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2024,33(9):1-13, DOI: 10.15888/j.cnki.csa.009638
Abstract:
Single-cell RNA sequencing (scRNA-seq) performs high-throughput sequencing analysis of the transcriptomes at the level of individual cells. Its primary application is to identify cell subpopulations with distinct functions, usually based on cell clustering. However, the high dimensionality, noise, and sparsity of scRNA-seq data make clustering challenging. Traditional clustering methods are inadequate, and most existing single-cell clustering approaches only consider gene expression patterns while ignoring relationships between cells. To address these issues, a self-optimizing single-cell clustering method with contrastive learning and graph neural network (scCLG) is proposed. This method employs an autoencoder to learn cellular feature distribution. First, it begins by constructing a cell-gene graph, which is encoded using a graph neural network to effectively harness information on intercellular relationships. Subgraph sampling and feature masking create augmented views for contrastive learning, further optimizing feature representation. Finally, a self-optimizing strategy is utilized to jointly train the clustering and feature modules, continually refining feature representation and clustering centers for more accurate clustering. Experiments on 10 real scRNA-seq datasets demonstrate that scCLG can learn robust representations of cell features, significantly surpassing other methods in clustering accuracy.
Single-cell RNA sequencing (scRNA-seq) performs high-throughput sequencing analysis of the transcriptomes at the level of individual cells. Its primary application is to identify cell subpopulations with distinct functions, usually based on cell clustering. However, the high dimensionality, noise, and sparsity of scRNA-seq data make clustering challenging. Traditional clustering methods are inadequate, and most existing single-cell clustering approaches only consider gene expression patterns while ignoring relationships between cells. To address these issues, a self-optimizing single-cell clustering method with contrastive learning and graph neural network (scCLG) is proposed. This method employs an autoencoder to learn cellular feature distribution. First, it begins by constructing a cell-gene graph, which is encoded using a graph neural network to effectively harness information on intercellular relationships. Subgraph sampling and feature masking create augmented views for contrastive learning, further optimizing feature representation. Finally, a self-optimizing strategy is utilized to jointly train the clustering and feature modules, continually refining feature representation and clustering centers for more accurate clustering. Experiments on 10 real scRNA-seq datasets demonstrate that scCLG can learn robust representations of cell features, significantly surpassing other methods in clustering accuracy.
2024,33(9):14-27, DOI: 10.15888/j.cnki.csa.009609
Abstract:
Dimensionality reduction plays a crucial role in machine learning and pattern recognition. The existing projection-based methods tend to solely utilize distance information or representation relationships among data points to maintain the data structure, which makes it difficult to effectively capture the nonlinear features and complex correlations of data manifolds in high-dimensional space. To address this issue, this study proposes a method: enhanced locality preserving projection with latent sparse representation learning (LPP_SRL). The method not only utilizes distance information to preserve the local structure of the data but also leverages multiple local linear representations to unveil the global nonlinear structure of the data. Moreover, to establish a connection between projection learning and sparse self-representation, this study employs a novel strategy by replacing the dictionary in sparse self-representation with reconstructed samples from the low-dimensional representation. This approach effectively filters out irrelevant features and noise, thereby better preserving the principal components in the original feature space. Extensive experiments conducted on multiple publicly available benchmark datasets have demonstrated the effectiveness and superiority of the proposed method.
Dimensionality reduction plays a crucial role in machine learning and pattern recognition. The existing projection-based methods tend to solely utilize distance information or representation relationships among data points to maintain the data structure, which makes it difficult to effectively capture the nonlinear features and complex correlations of data manifolds in high-dimensional space. To address this issue, this study proposes a method: enhanced locality preserving projection with latent sparse representation learning (LPP_SRL). The method not only utilizes distance information to preserve the local structure of the data but also leverages multiple local linear representations to unveil the global nonlinear structure of the data. Moreover, to establish a connection between projection learning and sparse self-representation, this study employs a novel strategy by replacing the dictionary in sparse self-representation with reconstructed samples from the low-dimensional representation. This approach effectively filters out irrelevant features and noise, thereby better preserving the principal components in the original feature space. Extensive experiments conducted on multiple publicly available benchmark datasets have demonstrated the effectiveness and superiority of the proposed method.
2024,33(9):28-37, DOI: 10.15888/j.cnki.csa.009623
Abstract:
Accurate estimation of tropical cyclone intensity is the basis of effective intensity prediction and is crucial for disaster forecasting. Current tropical cyclone intensity estimation technology based on deep learning shows superior performance, but there is still a problem of insufficient physical information fusion. Therefore, based on the deep learning framework, this study proposes a physical factor fusion for tropical cyclone intensity estimation model (PF-TCIE) to estimate the intensity of tropical cyclones in the northwest Pacific. PF-TCIE consists of a multi-channel satellite cloud image learning branch and a physical information extraction branch. The multi-channel satellite cloud image learning branch is used to extract tropical cyclone cloud system features, and the physical information extraction branch is used to extract physical factor features to constrain the learning of cloud system features. The data used in this article include Himawari-8 satellite data and ERA-5 reanalysis data. Experimental results show that after introducing multiple channels, the root mean squared error (RMSE) of the model is reduced by 3.7% compared with a single channel. At the same time, the introduction of physical information further reduces the model error by 8.5%. The RMSE of PF-TCIE finally reaches 4.83 m/s, which is better than most deep learning methods.
Accurate estimation of tropical cyclone intensity is the basis of effective intensity prediction and is crucial for disaster forecasting. Current tropical cyclone intensity estimation technology based on deep learning shows superior performance, but there is still a problem of insufficient physical information fusion. Therefore, based on the deep learning framework, this study proposes a physical factor fusion for tropical cyclone intensity estimation model (PF-TCIE) to estimate the intensity of tropical cyclones in the northwest Pacific. PF-TCIE consists of a multi-channel satellite cloud image learning branch and a physical information extraction branch. The multi-channel satellite cloud image learning branch is used to extract tropical cyclone cloud system features, and the physical information extraction branch is used to extract physical factor features to constrain the learning of cloud system features. The data used in this article include Himawari-8 satellite data and ERA-5 reanalysis data. Experimental results show that after introducing multiple channels, the root mean squared error (RMSE) of the model is reduced by 3.7% compared with a single channel. At the same time, the introduction of physical information further reduces the model error by 8.5%. The RMSE of PF-TCIE finally reaches 4.83 m/s, which is better than most deep learning methods.
2024,33(9):38-47, DOI: 10.15888/j.cnki.csa.009605
Abstract:
This study constructs a named entity recognition (NER) model suitable for the bone-sign interpretations of Han Chang’an City to solve the problem of the inability to classify some bone-sign interpretations due to the lack of key content. The original text of the bone-sign interpretations of Han Chang’an City is used as the dataset, and the begin, inside, outside, end (BIOE) annotation method is utilized to annotate the bone-sign interpretation entities. A multi-feature fusion network (MFFN) model is proposed, which not only considers the structural features of individual characters but also integrates the structural features of character-word combinations to enhance the model’s comprehension of the bone-sign interpretations. The experimental results demonstrate that the MFFN model can better identify the named entities of the bone-sign interpretations of Han Chang’an City and classify the bone-sign interpretations, outperforming existing NER models. This model provides historians and researchers with richer and more precise data support.
This study constructs a named entity recognition (NER) model suitable for the bone-sign interpretations of Han Chang’an City to solve the problem of the inability to classify some bone-sign interpretations due to the lack of key content. The original text of the bone-sign interpretations of Han Chang’an City is used as the dataset, and the begin, inside, outside, end (BIOE) annotation method is utilized to annotate the bone-sign interpretation entities. A multi-feature fusion network (MFFN) model is proposed, which not only considers the structural features of individual characters but also integrates the structural features of character-word combinations to enhance the model’s comprehension of the bone-sign interpretations. The experimental results demonstrate that the MFFN model can better identify the named entities of the bone-sign interpretations of Han Chang’an City and classify the bone-sign interpretations, outperforming existing NER models. This model provides historians and researchers with richer and more precise data support.
2024,33(9):48-57, DOI: 10.15888/j.cnki.csa.009612
Abstract:
In the task of few-shot open-set recognition (FSOSR), effectively distinguishing closed-set from open-set samples presents a notable challenge, especially in cases of sample scarcity. Current approaches exhibit uncertainty in describing boundaries for known class distributions, leading to insufficient discrimination between closed-set and open-set spaces. To tackle this issue, this study introduces a novel method for FSOSR leveraging feature decoupling and openness learning. The primary objective is to employ a feature decoupling module to compel the model to decouple class-specific features and open-set features, thereby accentuating the disparity between unknown and known classes. To achieve effective feature decoupling, an openness learning loss is introduced to facilitate the acquisition of open-set features. By integrating similarity metric values and anti-openness scores as the optimization target, the model is steered towards learning more discriminative feature representations. Experimental results on publicly datasets miniImageNet and tieredImageNet demonstrate that the proposed method substantially enhances the detection rate of unknown class samples while accurately classifying known classes.
