究。对主要的测试模型进行了分析和分类，同时，对故障密度等参数进行了初步的分析；最后，提出了一种基于模型的软件测试流程。
Abstract:
本文介绍了一个企业信息门户中单点登录系统的设计与实现。系统实现了一个基于Java EE架构的结合凭证加密和Web Services的单点登录系统，对门户用户进行统一认证和访问控制。论文详细阐述了该系统的总体结构、设计思想、工作原理和具体实现方案，目前系统已在部分省市的广电行业信息门户平台中得到了良好的应用。
本文介绍了一个企业信息门户中单点登录系统的设计与实现。系统实现了一个基于Java EE架构的结合凭证加密和Web Services的单点登录系统，对门户用户进行统一认证和访问控制。论文详细阐述了该系统的总体结构、设计思想、工作原理和具体实现方案，目前系统已在部分省市的广电行业信息门户平台中得到了良好的应用。
Abstract:
本文介绍了Visual C++6.0在对话框的多个文本框之间,通过回车键转移焦点的几种方法,并提出了一个改进方法.
本文介绍了Visual C++6.0在对话框的多个文本框之间,通过回车键转移焦点的几种方法,并提出了一个改进方法.
Abstract:
介绍了一种基于DWGDirectX在不依赖于AutoCAD平台的情况下实现DWG文件的显示、操作、添加的简单的实体的方法，并对该方法进行了分析和实现。
介绍了一种基于DWGDirectX在不依赖于AutoCAD平台的情况下实现DWG文件的显示、操作、添加的简单的实体的方法，并对该方法进行了分析和实现。
Abstract:
DICOM 是医学图像存储和传输的国际标准，DCMTK 是免费开源的针对DICOM 标准的开发包。解读DICOM 文件格式并解决DICOM 医学图像显示问题是医学图像处理的基础，对医学影像技术的研究具有重要意义。解读了DICOM 文件格式并介绍了调窗处理的原理，利用VC++和DCMTK 实现医学图像显示和调窗功能。
DICOM 是医学图像存储和传输的国际标准，DCMTK 是免费开源的针对DICOM 标准的开发包。解读DICOM 文件格式并解决DICOM 医学图像显示问题是医学图像处理的基础，对医学影像技术的研究具有重要意义。解读了DICOM 文件格式并介绍了调窗处理的原理，利用VC++和DCMTK 实现医学图像显示和调窗功能。
Abstract:
综合考虑基于构件组装技术的虚拟实验室的系统需求，分析了工作流驱动的动态虚拟实验室的业务处理模型，介绍了轻量级J2EE框架(SSH)与工作流系统(Shark和JaWE)的集成模型，提出了一种轻量级J2EE框架下工作流驱动的动态虚拟实验室的设计和实现方法，给出了虚拟实验项目的实现机制、数据流和控制流的管理方法，以及实验流程的动态组装方法，最后，以应用实例说明了本文方法的有效性。
综合考虑基于构件组装技术的虚拟实验室的系统需求，分析了工作流驱动的动态虚拟实验室的业务处理模型，介绍了轻量级J2EE框架(SSH)与工作流系统(Shark和JaWE)的集成模型，提出了一种轻量级J2EE框架下工作流驱动的动态虚拟实验室的设计和实现方法，给出了虚拟实验项目的实现机制、数据流和控制流的管理方法，以及实验流程的动态组装方法，最后，以应用实例说明了本文方法的有效性。
Abstract:
本文介绍了一种将DTD转换成ER图,并用XMLApplication将ER图描述成转换标准,然后根据该转换标准将XML文档转换为关系模型的方法.
本文介绍了一种将DTD转换成ER图,并用XMLApplication将ER图描述成转换标准,然后根据该转换标准将XML文档转换为关系模型的方法.
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Abstract:
论文对HDF数据格式和函数库进行研究,重点以栅格图像为例,详细论述如何利用VC++.net和VC#.net对光栅数据进行读取与处理,然后根据所得到的象素矩阵用描点法显示图像.论文是以国家气象中心开发Micaps3.0(气象信息综合分析处理系统)的课题研究为背景的.
论文对HDF数据格式和函数库进行研究,重点以栅格图像为例,详细论述如何利用VC++.net和VC#.net对光栅数据进行读取与处理,然后根据所得到的象素矩阵用描点法显示图像.论文是以国家气象中心开发Micaps3.0(气象信息综合分析处理系统)的课题研究为背景的.
Abstract:
本文介绍非实时操作系统Windows 2000下,利用VisualC++6.0开发实时数据采集的方法.所用到的数据采集卡是研华的PCL-818L.借助数据采集卡PCL-818L的DLLs中的API函数,提出三种实现高速实时数据采集的方法及优缺点.
本文介绍非实时操作系统Windows 2000下,利用VisualC++6.0开发实时数据采集的方法.所用到的数据采集卡是研华的PCL-818L.借助数据采集卡PCL-818L的DLLs中的API函数,提出三种实现高速实时数据采集的方法及优缺点.
Abstract:
排序是计算机程序设计中一种重要操作，本文论述了C语言中快速排序算法的改进，即快速排序与直接插入排序算法相结合的实现过程。在C语言程序设计中，实现大量的内部排序应用时，所寻求的目的就是找到一个简单、有效、快捷的算法。本文着重阐述快速排序的改进与提高过程，从基本的性能特征到基本的算法改进，通过不断的分析，实验，最后得出最佳的改进算法。
排序是计算机程序设计中一种重要操作，本文论述了C语言中快速排序算法的改进，即快速排序与直接插入排序算法相结合的实现过程。在C语言程序设计中，实现大量的内部排序应用时，所寻求的目的就是找到一个简单、有效、快捷的算法。本文着重阐述快速排序的改进与提高过程，从基本的性能特征到基本的算法改进，通过不断的分析，实验，最后得出最佳的改进算法。
Abstract:
随着Internet的迅速发展，网络资源越来越丰富，人们如何从网络上抽取信息也变得至关重要，尤其是占网络资源80%的Deep Web信息检索更是人们应该倍加关注的难点问题。为了更好的研究Deep Web爬虫技术，本文对有关Deep Web爬虫的内容进行了全面、详细地介绍。首先对Deep Web爬虫的定义及研究目标进行了阐述，接着介绍了近年来国内外关于Deep Web爬虫的研究进展，并对其加以分析。在此基础上展望了Deep Web爬虫的研究趋势，为下一步的研究奠定了基础。
随着Internet的迅速发展，网络资源越来越丰富，人们如何从网络上抽取信息也变得至关重要，尤其是占网络资源80%的Deep Web信息检索更是人们应该倍加关注的难点问题。为了更好的研究Deep Web爬虫技术，本文对有关Deep Web爬虫的内容进行了全面、详细地介绍。首先对Deep Web爬虫的定义及研究目标进行了阐述，接着介绍了近年来国内外关于Deep Web爬虫的研究进展，并对其加以分析。在此基础上展望了Deep Web爬虫的研究趋势，为下一步的研究奠定了基础。
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2020,29(12):13-25, DOI: 10.15888/j.cnki.csa.007701
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2019,28(12):1-8, DOI: 10.15888/j.cnki.csa.007195
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2018,27(10):39-45, DOI: 10.15888/j.cnki.csa.006591
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