IEEE Transactions on Systems Man Cybernetics-Systems最新文献_第3页

Stability Region Analysis for Nabla Linear Time Invariant Fractional Order Systems Nabla线性时不变分数阶系统的稳定域分析

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-04-01 DOI: 10.1109/TSMC.2025.3549640

Yiheng Wei;Linlin Zhao;Xuan Zhao;Jinde Cao

引用次数: 0

A Highly-Accurate Three-Way Decision-Incorporated Online Sparse Streaming Features Selection Model 一种高精度三向决策融合的在线稀疏流特征选择模型

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-03-31 DOI: 10.1109/TSMC.2025.3548648

Ruiyang Xu;Di Wu;Renfang Wang;Xin Luo

{"title":"A Highly-Accurate Three-Way Decision-Incorporated Online Sparse Streaming Features Selection Model","authors":"Ruiyang Xu;Di Wu;Renfang Wang;Xin Luo","doi":"10.1109/TSMC.2025.3548648","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3548648","url":null,"abstract":"An online streaming feature selection (OSFS) model is highly efficient in processing the high-dimensional streaming features. In practical big data-related applications, streaming features are mostly highly-incomplete due to various unpredictable reasons like the privacy protection, leading to the issue of online sparse streaming feature selection (OS2FS). The incomplete streaming features can lead to the uncertain relationship between the labels and sparse features during the feature selection process, yet existing OSFS and OS2FS models focus on the certain relationships, resulting in accuracy loss by improperly-selected features. To address this critical issue, this article presents a three <xref>(3)</xref>-way decision-incorporated OS2FS (3WDO) model with the following two-fold ideas: 1) utilizing the latent factor analysis (LFA) approach to pre-estimate the missing data of the concerned sparse streaming features and 2) integrating the three-way decision (3WD) into the streaming features selection process for appropriately modeling the uncertainty within the label-feature interactions. By doing so, the uncertain relationships between labels and sparse features are characterized by more information and looser tolerance, thereby minimizing the decision risk of feature selection. Experimental results on twelve real-world datasets demonstrate that the proposed 3WDO model significantly outperforms seven state-of-the-art OSFS and OS2FS models, which strongly supports its ability of addressing practical issues.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 6","pages":"4258-4272"},"PeriodicalIF":8.6,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072896","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}

引用次数: 0

Sharpness-Aware Cross-Domain Recommendation to Cold-Start Users 对冷启动用户的锐度感知跨域推荐

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-03-27 DOI: 10.1109/TSMC.2025.3549400

Guohang Zeng;Qian Zhang;Guangquan Zhang;Jie Lu

{"title":"Sharpness-Aware Cross-Domain Recommendation to Cold-Start Users","authors":"Guohang Zeng;Qian Zhang;Guangquan Zhang;Jie Lu","doi":"10.1109/TSMC.2025.3549400","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3549400","url":null,"abstract":"Cross-domain recommendation (CDR) is a promising paradigm inspired by transfer learning to solve the cold-start problem in recommender systems. Existing state-of-the-art CDR methods train an explicit mapping function to transfer the cold-start users from a data-rich source domain to a target domain. However, a limitation of these methods is that the mapping function is trained on overlapping users across domains, while only a small number of overlapping users are available for training. By visualizing the loss landscape of the existing CDR model, we find that training on a small number of overlapping users causes the model to converge to sharp minima, leading to poor generalization. Based on this observation, we leverage loss-geometry-based machine learning approach and propose a novel CDR method called sharpness-aware CDR (SCDR). Our proposed method simultaneously optimizes recommendation loss and loss sharpness, leading to better generalization with theoretical guarantees. Empirical studies on real-world datasets show that SCDR significantly outperforms other CDR models on cold-start recommendation tasks. Additionally, our method enhances the model’s robustness to adversarial attacks.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 6","pages":"4244-4257"},"PeriodicalIF":8.6,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072895","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}

引用次数: 0

DRIFT: A Dynamic Crowd Inflow Control System Using LSTM-Based Deep Reinforcement Learning 基于lstm的深度强化学习的动态人群流入控制系统

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-03-27 DOI: 10.1109/TSMC.2025.3549627