In the task of few-shot open-set recognition (FSOSR), effectively distinguishing closed-set from open-set samples presents a notable challenge, especially in cases of sample scarcity. Current approaches exhibit uncertainty in describing boundaries for known class distributions, leading to insufficient discrimination between closed-set and open-set spaces. To tackle this issue, this study introduces a novel method for FSOSR leveraging feature decoupling and openness learning. The primary objective is to employ a feature decoupling module to compel the model to decouple class-specific features and open-set features, thereby accentuating the disparity between unknown and known classes. To achieve effective feature decoupling, an openness learning loss is introduced to facilitate the acquisition of open-set features. By integrating similarity metric values and anti-openness scores as the optimization target, the model is steered towards learning more discriminative feature representations. Experimental results on publicly datasets miniImageNet and tieredImageNet demonstrate that the proposed method substantially enhances the detection rate of unknown class samples while accurately classifying known classes.
2024,33(9):58-64, DOI: 10.15888/j.cnki.csa.009615
Abstract:
Knowledge distillation (KD) is a technique that transfers knowledge from a complex model (teacher model) to a simpler model (student model). While many popular distillation methods currently focus on intermediate feature layers, response-based knowledge distillation (RKD) has regained its position among the SOTA models after decoupled knowledge distillation (DKD) was introduced. RKD leverages strong consistency constraints to split classic knowledge distillation into two parts, addressing the issue of high coupling. However, this approach overlooks the significant representation gap caused by the disparity in teacher-student network architectures, leading to the problem where smaller student models cannot effectively learn knowledge from teacher models. To solve this problem, this study proposes a diffusion model to narrow the representation gap between teacher and student models. This model transfers teacher features to train a lightweight diffusion model, which is then used to denoise the student model, thus reducing the representation gap between teacher and student models. Extensive experiments demonstrate that the proposed model achieves significant improvements over baseline models on CIFAR-100 and ImageNet datasets, maintaining good performance even when there is a large gap in teacher-student network architectures.
Knowledge distillation (KD) is a technique that transfers knowledge from a complex model (teacher model) to a simpler model (student model). While many popular distillation methods currently focus on intermediate feature layers, response-based knowledge distillation (RKD) has regained its position among the SOTA models after decoupled knowledge distillation (DKD) was introduced. RKD leverages strong consistency constraints to split classic knowledge distillation into two parts, addressing the issue of high coupling. However, this approach overlooks the significant representation gap caused by the disparity in teacher-student network architectures, leading to the problem where smaller student models cannot effectively learn knowledge from teacher models. To solve this problem, this study proposes a diffusion model to narrow the representation gap between teacher and student models. This model transfers teacher features to train a lightweight diffusion model, which is then used to denoise the student model, thus reducing the representation gap between teacher and student models. Extensive experiments demonstrate that the proposed model achieves significant improvements over baseline models on CIFAR-100 and ImageNet datasets, maintaining good performance even when there is a large gap in teacher-student network architectures.
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Available online: September 02, 2024 , DOI: 10.15888/j.cnki.csa.009660
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Effective detection of damage and foreign matter on transmission lines is very important for intelligent circuit inspection. However, it is difficult to collect data from different power companies to train a unified detection model due to the data island problem. Therefore, this study proposes a circuit defect detection method based on federated transfer learning by combining federated transfer learning and object detection algorithms. Specifically, a high-performance detection model is selected as the basic detection model, whose initial weight is frozen. The model adaptively learns from the data of different clients by using the low-rank decomposition of the weight matrix and inserting an adapter layer, so as to greatly reduce the number of the trainable parameters. An adaptive weight screening method is also proposed to accurately determine the low-rank decomposition of the weight layer and the insertion position of the adapter layer of the model. Through simple adaptive learning, the model can effectively adapt to the data distributions from different power companies. Experimental verification on a power dataset that closely resembles real-world conditions shows that the proposed model can adapt to different distributed detection scenarios under the premise of ensuring the security and privacy of customer data.
Effective detection of damage and foreign matter on transmission lines is very important for intelligent circuit inspection. However, it is difficult to collect data from different power companies to train a unified detection model due to the data island problem. Therefore, this study proposes a circuit defect detection method based on federated transfer learning by combining federated transfer learning and object detection algorithms. Specifically, a high-performance detection model is selected as the basic detection model, whose initial weight is frozen. The model adaptively learns from the data of different clients by using the low-rank decomposition of the weight matrix and inserting an adapter layer, so as to greatly reduce the number of the trainable parameters. An adaptive weight screening method is also proposed to accurately determine the low-rank decomposition of the weight layer and the insertion position of the adapter layer of the model. Through simple adaptive learning, the model can effectively adapt to the data distributions from different power companies. Experimental verification on a power dataset that closely resembles real-world conditions shows that the proposed model can adapt to different distributed detection scenarios under the premise of ensuring the security and privacy of customer data.
Available online: September 02, 2024 , DOI: 10.15888/j.cnki.csa.009661
Abstract:
This study proposes a CEEMDAN-SBiGRU combined prediction model with an optimized multi-head attention mechanism to enhance the precision of short-term power load forecasting and fully explore the complex correlation of power load data. The model improves two modules: feature extraction and feature fusion. Firstly, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is utilized to decompose the power load data into multiple intrinsic mode function (IMF) and a residual signal (RES); and a denoising autoencoder DAE is introduced to extract potential features from the data affected by meteorological factors, workday types, and temperature changes. Secondly, the extracted intricate features are fed into the stacked bidirectional gated recurrent unit (SBiGRU) module to obtain hidden states. Finally, the obtained hidden states are input into the optimized multi-head attention (OMHA) mechanism module,which incorporates residual mechanism and layer normalization, to accurately assign higher weights to important features and solve the problem of noise interference. The experimental results indicate that the CEEMDAN-SBiGRU-OMHA combined model achieves higher accuracy.
This study proposes a CEEMDAN-SBiGRU combined prediction model with an optimized multi-head attention mechanism to enhance the precision of short-term power load forecasting and fully explore the complex correlation of power load data. The model improves two modules: feature extraction and feature fusion. Firstly, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is utilized to decompose the power load data into multiple intrinsic mode function (IMF) and a residual signal (RES); and a denoising autoencoder DAE is introduced to extract potential features from the data affected by meteorological factors, workday types, and temperature changes. Secondly, the extracted intricate features are fed into the stacked bidirectional gated recurrent unit (SBiGRU) module to obtain hidden states. Finally, the obtained hidden states are input into the optimized multi-head attention (OMHA) mechanism module,which incorporates residual mechanism and layer normalization, to accurately assign higher weights to important features and solve the problem of noise interference. The experimental results indicate that the CEEMDAN-SBiGRU-OMHA combined model achieves higher accuracy.
Available online: September 02, 2024 , DOI: 10.15888/j.cnki.csa.009662
Abstract:
Alzheimer’s disease poses a significant public health challenge in the global aging society. One of its main clinical symptoms is the gradual decline in cognitive abilities. A crucial topic in Alzheimer’s disease research is to establish models that link cognitive performance with neuroimaging data to identify neuroimaging biomarkers associated with cognitive abilities. However, neuroimaging data often exhibit high dimensions, heavy-tailed distributions, and outliers. These characteristics not only reduce the accuracy and stability of models but also pose challenges for result explanations. To address these issues, this study uses sparse quantile regression to model and perform feature selection on data from the Alzheimer’s disease neuroimaging initiative (ADNI). This study also explores the distribution characteristics of cognitive scores at different quantiles and identifies specific brain regions associated with cognitive abilities. Experimental results demonstrate that sparse quantile regression successfully identifies the brain regions relevant to cognitive abilities at different quantiles. This research shows the potential of applying sparse quantile regression in neuroimaging data analysis and provides a novel perspective and approach for neuroimaging research.
Alzheimer’s disease poses a significant public health challenge in the global aging society. One of its main clinical symptoms is the gradual decline in cognitive abilities. A crucial topic in Alzheimer’s disease research is to establish models that link cognitive performance with neuroimaging data to identify neuroimaging biomarkers associated with cognitive abilities. However, neuroimaging data often exhibit high dimensions, heavy-tailed distributions, and outliers. These characteristics not only reduce the accuracy and stability of models but also pose challenges for result explanations. To address these issues, this study uses sparse quantile regression to model and perform feature selection on data from the Alzheimer’s disease neuroimaging initiative (ADNI). This study also explores the distribution characteristics of cognitive scores at different quantiles and identifies specific brain regions associated with cognitive abilities. Experimental results demonstrate that sparse quantile regression successfully identifies the brain regions relevant to cognitive abilities at different quantiles. This research shows the potential of applying sparse quantile regression in neuroimaging data analysis and provides a novel perspective and approach for neuroimaging research.
