The low-orbit satellite constellation is an important part of a space-ground integrated network. MEC servers can be deployed to realize on-orbit processing by offloading tasks to satellites. On the other hand, NDN natively provides mobility support for consumers, so integrating NDN into the satellite network can solve the difficult mobility management problems of IP. However, input parameters in current offloading algorithms are default obtained from users, and they rarely consider the existence of inter-satellite links, which are not suitable for NDN scenarios. Therefore, we propose an architecture, studies the task offloading problem in LEO satellites based on NDN and proposes an optimization algorithm. Simulation results show that the algorithm finally converges and has better performance than the other two algorithms.
{"title":"Offloading decision and resource allocation for NDN-based satellite edge computing","authors":"Haoru Xing, Jiangtian Lu, Xinyi Zhu, Jikun Qiu","doi":"10.1117/12.2682391","DOIUrl":"https://doi.org/10.1117/12.2682391","url":null,"abstract":"The low-orbit satellite constellation is an important part of a space-ground integrated network. MEC servers can be deployed to realize on-orbit processing by offloading tasks to satellites. On the other hand, NDN natively provides mobility support for consumers, so integrating NDN into the satellite network can solve the difficult mobility management problems of IP. However, input parameters in current offloading algorithms are default obtained from users, and they rarely consider the existence of inter-satellite links, which are not suitable for NDN scenarios. Therefore, we propose an architecture, studies the task offloading problem in LEO satellites based on NDN and proposes an optimization algorithm. Simulation results show that the algorithm finally converges and has better performance than the other two algorithms.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132885443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As two modes of direct contact between robots and external environment, visual and tactile play a critical role in improving robot perception ability. In the real environment, it is difficult for the robot to achieve high accuracy when classifying objects only by a single mode (visual or tactile). In order to improve the classification accuracy of robots, a novel visual-tactile fusion method is proposed in this paper. Firstly, the ResNet18 is selected as the backbone network to extract visual features. To improve the accuracy of object localization and recognition in the visual network, the Position-Channel Attention Mechanism (PCAM) block is added after conv3 and conv4 of ResNet18. Then, the four-layer one-dimensional convolutional neural network is used to extract tactile features, and the extracted tactile features are fused with visual features at the feature layer. Finally, the experimental results demonstrate that compared with the existing methods, on the self-made dataset VHAC-52, the proposed method has improved the AUC and ACC by 1.60% and 1.47%, respectively.
{"title":"Research on object classification based on visual-tactile fusion","authors":"Peng Zhang, Lu Bai, Dongri Shan","doi":"10.1117/12.2682381","DOIUrl":"https://doi.org/10.1117/12.2682381","url":null,"abstract":"As two modes of direct contact between robots and external environment, visual and tactile play a critical role in improving robot perception ability. In the real environment, it is difficult for the robot to achieve high accuracy when classifying objects only by a single mode (visual or tactile). In order to improve the classification accuracy of robots, a novel visual-tactile fusion method is proposed in this paper. Firstly, the ResNet18 is selected as the backbone network to extract visual features. To improve the accuracy of object localization and recognition in the visual network, the Position-Channel Attention Mechanism (PCAM) block is added after conv3 and conv4 of ResNet18. Then, the four-layer one-dimensional convolutional neural network is used to extract tactile features, and the extracted tactile features are fused with visual features at the feature layer. Finally, the experimental results demonstrate that compared with the existing methods, on the self-made dataset VHAC-52, the proposed method has improved the AUC and ACC by 1.60% and 1.47%, respectively.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133611240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Helicopters often encounter obstacles such as towers and high-voltage lines when flying at low altitude, and the safety problem is increasingly prominent. Optical images have high resolution, which provide rich color, texture, edge and other details of the detection object. Infrared images can still maintain the advantage of high detection rate at night or in the environment with poor visibility. Combining the characteristics and advantages of infrared and optical images, this paper designs a dual branch convolution neural network to detect helicopter flying obstacles. For infrared images, a single branch infrared image feature extraction network SBI-Net (Single Branch Infrared image Network) is designed to automatically extract the features of infrared images; For optical images, a single branch optical image feature extraction network SBO-Net (Single Branch Optical image Network) is designed to extract the features of optical images; Finally, the two networks are fused, and a dual branch feature fusion network IODBFF-Net (Dual Branch Feature Fusion Network model based on Infrared and Optical image) is proposed. The experimental results show that compared with infrared single branch network and optical single branch network, the detection accuracy of dual branch convolution neural network is improved by 2.06% and 40.25% respectively.