Xiao-Cheng Liao;Wei-Neng Chen;Xiao-Qi Guo;Jinghui Zhong;Da-Jiang Wang

{"title":"DRIFT: A Dynamic Crowd Inflow Control System Using LSTM-Based Deep Reinforcement Learning","authors":"Xiao-Cheng Liao;Wei-Neng Chen;Xiao-Qi Guo;Jinghui Zhong;Da-Jiang Wang","doi":"10.1109/TSMC.2025.3549627","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3549627","url":null,"abstract":"Crowd management plays a crucial role in improving travel efficiency and reducing potential risks caused by overcrowding in large public places. Crowd control at entrances is a common way in our daily life to avoid overcrowding, but nowadays the control of crowd inflow at the entrances of public places mainly relies on manual operation. In this article, we intend to propose a dynamic crowd inflow control system (DRIFT) to avoid risks of overcrowding and improve the throughput of public places. First, we formulate an optimization problem that maximizes throughput by adjusting the crowd inflow rate of each entrance in the public place. Through mathematical analysis and related proofs, we introduce a baseline for the aforementioned problem that can calculate the upper bound of static inflow rate. With this baseline, we can easily measure the performance of other dynamic inflow control algorithms. Second, we treat the proposed optimization problem as a real-time decision-making problem, and further propose the DRIFT system based on deep reinforcement learning to address it. Specifically, the strategy of DRIFT is a basic actor-critic framework adapting a shared long short term memory (LSTM) layer to extract scene feature information. Third, we train it through proximal policy optimization (PPO) to improve learning performance. The environment for experiments is a crowd simulation model of OpenAI Gym structure based on real scene data from the 1F floor of the Chengdudong Railway Station and Xizhimen Railway Station. In comparison experiments and ablation experiments, the strategy of our DRIFT outperforms all other comparison strategies, including the most recent strategy using reinforcement learning, in term of system crowd throughput and robustness.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 6","pages":"4202-4215"},"PeriodicalIF":8.6,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073438","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}

引用次数: 0

A New Multi-Target Domain Adaptation Method Based on Evidence Theory for Distribution Inconsistent Data Classification 分布不一致数据分类中一种基于证据理论的多目标域自适应方法

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-03-27 DOI: 10.1109/TSMC.2025.3548988

Linqing Huang;Jinfu Fan;Shi-Lin Wang;Kai Xu;Yong Liu

{"title":"A New Multi-Target Domain Adaptation Method Based on Evidence Theory for Distribution Inconsistent Data Classification","authors":"Linqing Huang;Jinfu Fan;Shi-Lin Wang;Kai Xu;Yong Liu","doi":"10.1109/TSMC.2025.3548988","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3548988","url":null,"abstract":"In the context of distribution inconsistent data classification, addressing distribution shift is crucial, typically accomplished through domain adaptation (DA) techniques. Once distributions are aligned between the source and target domains, the problem transforms into a conventional recognition task. This article introduces a new method called multitarget DA based on Evidence Theory (MET). For a given target domain, a random merger with other target domains is performed, generating distinct new target domains. Domain-invariant features corresponding to each new target domain are learned by minimizing distribution discrepancies separately between the source and different new target domains. The merging of target domains alters the distribution of the new target domain, leading to variations in the retained information within the learned domain-invariant features. For a query pattern in this target domain, multiple soft classification results (CCR) are obtained after aligning the distributions of the source and different new target domains. These soft CCR complement each other, and evidence theory is employed as a tool to represent and combine uncertain information, fusing these results. The weights for this fusion are automatically learned by minimizing the mean squared error between the combined results and the ground truth on labeled source domain data. The final class decision is determined through the weighted evidential combination of multiple pieces of soft CCR. MET is assessed on several datasets (i.e., Office+Caltech-10, VLSC, and V-RSIR) and compared to various advanced DA methods (e.g., GNN, MT, PAL, PTD, and so on) to validate its effectiveness. The experimental results demonstrate that MET usually can obtain a higher classification performance (i.e., the accuracy can be improved by 2% compared to many methods in most cases).","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 6","pages":"4125-4139"},"PeriodicalIF":8.6,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073429","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}

引用次数: 0

Practical Robust Formation Control for Nonlinear Multiagent Systems via Generative Adversarial Learning Framework: Theory and Experiment 基于生成对抗学习框架的非线性多智能体系统鲁棒群体控制：理论与实验

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-03-27 DOI: 10.1109/TSMC.2025.3550255

Nuan Wen;Mir Feroskhan

{"title":"Practical Robust Formation Control for Nonlinear Multiagent Systems via Generative Adversarial Learning Framework: Theory and Experiment","authors":"Nuan Wen;Mir Feroskhan","doi":"10.1109/TSMC.2025.3550255","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3550255","url":null,"abstract":"Cyber attacks and disturbances greatly impair the performance of formation tasks in multiagent systems (MASs). To achieve robust formation control against these challenges, this article proposes a generative adversarial learning framework that is theoretically transparent and practically applicable. Rather than relying on an end-to-end deep neural networks (DNNs) architecture, our work leverage a double robust structure that combine the representation capabilities of DNNs with established, theoretically grounded linear control theory, ultimately achieving a practical, learning-based robust formation for MASs. Initially, generative adversarial networks (GANs) are used to linearize agent dynamics under false data injection (FDI) attacks and external disturbances. Subsequently, a proportional-integral (PI) protocol is employed to achieve overall robust formation. We present rigorous theoretical analyses of both stages, demonstrating the guaranteed convergence of GANs training and the closed-loop formation errors. Our approach is directly validated through a series of physical experiments involving multi-quadrotors, demonstrating robustness against attacks and disturbances during formation flights, without the sim-to-real gap commonly encountered in learning-based control frameworks.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 6","pages":"4334-4347"},"PeriodicalIF":8.6,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073233","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}