Available online: September 02, 2024 , DOI: 10.15888/j.cnki.csa.009664
Abstract:
Point cloud segmentation is a crucial step in 3D visual guidance and scene understanding, whose quality directly affects the quality of 3D measurement or imaging. To improve the segmentation accuracy and solve the out-of-bounds problem, this study proposes a point cloud segmentation algorithm for 3D vision guidance. This algorithm generates initial supervoxel data and extracts boundary points based on the spatial position, curvature and normal vectors of the point cloud. Boundary refinement is then performed, which refers to the redistribution of boundary points to optimize supervoxels, by calculating the similarity measure between boundary points and neighboring supervoxels. Ultimately, candidate fragments are obtained based on region growing and merged according to their concavity and convexity to achieve object-level segmentation. Visualization and quantitative comparison show that this algorithm effectively solves the out-of-bounds problem and accurately segment complex point cloud models. The segmentation accuracy is 89.04% and the recall rate is 87.38%.
Point cloud segmentation is a crucial step in 3D visual guidance and scene understanding, whose quality directly affects the quality of 3D measurement or imaging. To improve the segmentation accuracy and solve the out-of-bounds problem, this study proposes a point cloud segmentation algorithm for 3D vision guidance. This algorithm generates initial supervoxel data and extracts boundary points based on the spatial position, curvature and normal vectors of the point cloud. Boundary refinement is then performed, which refers to the redistribution of boundary points to optimize supervoxels, by calculating the similarity measure between boundary points and neighboring supervoxels. Ultimately, candidate fragments are obtained based on region growing and merged according to their concavity and convexity to achieve object-level segmentation. Visualization and quantitative comparison show that this algorithm effectively solves the out-of-bounds problem and accurately segment complex point cloud models. The segmentation accuracy is 89.04% and the recall rate is 87.38%.
Available online: September 02, 2024 , DOI: 10.15888/j.cnki.csa.009666
Abstract:
Model obfuscation refers to the equivalent transformation of neural networks into another form, which is an efficient and low-cost technique for protecting neural networks. To detect the flaws of model obfuscation, researchers have proposed model deobfuscation techniques in the hope of improving model obfuscation methods. However, model deobfuscation techniques are not fully explored, with limited applicability and effectiveness. Therefore, this study proposes a model deobfuscation method based on neural machine translation (NMT). This method models a deobfuscation task as a seq2seq task. It provides a more detailed sequential representation of the obfuscated model, identifies and processes the obfuscated information in the weight parameters, and utilizes an NMT-based model for deobfuscation translation. The experimental results demonstrate that this method addresses the shortcomings of existing methods, effectively capturing the obfuscation features and restoring the architectures of models. It can serve as a general solution to model deobfuscation.
Model obfuscation refers to the equivalent transformation of neural networks into another form, which is an efficient and low-cost technique for protecting neural networks. To detect the flaws of model obfuscation, researchers have proposed model deobfuscation techniques in the hope of improving model obfuscation methods. However, model deobfuscation techniques are not fully explored, with limited applicability and effectiveness. Therefore, this study proposes a model deobfuscation method based on neural machine translation (NMT). This method models a deobfuscation task as a seq2seq task. It provides a more detailed sequential representation of the obfuscated model, identifies and processes the obfuscated information in the weight parameters, and utilizes an NMT-based model for deobfuscation translation. The experimental results demonstrate that this method addresses the shortcomings of existing methods, effectively capturing the obfuscation features and restoring the architectures of models. It can serve as a general solution to model deobfuscation.
Available online: August 28, 2024 , DOI: 10.15888/j.cnki.csa.009668
Abstract:
To address the issue of nonlinear radial distortion present in multimodal remote sensing images, this study proposes a method for matching multimodal remote sensing images that integrates phase symmetry features with rank-based local self-similarity. Initially, the local phase information of the images is utilized to construct a phase symmetry map, upon which feature extraction is performed using the features from the accelerated segment test (FAST) algorithm. Subsequently, a new feature descriptor named RPCLSS is constructed, which combines rank-based local self-similarity and phase congruency. Finally, the fast sample consensus (FSC) algorithm is employed to eliminate mismatched points. Comparative experiments are conducted on publicly available multi-source remote sensing image datasets, comparing the proposed method against five existing advanced matching methods. The results reveal that the proposed method outperforms these state-of-the-art methods in terms of the number of correct matching points, matching precision, and matching correctness.
To address the issue of nonlinear radial distortion present in multimodal remote sensing images, this study proposes a method for matching multimodal remote sensing images that integrates phase symmetry features with rank-based local self-similarity. Initially, the local phase information of the images is utilized to construct a phase symmetry map, upon which feature extraction is performed using the features from the accelerated segment test (FAST) algorithm. Subsequently, a new feature descriptor named RPCLSS is constructed, which combines rank-based local self-similarity and phase congruency. Finally, the fast sample consensus (FSC) algorithm is employed to eliminate mismatched points. Comparative experiments are conducted on publicly available multi-source remote sensing image datasets, comparing the proposed method against five existing advanced matching methods. The results reveal that the proposed method outperforms these state-of-the-art methods in terms of the number of correct matching points, matching precision, and matching correctness.
Available online: August 28, 2024 , DOI: 10.15888/j.cnki.csa.009670
Abstract:
Due to the lack of cooperative information, non-cooperative spacecraft cannot obtain pose data directly from sensors. Therefore, a pose recognition network based on inverse synthetic aperture radar (ISAR) images is proposed. Compared with the images taken by space photography satellites and simulation data, this kind of image is easier to obtain and cheaper, but there are some problems such as low resolution ratio and incomplete panel image. Therefore, in image preprocessing, the network uses YOLOX-tiny as a spacecraft clipping network by adjusting it to avoid the data marked in the image affecting the subsequent network training, so that the network only focuses on the region where the spacecraft is located. The enhanced Lee filter is used to remove image noise and improve image quality. In the backbone network, the STN module is added to make the network select the most relevant region attention, and the U-Net is designed into a dense residual block structure and combined with the CBAM module to reduce the feature loss during sampling and improve the accuracy of the model. In addition, multi-head self-attention is introduced to capture more global information. The experimental results show that the minimum, maximum, and average errors of this model are improved compared with some mainstream models, and the errors are averagely reduced by 0.5–0.6. All this proves that the network has a better pose recognition ability.
Due to the lack of cooperative information, non-cooperative spacecraft cannot obtain pose data directly from sensors. Therefore, a pose recognition network based on inverse synthetic aperture radar (ISAR) images is proposed. Compared with the images taken by space photography satellites and simulation data, this kind of image is easier to obtain and cheaper, but there are some problems such as low resolution ratio and incomplete panel image. Therefore, in image preprocessing, the network uses YOLOX-tiny as a spacecraft clipping network by adjusting it to avoid the data marked in the image affecting the subsequent network training, so that the network only focuses on the region where the spacecraft is located. The enhanced Lee filter is used to remove image noise and improve image quality. In the backbone network, the STN module is added to make the network select the most relevant region attention, and the U-Net is designed into a dense residual block structure and combined with the CBAM module to reduce the feature loss during sampling and improve the accuracy of the model. In addition, multi-head self-attention is introduced to capture more global information. The experimental results show that the minimum, maximum, and average errors of this model are improved compared with some mainstream models, and the errors are averagely reduced by 0.5–0.6. All this proves that the network has a better pose recognition ability.
Available online: August 28, 2024 , DOI: 10.15888/j.cnki.csa.009657
Abstract:
Current progressive secret image sharing schemes do not consider cheating attacks by dishonest participants, allowing them to use false shadow images for cheating attacks. To ensure successful progressive reconstruction, this study divides the bit plane of pixels into two parts and uses the Lagrange interpolation algorithm along with visual cryptography schemes to address this issue. The sliding window of the pixel bit plane is determined by a pseudo-random number, and authentication information is embedded into the sliding window through a filtering operation to achieve authentication capability. Additionally, different strategies for bit plane division produce different progressive reconstruction effects, enabling more flexible progressive reconstruction. Theoretical analysis and experimental results both demonstrate the effectiveness of the proposed scheme.
Current progressive secret image sharing schemes do not consider cheating attacks by dishonest participants, allowing them to use false shadow images for cheating attacks. To ensure successful progressive reconstruction, this study divides the bit plane of pixels into two parts and uses the Lagrange interpolation algorithm along with visual cryptography schemes to address this issue. The sliding window of the pixel bit plane is determined by a pseudo-random number, and authentication information is embedded into the sliding window through a filtering operation to achieve authentication capability. Additionally, different strategies for bit plane division produce different progressive reconstruction effects, enabling more flexible progressive reconstruction. Theoretical analysis and experimental results both demonstrate the effectiveness of the proposed scheme.
