Pub Date : 2019-12-01DOI: 10.1109/SPAC49953.2019.237877
Ruijing Jing, Y. Yao, Cheng Zhong, Yong Mu, Tao Wang, Xiuyu Zhang
In this paper, by incorporating implicit inverse technique into a dynamic surface based adaptive control design framework, we have developed a robust adaptive dynamic surface implicit inverse control for a class of nonlinear systems with unknown Prandtl-Ishilinskii (PI) hysteresis. Our development one hand is to eliminate the problem of “explosion of complexity” inherent in the backstepping method, on the other hand, instead of constructing the hysteresis inverse model to eliminate the hysteresis in the system, we eliminate the hysteresis by finding the optimal value of the PI performance index. And solve the difficulty of solving the hysteresis model by optimizing the method. In addition, the stability analysis shows that the system is semi-globally consistent and ultimately bounded, and the effectiveness of the proposed method is proved by simulation results.
{"title":"Adaptive indirect inverse control for nonlinear systems actuated by smart-material actuator*","authors":"Ruijing Jing, Y. Yao, Cheng Zhong, Yong Mu, Tao Wang, Xiuyu Zhang","doi":"10.1109/SPAC49953.2019.237877","DOIUrl":"https://doi.org/10.1109/SPAC49953.2019.237877","url":null,"abstract":"In this paper, by incorporating implicit inverse technique into a dynamic surface based adaptive control design framework, we have developed a robust adaptive dynamic surface implicit inverse control for a class of nonlinear systems with unknown Prandtl-Ishilinskii (PI) hysteresis. Our development one hand is to eliminate the problem of “explosion of complexity” inherent in the backstepping method, on the other hand, instead of constructing the hysteresis inverse model to eliminate the hysteresis in the system, we eliminate the hysteresis by finding the optimal value of the PI performance index. And solve the difficulty of solving the hysteresis model by optimizing the method. In addition, the stability analysis shows that the system is semi-globally consistent and ultimately bounded, and the effectiveness of the proposed method is proved by simulation results.","PeriodicalId":410003,"journal":{"name":"2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116443780","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}
Pub Date : 2019-12-01DOI: 10.1109/SPAC49953.2019.237875
Jipeng Zhao, Shaocheng Tong, Yong-ming Li
This work studies an observer-based fuzzy adaptive control problem for the uncertain nonlinear time-delay systems with unknown virtual and actual control gain functions. The Lyapunov-Krasovskii function is utilized to eliminate the unknown time delays. In order to estimate the uncertain nonlinear functions, Fuzzy Logic Systems(FLSs) are quoted. Then the fuzzy state observer is devised to handle the unavailable state issue. By using the backstepping control technique and bounded control method, a novel observer-based fuzzy adaptive backstepping control approach is developed. The rationality of the presented control methods is demonstrated by means of the Logarithm Lyapunov functions.
{"title":"Observer-based Adaptive Fuzzy Control for Uncertain Nonlinear time-delay systems","authors":"Jipeng Zhao, Shaocheng Tong, Yong-ming Li","doi":"10.1109/SPAC49953.2019.237875","DOIUrl":"https://doi.org/10.1109/SPAC49953.2019.237875","url":null,"abstract":"This work studies an observer-based fuzzy adaptive control problem for the uncertain nonlinear time-delay systems with unknown virtual and actual control gain functions. The Lyapunov-Krasovskii function is utilized to eliminate the unknown time delays. In order to estimate the uncertain nonlinear functions, Fuzzy Logic Systems(FLSs) are quoted. Then the fuzzy state observer is devised to handle the unavailable state issue. By using the backstepping control technique and bounded control method, a novel observer-based fuzzy adaptive backstepping control approach is developed. The rationality of the presented control methods is demonstrated by means of the Logarithm Lyapunov functions.","PeriodicalId":410003,"journal":{"name":"2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114160393","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}
Pub Date : 2019-12-01DOI: 10.1109/SPAC49953.2019.244101
Yijun Huang, Yaling Liang, Zhisong Han, Minghui Du
Gait recognition identifies a person based on gait feature which is a kind of unique biometric feature that can be acquired at a distance and needn’t cooperation. Gait features consist of abundant temporal features and spatial features. To make good use of the spatiotemporal information in gait features, we propose a two-stream network for gait recognition. In the temporal stream, we insert M3D architecture to an 2D network to capture the temporal information of different time perception domains. What’s more, we combine triplet loss, center loss with ID loss as our loss function to reduce the intra-class distance while increasing the inter-class distance which aids in classification. Our proposed method achieves a new state-of-the-art recognition accuracy in the CASIA-B database with the average rank-l accuracy of 95.63% on the NM subset, 90.86% on the BG subset and 72.15% on the CL subset.
