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2024,33(10):1-12, DOI: 10.15888/j.cnki.csa.009659, CSTR: 32024.14.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.
2024,33(10):13-25, DOI: 10.15888/j.cnki.csa.009662, CSTR: 32024.14.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.
2024,33(10):26-36, DOI: 10.15888/j.cnki.csa.009646, CSTR: 32024.14.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.
2024,33(10):37-46, DOI: 10.15888/j.cnki.csa.009635, CSTR: 32024.14.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.
2024,33(10):47-55, DOI: 10.15888/j.cnki.csa.009670, CSTR: 32024.14.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 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 reduced by 0.5–0.6. All this proves that the network has a better pose recognition ability.
2024,33(10):56-65, DOI: 10.15888/j.cnki.csa.009668, CSTR: 32024.14.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.
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Available online: October 25, 2024 , DOI: 10.15888/j.cnki.csa.009690
Abstract:
This study proposes a model called E2E-DRNet to address issues in manual diabetic retinopathy (DR) diagnosis, including poor classification performance, laborious processes, minimal differences in grades of retinal images, and inconspicuous lesions. This model is based on EfficientNetV2 and incorporates the efficient channel attention (ECA) module. By processing and optimizing a DR dataset, the Focal Loss function is introduced to address sample imbalance. The model achieves refined DR classification through two stages. Experimental results demonstrate that the proposed model performs well on both public and clinical datasets. Additionally, it enhances the interpretability of lesion regions in fundus images, thereby improving the efficiency of DR lesion screening and overcoming the limitations of manual diagnosis.
This study proposes a model called E2E-DRNet to address issues in manual diabetic retinopathy (DR) diagnosis, including poor classification performance, laborious processes, minimal differences in grades of retinal images, and inconspicuous lesions. This model is based on EfficientNetV2 and incorporates the efficient channel attention (ECA) module. By processing and optimizing a DR dataset, the Focal Loss function is introduced to address sample imbalance. The model achieves refined DR classification through two stages. Experimental results demonstrate that the proposed model performs well on both public and clinical datasets. Additionally, it enhances the interpretability of lesion regions in fundus images, thereby improving the efficiency of DR lesion screening and overcoming the limitations of manual diagnosis.
Available online: October 25, 2024 , DOI: 10.15888/j.cnki.csa.009691
Abstract:
As an Internet infrastructure, DNS is rarely subjected to deep monitoring by firewalls, allowing hackers and Asia-Pacific Telecommunity (APT) organizations to exploit DNS covert tunnels for data theft or network control and posing a significant threat to network security. In response to the easily bypassed nature of existing detection methods and their weak generalization capabilities, this study enhances the characterization method of DNS traffic and introduces the pcap features extraction CNN-Transformer (PFEC-Transformer) model. This model uses characterized decimal numerical sequences as input, conducts local feature extraction through CNN modules, and then analyzes long-distance dependency patterns between local features by using the Transformer for classification. The research builds datasets by collecting internet traffic and data packets generated by various DNS covert tunnel tools and conducts generalization testing with publicly available datasets containing traffic from unknown tunneling tools. Experimental results demonstrate that the model achieves an accuracy of 99.97% on the testing dataset and 92.12% on the generalization testing dataset, effectively showcasing its exceptional performance in detecting unknown DNS covert tunnels.
As an Internet infrastructure, DNS is rarely subjected to deep monitoring by firewalls, allowing hackers and Asia-Pacific Telecommunity (APT) organizations to exploit DNS covert tunnels for data theft or network control and posing a significant threat to network security. In response to the easily bypassed nature of existing detection methods and their weak generalization capabilities, this study enhances the characterization method of DNS traffic and introduces the pcap features extraction CNN-Transformer (PFEC-Transformer) model. This model uses characterized decimal numerical sequences as input, conducts local feature extraction through CNN modules, and then analyzes long-distance dependency patterns between local features by using the Transformer for classification. The research builds datasets by collecting internet traffic and data packets generated by various DNS covert tunnel tools and conducts generalization testing with publicly available datasets containing traffic from unknown tunneling tools. Experimental results demonstrate that the model achieves an accuracy of 99.97% on the testing dataset and 92.12% on the generalization testing dataset, effectively showcasing its exceptional performance in detecting unknown DNS covert tunnels.
Available online: October 25, 2024 , DOI: 10.15888/j.cnki.csa.009684
Abstract:
Underwater target detection has practical significance in ocean exploration. This study proposes a FERT-DETR network suitable for underwater target detection to address the issues of complex underwater environments and limited target feature extraction due to occlusion and overlap. The proposed model first introduces a feature extraction module, Faster EMA, to replace the BasicBlock of ResNet18 in RT-DETR, which can significantly improve its capability to extract features of underwater targets while effectively reducing the number of parameters and depth of the model. Secondly, a cascaded group attention module, AIFI-CGA, is used in the encoding part to reduce computational redundancy in multi-head attention and improve attention diversity. Finally, a feature pyramid for high-level filtering named HS-FPN is used to replace CCFM, achieving multi-level fusion and improving the accuracy and robustness of detection. The experimental results show that the proposed algorithm, FERT-DETR, improves detection accuracy by 3.1% and 1.7% compared to RT-DETR on the URPC2020 and DUO datasets respectively, compresses the number of parameters by 14.7%, and reduces computational complexity by 9.2%. It can effectively avoid missed and false detection of targets of different sizes in complex underwater environments.
Underwater target detection has practical significance in ocean exploration. This study proposes a FERT-DETR network suitable for underwater target detection to address the issues of complex underwater environments and limited target feature extraction due to occlusion and overlap. The proposed model first introduces a feature extraction module, Faster EMA, to replace the BasicBlock of ResNet18 in RT-DETR, which can significantly improve its capability to extract features of underwater targets while effectively reducing the number of parameters and depth of the model. Secondly, a cascaded group attention module, AIFI-CGA, is used in the encoding part to reduce computational redundancy in multi-head attention and improve attention diversity. Finally, a feature pyramid for high-level filtering named HS-FPN is used to replace CCFM, achieving multi-level fusion and improving the accuracy and robustness of detection. The experimental results show that the proposed algorithm, FERT-DETR, improves detection accuracy by 3.1% and 1.7% compared to RT-DETR on the URPC2020 and DUO datasets respectively, compresses the number of parameters by 14.7%, and reduces computational complexity by 9.2%. It can effectively avoid missed and false detection of targets of different sizes in complex underwater environments.
Available online: October 25, 2024 , DOI: 10.15888/j.cnki.csa.009696
Abstract:
The rapid growth of security inspection demand drives the development of intelligent security inspection technology. Due to the unique characteristics of X-ray images, detecting small contraband items is challenging. This study proposes an improved YOLOv8s network for contraband recognition to address this issue. Firstly, the Focal L1 Loss function is introduced to enhance CIoU and optimize the position and aspect ratio of prediction boxes to improve the network’s ability to identify contraband items. Improved deformable convolution is added to the shallow backbone network to capture features of contraband items in different directions. LSKA is incorporated into the SPPF module to expand the network’s receptive field, while the Swin-CS module captures global information and supplements dimensional interaction. Finally, three stacked attention blocks are used for processing, enhancing the network’s sensitivity towards small targets. The improved network achieves an average precision mean of 96.1% on the SIXray dataset, a 5.4% improvement over YOLOv8s with mAP50-95 reaching 0.682, a 4.5% increase. Experimental results indicate that the proposed model can accurately generate prediction boxes, effectively handle contraband detection in complex scenarios, and validate algorithm effectiveness.
The rapid growth of security inspection demand drives the development of intelligent security inspection technology. Due to the unique characteristics of X-ray images, detecting small contraband items is challenging. This study proposes an improved YOLOv8s network for contraband recognition to address this issue. Firstly, the Focal L1 Loss function is introduced to enhance CIoU and optimize the position and aspect ratio of prediction boxes to improve the network’s ability to identify contraband items. Improved deformable convolution is added to the shallow backbone network to capture features of contraband items in different directions. LSKA is incorporated into the SPPF module to expand the network’s receptive field, while the Swin-CS module captures global information and supplements dimensional interaction. Finally, three stacked attention blocks are used for processing, enhancing the network’s sensitivity towards small targets. The improved network achieves an average precision mean of 96.1% on the SIXray dataset, a 5.4% improvement over YOLOv8s with mAP50-95 reaching 0.682, a 4.5% increase. Experimental results indicate that the proposed model can accurately generate prediction boxes, effectively handle contraband detection in complex scenarios, and validate algorithm effectiveness.
Available online: October 25, 2024 , DOI: 10.15888/j.cnki.csa.009704
Abstract:
The AI diagnostic model based on deep learning relies heavily on high-quality detailed annotated data for algorithm training, but is affected by label noise information. To enhance the robustness of the model and prevent noisy label memory, a noise label sample selection (NLSS) model is proposed to fully mine the hidden information of noise samples and alleviate model overfitting. Firstly, distributed feature representations of the image are extracted by taking hybrid enhanced images as input. Secondly, the contrasive loss function is introduced to compare the similarity between the predicted label distribution of the sample and the real label distribution for sample evaluation and selection. Finally, based on sample selection, supervised information of the noisy label is re-corrected by the pseudo-label promotion strategy of the label redistribution module. Taking the PET/CT dataset of non-small cell lung cancer (NSCLC) patients as an example, results show that the proposed models outperform comparison models, reducing the interference of label noise in the diagnosis of lymph node metastasis.
The AI diagnostic model based on deep learning relies heavily on high-quality detailed annotated data for algorithm training, but is affected by label noise information. To enhance the robustness of the model and prevent noisy label memory, a noise label sample selection (NLSS) model is proposed to fully mine the hidden information of noise samples and alleviate model overfitting. Firstly, distributed feature representations of the image are extracted by taking hybrid enhanced images as input. Secondly, the contrasive loss function is introduced to compare the similarity between the predicted label distribution of the sample and the real label distribution for sample evaluation and selection. Finally, based on sample selection, supervised information of the noisy label is re-corrected by the pseudo-label promotion strategy of the label redistribution module. Taking the PET/CT dataset of non-small cell lung cancer (NSCLC) patients as an example, results show that the proposed models outperform comparison models, reducing the interference of label noise in the diagnosis of lymph node metastasis.