Available online: August 28, 2024 , DOI: 10.15888/j.cnki.csa.009663
Abstract:
Ensuring the precise maintenance and stable operation of mineral processing equipment has always been an important challenge for mining-related enterprises while developing predictive maintenance systems for equipment has become a crucial means to reduce maintenance costs and improve production efficiency. This study analyzes the functional requirements of predictive maintenance systems, designs architecture and overall functional structure for a predictive maintenance system based on a micro-service architecture, and elaborates on the key technologies of the system. Moreover, the study proposes an evaluation model for equipment health status based on a multi-scale CNN fusion attention mechanism, as well as a prediction model for current trend fusion based on CNN and BiLSTM, to support the construction of the predictive maintenance system. The completed system has been applied at Ansteel Group Guanbaoshan Mining Co. Ltd., where the proposed model undergoes testing. The results show that the proposed model outperforms existing models with its high accuracy and robustness. The developed system can provide precise equipment maintenance plans, reduce equipment maintenance costs, and improve enterprise production efficiency.
Ensuring the precise maintenance and stable operation of mineral processing equipment has always been an important challenge for mining-related enterprises while developing predictive maintenance systems for equipment has become a crucial means to reduce maintenance costs and improve production efficiency. This study analyzes the functional requirements of predictive maintenance systems, designs architecture and overall functional structure for a predictive maintenance system based on a micro-service architecture, and elaborates on the key technologies of the system. Moreover, the study proposes an evaluation model for equipment health status based on a multi-scale CNN fusion attention mechanism, as well as a prediction model for current trend fusion based on CNN and BiLSTM, to support the construction of the predictive maintenance system. The completed system has been applied at Ansteel Group Guanbaoshan Mining Co. Ltd., where the proposed model undergoes testing. The results show that the proposed model outperforms existing models with its high accuracy and robustness. The developed system can provide precise equipment maintenance plans, reduce equipment maintenance costs, and improve enterprise production efficiency.
Available online: August 28, 2024 , DOI: 10.15888/j.cnki.csa.009639
Abstract:
To improve the detection accuracy and speed of deep reinforcement learning object detection models, modifications are made to traditional models. To address inadequate feature extraction, a VGG16 feature extraction module integrated with a channel attention mechanism is introduced as the state input for reinforcement learning, enabling a more comprehensive capture of key information in images. To address inaccurate evaluation caused by relying solely on the intersection over union as a reward, an improved reward mechanism that also considers the distance between the center points and the aspect ratio of the ground truth box and the predicted box is employed, making the reward more reasonable. To accelerate the convergence of the training process and enhance the objectivity of the agent’s evaluation of current states and actions, the Dueling DQN algorithm is used for training. After conducting experiments on the PASCAL VOC2007 and PASCAL VOC2012 datasets, experimental results show that the detection model only needs 4–10 candidate boxes to detect the target. Compared with Caicedo-RL, the accuracy is improved by 9.8%, and the mean intersection over union between the predicted and ground truth boxes is increased by 5.6%.
To improve the detection accuracy and speed of deep reinforcement learning object detection models, modifications are made to traditional models. To address inadequate feature extraction, a VGG16 feature extraction module integrated with a channel attention mechanism is introduced as the state input for reinforcement learning, enabling a more comprehensive capture of key information in images. To address inaccurate evaluation caused by relying solely on the intersection over union as a reward, an improved reward mechanism that also considers the distance between the center points and the aspect ratio of the ground truth box and the predicted box is employed, making the reward more reasonable. To accelerate the convergence of the training process and enhance the objectivity of the agent’s evaluation of current states and actions, the Dueling DQN algorithm is used for training. After conducting experiments on the PASCAL VOC2007 and PASCAL VOC2012 datasets, experimental results show that the detection model only needs 4–10 candidate boxes to detect the target. Compared with Caicedo-RL, the accuracy is improved by 9.8%, and the mean intersection over union between the predicted and ground truth boxes is increased by 5.6%.
Available online: August 28, 2024 , DOI: 10.15888/j.cnki.csa.009643
Abstract:
The narrow spectral bands of hyperspectral images (HSI) provide rich information for many visual tasks, but also pose challenges for feature extraction. Despite various deep learning methods proposed by researchers, the advantages of these architectures are not fully combined. Therefore, this study proposes a high-frequency enhanced dual-branch hyperspectral image super-resolution network (HFEDB-Net) that effectively extracts spatial and spectral information of HSI by integrating the image spatial feature extraction advantage of convolutional neural network (CNN) with the adaptive capability and long-distance dependency extraction advantage of Transformers. HFEDB-Net consists of a high-frequency information enhancement branch and a backbone branch. In the high-frequency information enhancement branch, the high-frequency information of low-resolution and high-resolution HSI is extracted by using Laplacian pyramids, and the results serve as the input and label for the high-frequency branch. A spectral-enhanced Transformer is employed as the feature extraction method for this branch. In the backbone branch, a CNN with channel attention is utilized to extract spatial features and spectral information comprehensively. Finally, the results from both branches are combined through CNN to obtain the final reconstructed image. Additionally, the attention mechanism and encoder layers of the Transformer are respectively improved by using multi-head attention and multi-scale strategies to better extract spatial and spectral information from HSI. Experimental results demonstrate that HFEDB-Net outperforms current state-of-the-art methods in terms of quantitative evaluation metrics and visual effects on two public datasets.
The narrow spectral bands of hyperspectral images (HSI) provide rich information for many visual tasks, but also pose challenges for feature extraction. Despite various deep learning methods proposed by researchers, the advantages of these architectures are not fully combined. Therefore, this study proposes a high-frequency enhanced dual-branch hyperspectral image super-resolution network (HFEDB-Net) that effectively extracts spatial and spectral information of HSI by integrating the image spatial feature extraction advantage of convolutional neural network (CNN) with the adaptive capability and long-distance dependency extraction advantage of Transformers. HFEDB-Net consists of a high-frequency information enhancement branch and a backbone branch. In the high-frequency information enhancement branch, the high-frequency information of low-resolution and high-resolution HSI is extracted by using Laplacian pyramids, and the results serve as the input and label for the high-frequency branch. A spectral-enhanced Transformer is employed as the feature extraction method for this branch. In the backbone branch, a CNN with channel attention is utilized to extract spatial features and spectral information comprehensively. Finally, the results from both branches are combined through CNN to obtain the final reconstructed image. Additionally, the attention mechanism and encoder layers of the Transformer are respectively improved by using multi-head attention and multi-scale strategies to better extract spatial and spectral information from HSI. Experimental results demonstrate that HFEDB-Net outperforms current state-of-the-art methods in terms of quantitative evaluation metrics and visual effects on two public datasets.
Available online: August 28, 2024 , DOI: 10.15888/j.cnki.csa.009645
Abstract:
In recent years, the exacerbation of traffic congestion has sparked widespread interest in the research on traffic signal control algorithms. Current studies indicate that methods based on deep reinforcement learning (DRL) exhibit promising performance in simulated environments. However, challenges persist in their practical application, including substantial requirements for data and computational resources, as well as difficulties in achieving coordination between intersections. To address these challenges, this study proposes a novel traffic signal control algorithm based on a contextual multi-armed bandit model. In contrast to conventional algorithms, the proposed algorithm achieves efficient coordination between intersections by extracting the main arteries from the road network. Moreover, it employs a contextual multi-armed bandit model to facilitate rapid and effective traffic signal control. Finally, through extensive experimentation on both real and synthetic datasets, the superiority of the proposed algorithm over previous algorithms is empirically demonstrated.
In recent years, the exacerbation of traffic congestion has sparked widespread interest in the research on traffic signal control algorithms. Current studies indicate that methods based on deep reinforcement learning (DRL) exhibit promising performance in simulated environments. However, challenges persist in their practical application, including substantial requirements for data and computational resources, as well as difficulties in achieving coordination between intersections. To address these challenges, this study proposes a novel traffic signal control algorithm based on a contextual multi-armed bandit model. In contrast to conventional algorithms, the proposed algorithm achieves efficient coordination between intersections by extracting the main arteries from the road network. Moreover, it employs a contextual multi-armed bandit model to facilitate rapid and effective traffic signal control. Finally, through extensive experimentation on both real and synthetic datasets, the superiority of the proposed algorithm over previous algorithms is empirically demonstrated.