{"title":"Two-Stream Convolutional Network Extracting Effective Spatiotemporal Information for Gait Recognition","authors":"Yijun Huang, Yaling Liang, Zhisong Han, Minghui Du","doi":"10.1109/SPAC49953.2019.244101","DOIUrl":"https://doi.org/10.1109/SPAC49953.2019.244101","url":null,"abstract":"Gait recognition identifies a person based on gait feature which is a kind of unique biometric feature that can be acquired at a distance and needn’t cooperation. Gait features consist of abundant temporal features and spatial features. To make good use of the spatiotemporal information in gait features, we propose a two-stream network for gait recognition. In the temporal stream, we insert M3D architecture to an 2D network to capture the temporal information of different time perception domains. What’s more, we combine triplet loss, center loss with ID loss as our loss function to reduce the intra-class distance while increasing the inter-class distance which aids in classification. Our proposed method achieves a new state-of-the-art recognition accuracy in the CASIA-B database with the average rank-l accuracy of 95.63% on the NM subset, 90.86% on the BG subset and 72.15% on the CL subset.","PeriodicalId":410003,"journal":{"name":"2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131563526","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}
Pub Date : 2019-12-01DOI: 10.1109/SPAC49953.2019.237873
Ke Yang, Ya-Xin Zhou, Shiyuan Han, Ya Fang, Xiao-Yue Ma, Jin Zhou, Kang Yao
Road traffic flow monitoring is the main information for traffic safety management, traffic condition evaluation and decision-making. This paper mainly improved the accuracy of real-time traffic flow information by adding de-noising to preprocessing images, and has certain reference significance for improving road traffic conditions. At the same time, this article details some of the video processing technologies that play a major role in ITS.
{"title":"Video-Based Traffic Flow Monitoring Algorithm for Single Phase Position at An Intersection","authors":"Ke Yang, Ya-Xin Zhou, Shiyuan Han, Ya Fang, Xiao-Yue Ma, Jin Zhou, Kang Yao","doi":"10.1109/SPAC49953.2019.237873","DOIUrl":"https://doi.org/10.1109/SPAC49953.2019.237873","url":null,"abstract":"Road traffic flow monitoring is the main information for traffic safety management, traffic condition evaluation and decision-making. This paper mainly improved the accuracy of real-time traffic flow information by adding de-noising to preprocessing images, and has certain reference significance for improving road traffic conditions. At the same time, this article details some of the video processing technologies that play a major role in ITS.","PeriodicalId":410003,"journal":{"name":"2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126577471","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}
Pub Date : 2019-12-01DOI: 10.1109/SPAC49953.2019.237878
Dengxiu Yu, C. L. P. Chen, Gang Lu
The paper proposes the generative method of self-organized swarm with designated global leader. In previous work, the global leader of self-organized swarm is selected randomly. However, the global leader is designated in many situations and can not be replaced. To design the generative method of self-organized swarm with designated global leader, we propose the Molt Algorithm. Finally, the proposed method is verified by simulation.
{"title":"Generative Method of Self-Organized Swarm with Designated Global Leader","authors":"Dengxiu Yu, C. L. P. Chen, Gang Lu","doi":"10.1109/SPAC49953.2019.237878","DOIUrl":"https://doi.org/10.1109/SPAC49953.2019.237878","url":null,"abstract":"The paper proposes the generative method of self-organized swarm with designated global leader. In previous work, the global leader of self-organized swarm is selected randomly. However, the global leader is designated in many situations and can not be replaced. To design the generative method of self-organized swarm with designated global leader, we propose the Molt Algorithm. Finally, the proposed method is verified by simulation.","PeriodicalId":410003,"journal":{"name":"2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120931261","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}
Pub Date : 2019-12-01DOI: 10.1109/SPAC49953.2019.237870
Zhenyu Wang, Y. Zuo, Tie-shan Li, C. L. P. Chen, K. Yada
In the field of retail industry and marketing, identifying customer segments is one of the most important tasks. A meaningful segmentation is able to help the managers to enhance the quality of products and services for the targeting segments. Most of traditional methods used POS data to classify the customer loyalty as “heavy” segment while others are belonging to “light” segment. Based on the previous studies, this paper presents three improvements. Firstly, in addition to customer purchasing behavior, we also include RFID (Radio Frequency IDentification) data, which can accurately represent the consumers' in-store behavior. Secondly, this paper uses broad learning system (BLS) to analyze the consumer segmentation. BLS is one of the most state-of-the-art machine learning techniques, and quite efficient and effective for classification tasks. Thirdly, the customer behavior data used in this paper are collected from a real-world supermarket in Japan. We also consider the customer segmentation as a multi-label classification problem based on both of POS data and RFID data. In the experiment, the results were compared with other popular classification models, such as neural network and support vector machine, and it was found that BLS greatly reduced training time while guaranteeing accuracy.
