Pub Date : 2024-11-19DOI: 10.1109/TSMC.2024.3485292
{"title":"Information For Authors","authors":"","doi":"10.1109/TSMC.2024.3485292","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3485292","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"54 12","pages":"C4-C4"},"PeriodicalIF":8.6,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10758280","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-19DOI: 10.1109/TSMC.2024.3485300
{"title":"TechRxiv: Share Your Preprint Research with the World!","authors":"","doi":"10.1109/TSMC.2024.3485300","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3485300","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"54 12","pages":"7534-7534"},"PeriodicalIF":8.6,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10758271","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-19DOI: 10.1109/TSMC.2024.3495193
{"title":"TechRxiv: Share Your Preprint Research with the World!","authors":"","doi":"10.1109/TSMC.2024.3495193","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3495193","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"54 12","pages":"7909-7909"},"PeriodicalIF":8.6,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10758274","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142679370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-19DOI: 10.1109/TSMC.2024.3485294
{"title":"Information For Authors","authors":"","doi":"10.1109/TSMC.2024.3485294","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3485294","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"54 12","pages":"C4-C4"},"PeriodicalIF":8.6,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10758275","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142679358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article mainly focuses on the problem of observer-based adaptive event-triggered security fault-tolerant control (FTC) for attacked cyber-physical systems (CPSs). First, for the nonlinear characteristics in CPSs, the model is reconstructed based on the Takagi-Sugeno (T-S) fuzzy modeling technique. Then, a T-S fuzzy resilient observer is proposed to estimate the state as well as the actuator fault information. The proposed T-S fuzzy resilient observer proactively detects sensor transmission channels affected by dynamically changing denial-of-service (DoS) attacks, and proactively isolates contaminated data so as to reduce the impact of DoS attacks on the estimation effect. Based on the above content, an observer-based adaptive event-triggered security FTC scheme is proposed, which can ensure the stability of the CPSs and save the limited network resources between the controller and the actuator. Finally, simulation results are given to verify the effectiveness of the proposed scheme.
{"title":"Adaptive Event-Triggered Secure Control for Attacked Cyber–Physical Systems Based on Resilient Observer","authors":"Ximing Yang;Yue Long;Tieshan Li;Hanqing Yang;Hongjing Liang","doi":"10.1109/TSMC.2024.3491841","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3491841","url":null,"abstract":"This article mainly focuses on the problem of observer-based adaptive event-triggered security fault-tolerant control (FTC) for attacked cyber-physical systems (CPSs). First, for the nonlinear characteristics in CPSs, the model is reconstructed based on the Takagi-Sugeno (T-S) fuzzy modeling technique. Then, a T-S fuzzy resilient observer is proposed to estimate the state as well as the actuator fault information. The proposed T-S fuzzy resilient observer proactively detects sensor transmission channels affected by dynamically changing denial-of-service (DoS) attacks, and proactively isolates contaminated data so as to reduce the impact of DoS attacks on the estimation effect. Based on the above content, an observer-based adaptive event-triggered security FTC scheme is proposed, which can ensure the stability of the CPSs and save the limited network resources between the controller and the actuator. Finally, simulation results are given to verify the effectiveness of the proposed scheme.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 2","pages":"936-947"},"PeriodicalIF":8.6,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fuzzy rough sets have made considerable strides within the domain of machine learning and data mining and served as a valuable tool for feature selection. However, traditional models face challenges in computing fuzzy similarity relations. They oversimplify the treatment of diverse samples by assuming that they exist in the same class space, ignoring their labels and distribution information. Consequently, difficulties arise when dealing with data that exhibit considerable distribution variations across classes. To address this issue, this study proposes a directed fuzzy rough set model that better captures the inherent uncertainty in sample distribution compared with traditional models. In this model, class-subspace distribution information is seamlessly integrated into directed fuzzy binary relations. Furthermore, fuzzy rough approximation operators are redefined to accurately capture the uncertainty associated with class distribution, facilitating a comprehensive analysis of relevant properties concerning decision approximations for samples. Building on this background, a heuristic algorithm for feature selection and a K-nearest neighbor reduction classifier are developed. Comparative experiments with top-tier algorithms showcase the outstanding performance of our proposed model. This study provides a robust framework for addressing intricate machine learning and pattern recognition tasks.