Available online: August 28, 2024 , DOI: 10.15888/j.cnki.csa.009647
Abstract:
Cancer driver genes play a crucial role in the formation and progression of cancer. Accurate identification of cancer driver genes contributes to a deeper understanding of the mechanisms underlying cancer development and advances precision medicine. To address the heterogeneity and complexity challenges in the current field of cancer driver gene identification, this study presents the design and implementation of a cancer driver gene identification system, ACGAI, based on graph autoencoder and LightGBM. The system initially employs unsupervised learning with a graph autoencoder to grasp the complex topological structure of the biomolecular network. Subsequently, the generated embedding representations are concatenated with original gene features, forming gene-enhanced features input into LightGBM. After training, the system outputs predictive scores for each gene on the biomolecular network, achieving accurate identification of cancer driver genes. Finally, the system utilizes Web technology to create a user-friendly and highly interactive visualization interface, enabling cancer driver gene identification in the context of gene set analysis and providing biological interpretation for the identification results. Through rigorous testing, the system exhibits superior identification performance compared to other methods, demonstrating its effectiveness in identifying cancer driver genes.
Cancer driver genes play a crucial role in the formation and progression of cancer. Accurate identification of cancer driver genes contributes to a deeper understanding of the mechanisms underlying cancer development and advances precision medicine. To address the heterogeneity and complexity challenges in the current field of cancer driver gene identification, this study presents the design and implementation of a cancer driver gene identification system, ACGAI, based on graph autoencoder and LightGBM. The system initially employs unsupervised learning with a graph autoencoder to grasp the complex topological structure of the biomolecular network. Subsequently, the generated embedding representations are concatenated with original gene features, forming gene-enhanced features input into LightGBM. After training, the system outputs predictive scores for each gene on the biomolecular network, achieving accurate identification of cancer driver genes. Finally, the system utilizes Web technology to create a user-friendly and highly interactive visualization interface, enabling cancer driver gene identification in the context of gene set analysis and providing biological interpretation for the identification results. Through rigorous testing, the system exhibits superior identification performance compared to other methods, demonstrating its effectiveness in identifying cancer driver genes.
Available online: August 28, 2024 , DOI: 10.15888/j.cnki.csa.009648
Abstract:
With the development of global economic integration, cross-border trade has become an important driving force for global economic development. However, it is facing issues such as data security, information silos, and information asymmetry. Based on this, this study proposes a blockchain-based scheme for data sharing and access control in cross-border trade. The scheme uses a collaborative storage mechanism of blockchain and Inter planetary file system (IPFS) to effectively reduce the storage load of blockchain. In addition, adual key regression model combined with time dimension is adopted to encrypt and store data, as well as assign access permissions by setting different time periods, which limits the unnecessary access of data users outside a certain time span. Finally, corresponding smart contracts are designed to achieve efficient management of the entire life cycle flow of data, improving sharing efficiency. The experimental results show that the proposed scheme can achieve secure data sharing in cross-border trade and fine-grained access control for users.
With the development of global economic integration, cross-border trade has become an important driving force for global economic development. However, it is facing issues such as data security, information silos, and information asymmetry. Based on this, this study proposes a blockchain-based scheme for data sharing and access control in cross-border trade. The scheme uses a collaborative storage mechanism of blockchain and Inter planetary file system (IPFS) to effectively reduce the storage load of blockchain. In addition, adual key regression model combined with time dimension is adopted to encrypt and store data, as well as assign access permissions by setting different time periods, which limits the unnecessary access of data users outside a certain time span. Finally, corresponding smart contracts are designed to achieve efficient management of the entire life cycle flow of data, improving sharing efficiency. The experimental results show that the proposed scheme can achieve secure data sharing in cross-border trade and fine-grained access control for users.
Available online: August 28, 2024 , DOI: 10.15888/j.cnki.csa.009652
Abstract:
Face image generation requires high realism and controllability. This study proposes a new algorithm for face image generation that is jointly controlled by text and facial key points. The text constrains the generation of faces at a semantic level, while facial key points enable the model to control the generation of facial features, expressions, and details based on given facial information. The proposed algorithm improves the existing diffusion model and introduces additional components: text processing models (CM), keypoint control networks (KCN), and autoencoder networks (ACN). Specifically, the diffusion model is a noise inference algorithm based on the diffusion theory; CM is designed based on an attention mechanism to encode and store text information; KCN receives the location information of key points, enhancing the controllability of face generation; ACN alleviates the generation pressure of the diffusion model and reduces the time required to generate samples. In addition, to adapt to generating face images, this research constructs a dataset containing 30000 face images. In the proposed algorithm, given prerequisite text and a facial keypoint image, the model extracts feature information and keypoint information from the text, generating a highly realistic and controllable target face image. Compared with mainstream methods, the proposed algorithm improves the FID index by about 5%–23% and the IS index by about 3%–14%, which proves its superiority.
Face image generation requires high realism and controllability. This study proposes a new algorithm for face image generation that is jointly controlled by text and facial key points. The text constrains the generation of faces at a semantic level, while facial key points enable the model to control the generation of facial features, expressions, and details based on given facial information. The proposed algorithm improves the existing diffusion model and introduces additional components: text processing models (CM), keypoint control networks (KCN), and autoencoder networks (ACN). Specifically, the diffusion model is a noise inference algorithm based on the diffusion theory; CM is designed based on an attention mechanism to encode and store text information; KCN receives the location information of key points, enhancing the controllability of face generation; ACN alleviates the generation pressure of the diffusion model and reduces the time required to generate samples. In addition, to adapt to generating face images, this research constructs a dataset containing 30000 face images. In the proposed algorithm, given prerequisite text and a facial keypoint image, the model extracts feature information and keypoint information from the text, generating a highly realistic and controllable target face image. Compared with mainstream methods, the proposed algorithm improves the FID index by about 5%–23% and the IS index by about 3%–14%, which proves its superiority.
Available online: August 21, 2024 , DOI: 10.15888/j.cnki.csa.009646
Abstract:
Heart rate and saturation of peripheral capillary oxygenation (SpO2) are very important physiological indicators of human health. In recent years, non-contact heart rate and SpO2 detection methods based on imaging photoplethysmography (IPPG) have gradually become a research focus as they are convenient and freely-applied. The main work is as follows. First, the study introduces the background and research significance of non-contact detection methods. Secondly, two aspects of target region detection and region of interest (ROI) are selected to summarize and clarify the research status and future improvement direction. Thirdly, the detection methods of heart rate and SpO2 are summarized from three aspects: traditional method, signal processing combined with deep learning method and end-to-end method, and the data sets used in deep learning method and the detection effects displayed in each data set are sorted out. Finally, the paper points out the problems that need to be solved and the future research direction in this field.
Heart rate and saturation of peripheral capillary oxygenation (SpO2) are very important physiological indicators of human health. In recent years, non-contact heart rate and SpO2 detection methods based on imaging photoplethysmography (IPPG) have gradually become a research focus as they are convenient and freely-applied. The main work is as follows. First, the study introduces the background and research significance of non-contact detection methods. Secondly, two aspects of target region detection and region of interest (ROI) are selected to summarize and clarify the research status and future improvement direction. Thirdly, the detection methods of heart rate and SpO2 are summarized from three aspects: traditional method, signal processing combined with deep learning method and end-to-end method, and the data sets used in deep learning method and the detection effects displayed in each data set are sorted out. Finally, the paper points out the problems that need to be solved and the future research direction in this field.
Available online: August 21, 2024 , DOI: 10.15888/j.cnki.csa.009607
Abstract:
In imbalanced datasets, the presence of noise and class overlapping often leads to poor performance of traditional classifiers, resulting in minority class samples being difficult to classify accurately. To improve classification performance, a method for handling imbalanced data based on shared nearest neighbor density peak clustering and ensemble filtering mechanism is proposed. This method first uses the shared nearest neighbor density peak clustering algorithm to adaptively divide the minority class samples into multiple clusters. Then, based on the density and size within the clusters, oversampling weights are allocated to each cluster. During the synthesis within clusters, the local sparsity and clustering coefficient of the samples are considered to select neighboring samples and determine the weight range of linear interpolation, thus avoiding the generation of new samples in the majority class aggregation area. Finally, an ensemble filtering mechanism is introduced to eliminate noise and hard-to-learn boundary samples to regulate the decision boundary and improve the quality of generated samples. Compared with 5 oversampling methods, this algorithm performs better overall on 8 public datasets.
In imbalanced datasets, the presence of noise and class overlapping often leads to poor performance of traditional classifiers, resulting in minority class samples being difficult to classify accurately. To improve classification performance, a method for handling imbalanced data based on shared nearest neighbor density peak clustering and ensemble filtering mechanism is proposed. This method first uses the shared nearest neighbor density peak clustering algorithm to adaptively divide the minority class samples into multiple clusters. Then, based on the density and size within the clusters, oversampling weights are allocated to each cluster. During the synthesis within clusters, the local sparsity and clustering coefficient of the samples are considered to select neighboring samples and determine the weight range of linear interpolation, thus avoiding the generation of new samples in the majority class aggregation area. Finally, an ensemble filtering mechanism is introduced to eliminate noise and hard-to-learn boundary samples to regulate the decision boundary and improve the quality of generated samples. Compared with 5 oversampling methods, this algorithm performs better overall on 8 public datasets.