{"title":"Analysis of Customer Segmentation Based on Broad Learning System","authors":"Zhenyu Wang, Y. Zuo, Tie-shan Li, C. L. P. Chen, K. Yada","doi":"10.1109/SPAC49953.2019.237870","DOIUrl":"https://doi.org/10.1109/SPAC49953.2019.237870","url":null,"abstract":"In the field of retail industry and marketing, identifying customer segments is one of the most important tasks. A meaningful segmentation is able to help the managers to enhance the quality of products and services for the targeting segments. Most of traditional methods used POS data to classify the customer loyalty as “heavy” segment while others are belonging to “light” segment. Based on the previous studies, this paper presents three improvements. Firstly, in addition to customer purchasing behavior, we also include RFID (Radio Frequency IDentification) data, which can accurately represent the consumers' in-store behavior. Secondly, this paper uses broad learning system (BLS) to analyze the consumer segmentation. BLS is one of the most state-of-the-art machine learning techniques, and quite efficient and effective for classification tasks. Thirdly, the customer behavior data used in this paper are collected from a real-world supermarket in Japan. We also consider the customer segmentation as a multi-label classification problem based on both of POS data and RFID data. In the experiment, the results were compared with other popular classification models, such as neural network and support vector machine, and it was found that BLS greatly reduced training time while guaranteeing accuracy.","PeriodicalId":410003,"journal":{"name":"2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126742252","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}
Pub Date : 2019-12-01DOI: 10.1109/SPAC49953.2019.237871
Xinyu Li, Yongjie Zhu, Y. Zuo, Tie-shan Li, C. L. P. Chen
With the increasing attention of IMO to green shipping, and the increasingly strict restrictions on fuel regulatory and operating costs of shipping enterprises, no matter from the perspective of energy conservation and environmental protection or operating economy, ships should be put into actual operations in the future with lower fuel consumption and less emissions. At present, the researches and applications of maritime big data are mostly concentrated in the field of shipping schedules and cargoes. However, there are few studies focusing on the ship energy management. This paper proposes a fuel consumption prediction model based on the Broad Learning System (BLS) and the Danish RO-RO ship Ms Smyril is taken as the case ship. With the measured operation data, the fuel consumption prediction model of the ship is constructed by using data analysis and machine learning. Finally, compared with the existing fuel consumption prediction methods, it is proved that the prediction effects of this method are better. The rapidity of BLS can be used for real-time prediction of fuel consumption. When there are some mechanical failures of the ship which may cause the abnormal fuel consumption of the ship, it can help the engineers and the deck officers response quickly and address problems in time. It can also provide decision-making basis for navigation optimization.
{"title":"Prediction of Ship Fuel Consumption Based on Broad Learning System","authors":"Xinyu Li, Yongjie Zhu, Y. Zuo, Tie-shan Li, C. L. P. Chen","doi":"10.1109/SPAC49953.2019.237871","DOIUrl":"https://doi.org/10.1109/SPAC49953.2019.237871","url":null,"abstract":"With the increasing attention of IMO to green shipping, and the increasingly strict restrictions on fuel regulatory and operating costs of shipping enterprises, no matter from the perspective of energy conservation and environmental protection or operating economy, ships should be put into actual operations in the future with lower fuel consumption and less emissions. At present, the researches and applications of maritime big data are mostly concentrated in the field of shipping schedules and cargoes. However, there are few studies focusing on the ship energy management. This paper proposes a fuel consumption prediction model based on the Broad Learning System (BLS) and the Danish RO-RO ship Ms Smyril is taken as the case ship. With the measured operation data, the fuel consumption prediction model of the ship is constructed by using data analysis and machine learning. Finally, compared with the existing fuel consumption prediction methods, it is proved that the prediction effects of this method are better. The rapidity of BLS can be used for real-time prediction of fuel consumption. When there are some mechanical failures of the ship which may cause the abnormal fuel consumption of the ship, it can help the engineers and the deck officers response quickly and address problems in time. It can also provide decision-making basis for navigation optimization.","PeriodicalId":410003,"journal":{"name":"2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122114020","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}
Pub Date : 2019-12-01DOI: 10.1109/SPAC49953.2019.237884
Lei Lv, Ziming Chen, Zhenyu Lu
Searching appropriate step size and hyperparameter is the key to getting a robust convergence for gradient descent optimization algorithm. This study comes up with a novel gradient descent strategy based on reinforce learning, in which the gradient information of each time step is expressed as the state information of markov decision process in iterative optimization of neural network. We design a variable-view distance planner with a markov decision process as its recursive core for neural-network gradient descent. It combines the advantages of model-free learning and model-based learning, and fully utilizes the state transition information of the optimized neural-network objective function at each step. Experimental results show that the proposed method not only retains the merits of the model-free asymptotic optimal strategy but also enhances the utilization rate of samples compared with manually designed optimization algorithms.