{"title":"Helicopter flying obstacle detection based on the fusion of infrared and optical images","authors":"Zixin Xie, Gong Zhang, Zhengzheng Fang, Wei Xiong","doi":"10.1117/12.2682518","DOIUrl":"https://doi.org/10.1117/12.2682518","url":null,"abstract":"Helicopters often encounter obstacles such as towers and high-voltage lines when flying at low altitude, and the safety problem is increasingly prominent. Optical images have high resolution, which provide rich color, texture, edge and other details of the detection object. Infrared images can still maintain the advantage of high detection rate at night or in the environment with poor visibility. Combining the characteristics and advantages of infrared and optical images, this paper designs a dual branch convolution neural network to detect helicopter flying obstacles. For infrared images, a single branch infrared image feature extraction network SBI-Net (Single Branch Infrared image Network) is designed to automatically extract the features of infrared images; For optical images, a single branch optical image feature extraction network SBO-Net (Single Branch Optical image Network) is designed to extract the features of optical images; Finally, the two networks are fused, and a dual branch feature fusion network IODBFF-Net (Dual Branch Feature Fusion Network model based on Infrared and Optical image) is proposed. The experimental results show that compared with infrared single branch network and optical single branch network, the detection accuracy of dual branch convolution neural network is improved by 2.06% and 40.25% respectively.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"12715 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130791331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years, wind power has become more and more important in the energy component. In order to improve the prediction accuracy of wind farms and help management and scheduling, a multi-site short-term wind power spatiotemporal combination forecasting model based on dynamic graph convolution and graph attention is proposed. Firstly, graph convolution is used to realize neighbor aggregation of temporal features between multiple sites, and the graph attention mechanism is used to enhance its ability to extract spatial features. At the same time, in view of the problem that the traditional model cannot deal with the real-time change of graph node correlation, the adjacency matrix is dynamically constructed according to the correlation coefficient and distance between nodes in the graph convolution process. Finally, the Gated Recurrent Unit is used to process the context information of dynamic graph convolution output to complete the prediction of wind power. The experimental results show that the proposed combined model is optimal in the aspects of prediction accuracy, stability and multi-step prediction performance.
{"title":"Wind farm combination forecasting model based on dynamic graph attention","authors":"X. Liao, Yiqun Cheng","doi":"10.1117/12.2682328","DOIUrl":"https://doi.org/10.1117/12.2682328","url":null,"abstract":"In recent years, wind power has become more and more important in the energy component. In order to improve the prediction accuracy of wind farms and help management and scheduling, a multi-site short-term wind power spatiotemporal combination forecasting model based on dynamic graph convolution and graph attention is proposed. Firstly, graph convolution is used to realize neighbor aggregation of temporal features between multiple sites, and the graph attention mechanism is used to enhance its ability to extract spatial features. At the same time, in view of the problem that the traditional model cannot deal with the real-time change of graph node correlation, the adjacency matrix is dynamically constructed according to the correlation coefficient and distance between nodes in the graph convolution process. Finally, the Gated Recurrent Unit is used to process the context information of dynamic graph convolution output to complete the prediction of wind power. The experimental results show that the proposed combined model is optimal in the aspects of prediction accuracy, stability and multi-step prediction performance.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129608740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Runchong Dong, Jing Ma, Xingpei Chen, Wang Jianhua
In the actual operation process, some of the power system bad data identification methods have the problem of low accuracy, for this reason, a deep learning-based power system bad data identification method is designed to improve this defect. The data is collected from power system users, the phase deviation caused by non-integer sampling is reduced by high sampling rate, the measurement signal period is obtained, the operational state of the distribution network is evaluated based on deep learning, the state vector is calculated, the maximum standard residual value is found, the location of the bad data is obtained, and the bad data identification method is designed. Experimental results: The mean accuracy of the power system bad data identification method in the paper is: 78.26%, which indicates that the designed power system bad data identification method performs better after fully integrating the deep learning.