Available online: August 21, 2024 , DOI: 10.15888/j.cnki.csa.009614
Abstract:
Currently, in multimodal sentiment analysis tasks, there are problems such as insufficient single modal feature extraction and lack of stability in data fusion methods. This study proposes a method of optimizing modal features that uses interpolation to solve these problems. Firstly, the interpolation-optimized BERT and GRU models are applied to extract features, and both of the models are used to mine text, audio, and video information. Secondly, an improved attention mechanism is used to fuse text, audio, and video information, thus achieving modal fusion more stably. This method is tested on the MOSI and MOSEI datasets. The experimental results show that using interpolation can improve the accuracy of multi-modal sentiment analysis tasks based on optimizing modal features. This result verifies the effectiveness of interpolation.
Currently, in multimodal sentiment analysis tasks, there are problems such as insufficient single modal feature extraction and lack of stability in data fusion methods. This study proposes a method of optimizing modal features that uses interpolation to solve these problems. Firstly, the interpolation-optimized BERT and GRU models are applied to extract features, and both of the models are used to mine text, audio, and video information. Secondly, an improved attention mechanism is used to fuse text, audio, and video information, thus achieving modal fusion more stably. This method is tested on the MOSI and MOSEI datasets. The experimental results show that using interpolation can improve the accuracy of multi-modal sentiment analysis tasks based on optimizing modal features. This result verifies the effectiveness of interpolation.
Available online: August 21, 2024 , DOI: 10.15888/j.cnki.csa.009630
Abstract:
Light-weight image fusion algorithm is very important for human eye observation and machine recognition. By studying the importance of visual saliency in infrared and visible image fusion, a visual saliency map (VSM)-guided MSDNet fusion network is optimized and designed based on the SDNet fusion network. Firstly, the structure and channel numbers of SDNet are reduced to accelerate training and inference speed, and the learning ability of the light-weight model is enhanced by structural parameterization and reverse parameterization techniques. Then, for model training, the loss function guided by VSM is used to achieve model self-supervised training. Finally, at the end of the training, the image reconstruction branch is deleted. So the final light-weight model is obtained by the fusion of convolution parameters. Experiments show that the light-weight network can not only ensure image fusion quality but greatly improve the speed, making its porting in mobile terminals possible.
Light-weight image fusion algorithm is very important for human eye observation and machine recognition. By studying the importance of visual saliency in infrared and visible image fusion, a visual saliency map (VSM)-guided MSDNet fusion network is optimized and designed based on the SDNet fusion network. Firstly, the structure and channel numbers of SDNet are reduced to accelerate training and inference speed, and the learning ability of the light-weight model is enhanced by structural parameterization and reverse parameterization techniques. Then, for model training, the loss function guided by VSM is used to achieve model self-supervised training. Finally, at the end of the training, the image reconstruction branch is deleted. So the final light-weight model is obtained by the fusion of convolution parameters. Experiments show that the light-weight network can not only ensure image fusion quality but greatly improve the speed, making its porting in mobile terminals possible.
Available online: August 21, 2024 , DOI: 10.15888/j.cnki.csa.009635
Abstract:
Primary healthcare providers lack the ability to assess the risk of vaccination for children with certain illnesses. It is a viable solution to developing a risk prediction model for pediatric vaccination, by leveraging the experience of healthcare professionals in tertiary hospitals, to assist primary healthcare providers in swiftly identifying high-risk pediatric patients. This study proposes an intelligent method for vaccine recommendations based on a knowledge graph. Firstly, a method for medical named entity recognition called ELECTRA-BiGRU-CRF, based on pre-trained language models, is proposed for named entity extraction from outpatient electronic medical records. Secondly, a vaccination ontology is designed, with relationships and attributes defined, to construct a Chinese childhood vaccination knowledge graph based on Neo4j. Finally, a method for vaccine recommendations guided by significant categories using pre-trained language models is proposed based on the constructed knowledge graph. Experimental results indicate that the proposed methods can provide diagnostic assistance to physicians and offer support for deciding whether vaccines can be administered to children with certain illnesses.
Primary healthcare providers lack the ability to assess the risk of vaccination for children with certain illnesses. It is a viable solution to developing a risk prediction model for pediatric vaccination, by leveraging the experience of healthcare professionals in tertiary hospitals, to assist primary healthcare providers in swiftly identifying high-risk pediatric patients. This study proposes an intelligent method for vaccine recommendations based on a knowledge graph. Firstly, a method for medical named entity recognition called ELECTRA-BiGRU-CRF, based on pre-trained language models, is proposed for named entity extraction from outpatient electronic medical records. Secondly, a vaccination ontology is designed, with relationships and attributes defined, to construct a Chinese childhood vaccination knowledge graph based on Neo4j. Finally, a method for vaccine recommendations guided by significant categories using pre-trained language models is proposed based on the constructed knowledge graph. Experimental results indicate that the proposed methods can provide diagnostic assistance to physicians and offer support for deciding whether vaccines can be administered to children with certain illnesses.
Available online: August 21, 2024 , DOI: 10.15888/j.cnki.csa.009654
Abstract:
Aiming at the problems of insufficient light, low contrast, and information loss in images taken by imaging devices at night or in low-light environments, an improved dark image enhancement network named RelightGAN is designed based on generative adversarial networks (GANs). It contains two discriminators and one generator, and the generator is jointly constrained by two sets of adversarial losses and cyclic losses to generate a better illumination layer. To enhance the recovery of image details during network training, a residual network is introduced to solve the gradient vanishing problem. At the same time, a hybrid attention mechanism CBAM structure is introduced to increase the generator’s attention to important information and structures in the image, enhancing network expression capability. By comparing the image enhanced by RelightGAN with those enhanced by other dark image enhancement networks, the peak signal-to-noise ratio (PSNR) of the former is improved by 12.81% and the structural similarity (SSIM) is enhanced by 5.95%. Experimental results show that the RelightGAN network combines the advantages of traditional algorithms and neural networks to improve dark scene images and image visibility.
Aiming at the problems of insufficient light, low contrast, and information loss in images taken by imaging devices at night or in low-light environments, an improved dark image enhancement network named RelightGAN is designed based on generative adversarial networks (GANs). It contains two discriminators and one generator, and the generator is jointly constrained by two sets of adversarial losses and cyclic losses to generate a better illumination layer. To enhance the recovery of image details during network training, a residual network is introduced to solve the gradient vanishing problem. At the same time, a hybrid attention mechanism CBAM structure is introduced to increase the generator’s attention to important information and structures in the image, enhancing network expression capability. By comparing the image enhanced by RelightGAN with those enhanced by other dark image enhancement networks, the peak signal-to-noise ratio (PSNR) of the former is improved by 12.81% and the structural similarity (SSIM) is enhanced by 5.95%. Experimental results show that the RelightGAN network combines the advantages of traditional algorithms and neural networks to improve dark scene images and image visibility.
Available online: August 21, 2024 , DOI: 10.15888/j.cnki.csa.009656
Abstract:
Vertical federated learning improves the value of data utilization by combining local data features from multiple parties and jointly training the target model without leaking data privacy. It has received widespread attention from companies and institutions in the industry. During the training process, the intermediate embeddings uploaded by clients and the gradients returned by the server require a huge amount of communication, and thus the communication cost becomes a key bottleneck limiting the practical application of vertical federated learning. Consequently, current research focuses on designing effective algorithms to reduce the communication amount and improve communication efficiency. To improve the communication efficiency of vertical federated learning, this study proposes an efficient compression algorithm based on embedding and gradient bidirectional compression. For the embedding representation uploaded by the client, an improved sparsification method combined with a cache reuse mechanism is employed. For the gradient information distributed by the server, a mechanism combining discrete quantization and Huffman coding is used. Experimental results show that the proposed algorithm can reduce the communication volume by about 85%, improve communication efficiency, and reduce the overall training time while maintaining almost the same accuracy as the uncompressed scenario.
Vertical federated learning improves the value of data utilization by combining local data features from multiple parties and jointly training the target model without leaking data privacy. It has received widespread attention from companies and institutions in the industry. During the training process, the intermediate embeddings uploaded by clients and the gradients returned by the server require a huge amount of communication, and thus the communication cost becomes a key bottleneck limiting the practical application of vertical federated learning. Consequently, current research focuses on designing effective algorithms to reduce the communication amount and improve communication efficiency. To improve the communication efficiency of vertical federated learning, this study proposes an efficient compression algorithm based on embedding and gradient bidirectional compression. For the embedding representation uploaded by the client, an improved sparsification method combined with a cache reuse mechanism is employed. For the gradient information distributed by the server, a mechanism combining discrete quantization and Huffman coding is used. Experimental results show that the proposed algorithm can reduce the communication volume by about 85%, improve communication efficiency, and reduce the overall training time while maintaining almost the same accuracy as the uncompressed scenario.