{"title":"A novel neural-network gradient optimization algorithm based on reinforcement learning","authors":"Lei Lv, Ziming Chen, Zhenyu Lu","doi":"10.1109/SPAC49953.2019.237884","DOIUrl":"https://doi.org/10.1109/SPAC49953.2019.237884","url":null,"abstract":"Searching appropriate step size and hyperparameter is the key to getting a robust convergence for gradient descent optimization algorithm. This study comes up with a novel gradient descent strategy based on reinforce learning, in which the gradient information of each time step is expressed as the state information of markov decision process in iterative optimization of neural network. We design a variable-view distance planner with a markov decision process as its recursive core for neural-network gradient descent. It combines the advantages of model-free learning and model-based learning, and fully utilizes the state transition information of the optimized neural-network objective function at each step. Experimental results show that the proposed method not only retains the merits of the model-free asymptotic optimal strategy but also enhances the utilization rate of samples compared with manually designed optimization algorithms.","PeriodicalId":410003,"journal":{"name":"2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123770026","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}
Pub Date : 2019-12-01DOI: 10.1109/SPAC49953.2019.243773
Wei Zhang, Yong-ming Li
An event-trigger control approach of nonlinear systems is presented. The saturated controllers are given by applying event-trigger condition. Using Lyapunov-Krasovskii functional technique, we prove the asymptotically stability of fuzzy systems. The controllers are obtained by the matrix inequalities. Finally, the method is substantiated with numerical example.
{"title":"H-infinity Control for Nonlinear Systems Using Event-triggered Method","authors":"Wei Zhang, Yong-ming Li","doi":"10.1109/SPAC49953.2019.243773","DOIUrl":"https://doi.org/10.1109/SPAC49953.2019.243773","url":null,"abstract":"An event-trigger control approach of nonlinear systems is presented. The saturated controllers are given by applying event-trigger condition. Using Lyapunov-Krasovskii functional technique, we prove the asymptotically stability of fuzzy systems. The controllers are obtained by the matrix inequalities. Finally, the method is substantiated with numerical example.","PeriodicalId":410003,"journal":{"name":"2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122841167","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}
Pub Date : 2019-12-01DOI: 10.1109/SPAC49953.2019.237867
Xinyue Li, Samuel Cheng
Fully Convolutional Residual Network (FCRN) has already become one of the most significant models for depth map prediction. It has achieved high quality results but has problem in reconstructing the human outline. On this basis, we present our method, the purpose of which is to reinforce human reconstruction in depth prediction. Our main idea is to merge Mask R-CNN with FCRN, so we present our modified FCRN. Our modified FCRN, which can also be regarded as an improvement of FCRN through Mask R-CNN, is designed on the basis of attention mechanism and optimized on the basis of transfer learning. It needs to work with the original FCRN. For a single RGB image, first of all, Mask RCNN receives it as input and generates the mask images for the “person” instances. Then, the input image and the mask image are fed jointly to our modified FCRN which can give a new result in generating the depth map. After that, we present a depth filter to combine the raw result given by the original FCRN with the new result given by the modified FCRN. Our final result is generated through the depth filter. Both the image result and the metric result given by our experiment can illustrate that our method has the ability to improve the performance of FCRN in human outline reconstruction through Mask R-CNN.
{"title":"Human Outline Reconstruction in Depth Prediction","authors":"Xinyue Li, Samuel Cheng","doi":"10.1109/SPAC49953.2019.237867","DOIUrl":"https://doi.org/10.1109/SPAC49953.2019.237867","url":null,"abstract":"Fully Convolutional Residual Network (FCRN) has already become one of the most significant models for depth map prediction. It has achieved high quality results but has problem in reconstructing the human outline. On this basis, we present our method, the purpose of which is to reinforce human reconstruction in depth prediction. Our main idea is to merge Mask R-CNN with FCRN, so we present our modified FCRN. Our modified FCRN, which can also be regarded as an improvement of FCRN through Mask R-CNN, is designed on the basis of attention mechanism and optimized on the basis of transfer learning. It needs to work with the original FCRN. For a single RGB image, first of all, Mask RCNN receives it as input and generates the mask images for the “person” instances. Then, the input image and the mask image are fed jointly to our modified FCRN which can give a new result in generating the depth map. After that, we present a depth filter to combine the raw result given by the original FCRN with the new result given by the modified FCRN. Our final result is generated through the depth filter. Both the image result and the metric result given by our experiment can illustrate that our method has the ability to improve the performance of FCRN in human outline reconstruction through Mask R-CNN.","PeriodicalId":410003,"journal":{"name":"2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124102097","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：