Available online: October 25, 2024 , DOI: 10.15888/j.cnki.csa.009706
Abstract:
Most current recommendation models often overlook the importance of features during feature interactions, leading to low accuracy. To address this issue, an enhanced recommendation model combining feature selection and the cross network is proposed. The SENet network is employed to filter out unimportant features before feature interaction, enabling the extraction of more valuable interaction information. On this basis, parallel cross network and deep neural network are utilized to capture explicit and implicit feature interactions. Additionally, low-rank techniques are intro-duced in the cross network, transforming weight vectors into low-rank matrices to maintain model performance and reduce model training costs. Comparative experiments on the datasets of MovieLens-1M and Criteo demonstrate that the proposed recommendation model is significantly superior to other models in terms of AUC metrics, which proves the effectiveness of the proposed recommendation model.
Most current recommendation models often overlook the importance of features during feature interactions, leading to low accuracy. To address this issue, an enhanced recommendation model combining feature selection and the cross network is proposed. The SENet network is employed to filter out unimportant features before feature interaction, enabling the extraction of more valuable interaction information. On this basis, parallel cross network and deep neural network are utilized to capture explicit and implicit feature interactions. Additionally, low-rank techniques are intro-duced in the cross network, transforming weight vectors into low-rank matrices to maintain model performance and reduce model training costs. Comparative experiments on the datasets of MovieLens-1M and Criteo demonstrate that the proposed recommendation model is significantly superior to other models in terms of AUC metrics, which proves the effectiveness of the proposed recommendation model.
Available online: October 25, 2024 , DOI: 10.15888/j.cnki.csa.009710
Abstract:
With the continuous development of industrial automation, the three-dimensional reconstruction technology of workpieces is playing an increasingly important role in the manufacturing industry. In actual working environments, there is a common problem of stacking workpieces, which significantly impacts subsequent work including robot recognition and grasping. Currently, it is hard for 3D reconstruction to extract image feature points and achieve accurate feature registration in workpieces with weak textures. To address the above issues, this study proposes a 3D reconstruction method for stacked workpieces based on deep learning with multi-view stereo matching. Firstly, multiple images from different perspectives are input through a DCNv2-based feature pyramid network for feature extraction. Then, homography transformation is performed to construct cost volumes, and a unified cost volume is obtained through variance aggregation. In the regularization section of the cost volume, an SE channel attention module is introduced to improve the feature expression ability of the network and enhance the performance and generalization ability of the model. This method exhibits good performance on the Danish Technical University (DTU) dataset. The point cloud model of stacked workpieces generated by this method is of great significance for future applications of industrial automation.
With the continuous development of industrial automation, the three-dimensional reconstruction technology of workpieces is playing an increasingly important role in the manufacturing industry. In actual working environments, there is a common problem of stacking workpieces, which significantly impacts subsequent work including robot recognition and grasping. Currently, it is hard for 3D reconstruction to extract image feature points and achieve accurate feature registration in workpieces with weak textures. To address the above issues, this study proposes a 3D reconstruction method for stacked workpieces based on deep learning with multi-view stereo matching. Firstly, multiple images from different perspectives are input through a DCNv2-based feature pyramid network for feature extraction. Then, homography transformation is performed to construct cost volumes, and a unified cost volume is obtained through variance aggregation. In the regularization section of the cost volume, an SE channel attention module is introduced to improve the feature expression ability of the network and enhance the performance and generalization ability of the model. This method exhibits good performance on the Danish Technical University (DTU) dataset. The point cloud model of stacked workpieces generated by this method is of great significance for future applications of industrial automation.
Available online: October 25, 2024 , DOI: 10.15888/j.cnki.csa.009713
Abstract:
Braille conversion technology is crucial for advancing information accessibility for the blind. With the rapid advancement of information globalization, the blind are increasingly exposed to bilingual information in both Chinese and English. While existing braille conversion systems have successfully translated Chinese and English into braille, they fall short in accurately converting punctuation, including poor differentiation of punctuation with multiple uses and lack of error correction for the mixed use of Chinese and English punctuation. Failure to address these issues may lead to misunderstanding of text by the blind. This study delves into these problems, designing and implementing a bilingual braille conversion system capable of distinguishing multipurpose punctuation and correcting the mixed use of punctuation. The performance of the system is evaluated by using a dataset based on BLCU Chinese Corpus. The results demonstrate that the proposed system accurately distinguishes multipurpose punctuation and corrects the mixed use of Chinese and English punctuation according to language types and context, outperforming other braille conversion systems. Overall, this research has significant potential for promoting information accessibility in China.
Braille conversion technology is crucial for advancing information accessibility for the blind. With the rapid advancement of information globalization, the blind are increasingly exposed to bilingual information in both Chinese and English. While existing braille conversion systems have successfully translated Chinese and English into braille, they fall short in accurately converting punctuation, including poor differentiation of punctuation with multiple uses and lack of error correction for the mixed use of Chinese and English punctuation. Failure to address these issues may lead to misunderstanding of text by the blind. This study delves into these problems, designing and implementing a bilingual braille conversion system capable of distinguishing multipurpose punctuation and correcting the mixed use of punctuation. The performance of the system is evaluated by using a dataset based on BLCU Chinese Corpus. The results demonstrate that the proposed system accurately distinguishes multipurpose punctuation and corrects the mixed use of Chinese and English punctuation according to language types and context, outperforming other braille conversion systems. Overall, this research has significant potential for promoting information accessibility in China.
Available online: September 29, 2024 , DOI: 10.15888/j.cnki.csa.009672
Abstract:
When dealing with large-scale multi-objective optimization problem (LSMOP), the MOEA/D algorithm shows poor scalability in the decision space and a tendency to converge to local optima as the dimensionality of decision variables increases. To address this issue, this study proposes a large-scale MOEA/D algorithm with multiple strategies (MSMOEA/D). The MSMOEA/D algorithm introduces a hybrid initialization strategy based on autoencoders in the optimization process to expand the coverage of the initial population, thus promoting global search. Moreover, a neighborhood adjustment strategy based on aggregation functions is proposed, which can more accurately control the search range during the search process by adjusting neighborhood sizes, thereby avoiding low search efficiency caused by excessively large or small neighborhoods. Furthermore, a mutation-selection strategy based on non-dominated sorting is adopted during the optimization process. Different subproblems select their mutation strategies according to the number of individuals in the first level of non-dominated sorting to avoid the population falling into local optima and enhance the overall performance of the algorithm. Finally, the MSMOEA/D algorithm and other existing algorithms are evaluated using LSMOP and DTLZ test problems. Experimental results verify the effectiveness of the proposed algorithm for solving LSMOPs.
When dealing with large-scale multi-objective optimization problem (LSMOP), the MOEA/D algorithm shows poor scalability in the decision space and a tendency to converge to local optima as the dimensionality of decision variables increases. To address this issue, this study proposes a large-scale MOEA/D algorithm with multiple strategies (MSMOEA/D). The MSMOEA/D algorithm introduces a hybrid initialization strategy based on autoencoders in the optimization process to expand the coverage of the initial population, thus promoting global search. Moreover, a neighborhood adjustment strategy based on aggregation functions is proposed, which can more accurately control the search range during the search process by adjusting neighborhood sizes, thereby avoiding low search efficiency caused by excessively large or small neighborhoods. Furthermore, a mutation-selection strategy based on non-dominated sorting is adopted during the optimization process. Different subproblems select their mutation strategies according to the number of individuals in the first level of non-dominated sorting to avoid the population falling into local optima and enhance the overall performance of the algorithm. Finally, the MSMOEA/D algorithm and other existing algorithms are evaluated using LSMOP and DTLZ test problems. Experimental results verify the effectiveness of the proposed algorithm for solving LSMOPs.
Available online: September 29, 2024 , DOI: 10.15888/j.cnki.csa.009678
Abstract:
The quality of steel surface defect inspection directly affects industrial production safety and machine performance. However, in real factories, steel quality control is limited by equipment conditions, making it challenging to achieve high-precision and real-time inspection. To solve this problem, a lightweight YOLOv8n detection algorithm with multi-scale fusion is proposed. Firstly, a lightweight multi-scale fusion backbone network (RepHGnetv2) is introduced, combining HGNetv2 and RepConv to improve the feature extraction and generalization capabilities of Backbone and reduce the complexity of the model. In the Head part, the ordinary convolution of the original algorithm is replaced with the ADown downsampling module, which reduces computational complexity and improves semantic retention. Finally, the loss function of the original algorithm is replaced by SlideLoss to address sample imbalance. Ablation and comparison experiments are conducted on the NEU-DET dataset. Compared with the original algorithm, the improved algorithm increases precision by 9.3%, reduces the model size by 25.5%, decreases computational complexity by 17.2%, and improves FPS to a certain extent. Comparative experiments are conducted on the VOC2012 dataset to evaluate the generalizability of the improved algorithm, and the results show that the improved algorithm exhibits strong generalizability and effectively improves the accuracy and efficiency of defect detection.
The quality of steel surface defect inspection directly affects industrial production safety and machine performance. However, in real factories, steel quality control is limited by equipment conditions, making it challenging to achieve high-precision and real-time inspection. To solve this problem, a lightweight YOLOv8n detection algorithm with multi-scale fusion is proposed. Firstly, a lightweight multi-scale fusion backbone network (RepHGnetv2) is introduced, combining HGNetv2 and RepConv to improve the feature extraction and generalization capabilities of Backbone and reduce the complexity of the model. In the Head part, the ordinary convolution of the original algorithm is replaced with the ADown downsampling module, which reduces computational complexity and improves semantic retention. Finally, the loss function of the original algorithm is replaced by SlideLoss to address sample imbalance. Ablation and comparison experiments are conducted on the NEU-DET dataset. Compared with the original algorithm, the improved algorithm increases precision by 9.3%, reduces the model size by 25.5%, decreases computational complexity by 17.2%, and improves FPS to a certain extent. Comparative experiments are conducted on the VOC2012 dataset to evaluate the generalizability of the improved algorithm, and the results show that the improved algorithm exhibits strong generalizability and effectively improves the accuracy and efficiency of defect detection.
Available online: September 27, 2024 , DOI: 10.15888/j.cnki.csa.009701
Abstract:
In this study, a multi-branch network that integrates multi-scale frequency features and depth map features trained by generative adversarial network (GAN) is proposed. Specifically, edge texture information in high-frequency features is beneficial to capturing moire patterns. Low-frequency features are more sensitive to color distortion. Depth maps are more discriminative than RGB images from the visual level as auxiliary information. Supervised multi-view contrastive learning is employed to further enhance multi-view feature learning. Moreover, a two-stage bilinear feature fusion method is proposed to effectively integrate multi-branch features from different views. To evaluate the model, ablation experiments, feature fusion comparison experiments, intra-set experiments and inter-set experiments are conducted on four widely used public datasets, namely CASIA-FASD, Replay-Attack, MSU-MFSD, and OULU-NPU. The experiment result shows that the average HTER of the proposed model on the four tested protocols is 5% (20.3% to 15.0%) better than the DFA method in the inter-set evaluation.