Available online: August 21, 2024 , DOI: 10.15888/j.cnki.csa.009651
Abstract:
Most existing anomaly detection methods focus on algorithm efficiency and accuracy while overlooking the interpretability of detected anomalous objects. Counterfactual explanation, a research hot spot in interpretable machine learning, aims to explain model decisions by perturbing the features of the instances under study and generating counterfactual examples. In practical applications, there may be causal relationships among features. However, most existing counterfactual-based interpretability methods concentrate on how to generate more diverse counterfactual examples, overlooking the causal relationships among features. Consequently, unreasonable counterfactual explanations may be produced. To address this issue, this study proposes an algorithm to interpret anomaly via reasonable counterfactuals (IARC) that consider causal constraints. In the process of generating counterfactual explanations, the proposed method incorporates the causality between features into the objective function to evaluate the feasibility of each perturbation, and employs an improved genetic algorithm for solution optimization, thereby generating rational counterfactual explanations. Additionally, a novel measurement metric is introduced to quantify the degree of contradiction in the generated counterfactual explanations. Comparative experiments and detailed case studies are conducted on multiple real-world datasets, benchmarking the proposed method against several state-of-the-art methods. The experimental results demonstrate that the proposed method can generate highly rational counterfactual explanations for anomalous objects.
Most existing anomaly detection methods focus on algorithm efficiency and accuracy while overlooking the interpretability of detected anomalous objects. Counterfactual explanation, a research hot spot in interpretable machine learning, aims to explain model decisions by perturbing the features of the instances under study and generating counterfactual examples. In practical applications, there may be causal relationships among features. However, most existing counterfactual-based interpretability methods concentrate on how to generate more diverse counterfactual examples, overlooking the causal relationships among features. Consequently, unreasonable counterfactual explanations may be produced. To address this issue, this study proposes an algorithm to interpret anomaly via reasonable counterfactuals (IARC) that consider causal constraints. In the process of generating counterfactual explanations, the proposed method incorporates the causality between features into the objective function to evaluate the feasibility of each perturbation, and employs an improved genetic algorithm for solution optimization, thereby generating rational counterfactual explanations. Additionally, a novel measurement metric is introduced to quantify the degree of contradiction in the generated counterfactual explanations. Comparative experiments and detailed case studies are conducted on multiple real-world datasets, benchmarking the proposed method against several state-of-the-art methods. The experimental results demonstrate that the proposed method can generate highly rational counterfactual explanations for anomalous objects.
Available online: August 21, 2024 , DOI: 10.15888/j.cnki.csa.009658
Abstract:
The partially linear model, as an important type of semiparametric regression models, is widely used across various fields due to its flexible adaptability in the analysis of complex data structures. However, in the era of big data, the research and application of this model are faced with multiple challenges, with the most critical ones being computing speed and data storage. This study considers the scenario of data streams continuously observed in the form of data blocks and proposes an online estimation method for the parameters of the linear part and the unknown function of the nonlinear part in the partially linear model. This method enables real-time estimation using only the current data block and previously computed summary statistics. To verify the effectiveness, the unit data block size and the total sample size of the data streams are changed respectively in numerical simulations, so that the bias, standard error and mean squared error between the online estimation method and the traditional one can be compared. The experiments demonstrate that, compared to the traditional method, the proposed approach offers the advantages of rapid computation and unnecessary review of historical data, while being close to the traditional method in terms of mean squared error. Finally, based on the data from the China general social survey (CGSS), this study applies the online estimation method to analyze the factors influencing the quality of life of the working-age population in China. The results indicate that full-time work within the range of 30 to 60 hours per week positively contributes to improving the quality of life, providing valuable references for relevant policy formulation.
The partially linear model, as an important type of semiparametric regression models, is widely used across various fields due to its flexible adaptability in the analysis of complex data structures. However, in the era of big data, the research and application of this model are faced with multiple challenges, with the most critical ones being computing speed and data storage. This study considers the scenario of data streams continuously observed in the form of data blocks and proposes an online estimation method for the parameters of the linear part and the unknown function of the nonlinear part in the partially linear model. This method enables real-time estimation using only the current data block and previously computed summary statistics. To verify the effectiveness, the unit data block size and the total sample size of the data streams are changed respectively in numerical simulations, so that the bias, standard error and mean squared error between the online estimation method and the traditional one can be compared. The experiments demonstrate that, compared to the traditional method, the proposed approach offers the advantages of rapid computation and unnecessary review of historical data, while being close to the traditional method in terms of mean squared error. Finally, based on the data from the China general social survey (CGSS), this study applies the online estimation method to analyze the factors influencing the quality of life of the working-age population in China. The results indicate that full-time work within the range of 30 to 60 hours per week positively contributes to improving the quality of life, providing valuable references for relevant policy formulation.
Available online: August 21, 2024 , DOI: 10.15888/j.cnki.csa.009659
Abstract:
Action recognition is an important technology in computer vision, which can be categorized into video-based and skeleton-based action recognition according to different input data. The 3D skeleton data avoids the influence of illumination, occlusion, and other factors, yielding more accurate action descriptions. Now, human action recognition based on 3D skeleton has been paid more attention. Methods for human action recognition based on a 3D skeleton can be divided into the end-to-end black-box method and the pattern recognition-based white-box method. The black-box method in deep learning involves large parameters and can learn classification knowledge from a large amount of data. However, deep learning is difficult to explain and can only provide an overall recognition result. Compared with the black-box method, the white-box method has an explainable recognition process and an adjustable algorithm. Nevertheless, some white-box methods only focus on algorithmic improvements, using formulas to represent and classify actions, without reflecting the difference and connection among actions. Therefore, this study designs a white-box method with a visible classification process. This method uses a tree structure to organize action data hierarchically, constructing an individual classification hierarchy according to the differences between the same actions and an action classification hierarchy according to the discrepancies among different actions. Various measurement algorithms are also incorporated into the system. This study selects the nearest neighbor and dynamic time warping algorithms for experiments. The advantage of a hierarchical structure is that a variety of knowledge can be implanted to it according to various requirements so that actions can be classified from different perspectives. In the experiments, key posture knowledge and human body structure knowledge are implanted into the hierarchy structure. With the implantation of knowledge, the hierarchy structure dynamically changes.
Action recognition is an important technology in computer vision, which can be categorized into video-based and skeleton-based action recognition according to different input data. The 3D skeleton data avoids the influence of illumination, occlusion, and other factors, yielding more accurate action descriptions. Now, human action recognition based on 3D skeleton has been paid more attention. Methods for human action recognition based on a 3D skeleton can be divided into the end-to-end black-box method and the pattern recognition-based white-box method. The black-box method in deep learning involves large parameters and can learn classification knowledge from a large amount of data. However, deep learning is difficult to explain and can only provide an overall recognition result. Compared with the black-box method, the white-box method has an explainable recognition process and an adjustable algorithm. Nevertheless, some white-box methods only focus on algorithmic improvements, using formulas to represent and classify actions, without reflecting the difference and connection among actions. Therefore, this study designs a white-box method with a visible classification process. This method uses a tree structure to organize action data hierarchically, constructing an individual classification hierarchy according to the differences between the same actions and an action classification hierarchy according to the discrepancies among different actions. Various measurement algorithms are also incorporated into the system. This study selects the nearest neighbor and dynamic time warping algorithms for experiments. The advantage of a hierarchical structure is that a variety of knowledge can be implanted to it according to various requirements so that actions can be classified from different perspectives. In the experiments, key posture knowledge and human body structure knowledge are implanted into the hierarchy structure. With the implantation of knowledge, the hierarchy structure dynamically changes.
Available online: August 21, 2024 , DOI: 10.15888/j.cnki.csa.009665
Abstract:
Intelligent tongue diagnosis is of great significance in assisting doctors in medical treatment. At present, intelligent tongue diagnosis is mainly focused on the prediction and classification of single tongue image features, making it difficult to provide substantial help in the diagnostic process. To make up for this deficiency, research of accurate prediction and classification is carried out from the level of tongue image syndrome to assist doctors in diagnosing diseases. The TUNet is used to segment the tongue, and a parallel residual network PMANet integrated with the multi-attention mechanism is proposed to classify the syndrome of tongue image. the pixel accuracy (PA), mean intersection over union (MIoU) and Dice coefficient of TUNet reach 99.7%, 98.4%, and 99.2%, respectively, improved by 3.2%, 9.0%, and 4.8% compared with the baseline U-Net. In the research of tongue image syndrome classification, PMA’s total amount of parameters is 12.34M, slightly higher than that of EfficientNet, and its total amount of floating-point calculations is 1.021G, significantly lower than all compared networks. Under the background of a lower amount of both parameters and floating-point calculations, the classification accuracy of PMANet reaches 95.7%, achieving a balance between precision, parameter amount, and floating-point calculations amount. This method provides support for the research of intelligent tongue diagnosis and is expected to promote the modernization of TCM tongue diagnosis.