{"title":"A deep learning-based approach for identifying bad data in power systems","authors":"Runchong Dong, Jing Ma, Xingpei Chen, Wang Jianhua","doi":"10.1117/12.2682551","DOIUrl":"https://doi.org/10.1117/12.2682551","url":null,"abstract":"In the actual operation process, some of the power system bad data identification methods have the problem of low accuracy, for this reason, a deep learning-based power system bad data identification method is designed to improve this defect. The data is collected from power system users, the phase deviation caused by non-integer sampling is reduced by high sampling rate, the measurement signal period is obtained, the operational state of the distribution network is evaluated based on deep learning, the state vector is calculated, the maximum standard residual value is found, the location of the bad data is obtained, and the bad data identification method is designed. Experimental results: The mean accuracy of the power system bad data identification method in the paper is: 78.26%, which indicates that the designed power system bad data identification method performs better after fully integrating the deep learning.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124426319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xuewen Chen, F. Jiang, Wensheng Huang, Wanyong Xing, Yan Lv, Yuzhong Wang, Dan Li, Guan Wu, Muyu Qi
This paper takes the toll station system as the research object, and based on the analysis of the current situation and demand of domestic toll collection business, defines the intelligent toll station for the purpose of credit creation and unmanned toll station construction, and designs the overall scheme architecture of intelligent toll station. Based on super-fusion structure technology, disaster tolerance technology, health insight technology, intelligent anti-theft network monitoring technology, self-service payment terminal is studied. Based on virtualization, Docker, micro-service architecture, distributed cluster and other technologies, this paper designs and studies cloud-based systems (cloud toll hall), and develops software and hardware systems of intelligent toll station. The key technology of intelligent toll station is applied in Guangdong province and evaluated according to the needs of intelligent toll station. In order to ensure the application effect of the intelligent toll collection system, the evaluation method of the application effect is studied. The evaluation index system of the application effect of the intelligent toll collection system at the station level, including 3 first-level indicators and 13 second-level indicators, is constructed through on-site investigation, discussion, expert consultation and other forms, considering toll station passage, service and resource utilization. The toll collection system and effect evaluation method are applied and validated in Guangdong Province..
{"title":"Research on key technologies and application effect evaluation of intelligent toll station based on localized platform","authors":"Xuewen Chen, F. Jiang, Wensheng Huang, Wanyong Xing, Yan Lv, Yuzhong Wang, Dan Li, Guan Wu, Muyu Qi","doi":"10.1117/12.2682499","DOIUrl":"https://doi.org/10.1117/12.2682499","url":null,"abstract":"This paper takes the toll station system as the research object, and based on the analysis of the current situation and demand of domestic toll collection business, defines the intelligent toll station for the purpose of credit creation and unmanned toll station construction, and designs the overall scheme architecture of intelligent toll station. Based on super-fusion structure technology, disaster tolerance technology, health insight technology, intelligent anti-theft network monitoring technology, self-service payment terminal is studied. Based on virtualization, Docker, micro-service architecture, distributed cluster and other technologies, this paper designs and studies cloud-based systems (cloud toll hall), and develops software and hardware systems of intelligent toll station. The key technology of intelligent toll station is applied in Guangdong province and evaluated according to the needs of intelligent toll station. In order to ensure the application effect of the intelligent toll collection system, the evaluation method of the application effect is studied. The evaluation index system of the application effect of the intelligent toll collection system at the station level, including 3 first-level indicators and 13 second-level indicators, is constructed through on-site investigation, discussion, expert consultation and other forms, considering toll station passage, service and resource utilization. The toll collection system and effect evaluation method are applied and validated in Guangdong Province..","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122256662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Infrared target images have low signal-to-noise ratio, blurred edges and missing textures, which make it a great challenge to identify the target and achieve stable tracking in the tracking process. However, ordinary target trackers use feature fusion as a convolutional operation, which is a local matching process that easily leads to the absence of high-level semantic information of the image, and is further limited on infrared images. Inspired by transformer, its attention mechanism can capture global features, as well as contextual relationships between features, and can well establish the association between remote features , long-range dependency and other advantages, we designed transofmer-based infrared target tracker, which is a network that performs feature enhancement and fusion on infrared images by tranformer, and classifies and regresses targets by classification head, and has proved the effectiveness of the method by conducting extensive experiments on challenging benchmarks.
{"title":"Infrared target tracking based on transformer","authors":"Zhou Xi, Xiaohong Li","doi":"10.1117/12.2682473","DOIUrl":"https://doi.org/10.1117/12.2682473","url":null,"abstract":"Infrared target images have low signal-to-noise ratio, blurred edges and missing textures, which make it a great challenge to identify the target and achieve stable tracking in the tracking process. However, ordinary target trackers use feature fusion as a convolutional operation, which is a local matching process that easily leads to the absence of high-level semantic information of the image, and is further limited on infrared images. Inspired by transformer, its attention mechanism can capture global features, as well as contextual relationships between features, and can well establish the association between remote features , long-range dependency and other advantages, we designed transofmer-based infrared target tracker, which is a network that performs feature enhancement and fusion on infrared images by tranformer, and classifies and regresses targets by classification head, and has proved the effectiveness of the method by conducting extensive experiments on challenging benchmarks.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117195988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Computer simulation technology has changed the traditional process of comparison and selection of architectural design schemes. Unlike the traditional process, which relies more on the experience of architects, computer algorithms can realize the calculation of quantitative indicators for complex problems and provide objective basis for scheme comparison and selection. Combined with the application of engineering projects, this article expounds the application of computer-aided design in architectural design through the method of calculating visibility percent of quantitative indicators to judge the visibility of the interior display space design in small shopping centres by multi-software simulation.
