Pub Date : 2025-11-19DOI: 10.1109/TSMC.2025.3627727
{"title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems Information for Authors","authors":"","doi":"10.1109/TSMC.2025.3627727","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3627727","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 12","pages":"C4-C4"},"PeriodicalIF":8.7,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11260921","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145546975","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 : 2025-11-19DOI: 10.1109/TSMC.2025.3630255
{"title":"Thank You for Your Authorship","authors":"","doi":"10.1109/TSMC.2025.3630255","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3630255","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 12","pages":"9954-9954"},"PeriodicalIF":8.7,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11260917","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145546974","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 : 2025-11-19DOI: 10.1109/TSMC.2025.3627739
{"title":"IEEE Systems, Man, and Cybernetics Society Information","authors":"","doi":"10.1109/TSMC.2025.3627739","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3627739","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 12","pages":"C3-C3"},"PeriodicalIF":8.7,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11260909","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145546960","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 : 2025-11-19DOI: 10.1109/TSMC.2025.3630757
Dan Li;Hye-Bin Shin;Seong-Whan Lee
Current noninvasive electroencephalography (EEG)-based brain–computer interface (BCI) systems face a fundamental scalability barrier: they either suffer catastrophic forgetting (CF) when learning from new users or require centralized management and use of sensitive neural data from previous users-making real-world deployment impractical. To address this, we introduce subject-incremental continual adaptation (SI-CA), a novel paradigm that models cross-subject continual learning (CL), where knowledge transfer and limited replay sustain stable performance as new subjects are introduced, enabling continual decoding without forgetting. Building on this paradigm, we propose a novel CL framework that achieves memory-efficient adaptation by integrating an extendable architecture with prototype-based consistency regularization and limited replay to mitigate CF. The effectiveness of our proposed method has been validated on three benchmark EEG-BCI datasets. Experimental results demonstrate that the proposed method can effectively reduce reliance on historical samples during CL, while maintaining stable decoding performance for previously learned individuals and ensuring reliable motor decoding for newly encountered ones. This holds significant importance for the development of scalable, privacy-preserving, and stable neural interface systems.
{"title":"Toward Memory-Efficient Continual Adaptation for MI-EEG Decoding in BCIs","authors":"Dan Li;Hye-Bin Shin;Seong-Whan Lee","doi":"10.1109/TSMC.2025.3630757","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3630757","url":null,"abstract":"Current noninvasive electroencephalography (EEG)-based brain–computer interface (BCI) systems face a fundamental scalability barrier: they either suffer catastrophic forgetting (CF) when learning from new users or require centralized management and use of sensitive neural data from previous users-making real-world deployment impractical. To address this, we introduce subject-incremental continual adaptation (SI-CA), a novel paradigm that models cross-subject continual learning (CL), where knowledge transfer and limited replay sustain stable performance as new subjects are introduced, enabling continual decoding without forgetting. Building on this paradigm, we propose a novel CL framework that achieves memory-efficient adaptation by integrating an extendable architecture with prototype-based consistency regularization and limited replay to mitigate CF. The effectiveness of our proposed method has been validated on three benchmark EEG-BCI datasets. Experimental results demonstrate that the proposed method can effectively reduce reliance on historical samples during CL, while maintaining stable decoding performance for previously learned individuals and ensuring reliable motor decoding for newly encountered ones. This holds significant importance for the development of scalable, privacy-preserving, and stable neural interface systems.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 1","pages":"766-778"},"PeriodicalIF":8.7,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145760912","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 : 2025-11-19DOI: 10.1109/TSMC.2025.3627725
{"title":"IEEE Systems, Man, and Cybernetics Society Information","authors":"","doi":"10.1109/TSMC.2025.3627725","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3627725","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 12","pages":"C3-C3"},"PeriodicalIF":8.7,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11260918","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145546966","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 : 2025-11-19DOI: 10.1109/TSMC.2025.3627737
{"title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems Information for Authors","authors":"","doi":"10.1109/TSMC.2025.3627737","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3627737","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 12","pages":"C4-C4"},"PeriodicalIF":8.7,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11260920","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145546969","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 : 2025-11-14DOI: 10.1109/TSMC.2025.3630653
Tengbo Li;Huorong Ren;Yihui Hu;Xu Lu;Zhiwu Li
This study tackles the challenge of robust fault diagnosis in discrete event systems (DESs) that experience permanent observation losses using labeled Petri nets (LPNs). We consider the scenario that the initially observable transitions may become unobservable before their firings. Especially, the case that some, instead of all, of the transitions with a shared label may become unobservable is also taken into account. In such a scenario, the diagnosers in the existing methods may not report correct diagnostic results. This article presents a novel notion to ensure robust diagnosability for LPNs, aimed at overcoming the issue of permanent observation loss. To avert enumerating all the reachable markings, a structure called a tagged basis reachability graph (t-BRG) is developed, based on which all subsets of observable transitions, called diagnosis transition sets (DTSs), that ensure the diagnosability of the plant independently are calculated. Then, a special class of verifiers to assess the robust diagnosability of a system experiencing permanent observation loss is developed. Finally, an online diagnosis method performed by a set of diagnosers is presented and demonstrated by examples.