Intelligent tongue diagnosis is of great significance in assisting doctors in medical treatment. At present, intelligent tongue diagnosis is mainly focused on the prediction and classification of single tongue image features, making it difficult to provide substantial help in the diagnostic process. To make up for this deficiency, research of accurate prediction and classification is carried out from the level of tongue image syndrome to assist doctors in diagnosing diseases. The TUNet is used to segment the tongue, and a parallel residual network PMANet integrated with the multi-attention mechanism is proposed to classify the syndrome of tongue image. the pixel accuracy (PA), mean intersection over union (MIoU) and Dice coefficient of TUNet reach 99.7%, 98.4%, and 99.2%, respectively, improved by 3.2%, 9.0%, and 4.8% compared with the baseline U-Net. In the research of tongue image syndrome classification, PMA’s total amount of parameters is 12.34M, slightly higher than that of EfficientNet, and its total amount of floating-point calculations is 1.021G, significantly lower than all compared networks. Under the background of a lower amount of both parameters and floating-point calculations, the classification accuracy of PMANet reaches 95.7%, achieving a balance between precision, parameter amount, and floating-point calculations amount. This method provides support for the research of intelligent tongue diagnosis and is expected to promote the modernization of TCM tongue diagnosis.
Available online: August 21, 2024 , DOI: 10.15888/j.cnki.csa.009649
Abstract:
To address the issue of low accuracy and susceptibility to interference from external factors in unconstrained environments, a convolution and attention double-branch parallel feature cross-fusion gaze estimation method is proposed to enhance feature fusion effectiveness and network performance. Firstly, the Mobile-Former network is enhanced by introducing a linear attention mechanism and partial convolution. This effectively improves the feature extraction capability while reducing computing costs. Additionally, a branch of the ResNet50 head pose feature estimation network, pre-trained on the 300W-LP dataset, is added to enhance gaze estimation accuracy. A Sigmoid function is used as a gating unit to screen effective features. Finally, facial images are inputted into the neural network for feature extraction and fusion, and the 3D gaze estimation direction is outputted. The model is evaluated on the MPIIFaceGaze and Gaze360 datasets, and the average angle error of the proposed method is 3.70° and 10.82°, respectively. The network model is verified to accurately estimate the 3D gaze direction and reduce computational complexity compared to other mainstream 3D gaze estimation methods.
To address the issue of low accuracy and susceptibility to interference from external factors in unconstrained environments, a convolution and attention double-branch parallel feature cross-fusion gaze estimation method is proposed to enhance feature fusion effectiveness and network performance. Firstly, the Mobile-Former network is enhanced by introducing a linear attention mechanism and partial convolution. This effectively improves the feature extraction capability while reducing computing costs. Additionally, a branch of the ResNet50 head pose feature estimation network, pre-trained on the 300W-LP dataset, is added to enhance gaze estimation accuracy. A Sigmoid function is used as a gating unit to screen effective features. Finally, facial images are inputted into the neural network for feature extraction and fusion, and the 3D gaze estimation direction is outputted. The model is evaluated on the MPIIFaceGaze and Gaze360 datasets, and the average angle error of the proposed method is 3.70° and 10.82°, respectively. The network model is verified to accurately estimate the 3D gaze direction and reduce computational complexity compared to other mainstream 3D gaze estimation methods.
Available online: August 21, 2024 , DOI: 10.15888/j.cnki.csa.009650
Abstract:
Traditional sleep staging models are difficult to deploy in devices with limited computing power due to high requirements of computational resources. In this study, a lightweight sleep analysis system based on single-channel EEG signals is developed, which deploys a GhostNet-optimized neural network model named GhostSleepNet to assess sleep staging and sleep quality. Users only need to use a brain loop and connect it to this system to achieve sleep staging with high accuracy in a home environment. In this system, convolutional neural networks (CNN) are responsible for extracting higher-order features, GhostNet is designed to maintain the accuracy of CNN extracted features while reducing the parameters of the model to improve the computational efficiency, and gated recurrent unit (GRU) focuses on capturing long-term dependencies and cyclic changes in sleep data. Verification of the five classification tasks on the Sleep-EDF dataset shows that the sleep staging accuracy of GhostSleepNet reaches 84.17%, which is 3%–5% lower than that of traditional sleep staging models. However, the number of FLOPs is only 5 041 111 040, and the computational complexity decreases by 20%–45%, contributing to the development of sleep staging for mobile devices.
Traditional sleep staging models are difficult to deploy in devices with limited computing power due to high requirements of computational resources. In this study, a lightweight sleep analysis system based on single-channel EEG signals is developed, which deploys a GhostNet-optimized neural network model named GhostSleepNet to assess sleep staging and sleep quality. Users only need to use a brain loop and connect it to this system to achieve sleep staging with high accuracy in a home environment. In this system, convolutional neural networks (CNN) are responsible for extracting higher-order features, GhostNet is designed to maintain the accuracy of CNN extracted features while reducing the parameters of the model to improve the computational efficiency, and gated recurrent unit (GRU) focuses on capturing long-term dependencies and cyclic changes in sleep data. Verification of the five classification tasks on the Sleep-EDF dataset shows that the sleep staging accuracy of GhostSleepNet reaches 84.17%, which is 3%–5% lower than that of traditional sleep staging models. However, the number of FLOPs is only 5 041 111 040, and the computational complexity decreases by 20%–45%, contributing to the development of sleep staging for mobile devices.
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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:
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:
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:
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.
2022,31(5):1-20, DOI: 10.15888/j.cnki.csa.008463
Abstract:
深度学习方法的提出使得机器学习研究领域得到了巨大突破, 但是却需要大量的人工标注数据来辅助完成. 在实际问题中, 受限于人力成本, 许多应用需要对从未见过的实例类别进行推理判断. 为此, 零样本学习(zero-shot learning, ZSL)应运而生. 图作为一种表示事物之间联系的自然数据结构, 目前在零样本学习中受到了越来越多的关注. 本文对零样本图学习方法进行了系统综述. 首先概述了零样本学习和图学习的定义, 并总结了零样本学习现有的解决方案思想. 然后依据图的不同利用方式对目前零样本图学习的方法体系进行了分类. 接下来讨论了零样本图学习所涉及到的评估准则和数据集. 最后指明了零样本图学习进一步研究中需要解决的问题以及未来可能的发展方向.
深度学习方法的提出使得机器学习研究领域得到了巨大突破, 但是却需要大量的人工标注数据来辅助完成. 在实际问题中, 受限于人力成本, 许多应用需要对从未见过的实例类别进行推理判断. 为此, 零样本学习(zero-shot learning, ZSL)应运而生. 图作为一种表示事物之间联系的自然数据结构, 目前在零样本学习中受到了越来越多的关注. 本文对零样本图学习方法进行了系统综述. 首先概述了零样本学习和图学习的定义, 并总结了零样本学习现有的解决方案思想. 然后依据图的不同利用方式对目前零样本图学习的方法体系进行了分类. 接下来讨论了零样本图学习所涉及到的评估准则和数据集. 最后指明了零样本图学习进一步研究中需要解决的问题以及未来可能的发展方向.
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。
2019,28(6):1-12, DOI: 10.15888/j.cnki.csa.006915
Abstract:
知识图谱是以图的形式表现客观世界中的概念和实体及其之间关系的知识库，是语义搜索、智能问答、决策支持等智能服务的基础技术之一.目前，知识图谱的内涵还不够清晰；且因建档不全，已有知识图谱的使用率和重用率不高.为此，本文给出知识图谱的定义，辨析其与本体等相关概念的关系.本体是知识图谱的模式层和逻辑基础，知识图谱是本体的实例化；本体研究成果可以作为知识图谱研究的基础，促进知识图谱的更快发展和更广应用.本文罗列分析了国内外已有的主要通用知识图谱和行业知识图谱及其构建、存储及检索方法，以提高其使用率和重用率.最后指出知识图谱未来的研究方向.
知识图谱是以图的形式表现客观世界中的概念和实体及其之间关系的知识库，是语义搜索、智能问答、决策支持等智能服务的基础技术之一.目前，知识图谱的内涵还不够清晰；且因建档不全，已有知识图谱的使用率和重用率不高.为此，本文给出知识图谱的定义，辨析其与本体等相关概念的关系.本体是知识图谱的模式层和逻辑基础，知识图谱是本体的实例化；本体研究成果可以作为知识图谱研究的基础，促进知识图谱的更快发展和更广应用.本文罗列分析了国内外已有的主要通用知识图谱和行业知识图谱及其构建、存储及检索方法，以提高其使用率和重用率.最后指出知识图谱未来的研究方向.
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:
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。
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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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