{"title":"Research on visibility of interior display space in small shopping centre based on multi-software simulation","authors":"Haoxu Guo, Zhangrui Shi, Mengren Deng","doi":"10.1117/12.2682369","DOIUrl":"https://doi.org/10.1117/12.2682369","url":null,"abstract":"Computer simulation technology has changed the traditional process of comparison and selection of architectural design schemes. Unlike the traditional process, which relies more on the experience of architects, computer algorithms can realize the calculation of quantitative indicators for complex problems and provide objective basis for scheme comparison and selection. Combined with the application of engineering projects, this article expounds the application of computer-aided design in architectural design through the method of calculating visibility percent of quantitative indicators to judge the visibility of the interior display space design in small shopping centres by multi-software simulation.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116303138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
For the integrated navigation system GNSS/SINS in the process of data fusion, the traditional filtering algorithm does not consider the correlation between the two system and the poor robustness when measurement outliers occur, this paper proposes an iterated split covariance intersection filter algorithm to fuse the data of the two systems. It combines the Split CIF and the Gauss-Newton iteration and separate state covariance matrix into independent parts and dependent parts, and adjusts the posterior state estimation by calculating the Kalman filter gain iteratively during the measurement update process to reduce the error caused by outliers. The simulation show that the Iterated Split CIF based integrated navigation system has higher accuracy and better robustness. Compared with Split CIF and Kalman filter, the east velocity error is reduced by 30% and 35% respectively, and the latitude error is reduced by 22% and 30% respectively. In addition, the position accuracy still remains at a high level when outliers occur, so the algorithm has good robustness.
{"title":"Integrated navigation and location algorithm based on iterated split CIF","authors":"Xin Zheng, Dalong Zhang, Teng He","doi":"10.1117/12.2682446","DOIUrl":"https://doi.org/10.1117/12.2682446","url":null,"abstract":"For the integrated navigation system GNSS/SINS in the process of data fusion, the traditional filtering algorithm does not consider the correlation between the two system and the poor robustness when measurement outliers occur, this paper proposes an iterated split covariance intersection filter algorithm to fuse the data of the two systems. It combines the Split CIF and the Gauss-Newton iteration and separate state covariance matrix into independent parts and dependent parts, and adjusts the posterior state estimation by calculating the Kalman filter gain iteratively during the measurement update process to reduce the error caused by outliers. The simulation show that the Iterated Split CIF based integrated navigation system has higher accuracy and better robustness. Compared with Split CIF and Kalman filter, the east velocity error is reduced by 30% and 35% respectively, and the latitude error is reduced by 22% and 30% respectively. In addition, the position accuracy still remains at a high level when outliers occur, so the algorithm has good robustness.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"02 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127452896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The frequent occurrences of surge current[1] of memory architecture in FeRAM, and the lack of necessary formula derivation in related researches of FeRAM are main aspects of what this work focuses. Therefore, the surge protection circuit is designed and simulated by this work with solid memory architecture of FeRAM to decrease the surge current and average power consumption creatively. The surge protection circuit is composed of a MOSFET and an RC delay circuit, which forces the generation of the surge current to be slowed down by mandatory precharge of the capacitor. On the other hand, necessary circuit analysis and formula derivation are concluded to predict the DC operating point of sensitive amplifier and build a clearly defined limits of operating voltage and the ratio of the maximum ferroelectric capacitance and bitline capacitance in the read/write operation of FeRAM.
{"title":"Design and analysis of surge protection circuit in the memory architecture of FeRAM","authors":"Dongsen Yang, Shenmin Zhang","doi":"10.1117/12.2682467","DOIUrl":"https://doi.org/10.1117/12.2682467","url":null,"abstract":"The frequent occurrences of surge current[1] of memory architecture in FeRAM, and the lack of necessary formula derivation in related researches of FeRAM are main aspects of what this work focuses. Therefore, the surge protection circuit is designed and simulated by this work with solid memory architecture of FeRAM to decrease the surge current and average power consumption creatively. The surge protection circuit is composed of a MOSFET and an RC delay circuit, which forces the generation of the surge current to be slowed down by mandatory precharge of the capacitor. On the other hand, necessary circuit analysis and formula derivation are concluded to predict the DC operating point of sensitive amplifier and build a clearly defined limits of operating voltage and the ratio of the maximum ferroelectric capacitance and bitline capacitance in the read/write operation of FeRAM.","PeriodicalId":440430,"journal":{"name":"International Conference on Electronic Technology and Information Science","volume":"12715 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130904829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：