{"title":"Robust Fault Diagnosis Against Permanent Loss of Observations Using Labeled Petri Nets","authors":"Tengbo Li;Huorong Ren;Yihui Hu;Xu Lu;Zhiwu Li","doi":"10.1109/TSMC.2025.3630653","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3630653","url":null,"abstract":"This study tackles the challenge of robust fault diagnosis in discrete event systems (DESs) that experience permanent observation losses using labeled Petri nets (LPNs). We consider the scenario that the initially observable transitions may become unobservable before their firings. Especially, the case that some, instead of all, of the transitions with a shared label may become unobservable is also taken into account. In such a scenario, the diagnosers in the existing methods may not report correct diagnostic results. This article presents a novel notion to ensure robust diagnosability for LPNs, aimed at overcoming the issue of permanent observation loss. To avert enumerating all the reachable markings, a structure called a tagged basis reachability graph (t-BRG) is developed, based on which all subsets of observable transitions, called diagnosis transition sets (DTSs), that ensure the diagnosability of the plant independently are calculated. Then, a special class of verifiers to assess the robust diagnosability of a system experiencing permanent observation loss is developed. Finally, an online diagnosis method performed by a set of diagnosers is presented and demonstrated by examples.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 1","pages":"737-750"},"PeriodicalIF":8.7,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145760915","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}
This study investigates a scheduling problem involving dual-arm cluster tools (CTs) that simultaneously handle two types of wafers, considering both wafer priority and residency time constraints. The two types of wafers have their own processing routes and processing times at each step. To fully utilize the resources of the CTs, we use the fewest processing modules (PMs) to produce one type of wafers with maximum productivity, and use the available PMs to produce the other type of wafers. Based on this, we introduce a swap sequence for scheduling a dual-arm robot, which is simple to implement and supports periodic operations. Without affecting the priority wafer production, we provide the necessary and sufficient conditions for scheduling a CT that processes two types of wafers, and present the optimal PM configuration. A high-performance algorithm is developed to determine an optimal periodic schedule, with its practicality and feasibility illustrated through several examples.
{"title":"A Periodic Scheduling Method for Dual-Arm Cluster Tools Considering Wafer Priority and Residency Time Constraint","authors":"Jufeng Wang;Chunfeng Liu;MengChu Zhou;Abdullah Abusorrah","doi":"10.1109/TSMC.2025.3629134","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3629134","url":null,"abstract":"This study investigates a scheduling problem involving dual-arm cluster tools (CTs) that simultaneously handle two types of wafers, considering both wafer priority and residency time constraints. The two types of wafers have their own processing routes and processing times at each step. To fully utilize the resources of the CTs, we use the fewest processing modules (PMs) to produce one type of wafers with maximum productivity, and use the available PMs to produce the other type of wafers. Based on this, we introduce a swap sequence for scheduling a dual-arm robot, which is simple to implement and supports periodic operations. Without affecting the priority wafer production, we provide the necessary and sufficient conditions for scheduling a CT that processes two types of wafers, and present the optimal PM configuration. A high-performance algorithm is developed to determine an optimal periodic schedule, with its practicality and feasibility illustrated through several examples.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 1","pages":"779-788"},"PeriodicalIF":8.7,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145760889","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 : 2025-11-14DOI: 10.1109/TSMC.2025.3629555
Guojie Li;Ziwei Fan;Zhiwen Yu;Kaixiang Yang;C. L. Philip Chen
Due to its exceptional feature representation capabilities and high computational efficiency, the broad learning system (BLS) has been widely employed in various classification tasks. Nevertheless, BLS encounters considerable challenges in semi-supervised classification tasks involving complex heterogeneous data, given the data’s high-dimensional and noisy nature, coupled with a limited number of available labeled samples. To tackle these challenges, this article introduces a semi-supervised BLS based on distance constraint regularization (DRBLS) and a semi-supervised broad ensemble method (E-DRBLS) for high-dimensional data. Specifically, we present a distance constraint regularization (DR) that utilizes both labeled and unlabeled data to derive an optimal projection matrix, which maximizes the preservation of the original data’s intrinsic distribution structure. DR is designed to minimize intraclass distance, maximize interclass distance, and minimize the distance between neighboring samples. To boost the performance of BLS in semi-supervised classification, we integrate DR and BLS to construct the semi-supervised classifier DRBLS. Finally, we propose a mixed dimensionality reduction space generation (MDRSG) method that generates multiple high-quality and diverse mixed dimensionality reduction spaces (MDRSs). Based on MDRS, an ensemble framework, E-DRBLS, is developed for semi-supervised classification tasks targeting high-dimensional data. Comprehensive experiments confirm the superiority of the proposed methods.