In this study, a multi-branch network that integrates multi-scale frequency features and depth map features trained by generative adversarial network (GAN) is proposed. Specifically, edge texture information in high-frequency features is beneficial to capturing moire patterns. Low-frequency features are more sensitive to color distortion. Depth maps are more discriminative than RGB images from the visual level as auxiliary information. Supervised multi-view contrastive learning is employed to further enhance multi-view feature learning. Moreover, a two-stage bilinear feature fusion method is proposed to effectively integrate multi-branch features from different views. To evaluate the model, ablation experiments, feature fusion comparison experiments, intra-set experiments and inter-set experiments are conducted on four widely used public datasets, namely CASIA-FASD, Replay-Attack, MSU-MFSD, and OULU-NPU. The experiment result shows that the average HTER of the proposed model on the four tested protocols is 5% (20.3% to 15.0%) better than the DFA method in the inter-set evaluation.
Available online: September 27, 2024 , DOI: 10.15888/j.cnki.csa.009702
Abstract:
Compared with centralized cloud computing frameworks, edge computing deploys additional “edge servers” between a cloud center and on-site intelligent devices to support those devices to quickly and efficiently complete computing tasks and event processing. In an edge computing system, there are a large number of on-site intelligent devices and heterogeneous edge computing servers. Also, stored data is sensitive and requires high privacy. These characteristics of edge computing systems make it difficult to ensure network security. Solving information and network security of edge computing systems is the key to the large-scale industrialization of edge computing technology. However, due to the limitations of computing capacity, network capacity, and storage capacity of edge server devices and on-site intelligent devices, traditional computer network security technology may not fully meet the requirements. Analyzing effective sensitive data protection technologies suitable for edge computing systems, such as federated learning, lightweight encryption, confused and virtual location information, and anonymous identity authentication, and exploring new technologies such as artificial intelligence and blockchain to prevent malicious attacks in edge computing will greatly promote the industrial development of edge computing.
Compared with centralized cloud computing frameworks, edge computing deploys additional “edge servers” between a cloud center and on-site intelligent devices to support those devices to quickly and efficiently complete computing tasks and event processing. In an edge computing system, there are a large number of on-site intelligent devices and heterogeneous edge computing servers. Also, stored data is sensitive and requires high privacy. These characteristics of edge computing systems make it difficult to ensure network security. Solving information and network security of edge computing systems is the key to the large-scale industrialization of edge computing technology. However, due to the limitations of computing capacity, network capacity, and storage capacity of edge server devices and on-site intelligent devices, traditional computer network security technology may not fully meet the requirements. Analyzing effective sensitive data protection technologies suitable for edge computing systems, such as federated learning, lightweight encryption, confused and virtual location information, and anonymous identity authentication, and exploring new technologies such as artificial intelligence and blockchain to prevent malicious attacks in edge computing will greatly promote the industrial development of edge computing.
Available online: September 27, 2024 , DOI: 10.15888/j.cnki.csa.009693
Abstract:
To achieve intelligent citrus picking, fast and accurate identification of citrus in the orchard environment becomes critical. Aiming at the defective adaptation of existing target detection algorithms to the environment and low efficiency, this study proposes a lightweight citrus maturity detection algorithm based on the YOLOv8n model, YOLOv8n-CMD (YOLOv8n citrus maturity detection). Firstly, the Backbone network structure is optimized to improve the detection of small targets. Secondly, the CBAM attention mechanism is added to improve the classification effect of the model. Then, Ghost convolution is introduced, and the neck C2f module in the original YOLOv8 model is combined with Ghost to reduce the amount of computation and that of parameters. Finally, the SimSPPF module is used in place of the original pyramidal pooling layer to improve model detection efficiency. Experimental results show that the YOLOv8n-CMD algorithm reduces the number of parameters and computation by 31.8% and 7.4%, respectively, and improves the accuracy by 3.0%, which is more suitable for citrus detection research in the orchard environment.
To achieve intelligent citrus picking, fast and accurate identification of citrus in the orchard environment becomes critical. Aiming at the defective adaptation of existing target detection algorithms to the environment and low efficiency, this study proposes a lightweight citrus maturity detection algorithm based on the YOLOv8n model, YOLOv8n-CMD (YOLOv8n citrus maturity detection). Firstly, the Backbone network structure is optimized to improve the detection of small targets. Secondly, the CBAM attention mechanism is added to improve the classification effect of the model. Then, Ghost convolution is introduced, and the neck C2f module in the original YOLOv8 model is combined with Ghost to reduce the amount of computation and that of parameters. Finally, the SimSPPF module is used in place of the original pyramidal pooling layer to improve model detection efficiency. Experimental results show that the YOLOv8n-CMD algorithm reduces the number of parameters and computation by 31.8% and 7.4%, respectively, and improves the accuracy by 3.0%, which is more suitable for citrus detection research in the orchard environment.
Available online: September 27, 2024 , DOI: 10.15888/j.cnki.csa.009677
Abstract:
To avoid eye image disappearance and inaccurate head pose estimation during image capture, a non-contact method for acquiring eye information is employed to collect facial images, determining the pilot’s current gaze direction from a single image frame. Concurrently, considering the poor classification of current networks due to the neglect of visual obstruction caused by head movements, with a combination of facial images and head poses, a multimodal data fusion network for the pilot’s gaze region classification is proposed using an improved MobileVit model. Firstly, a multi-modal data fusion module is introduced to address the problem of overfitting resulting from size imbalances during feature concatenation. Additionally, an inverse residual block based on a parallel branch SE mechanism is proposed to fully leverage spatial and channel feature information in the shallow layers of the network. Moreover, multi-scale features are captured by integrating the global attention mechanism from the Transformer. Finally, the Mobile block structure is redesigned and the depthwise separable convolution is utilized to reduce model complexity. Experimental comparisons with mainstream baseline models are conducted using a self-made dataset FlyGaze. The results demonstrate that the PilotT model achieves classification accuracies exceeding 92% for gaze regions 0, 3, 4, and 5, with robust adaptability to facial deflection. These findings hold practical significance for enhancing flight training quality and facilitating pilot intention recognition and fatigue assessment.
To avoid eye image disappearance and inaccurate head pose estimation during image capture, a non-contact method for acquiring eye information is employed to collect facial images, determining the pilot’s current gaze direction from a single image frame. Concurrently, considering the poor classification of current networks due to the neglect of visual obstruction caused by head movements, with a combination of facial images and head poses, a multimodal data fusion network for the pilot’s gaze region classification is proposed using an improved MobileVit model. Firstly, a multi-modal data fusion module is introduced to address the problem of overfitting resulting from size imbalances during feature concatenation. Additionally, an inverse residual block based on a parallel branch SE mechanism is proposed to fully leverage spatial and channel feature information in the shallow layers of the network. Moreover, multi-scale features are captured by integrating the global attention mechanism from the Transformer. Finally, the Mobile block structure is redesigned and the depthwise separable convolution is utilized to reduce model complexity. Experimental comparisons with mainstream baseline models are conducted using a self-made dataset FlyGaze. The results demonstrate that the PilotT model achieves classification accuracies exceeding 92% for gaze regions 0, 3, 4, and 5, with robust adaptability to facial deflection. These findings hold practical significance for enhancing flight training quality and facilitating pilot intention recognition and fatigue assessment.
Available online: September 27, 2024 , DOI: 10.15888/j.cnki.csa.009679
Abstract:
In this study, a detection method for tuberculosis pathogens based on Faster R-CNN is proposed to detect tuberculosis with higher accuracy and lower missed detection rate. First, the Mosaic data enhancement method is used to expand the dataset to improve the generalization ability of the model. At the same time, the K-means clustering algorithm is introduced to re-cluster the used dataset to generate the initial candidate box size of the paired anchor points. Secondly, the original feature extraction network in Faster R-CNN is replaced with Res2Net, and all its convolution kernels are replaced with empty convolution. This can bring a larger receptive field compared with the original convolution when the number of parameters remains unchanged. Furthermore, the improved GC-FPN module is introduced to make the model pay more attention to small target information while being lightweight. Finally, ROI Align is introduced to solve the problem of deviation between the candidate box and the initial regression position. The experimental results show that, compared with the original Faster R-CNN algorithm, the improved Faster R-CNN model has a 2.7% higher accuracy and an 1.4% higher recall rate on the open data set. This algorithm has been verified on the dataset of tuberculosis images and possesses high accuracy.
In this study, a detection method for tuberculosis pathogens based on Faster R-CNN is proposed to detect tuberculosis with higher accuracy and lower missed detection rate. First, the Mosaic data enhancement method is used to expand the dataset to improve the generalization ability of the model. At the same time, the K-means clustering algorithm is introduced to re-cluster the used dataset to generate the initial candidate box size of the paired anchor points. Secondly, the original feature extraction network in Faster R-CNN is replaced with Res2Net, and all its convolution kernels are replaced with empty convolution. This can bring a larger receptive field compared with the original convolution when the number of parameters remains unchanged. Furthermore, the improved GC-FPN module is introduced to make the model pay more attention to small target information while being lightweight. Finally, ROI Align is introduced to solve the problem of deviation between the candidate box and the initial regression position. The experimental results show that, compared with the original Faster R-CNN algorithm, the improved Faster R-CNN model has a 2.7% higher accuracy and an 1.4% higher recall rate on the open data set. This algorithm has been verified on the dataset of tuberculosis images and possesses high accuracy.
Available online: September 27, 2024 , DOI: 10.15888/j.cnki.csa.009680
Abstract:
An improved YOLOv8 algorithm for underwater target detection is proposed to prevent missed detection of objects with different scales and overlapping occlusion. Firstly, deformable convolutions are introduced into the backbone network (deformable convolution network, DCN) to improve the feature extraction capability of the model by means of the adaptive deformation mechanism of convolution kernels. Secondly, a module combining atrous convolution and spatial pyramid, termed ASPF, is designed to expand the receptive field of the output feature map and improve the perception ability of the model for detecting underwater targets of multiple scales. Finally, the loss function is improved to optimize the training process of the model and improve detection accuracy. The improved algorithm is tested on the URPC data set, and the results show that its detection accuracy reaches 87.3%, which is 3.4% higher than that of the original YOLOv8 algorithm. Moreover, it can accurately detect underwater targets with different scales and overlapping occlusion.