{"title":"Feature Selection and Classification Based on Directed Fuzzy Rough Sets","authors":"Changyue Wang;Changzhong Wang;Shuang An;Weiping Ding;Yuhua Qian","doi":"10.1109/TSMC.2024.3492337","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3492337","url":null,"abstract":"Fuzzy rough sets have made considerable strides within the domain of machine learning and data mining and served as a valuable tool for feature selection. However, traditional models face challenges in computing fuzzy similarity relations. They oversimplify the treatment of diverse samples by assuming that they exist in the same class space, ignoring their labels and distribution information. Consequently, difficulties arise when dealing with data that exhibit considerable distribution variations across classes. To address this issue, this study proposes a directed fuzzy rough set model that better captures the inherent uncertainty in sample distribution compared with traditional models. In this model, class-subspace distribution information is seamlessly integrated into directed fuzzy binary relations. Furthermore, fuzzy rough approximation operators are redefined to accurately capture the uncertainty associated with class distribution, facilitating a comprehensive analysis of relevant properties concerning decision approximations for samples. Building on this background, a heuristic algorithm for feature selection and a K-nearest neighbor reduction classifier are developed. Comparative experiments with top-tier algorithms showcase the outstanding performance of our proposed model. This study provides a robust framework for addressing intricate machine learning and pattern recognition tasks.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 1","pages":"699-711"},"PeriodicalIF":8.6,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-19DOI: 10.1109/TSMC.2024.3485290
{"title":"IEEE Systems, Man, and Cybernetics Society Information","authors":"","doi":"10.1109/TSMC.2024.3485290","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3485290","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"54 12","pages":"C3-C3"},"PeriodicalIF":8.6,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10758277","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-19DOI: 10.1109/TSMC.2024.3485296
{"title":"IEEE Systems, Man, and Cybernetics Society Information","authors":"","doi":"10.1109/TSMC.2024.3485296","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3485296","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"54 12","pages":"C3-C3"},"PeriodicalIF":8.6,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10758272","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142679368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Prognostics and health management (PHM) has garnered significant attention in industrial fields, particularly due to its successful application in managing battery degradation. However, current approaches are inadequate in addressing multiple thresholds, including both theoretical formulation and practical computational complexity. These limitations hinder the development and implementation of threshold-varying assessments, thereby impeding the advancement of PHM application. This article investigates prognostic applications with different failure thresholds and highlights the importance of failure threshold selection. In addition, theoretical evaluation and analysis are provided for multiple threshold settings, encompassing both discrete and continuous series. This introduces a novel technical domain for prognostic applications. The effectiveness of threshold-varying assessment is verified with several different approaches on real battery degradation experiments. Furthermore, we demonstrate the practical significance of threshold-varying assessments in enabling on-demand scheduling for maintenance or replacement of spare parts. Most importantly, to meet the real-time requirements of practical prognostic applications, this article also discusses the computational complexity of threshold-varying assessment and finds an applicable solution for this common difficulty.
{"title":"Threshold-Varying Assessment for Prognostics and Health Management","authors":"Dongzhen Lyu;Enhui Liu;Bin Zhang;Enrico Zio;Tao Yang;Jiawei Xiang","doi":"10.1109/TSMC.2024.3489879","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3489879","url":null,"abstract":"Prognostics and health management (PHM) has garnered significant attention in industrial fields, particularly due to its successful application in managing battery degradation. However, current approaches are inadequate in addressing multiple thresholds, including both theoretical formulation and practical computational complexity. These limitations hinder the development and implementation of threshold-varying assessments, thereby impeding the advancement of PHM application. This article investigates prognostic applications with different failure thresholds and highlights the importance of failure threshold selection. In addition, theoretical evaluation and analysis are provided for multiple threshold settings, encompassing both discrete and continuous series. This introduces a novel technical domain for prognostic applications. The effectiveness of threshold-varying assessment is verified with several different approaches on real battery degradation experiments. Furthermore, we demonstrate the practical significance of threshold-varying assessments in enabling on-demand scheduling for maintenance or replacement of spare parts. Most importantly, to meet the real-time requirements of practical prognostic applications, this article also discusses the computational complexity of threshold-varying assessment and finds an applicable solution for this common difficulty.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 1","pages":"685-698"},"PeriodicalIF":8.6,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-19DOI: 10.1109/TSMC.2024.3491595
Zehui Mao;Donghao Liu;Kai Ju;Bin Jiang;Xing-Gang Yan
In this article, a novel task search and allocation strategy is developed for heterogeneous multiagent systems with limited search range and communication constraints, which includes three processes: 1) task search; 2) task allocation; and 3) formation recovery. In order to optimize task search efficiency under communication constraints, a multigroup task search strategy is proposed by minimizing the average overlap degree between agents’ search ranges, which divides agents into multiple groups and establishes intragroup communication links. According to the communication link and group allocation results, an optimal search formation is designed for each group to maximize their individual search ranges. For transmitting information between different groups, by employing the agent with the highest communication efficiency within the discovery agent’s group as the relay agent, a communication relay strategy is proposed to transmit the task information to other groups. Then, a task allocation strategy based on communication relays is designed to achieve global task allocation by using the estimated state information of all agents. Moreover, to ensure the sustainability of task search and allocation, an intergroup scheduling strategy is proposed to recover the optimal search formation after agents complete the task-related works. Simulation results verify the effectiveness of the proposed task search and allocation strategy.
{"title":"Task Search and Allocation Strategy for Heterogeneous Multiagent Systems Under Communication Constraints","authors":"Zehui Mao;Donghao Liu;Kai Ju;Bin Jiang;Xing-Gang Yan","doi":"10.1109/TSMC.2024.3491595","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3491595","url":null,"abstract":"In this article, a novel task search and allocation strategy is developed for heterogeneous multiagent systems with limited search range and communication constraints, which includes three processes: 1) task search; 2) task allocation; and 3) formation recovery. In order to optimize task search efficiency under communication constraints, a multigroup task search strategy is proposed by minimizing the average overlap degree between agents’ search ranges, which divides agents into multiple groups and establishes intragroup communication links. According to the communication link and group allocation results, an optimal search formation is designed for each group to maximize their individual search ranges. For transmitting information between different groups, by employing the agent with the highest communication efficiency within the discovery agent’s group as the relay agent, a communication relay strategy is proposed to transmit the task information to other groups. Then, a task allocation strategy based on communication relays is designed to achieve global task allocation by using the estimated state information of all agents. Moreover, to ensure the sustainability of task search and allocation, an intergroup scheduling strategy is proposed to recover the optimal search formation after agents complete the task-related works. Simulation results verify the effectiveness of the proposed task search and allocation strategy.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 1","pages":"550-562"},"PeriodicalIF":8.6,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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