{"title":"Semi-Supervised Ensemble Classifier Based on Distance Constraint for High-Dimensional Data","authors":"Guojie Li;Ziwei Fan;Zhiwen Yu;Kaixiang Yang;C. L. Philip Chen","doi":"10.1109/TSMC.2025.3629555","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3629555","url":null,"abstract":"Due to its exceptional feature representation capabilities and high computational efficiency, the broad learning system (BLS) has been widely employed in various classification tasks. Nevertheless, BLS encounters considerable challenges in semi-supervised classification tasks involving complex heterogeneous data, given the data’s high-dimensional and noisy nature, coupled with a limited number of available labeled samples. To tackle these challenges, this article introduces a semi-supervised BLS based on distance constraint regularization (DRBLS) and a semi-supervised broad ensemble method (E-DRBLS) for high-dimensional data. Specifically, we present a distance constraint regularization (DR) that utilizes both labeled and unlabeled data to derive an optimal projection matrix, which maximizes the preservation of the original data’s intrinsic distribution structure. DR is designed to minimize intraclass distance, maximize interclass distance, and minimize the distance between neighboring samples. To boost the performance of BLS in semi-supervised classification, we integrate DR and BLS to construct the semi-supervised classifier DRBLS. Finally, we propose a mixed dimensionality reduction space generation (MDRSG) method that generates multiple high-quality and diverse mixed dimensionality reduction spaces (MDRSs). Based on MDRS, an ensemble framework, E-DRBLS, is developed for semi-supervised classification tasks targeting high-dimensional data. Comprehensive experiments confirm the superiority of the proposed methods.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 1","pages":"724-736"},"PeriodicalIF":8.7,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778221","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 : 2025-11-11DOI: 10.1109/TSMC.2025.3628874
Wei Song;Mingshuo Song;Haojie Zhou;Xiaoyan Sun;Yaochu Jin;Songbai Liu;Qiuzhen Lin;Shengxiang Yang
Recently, machine learning-embedded large-scale multiobjective evolutionary algorithms (LMOEAs) have shown great promise in solving large-scale multiobjective optimization problems (LMOPs). However, the fast convergence of the population to the true Pareto-optimal front (POF) and even distribution of the obtained Pareto-optimal solutions (POSs) on the POF are not adequately considered when tackling an LMOP. Besides, existing LMOEAs typically pair solutions with a matching rule and employ a network to learn the evolution pattern among the obtained solution pairs. It is difficult to learn various evolution patterns through a simple network, which hinders the collaboration of different patterns for enhancing the search capability. Facing such difficulties, this article proposes an LMOEA with multipattern learning and collaboration (LMOEA-MLC), where a single-hidden-layer multioutput network (SMN) is established to learn inductive and hybrid evolution patterns. Specifically, two inductive ones can be learned with the solution pairs built by two matching rules toward fast convergence and even distribution, respectively. Moreover, the solution pairs considering the fusion of the two inductive ones are collected, enabling SMN to learn a hybrid one and thus making a tradeoff between fast convergence and even distribution. Besides, the learned evolution patterns collaborate to enhance the search capability due to the distinct patterns. To enhance learning speed, SMN’s parameters are updated by an incremental random vector functional link (IRVFL). In our experiments, comprehensive comparisons with eight state-of-the-art LMOEAs demonstrate the significant performance improvement of LMOEA-MLC in handling LMOPs.
{"title":"Multipattern Learning and Collaboration-Based Evolutionary Optimizer for Large-Scale Multiobjective Optimization","authors":"Wei Song;Mingshuo Song;Haojie Zhou;Xiaoyan Sun;Yaochu Jin;Songbai Liu;Qiuzhen Lin;Shengxiang Yang","doi":"10.1109/TSMC.2025.3628874","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3628874","url":null,"abstract":"Recently, machine learning-embedded large-scale multiobjective evolutionary algorithms (LMOEAs) have shown great promise in solving large-scale multiobjective optimization problems (LMOPs). However, the fast convergence of the population to the true Pareto-optimal front (POF) and even distribution of the obtained Pareto-optimal solutions (POSs) on the POF are not adequately considered when tackling an LMOP. Besides, existing LMOEAs typically pair solutions with a matching rule and employ a network to learn the evolution pattern among the obtained solution pairs. It is difficult to learn various evolution patterns through a simple network, which hinders the collaboration of different patterns for enhancing the search capability. Facing such difficulties, this article proposes an LMOEA with multipattern learning and collaboration (LMOEA-MLC), where a single-hidden-layer multioutput network (SMN) is established to learn inductive and hybrid evolution patterns. Specifically, two inductive ones can be learned with the solution pairs built by two matching rules toward fast convergence and even distribution, respectively. Moreover, the solution pairs considering the fusion of the two inductive ones are collected, enabling SMN to learn a hybrid one and thus making a tradeoff between fast convergence and even distribution. Besides, the learned evolution patterns collaborate to enhance the search capability due to the distinct patterns. To enhance learning speed, SMN’s parameters are updated by an incremental random vector functional link (IRVFL). In our experiments, comprehensive comparisons with eight state-of-the-art LMOEAs demonstrate the significant performance improvement of LMOEA-MLC in handling LMOPs.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 1","pages":"751-765"},"PeriodicalIF":8.7,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145760907","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}
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：