An improved YOLOv8 algorithm for underwater target detection is proposed to prevent missed detection of objects with different scales and overlapping occlusion. Firstly, deformable convolutions are introduced into the backbone network (deformable convolution network, DCN) to improve the feature extraction capability of the model by means of the adaptive deformation mechanism of convolution kernels. Secondly, a module combining atrous convolution and spatial pyramid, termed ASPF, is designed to expand the receptive field of the output feature map and improve the perception ability of the model for detecting underwater targets of multiple scales. Finally, the loss function is improved to optimize the training process of the model and improve detection accuracy. The improved algorithm is tested on the URPC data set, and the results show that its detection accuracy reaches 87.3%, which is 3.4% higher than that of the original YOLOv8 algorithm. Moreover, it can accurately detect underwater targets with different scales and overlapping occlusion.
Available online: September 27, 2024 , DOI: 10.15888/j.cnki.csa.009681
Abstract:
In multi-object tracking tasks, the interference of external noise can lead to unreliable system modeling of traditional methods, thus reducing the accuracy of object position prediction; and the congestion and obstruction caused by dense crowds seriously affect the reliability of the object appearance, resulting in incorrect identity association. To address these issues, this study proposes a multi-object tracking algorithm Ecsort. This algorithm improves position prediction accuracy by introducing a noise compensation module based on traditional motion prediction to reduce errors caused by noise interference. Secondly, this algorithm introduces a feature similarity matching module It can achieve accurate identity association by learning discriminative appearance features of objects and combining the advantages of motion cues and discriminative appearance features. Extensive experimental results on multi-object tracking benchmark datasets demonstrate that, compared to the baseline model, this method improves ID F1 score (IDF1), higher order tracking accuracy (HOTA), association accuracy (AssA), and detection accuracy (DetA) by 1.1%, 0.5%, 0.6%, and 0.3% respectively on the MOT17 test set, and by 2.3%, 1.9%, 3.4%, and 0.2% respectively on the MOT20 test set.
In multi-object tracking tasks, the interference of external noise can lead to unreliable system modeling of traditional methods, thus reducing the accuracy of object position prediction; and the congestion and obstruction caused by dense crowds seriously affect the reliability of the object appearance, resulting in incorrect identity association. To address these issues, this study proposes a multi-object tracking algorithm Ecsort. This algorithm improves position prediction accuracy by introducing a noise compensation module based on traditional motion prediction to reduce errors caused by noise interference. Secondly, this algorithm introduces a feature similarity matching module It can achieve accurate identity association by learning discriminative appearance features of objects and combining the advantages of motion cues and discriminative appearance features. Extensive experimental results on multi-object tracking benchmark datasets demonstrate that, compared to the baseline model, this method improves ID F1 score (IDF1), higher order tracking accuracy (HOTA), association accuracy (AssA), and detection accuracy (DetA) by 1.1%, 0.5%, 0.6%, and 0.3% respectively on the MOT17 test set, and by 2.3%, 1.9%, 3.4%, and 0.2% respectively on the MOT20 test set.
Available online: September 27, 2024 , DOI: 10.15888/j.cnki.csa.009682
Abstract:
An optimized bilinear structure based on ResNet34, termed OBSR-Net, is proposed for more accurate and quick facial expression recognition. OBSR-Net adopts a bilinear network structure as its overall framework and incorporates ResNet34 as the backbone network to model the local paired feature interaction by translation invariance, to extract more complete and effective features. At the same time, transfer learning mitigates the limitations imposed by small sample image data sets of facial expressions on deep learning. In addition, gradient concentration, a new general optimization technique, is utilized during the training process. This technique operates directly on gradients by concentrating gradient vectors to zero mean, which can be regarded as a projected gradient descent method with a constrained loss function. Experiments on two public datasets, namely Fer2013 and CK+, reveal that OBSR-Net achieves recognition accuracy of 77.65% and 98.82%, respectively. The experimental results show that OBSR-Net is more competitive than other advanced facial expression recognition methods.
An optimized bilinear structure based on ResNet34, termed OBSR-Net, is proposed for more accurate and quick facial expression recognition. OBSR-Net adopts a bilinear network structure as its overall framework and incorporates ResNet34 as the backbone network to model the local paired feature interaction by translation invariance, to extract more complete and effective features. At the same time, transfer learning mitigates the limitations imposed by small sample image data sets of facial expressions on deep learning. In addition, gradient concentration, a new general optimization technique, is utilized during the training process. This technique operates directly on gradients by concentrating gradient vectors to zero mean, which can be regarded as a projected gradient descent method with a constrained loss function. Experiments on two public datasets, namely Fer2013 and CK+, reveal that OBSR-Net achieves recognition accuracy of 77.65% and 98.82%, respectively. The experimental results show that OBSR-Net is more competitive than other advanced facial expression recognition methods.
Available online: September 27, 2024 , DOI: 10.15888/j.cnki.csa.009683
Abstract:
The development of deep learning technology invites most research to consider short-term precipitation nowcasting as a prediction task of radar echo sequences. Due to the nonlinear spatiotemporal transformations involved in the complexity of precipitation, existing short-term nowcasting methods have problems such as low accuracy, short extrapolation time, and difficulty in dealing with complex nonlinear spatiotemporal transformations. To address these issues, this study proposes an S-UNet short-term precipitation forecasting network based on U-Net and LSTM. Firstly, the study introduces the S-UNet layer (SL) module to help the network better extract radar sequence features and construct the overall trend of spatiotemporal changes, thereby improving the network efficiency and increasing the extrapolation duration. Secondly, to better address the complexity of radar echo deformation, accumulation, and dissipation, and to enhance the network’s ability to capture complex spatial relationships and simulate movement trajectories, this study constructs the radar feature (RF) module based on LSTM. Finally, by combining the SL module and the RF module with the U-Net framework, the S-UNet short-term precipitation nowcasting network is proposed, achieving remarkable performance on the KNMI dataset. Experimental results show that, compared with the mainstream methods, on the KNMI’s NL-50 and NL-20 datasets, the proposed method improves the Heidke skill score (HSS) and critical success index (CSI) by 5.25% (6.57%) and 2.17% (4.75%) respectively, reaching 0.30(0.29) and 0.72(0.58); the accuracy increases by 2.10% (1.35%), reaching 0.80 (0.80); and the false acceptance rate decreases by 4.27% (1.80%), dropping to 0.24 (0.38). Additionally, the effectiveness of the proposed modules and their combination methods are verified through ablation experiments.
The development of deep learning technology invites most research to consider short-term precipitation nowcasting as a prediction task of radar echo sequences. Due to the nonlinear spatiotemporal transformations involved in the complexity of precipitation, existing short-term nowcasting methods have problems such as low accuracy, short extrapolation time, and difficulty in dealing with complex nonlinear spatiotemporal transformations. To address these issues, this study proposes an S-UNet short-term precipitation forecasting network based on U-Net and LSTM. Firstly, the study introduces the S-UNet layer (SL) module to help the network better extract radar sequence features and construct the overall trend of spatiotemporal changes, thereby improving the network efficiency and increasing the extrapolation duration. Secondly, to better address the complexity of radar echo deformation, accumulation, and dissipation, and to enhance the network’s ability to capture complex spatial relationships and simulate movement trajectories, this study constructs the radar feature (RF) module based on LSTM. Finally, by combining the SL module and the RF module with the U-Net framework, the S-UNet short-term precipitation nowcasting network is proposed, achieving remarkable performance on the KNMI dataset. Experimental results show that, compared with the mainstream methods, on the KNMI’s NL-50 and NL-20 datasets, the proposed method improves the Heidke skill score (HSS) and critical success index (CSI) by 5.25% (6.57%) and 2.17% (4.75%) respectively, reaching 0.30(0.29) and 0.72(0.58); the accuracy increases by 2.10% (1.35%), reaching 0.80 (0.80); and the false acceptance rate decreases by 4.27% (1.80%), dropping to 0.24 (0.38). Additionally, the effectiveness of the proposed modules and their combination methods are verified through ablation experiments.
Available online: September 27, 2024 , DOI: 10.15888/j.cnki.csa.009640
Abstract:
To extract rich entity information and normalized expressions from biomedical literature, this study proposes a multi-granularity feature fusion approach for biomedical named entity recognition and normalization (MGFFA). By integrating character-level, word-level, and concept-level textual information, the model significantly enhances its learning capability. It also incorporates a memory bank for storing and synthesizing information from different levels to achieve a deeper understanding of the complex relationships between entities and their normalized labels. With the integration of pre-trained models, MGFFA captures not only coarse-grained semantic representations of text but also conducts detailed analysis at the morphological level, thereby comprehensively improving the recognition accuracy of long-span entities. Experimental results on the NCBI and NC5CDR datasets demonstrate that the model outperforms other baseline models overall.
To extract rich entity information and normalized expressions from biomedical literature, this study proposes a multi-granularity feature fusion approach for biomedical named entity recognition and normalization (MGFFA). By integrating character-level, word-level, and concept-level textual information, the model significantly enhances its learning capability. It also incorporates a memory bank for storing and synthesizing information from different levels to achieve a deeper understanding of the complex relationships between entities and their normalized labels. With the integration of pre-trained models, MGFFA captures not only coarse-grained semantic representations of text but also conducts detailed analysis at the morphological level, thereby comprehensively improving the recognition accuracy of long-span entities. Experimental results on the NCBI and NC5CDR datasets demonstrate that the model outperforms other baseline models overall.
Available online: September 24, 2024 , DOI: 10.15888/j.cnki.csa.009636
Abstract:
Numerous real-world tasks require the collaboration of multiple agents, often with limited communication and incomplete observations. Deep multi-agent reinforcement learning (Deep-MARL) algorithms show remarkable effectiveness in tackling such challenging scenarios. Among these algorithms, QTRAN and QTRAN++ are representative approaches capable of learning a broad class of joint-action value functions with strong theoretical guarantees. However, the performance of QTRAN and QTRAN++ is hindered by their reliance on a single joint action-value estimator and their neglect of preprocessing agent observations. This study introduces a novel algorithm called OPTQTRAN, which significantly improves upon the performance of QTRAN and QTRAN++. Firstly, the study proposes a dual joint action-value estimator structure that leverages a decomposition network module to compute additional joint action-values. To ensure accurate computation of joint action-value estimators, it designs an adaptive network that facilitates efficient value function learning. Additionally, it introduces a multi-unit network that groups agent observations into different units for effective estimation of utility functions. Extensive experiments conducted on the widely-used StarCraft benchmark across diverse scenarios demonstrate that the proposed approach outperforms state-of-the-art MARL methods.
Numerous real-world tasks require the collaboration of multiple agents, often with limited communication and incomplete observations. Deep multi-agent reinforcement learning (Deep-MARL) algorithms show remarkable effectiveness in tackling such challenging scenarios. Among these algorithms, QTRAN and QTRAN++ are representative approaches capable of learning a broad class of joint-action value functions with strong theoretical guarantees. However, the performance of QTRAN and QTRAN++ is hindered by their reliance on a single joint action-value estimator and their neglect of preprocessing agent observations. This study introduces a novel algorithm called OPTQTRAN, which significantly improves upon the performance of QTRAN and QTRAN++. Firstly, the study proposes a dual joint action-value estimator structure that leverages a decomposition network module to compute additional joint action-values. To ensure accurate computation of joint action-value estimators, it designs an adaptive network that facilitates efficient value function learning. Additionally, it introduces a multi-unit network that groups agent observations into different units for effective estimation of utility functions. Extensive experiments conducted on the widely-used StarCraft benchmark across diverse scenarios demonstrate that the proposed approach outperforms state-of-the-art MARL methods.
Available online: September 24, 2024 , DOI: 10.15888/j.cnki.csa.009642
Abstract:
Mini-programs have been widely used in recent years, causing widespread privacy and security concerns for carrying a large amount of sensitive user data. Existing privacy and security analysis techniques for traditional mobile applications cannot be directly applied to mini-programs. On the one hand, it is difficult for existing methods to effectively analyze the privacy transfer caused by the closed-source mini-program framework and the cross-scope privacy transfer caused by the JavaScript closures, resulting in a lack of analysis results. On the other hand, the mechanism of dynamic sub-package loading leads to incomplete analysis scope, further resulting in a lack of analysis results. This study proposes a hybrid dynamic/static method for analyzing the privacy collection behaviors in mini-programs. First, this method constructs a data propagation path based on either control flow or data dependency for different unit boundaries in the mini-programs, namely the mini-program privacy propagation flow graph. Furthermore, this method effectively explores the mini-program UI by learning and transferring traditional mobile application UI design knowledge, and using the control flow association between UI events and page transition information as a guide, thereby triggering the sub-package loading process. The corresponding sub-package code is analyzed and integrated with existing analysis results to form a more comprehensive mini-program privacy propagation flow graph. This study implements the tracking of sensitive data in mini-programs through the privacy propagation flow graph. Based on the above method, this study implements MiniSafe, a privacy collection behavior analysis tool for mini-programs. The evaluation results show that MiniSafe achieves 90.4% and 87.4% in precision and recall respectively, both of which outperform existing work. MiniSafe detects an average of 7 sensitive data collection behaviors in each mini-program. By considering sensitive data collection behaviors in mini-program sub-packages, the overall detection number has increased by 42.9%, demonstrating good detection performance and practical usability.
Mini-programs have been widely used in recent years, causing widespread privacy and security concerns for carrying a large amount of sensitive user data. Existing privacy and security analysis techniques for traditional mobile applications cannot be directly applied to mini-programs. On the one hand, it is difficult for existing methods to effectively analyze the privacy transfer caused by the closed-source mini-program framework and the cross-scope privacy transfer caused by the JavaScript closures, resulting in a lack of analysis results. On the other hand, the mechanism of dynamic sub-package loading leads to incomplete analysis scope, further resulting in a lack of analysis results. This study proposes a hybrid dynamic/static method for analyzing the privacy collection behaviors in mini-programs. First, this method constructs a data propagation path based on either control flow or data dependency for different unit boundaries in the mini-programs, namely the mini-program privacy propagation flow graph. Furthermore, this method effectively explores the mini-program UI by learning and transferring traditional mobile application UI design knowledge, and using the control flow association between UI events and page transition information as a guide, thereby triggering the sub-package loading process. The corresponding sub-package code is analyzed and integrated with existing analysis results to form a more comprehensive mini-program privacy propagation flow graph. This study implements the tracking of sensitive data in mini-programs through the privacy propagation flow graph. Based on the above method, this study implements MiniSafe, a privacy collection behavior analysis tool for mini-programs. The evaluation results show that MiniSafe achieves 90.4% and 87.4% in precision and recall respectively, both of which outperform existing work. MiniSafe detects an average of 7 sensitive data collection behaviors in each mini-program. By considering sensitive data collection behaviors in mini-program sub-packages, the overall detection number has increased by 42.9%, demonstrating good detection performance and practical usability.
Available online: September 24, 2024 , DOI: 10.15888/j.cnki.csa.009653
Abstract:
With the increasing complexity of mobile applications, existing privacy leak detection methods based on user intent face greater challenges. On the one hand, traditional privacy leak detection, which is based on APP-level user intent, only focuses on whether the privacy collection behavior of the application aligns with its core functional requirements. This approach is not suitable for today’s mobile APP security detection, which has broad functionalities and diverse user intents, necessitating a more fine-grained user intent classification. On the other hand, current research mainly focuses on evaluating whether the privacy collection behaviors triggered by interface widgets, such as icons, are consistent with user intent. However, the improper design and misuse of icons are very common, which limits the effectiveness of privacy risk assessments that rely solely on widget-based user intents. Therefore, a comprehensive understanding of user intent at the overall interface level is still needed. In response to the above issues, this study first extracts and summarizes a fine-grained user intent list suitable for privacy compliance detection based on Chinese privacy policies. Then, based on the characteristics of mobile application interface design, a multi-classification model with multi-modal feature fusion is designed and implemented to identify the user intent reflected by the entire mobile interface. Evaluation results show that the intent extraction tool in this study has achieved 83% in both precision and recall, and the user intent classification model reaches 80% and 83% in precision and recall, respectively, demonstrating good detection effectiveness and practical usability.
With the increasing complexity of mobile applications, existing privacy leak detection methods based on user intent face greater challenges. On the one hand, traditional privacy leak detection, which is based on APP-level user intent, only focuses on whether the privacy collection behavior of the application aligns with its core functional requirements. This approach is not suitable for today’s mobile APP security detection, which has broad functionalities and diverse user intents, necessitating a more fine-grained user intent classification. On the other hand, current research mainly focuses on evaluating whether the privacy collection behaviors triggered by interface widgets, such as icons, are consistent with user intent. However, the improper design and misuse of icons are very common, which limits the effectiveness of privacy risk assessments that rely solely on widget-based user intents. Therefore, a comprehensive understanding of user intent at the overall interface level is still needed. In response to the above issues, this study first extracts and summarizes a fine-grained user intent list suitable for privacy compliance detection based on Chinese privacy policies. Then, based on the characteristics of mobile application interface design, a multi-classification model with multi-modal feature fusion is designed and implemented to identify the user intent reflected by the entire mobile interface. Evaluation results show that the intent extraction tool in this study has achieved 83% in both precision and recall, and the user intent classification model reaches 80% and 83% in precision and recall, respectively, demonstrating good detection effectiveness and practical usability.
Available online: September 24, 2024 , DOI: 10.15888/j.cnki.csa.009667
Abstract:
To improve the accuracy and real-time performance of vehicle recognition, this study proposes a vehicle recognition method based on transfer learning. This optimized method improves the accuracy of vehicle recognition, reduces model training time, and improves the robustness of the model by integrating convolutional neural networks and support vector machines. This method first uses a convolutional neural network to train its network on the CIFAR-10 data set. Residual optimization is then applied to a deeper pre-trained network to extract fine-grained features. During the parameter transfer process of the model network, only the pre-trained parameters of the convolutional layer are transferred, and a fully connected layer is added for fine-tuning on the vehicle data set. Finally, the extracted features are directly put into the support vector machine for classification. Detailed model experiments and result analysis demonstrate that this method achieves the highest recognition accuracy of 97.56% and a recognition time of 260 ms per single image, indicating optimized performance in both recognition time and accuracy.
To improve the accuracy and real-time performance of vehicle recognition, this study proposes a vehicle recognition method based on transfer learning. This optimized method improves the accuracy of vehicle recognition, reduces model training time, and improves the robustness of the model by integrating convolutional neural networks and support vector machines. This method first uses a convolutional neural network to train its network on the CIFAR-10 data set. Residual optimization is then applied to a deeper pre-trained network to extract fine-grained features. During the parameter transfer process of the model network, only the pre-trained parameters of the convolutional layer are transferred, and a fully connected layer is added for fine-tuning on the vehicle data set. Finally, the extracted features are directly put into the support vector machine for classification. Detailed model experiments and result analysis demonstrate that this method achieves the highest recognition accuracy of 97.56% and a recognition time of 260 ms per single image, indicating optimized performance in both recognition time and accuracy.
Available online: September 24, 2024 , DOI: 10.15888/j.cnki.csa.009669
Abstract:
Time series imputation aims to restore data integrity by filling in missing values based on existing data. Currently, RNN-based imputation methods suffer from large errors, and increasing the number of network layers often leads to exploding and vanishing gradients. Additionally, GAN-based and VAE-based imputation methods frequently encounter challenges such as training difficulties and pattern collapse. To address these challenges, this study proposes a time series imputation model named diffusion model and time-frequency attention (DTFA), which reconstructs missing data from Gaussian noise through reverse diffusion. Specifically, this study utilizes multi-scale convolutional modules and two-dimensional attention mechanisms to capture temporal dependencies in time-domain data and employs MLPs and two-dimensional attention mechanisms to learn real and imaginary parts of frequency-domain data. This study also implements a linear imputation module to augment the existing observed data, thereby providing better guidance for model imputation. Finally, this study trains a noise estimation network by minimizing the Euclidean distance between real noise and estimated noise and then utilizes reverse diffusion to fill in the missing values in time series data. The experimental results demonstrate that DTFA outperforms mainstream baseline models in terms of imputation effectiveness on three public datasets: ETTm1, WindPower, and Electricity.
Time series imputation aims to restore data integrity by filling in missing values based on existing data. Currently, RNN-based imputation methods suffer from large errors, and increasing the number of network layers often leads to exploding and vanishing gradients. Additionally, GAN-based and VAE-based imputation methods frequently encounter challenges such as training difficulties and pattern collapse. To address these challenges, this study proposes a time series imputation model named diffusion model and time-frequency attention (DTFA), which reconstructs missing data from Gaussian noise through reverse diffusion. Specifically, this study utilizes multi-scale convolutional modules and two-dimensional attention mechanisms to capture temporal dependencies in time-domain data and employs MLPs and two-dimensional attention mechanisms to learn real and imaginary parts of frequency-domain data. This study also implements a linear imputation module to augment the existing observed data, thereby providing better guidance for model imputation. Finally, this study trains a noise estimation network by minimizing the Euclidean distance between real noise and estimated noise and then utilizes reverse diffusion to fill in the missing values in time series data. The experimental results demonstrate that DTFA outperforms mainstream baseline models in terms of imputation effectiveness on three public datasets: ETTm1, WindPower, and Electricity.
Available online: September 24, 2024 , DOI: 10.15888/j.cnki.csa.009671
Abstract:
The diagnosis of depression is an important research direction in the medical field. However, existing methods for diagnosing depression face problems such as high cost, low efficiency, low accuracy, and weak interpretability. To solve these problems, this study proposes an automatic algorithm for depression diagnosis based on sleep EEG signals, combined with sleep staging. This method first combines convolutional neural networks with bidirectional long short-term memory neural networks to extract advanced features of sleep signals. At the same time, it analyzes the correlation among different sleep stages, improving the accuracy and interpretability of sleep staging. The experimental results show that this method achieves the highest accuracy of 95.82% on the public dataset Sleep-EDF, surpassing most existing methods. Subsequently, based on the results of sleep staging, the compression net 2 dimension (DepNet2D) model combined with convolutional neural networks is proposed to extract features and classify EEG data during the REM phase. This model can effectively learn the spatiotemporal dependencies of sleep EEG, capture the feature patterns of brain activity in patients with depression, and improve the accuracy of identifying the spectral features of patients. The experimental results show that in the diagnosis of depression, the proposed method in this study reaches accuracy of 88.82%, which is higher than that of traditional models. The proposed method enhances the interpretability of depression diagnosis and has practical value for modern depression research and analysis, providing new ideas and methods for research and clinical practice in the field of mental health.
The diagnosis of depression is an important research direction in the medical field. However, existing methods for diagnosing depression face problems such as high cost, low efficiency, low accuracy, and weak interpretability. To solve these problems, this study proposes an automatic algorithm for depression diagnosis based on sleep EEG signals, combined with sleep staging. This method first combines convolutional neural networks with bidirectional long short-term memory neural networks to extract advanced features of sleep signals. At the same time, it analyzes the correlation among different sleep stages, improving the accuracy and interpretability of sleep staging. The experimental results show that this method achieves the highest accuracy of 95.82% on the public dataset Sleep-EDF, surpassing most existing methods. Subsequently, based on the results of sleep staging, the compression net 2 dimension (DepNet2D) model combined with convolutional neural networks is proposed to extract features and classify EEG data during the REM phase. This model can effectively learn the spatiotemporal dependencies of sleep EEG, capture the feature patterns of brain activity in patients with depression, and improve the accuracy of identifying the spectral features of patients. The experimental results show that in the diagnosis of depression, the proposed method in this study reaches accuracy of 88.82%, which is higher than that of traditional models. The proposed method enhances the interpretability of depression diagnosis and has practical value for modern depression research and analysis, providing new ideas and methods for research and clinical practice in the field of mental health.
Available online: September 24, 2024 , DOI: 10.15888/j.cnki.csa.009673
Abstract:
In the field of precipitation nowcasting, the existing radar echo extrapolation methods based on deep learning have some shortcomings. In terms of image quality, the prediction images are indistinct and deficient in small-scale details, while in terms of prediction accuracy, the precipitation results are not accurate enough. This study proposes a multi-scale generative adversarial (MCGAN) model, which consists of a multi-scale convolutional generator and a fully convolutional discriminator. The generator part adopts an encoder-decoder architecture, which mainly includes multi-scale convolutional blocks and downsampling gating units. Using the dynamic spatiotemporal variability loss function, the MCGAN model is trained under the generative adversarial network (GAN) framework to achieve more accurate and clearer predictions of echo intensity and distribution. Verified in the Shanghai public radar dataset, the performance of the model in this study decreases by 11.15% in the MSE index in image quality evaluation, and increases by 8.99% and 2.95% in the SSIM index and PSNR index compared with the mainstream deep learning models, respectively. In the evaluation of prediction accuracy, the CSI, POD, and HSS indexes increase by 11.92%, 15.89%, and 9.01% on average, and the FAR index decreases by 14.81% on average. In addition, the role of each component of the MCGAN model is demonstrated by ablation experiments.
In the field of precipitation nowcasting, the existing radar echo extrapolation methods based on deep learning have some shortcomings. In terms of image quality, the prediction images are indistinct and deficient in small-scale details, while in terms of prediction accuracy, the precipitation results are not accurate enough. This study proposes a multi-scale generative adversarial (MCGAN) model, which consists of a multi-scale convolutional generator and a fully convolutional discriminator. The generator part adopts an encoder-decoder architecture, which mainly includes multi-scale convolutional blocks and downsampling gating units. Using the dynamic spatiotemporal variability loss function, the MCGAN model is trained under the generative adversarial network (GAN) framework to achieve more accurate and clearer predictions of echo intensity and distribution. Verified in the Shanghai public radar dataset, the performance of the model in this study decreases by 11.15% in the MSE index in image quality evaluation, and increases by 8.99% and 2.95% in the SSIM index and PSNR index compared with the mainstream deep learning models, respectively. In the evaluation of prediction accuracy, the CSI, POD, and HSS indexes increase by 11.92%, 15.89%, and 9.01% on average, and the FAR index decreases by 14.81% on average. In addition, the role of each component of the MCGAN model is demonstrated by ablation experiments.
Available online: September 24, 2024 , DOI: 10.15888/j.cnki.csa.009675
Abstract:
As the demand for unmanned aerial vehicle (UAV) applications continues to expand, the design of disturbance rejection controllers which aim to ensure that UAVs can complete designated tasks as required has received significant attention. Traditional control algorithms widely used currently exhibit good stability but poor disturbance rejection capability. To address this issue, a hybrid disturbance rejection controller based on an improved twin delayed deep deterministic policy gradient (TD3) algorithm is proposed. This method utilizes nonlinear model predictive control (NMPC) as the base controller and introduces a disturbance compensator based on improved TD3 for hybrid control. This approach combines the advantages of the NMPC controller as well as addresses the shortcomings in disturbance rejection of traditional control algorithms. This study introduces a multi-head attention (MA) mechanism and long short-term memory (LSTM) network into the Actor network of TD3, enhancing TD3’s ability to capture spatial management information and temporal correlation information. Additionally, a continuous logarithmic reward function is introduced to improve training stability and convergence speed, and training is conducted using random task scenarios with random disturbances to enhance model generalization. In experiments, the NMPC-MALSTM-TD3 architecture is compared with architectures using DDPG, SAC, TD3, and PPO algorithms as disturbance compensators. Experimental results demonstrate that the NMPC-MALSTM-TD3 architecture exhibits the most excellent disturbance rejection capabilities and a smaller influence on the stability and real-time performance of NMPC.
As the demand for unmanned aerial vehicle (UAV) applications continues to expand, the design of disturbance rejection controllers which aim to ensure that UAVs can complete designated tasks as required has received significant attention. Traditional control algorithms widely used currently exhibit good stability but poor disturbance rejection capability. To address this issue, a hybrid disturbance rejection controller based on an improved twin delayed deep deterministic policy gradient (TD3) algorithm is proposed. This method utilizes nonlinear model predictive control (NMPC) as the base controller and introduces a disturbance compensator based on improved TD3 for hybrid control. This approach combines the advantages of the NMPC controller as well as addresses the shortcomings in disturbance rejection of traditional control algorithms. This study introduces a multi-head attention (MA) mechanism and long short-term memory (LSTM) network into the Actor network of TD3, enhancing TD3’s ability to capture spatial management information and temporal correlation information. Additionally, a continuous logarithmic reward function is introduced to improve training stability and convergence speed, and training is conducted using random task scenarios with random disturbances to enhance model generalization. In experiments, the NMPC-MALSTM-TD3 architecture is compared with architectures using DDPG, SAC, TD3, and PPO algorithms as disturbance compensators. Experimental results demonstrate that the NMPC-MALSTM-TD3 architecture exhibits the most excellent disturbance rejection capabilities and a smaller influence on the stability and real-time performance of NMPC.
Available online: September 24, 2024 , DOI: 10.15888/j.cnki.csa.009676
Abstract:
To address the problems of skin lesions, such as varied sizes, low contrast with surrounding skin, blurred and irregular boundaries, artifacts, and hair interference, this study proposes a skin lesion segmentation algorithm that combines edge enhancement with multi-scale information fusion. The algorithm consists of an encoder, a multi-scale sensing module, an edge enhancement module, and a lightweight decoder. Firstly, a Transformer module is built in the encoder to extract global information, and convolution operations are used to extract local information. Secondly, a multi-scale sensing module is designed to integrate multi-scale features using a gated atrous convolution pyramid module with a dense connection structure. An edge enhancement module is constructed, utilizing deep features to promote the exploration of edge features to better retain details and edge information. Finally, a lightweight decoder is designed, employing the CARAFE lightweight operator for upsampling, to maintain high segmentation accuracy with fewer parameters. Comparative experiments on open data sets ISIC2016 and ISIC2018 show that the segmentation accuracy of the proposed algorithm is higher than that of other popular algorithms.
To address the problems of skin lesions, such as varied sizes, low contrast with surrounding skin, blurred and irregular boundaries, artifacts, and hair interference, this study proposes a skin lesion segmentation algorithm that combines edge enhancement with multi-scale information fusion. The algorithm consists of an encoder, a multi-scale sensing module, an edge enhancement module, and a lightweight decoder. Firstly, a Transformer module is built in the encoder to extract global information, and convolution operations are used to extract local information. Secondly, a multi-scale sensing module is designed to integrate multi-scale features using a gated atrous convolution pyramid module with a dense connection structure. An edge enhancement module is constructed, utilizing deep features to promote the exploration of edge features to better retain details and edge information. Finally, a lightweight decoder is designed, employing the CARAFE lightweight operator for upsampling, to maintain high segmentation accuracy with fewer parameters. Comparative experiments on open data sets ISIC2016 and ISIC2018 show that the segmentation accuracy of the proposed algorithm is higher than that of other popular algorithms.
Available online: September 24, 2024 , DOI: 10.15888/j.cnki.csa.009685
Abstract:
This study proposes a comprehensive solution that combines deep survival analysis, data segmentation, and data imputation to address the issue of statistical predictive maintenance for elevators, which is characterized by low frequency and irregular time periods. This study establishes both dynamic and static survival vectors to capture factors influencing major fault risks. Additionally, to tackle left censoring in recorded data, this research employs data imputation and explores the impact of different imputation methods and segmentation strategies on the accuracy of deep survival models. Finally, this study utilizes SHAP to analyze deep survival models in elevators to reveal the dynamic influence of various factors on fault risks. The results indicate that a model combining rough data segmentation with Cox imputation demonstrates strong predictive capability and accuracy. The DeepSurv model excels in predictive capability and stability. The contribution of factors such as elevator age and lifting height to major fault risks can shift under specific conditions.
This study proposes a comprehensive solution that combines deep survival analysis, data segmentation, and data imputation to address the issue of statistical predictive maintenance for elevators, which is characterized by low frequency and irregular time periods. This study establishes both dynamic and static survival vectors to capture factors influencing major fault risks. Additionally, to tackle left censoring in recorded data, this research employs data imputation and explores the impact of different imputation methods and segmentation strategies on the accuracy of deep survival models. Finally, this study utilizes SHAP to analyze deep survival models in elevators to reveal the dynamic influence of various factors on fault risks. The results indicate that a model combining rough data segmentation with Cox imputation demonstrates strong predictive capability and accuracy. The DeepSurv model excels in predictive capability and stability. The contribution of factors such as elevator age and lifting height to major fault risks can shift under specific conditions.
Available online: September 24, 2024 , DOI: 10.15888/j.cnki.csa.009688
Abstract:
Accurate prediction of PM2.5 concentration is essential for public health and environmental protection, but its nonlinearity, variability, and complexity make it difficult. Based on this, this study proposes a gene expression programming algorithm based on virus evolution (VE-GEP) to predict PM2.5 concentration in response to the shortcomings of traditional GEP. The algorithm introduces a resurrection mechanism and a mutagenic restart mechanism based on GEP. The resurrection mechanism removes poor-quality individuals from the population and improves individual quality in the population. The mutagenic restart mechanism increases population diversity and enhances algorithm optimization-seeking ability by introducing high-quality genes and new individuals. Experimental results show that the VE-GEP algorithm improves the prediction models to different degrees compared to GEP, DSCE-GEP, and CNN-LSTM in spring, summer, and fall, with improvements in the fitness of 1.28%/0.1%/0.13%, 1.86%/1.29%/0.42%, and 0.57%/0.24%/0.29%, respectively, which provides new ideas and methods for PM2.5 concentration prediction studies.
Accurate prediction of PM2.5 concentration is essential for public health and environmental protection, but its nonlinearity, variability, and complexity make it difficult. Based on this, this study proposes a gene expression programming algorithm based on virus evolution (VE-GEP) to predict PM2.5 concentration in response to the shortcomings of traditional GEP. The algorithm introduces a resurrection mechanism and a mutagenic restart mechanism based on GEP. The resurrection mechanism removes poor-quality individuals from the population and improves individual quality in the population. The mutagenic restart mechanism increases population diversity and enhances algorithm optimization-seeking ability by introducing high-quality genes and new individuals. Experimental results show that the VE-GEP algorithm improves the prediction models to different degrees compared to GEP, DSCE-GEP, and CNN-LSTM in spring, summer, and fall, with improvements in the fitness of 1.28%/0.1%/0.13%, 1.86%/1.29%/0.42%, and 0.57%/0.24%/0.29%, respectively, which provides new ideas and methods for PM2.5 concentration prediction studies.
Available online: September 24, 2024 , DOI: 10.15888/j.cnki.csa.009699
Abstract:
Aiming at the problems of low contrast, poor recognition accuracy, and difficult detection of infrared targets in aerial scenes, this study proposes an aerial infrared target detection algorithm based on attention and quantization awareness. Firstly, the DC-ELAN module is innovatively constructed by using DCNv2 to replace the 3×3 convolution in the ELAN module, which effectively improves the ability of the model to capture local and global features, and then strengthens the feature representation ability of the network. Secondly, by cleverly integrating the SE attention mechanism into the SPPCSPC module and the ELAN module, the SE-SPPCSPC module and the SE-ELAN module are designed, which helps to enhance the spatial self-attention of the feature map, and the model can better focus on target areas. In addition, the QARepVGG module is introduced to improve the quantization awareness of the model and enhance its robustness to quantization errors. Finally, the DyHead module is introduced, which can dynamically adjust the detection head according to different input images, improve the detection ability of the model to targets of different sizes and shapes, and further improve the accuracy and robustness of infrared target detection. Experimental results show that compared with the original model, the improved YOLOv7-tiny model has 3.4% and 4.8% increases in mAP@0.5 and mAP@0.5:0.95 values without increasing the amount of calculation, which significantly improves model detection accuracy.
Aiming at the problems of low contrast, poor recognition accuracy, and difficult detection of infrared targets in aerial scenes, this study proposes an aerial infrared target detection algorithm based on attention and quantization awareness. Firstly, the DC-ELAN module is innovatively constructed by using DCNv2 to replace the 3×3 convolution in the ELAN module, which effectively improves the ability of the model to capture local and global features, and then strengthens the feature representation ability of the network. Secondly, by cleverly integrating the SE attention mechanism into the SPPCSPC module and the ELAN module, the SE-SPPCSPC module and the SE-ELAN module are designed, which helps to enhance the spatial self-attention of the feature map, and the model can better focus on target areas. In addition, the QARepVGG module is introduced to improve the quantization awareness of the model and enhance its robustness to quantization errors. Finally, the DyHead module is introduced, which can dynamically adjust the detection head according to different input images, improve the detection ability of the model to targets of different sizes and shapes, and further improve the accuracy and robustness of infrared target detection. Experimental results show that compared with the original model, the improved YOLOv7-tiny model has 3.4% and 4.8% increases in mAP@0.5 and mAP@0.5:0.95 values without increasing the amount of calculation, which significantly improves model detection accuracy.
Available online: September 24, 2024 , DOI: 10.15888/j.cnki.csa.009695
Abstract:
A retail commodity detection algorithm based on improved YOLOv8s is proposed in response to the difficulty in accurately extracting global features and irrelevant feature interference caused by retail commodity rotation and deformation. Firstly, using normalized deformable convolutions to replace some standard convolutions enhances the ability to extract global features by fully capturing long-range dependencies and highlighting key channel features. Secondly, using an improved dynamic detection head and a multi-attention mechanism based on spatial perception, scale perception, and task perception captures more discriminative local features of goods to suppress irrelevant feature interference. Finally, the InnerEIoU loss function is used to replace CIoU to reduce the missed detection rate of goods. Experimental results show that the proposed algorithm achieves an mAP@0.5:0.95 of 93.3% on the RPC retail commodity dataset, which is 1.5% higher than the original algorithm and better than other mainstream detection algorithms. At the same time, the number of model parameters and the amount of computation decrease by 10.0% and 6.5% respectively, enabling accurate retail commodity detection in practical scenarios with limited storage and computing resources.
A retail commodity detection algorithm based on improved YOLOv8s is proposed in response to the difficulty in accurately extracting global features and irrelevant feature interference caused by retail commodity rotation and deformation. Firstly, using normalized deformable convolutions to replace some standard convolutions enhances the ability to extract global features by fully capturing long-range dependencies and highlighting key channel features. Secondly, using an improved dynamic detection head and a multi-attention mechanism based on spatial perception, scale perception, and task perception captures more discriminative local features of goods to suppress irrelevant feature interference. Finally, the InnerEIoU loss function is used to replace CIoU to reduce the missed detection rate of goods. Experimental results show that the proposed algorithm achieves an mAP@0.5:0.95 of 93.3% on the RPC retail commodity dataset, which is 1.5% higher than the original algorithm and better than other mainstream detection algorithms. At the same time, the number of model parameters and the amount of computation decrease by 10.0% and 6.5% respectively, enabling accurate retail commodity detection in practical scenarios with limited storage and computing resources.
Available online: September 24, 2024 , DOI: 10.15888/j.cnki.csa.009641
Abstract:
Road information is of great significance and value in remote sensing images, and thus the accurate extraction of roads is crucial for many applications. However, there are two main challenges in road recognition. Firstly, the background of satellite images is complex and diverse, while the morphology of roads is also complex and diverse, which poses a challenge to automatic road recognition. Secondly, road pixels only account for a small portion of the entire image, leading to class imbalance. To address these challenges, this study proposes an automatic road recognition algorithm based on an improved SegFormer model. The algorithm employs two main strategies to improve the recognition performance. Firstly, spatial attention modules are added to the output of each stage of the SegFormer encoder. This module helps to weaken the interference from complex backgrounds and enhance the attention to road areas. By introducing spatial attention mechanisms, the model can better capture the features of roads, thereby improving recognition accuracy. Secondly, a hybrid loss function that combines pixel contrast loss and cross-entropy loss is used. Such a loss function can better handle class imbalance problems and make the model place more focus on training road categories. By optimizing the training process, the model can better learn road feature representation, thereby improving recognition accuracy. Comparative experimental analysis shows that the improved model achieves an approximate 3.3% improvement in the mIoU metric on the test set.
Road information is of great significance and value in remote sensing images, and thus the accurate extraction of roads is crucial for many applications. However, there are two main challenges in road recognition. Firstly, the background of satellite images is complex and diverse, while the morphology of roads is also complex and diverse, which poses a challenge to automatic road recognition. Secondly, road pixels only account for a small portion of the entire image, leading to class imbalance. To address these challenges, this study proposes an automatic road recognition algorithm based on an improved SegFormer model. The algorithm employs two main strategies to improve the recognition performance. Firstly, spatial attention modules are added to the output of each stage of the SegFormer encoder. This module helps to weaken the interference from complex backgrounds and enhance the attention to road areas. By introducing spatial attention mechanisms, the model can better capture the features of roads, thereby improving recognition accuracy. Secondly, a hybrid loss function that combines pixel contrast loss and cross-entropy loss is used. Such a loss function can better handle class imbalance problems and make the model place more focus on training road categories. By optimizing the training process, the model can better learn road feature representation, thereby improving recognition accuracy. Comparative experimental analysis shows that the improved model achieves an approximate 3.3% improvement in the mIoU metric on the test set.
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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:
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:
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.
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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。
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.
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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.
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:
排序是计算机程序设计中一种重要操作，本文论述了C语言中快速排序算法的改进，即快速排序与直接插入排序算法相结合的实现过程。在C语言程序设计中，实现大量的内部排序应用时，所寻求的目的就是找到一个简单、有效、快捷的算法。本文着重阐述快速排序的改进与提高过程，从基本的性能特征到基本的算法改进，通过不断的分析，实验，最后得出最佳的改进算法。
排序是计算机程序设计中一种重要操作，本文论述了C语言中快速排序算法的改进，即快速排序与直接插入排序算法相结合的实现过程。在C语言程序设计中，实现大量的内部排序应用时，所寻求的目的就是找到一个简单、有效、快捷的算法。本文着重阐述快速排序的改进与提高过程，从基本的性能特征到基本的算法改进，通过不断的分析，实验，最后得出最佳的改进算法。
Abstract:
随着面向对象软件开发技术的广泛应用和软件测试自动化的要求，基于模型的软件测试逐渐得到了软件开发人员和软件测试人员的认可和接受。基于模型的软件测试是软件编码阶段的主要测试方法之一，具有测试效率高、排除逻辑复杂故障测试效果好等特点。但是误报、漏报和故障机理有待进一步研究。对主要的测试模型进行了分析和分类，同时，对故障密度等参数进行了初步的分析；最后，提出了一种基于模型的软件测试流程。
随着面向对象软件开发技术的广泛应用和软件测试自动化的要求，基于模型的软件测试逐渐得到了软件开发人员和软件测试人员的认可和接受。基于模型的软件测试是软件编码阶段的主要测试方法之一，具有测试效率高、排除逻辑复杂故障测试效果好等特点。但是误报、漏报和故障机理有待进一步研究。对主要的测试模型进行了分析和分类，同时，对故障密度等参数进行了初步的分析；最后，提出了一种基于模型的软件测试流程。
Abstract:
本文介绍了一个企业信息门户中单点登录系统的设计与实现。系统实现了一个基于Java EE架构的结合凭证加密和Web Services的单点登录系统，对门户用户进行统一认证和访问控制。论文详细阐述了该系统的总体结构、设计思想、工作原理和具体实现方案，目前系统已在部分省市的广电行业信息门户平台中得到了良好的应用。
本文介绍了一个企业信息门户中单点登录系统的设计与实现。系统实现了一个基于Java EE架构的结合凭证加密和Web Services的单点登录系统，对门户用户进行统一认证和访问控制。论文详细阐述了该系统的总体结构、设计思想、工作原理和具体实现方案，目前系统已在部分省市的广电行业信息门户平台中得到了良好的应用。
Abstract:
本文介绍了Visual C++6.0在对话框的多个文本框之间,通过回车键转移焦点的几种方法,并提出了一个改进方法.
本文介绍了Visual C++6.0在对话框的多个文本框之间,通过回车键转移焦点的几种方法,并提出了一个改进方法.
Abstract:
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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Abstract:
论文对HDF数据格式和函数库进行研究,重点以栅格图像为例,详细论述如何利用VC++.net和VC#.net对光栅数据进行读取与处理,然后根据所得到的象素矩阵用描点法显示图像.论文是以国家气象中心开发Micaps3.0(气象信息综合分析处理系统)的课题研究为背景的.
论文对HDF数据格式和函数库进行研究,重点以栅格图像为例,详细论述如何利用VC++.net和VC#.net对光栅数据进行读取与处理,然后根据所得到的象素矩阵用描点法显示图像.论文是以国家气象中心开发Micaps3.0(气象信息综合分析处理系统)的课题研究为背景的.
Abstract:
本文介绍非实时操作系统Windows 2000下,利用VisualC++6.0开发实时数据采集的方法.所用到的数据采集卡是研华的PCL-818L.借助数据采集卡PCL-818L的DLLs中的API函数,提出三种实现高速实时数据采集的方法及优缺点.
本文介绍非实时操作系统Windows 2000下,利用VisualC++6.0开发实时数据采集的方法.所用到的数据采集卡是研华的PCL-818L.借助数据采集卡PCL-818L的DLLs中的API函数,提出三种实现高速实时数据采集的方法及优缺点.
Abstract:
排序是计算机程序设计中一种重要操作，本文论述了C语言中快速排序算法的改进，即快速排序与直接插入排序算法相结合的实现过程。在C语言程序设计中，实现大量的内部排序应用时，所寻求的目的就是找到一个简单、有效、快捷的算法。本文着重阐述快速排序的改进与提高过程，从基本的性能特征到基本的算法改进，通过不断的分析，实验，最后得出最佳的改进算法。
排序是计算机程序设计中一种重要操作，本文论述了C语言中快速排序算法的改进，即快速排序与直接插入排序算法相结合的实现过程。在C语言程序设计中，实现大量的内部排序应用时，所寻求的目的就是找到一个简单、有效、快捷的算法。本文着重阐述快速排序的改进与提高过程，从基本的性能特征到基本的算法改进，通过不断的分析，实验，最后得出最佳的改进算法。
Abstract:
随着Internet的迅速发展，网络资源越来越丰富，人们如何从网络上抽取信息也变得至关重要，尤其是占网络资源80%的Deep Web信息检索更是人们应该倍加关注的难点问题。为了更好的研究Deep Web爬虫技术，本文对有关Deep Web爬虫的内容进行了全面、详细地介绍。首先对Deep Web爬虫的定义及研究目标进行了阐述，接着介绍了近年来国内外关于Deep Web爬虫的研究进展，并对其加以分析。在此基础上展望了Deep Web爬虫的研究趋势，为下一步的研究奠定了基础。
随着Internet的迅速发展，网络资源越来越丰富，人们如何从网络上抽取信息也变得至关重要，尤其是占网络资源80%的Deep Web信息检索更是人们应该倍加关注的难点问题。为了更好的研究Deep Web爬虫技术，本文对有关Deep Web爬虫的内容进行了全面、详细地介绍。首先对Deep Web爬虫的定义及研究目标进行了阐述，接着介绍了近年来国内外关于Deep Web爬虫的研究进展，并对其加以分析。在此基础上展望了Deep Web爬虫的研究趋势，为下一步的研究奠定了基础。
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2021,30(9):41-52, DOI: 10.15888/j.cnki.csa.008078
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2021,30(8):14-21, DOI: 10.15888/j.cnki.csa.008003
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2021,30(7):13-21, DOI: 10.15888/j.cnki.csa.007992
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2021,30(7):1-12, DOI: 10.15888/j.cnki.csa.007973
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2021,30(6):18-27, DOI: 10.15888/j.cnki.csa.007956
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2021,30(5):21-30, DOI: 10.15888/j.cnki.csa.007916
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2021,30(3):1-13, DOI: 10.15888/j.cnki.csa.007839
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2021,30(1):10-18, DOI: 10.15888/j.cnki.csa.007776
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2020,29(12):13-25, DOI: 10.15888/j.cnki.csa.007701
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2020,29(11):29-39, DOI: 10.15888/j.cnki.csa.007705
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2020,29(9):16-25, DOI: 10.15888/j.cnki.csa.007632
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2020,29(9):26-31, DOI: 10.15888/j.cnki.csa.007617
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2020,29(9):1-15, DOI: 10.15888/j.cnki.csa.007489
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2020,29(8):1-8, DOI: 10.15888/j.cnki.csa.007478
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2020,29(6):56-63, DOI: 10.15888/j.cnki.csa.007460
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2020,29(6):1-12, DOI: 10.15888/j.cnki.csa.007431
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2020,29(6):13-21, DOI: 10.15888/j.cnki.csa.007430
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2020,29(3):29-38, DOI: 10.15888/j.cnki.csa.007315
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2020,29(3):1-10, DOI: 10.15888/j.cnki.csa.007301
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2020,29(3):11-19, DOI: 10.15888/j.cnki.csa.007261
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2020,29(2):1-8, DOI: 10.15888/j.cnki.csa.007275
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2020,29(2):9-27, DOI: 10.15888/j.cnki.csa.007262
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2020,29(1):14-21, DOI: 10.15888/j.cnki.csa.007243
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2020,29(1):22-28, DOI: 10.15888/j.cnki.csa.007237
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2019,28(12):1-8, DOI: 10.15888/j.cnki.csa.007195
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2019,28(11):1-9, DOI: 10.15888/j.cnki.csa.007156
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2019,28(9):1-8, DOI: 10.15888/j.cnki.csa.007037
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2019,28(7):1-8, DOI: 10.15888/j.cnki.csa.006988
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2019,28(6):1-12, DOI: 10.15888/j.cnki.csa.006915
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2019,28(4):205-211, DOI: 10.15888/j.cnki.csa.006739
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2019,28(3):1-9, DOI: 10.15888/j.cnki.csa.006812
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2019,28(3):10-17, DOI: 10.15888/j.cnki.csa.006806
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2019,28(1):1-9, DOI: 10.15888/j.cnki.csa.006720
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2019,28(1):10-16, DOI: 10.15888/j.cnki.csa.006707
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2018,27(10):39-45, DOI: 10.15888/j.cnki.csa.006591
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2018,27(10):33-38, DOI: 10.15888/j.cnki.csa.006560
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2018,27(9):47-51, DOI: 10.15888/j.cnki.csa.006542
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2018,27(8):10-18, DOI: 10.15888/j.cnki.csa.006456
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2018,27(7):1-10, DOI: 10.15888/j.cnki.csa.006451
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2018,27(5):1-9, DOI: 10.15888/j.cnki.csa.006336
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2018,27(4):1-9, DOI: 10.15888/j.cnki.csa.006273
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2018,27(1):1-9, DOI: 10.15888/j.cnki.csa.006169
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2017,26(12):32-36, DOI: 10.15888/j.cnki.csa.006129
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2017,26(12):25-31, DOI: 10.15888/j.cnki.csa.006096
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2017,26(6):36-39, DOI: 10.15888/j.cnki.csa.005807
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2017,26(4):1-8, DOI: 10.15888/j.cnki.csa.005773
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2017,26(2):30-36, DOI: 10.15888/j.cnki.csa.005626
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2016,25(9):17-26, DOI: 10.15888/j.cnki.csa.005354
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2016,25(9):10-16, DOI: 10.15888/j.cnki.csa.005330
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2016,25(8):1-7, DOI: 10.15888/j.cnki.csa.005283
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2016,25(6):9-16, DOI: 10.15888/j.cnki.csa.005173
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2016,25(6):1-8, DOI: 10.15888/j.cnki.csa.005206
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Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-3
Address：4# South Fourth Street, Zhongguancun,Haidian, Beijing,Postal Code：100190
Phone：010-62661041 Fax： Email：csa (a) iscas.ac.cn
Technical Support：Beijing Qinyun Technology Development Co., Ltd.