引用次数: 0

Dynamic Event-Triggered Cluster Consensus for Multiagent Systems Under DoS Attacks With Antagonistic Interactions 具有对抗交互的DoS攻击下多智能体系统的动态事件触发聚类一致性

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-03-26 DOI: 10.1109/TSMC.2025.3549726

Siwen Zhou;Yang Liu;Xinxi Lu;Wenling Li;Jiang Long

{"title":"Dynamic Event-Triggered Cluster Consensus for Multiagent Systems Under DoS Attacks With Antagonistic Interactions","authors":"Siwen Zhou;Yang Liu;Xinxi Lu;Wenling Li;Jiang Long","doi":"10.1109/TSMC.2025.3549726","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3549726","url":null,"abstract":"In this article, we investigate the cluster consensus problem of multiagent systems (MASs) with general linear dynamics and weighted antagonistic interactions under aperiodic denial-of-service (DoS) attacks. First, an event-triggered communication mechanism is designed to efficiently reduce unnecessary message transmission over unreliable networks, where the event-triggered function with dynamically varying thresholds is designed based on the state estimators instead of the real-time states so that continuous communication can be avoided both in controller updates and triggering threshold detection. Then, a novel event-triggered cluster consensus protocol is designed for MASs with DoS attacks and structurally balanced signed digraphs, where Zeno behavior can be strictly excluded by the proposed triggering mechanism. Furthermore, to cope with the structurally unbalanced signed digraph, an improved event-triggered resilient consensus protocol is developed by introducing a pinning control strategy. By utilizing the piecewise Lyapunov functional approach, some sufficient conditions are derived for the cluster consensus under structurally balanced and unbalanced signed digraphs, while the selection principles of event-triggered parameters and resilient controller gains are obtained. Finally, the validity of the theoretical results is verified by practical simulation examples.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 6","pages":"4362-4374"},"PeriodicalIF":8.6,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073418","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}

引用次数: 0

Group Multirole Assignment With General Conflict 具有一般冲突的组多角色分配

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-03-26 DOI: 10.1109/TSMC.2025.3549602

Shiyu Wu;Shenglin Li;Haibin Zhu;Tianxing Wang;Libo Zhang

{"title":"Group Multirole Assignment With General Conflict","authors":"Shiyu Wu;Shenglin Li;Haibin Zhu;Tianxing Wang;Libo Zhang","doi":"10.1109/TSMC.2025.3549602","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3549602","url":null,"abstract":"Role-based collaboration (RBC) is a novel problem-solving paradigm to facilitate collaboration. Group multirole assignment (GMRA), an extension of group role assignment (GRA), is a critical step in the RBC process, enabling the formation of efficient collaborative teams by reasonably assigning roles to agents. Recognized as a significant determinant impacting assignment and collaboration, conflict has been delineated and incorporated into GMRA. Nevertheless, the specified conflict is characterized as an oppositional conflict (OC), signifying that conflicting agents are engaged in conflict across the entirety of the role set. Rather than completely OC, which is highly specific, a more prevalent relationship involves conflict in certain aspects while remaining conflict-free in others. Therefore, we propose the concept of general conflict (GC) to describe the more common and realistic conflict relationship, offering a broader and novel perspective to depict conflict relationships. Then, we formalize these two problems by considering GC avoidance in GRA and GMRA, called GRA with GC (GRAGC) and GMRA with GC (GMRAGC), respectively. Furthermore, we establish the necessary conditions for the GRAGC and GMRAGC problems through a graph-theoretical lens, accompanied by a thorough analysis of their mathematical nature. Additionally, we propose practical solutions and refine methodologies to address both problems. The effectiveness of the improved algorithms utilizing necessary conditions is verified by simulations, which also provides evidence supporting the advantages of conflict avoidance.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 6","pages":"4188-4201"},"PeriodicalIF":8.6,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073436","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}

引用次数: 0

Collaborative Control of Human–Machine Game in Lateral and Longitudinal Dimensions Considering Dynamic Allocation of Driving Authority 考虑驾驶权限动态分配的横向和纵向人机博弈协同控制

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-03-26 DOI: 10.1109/TSMC.2025.3549424

Xiang Chen;Wenlong Ding;Wanzhong Zhao;Chunyan Wang

{"title":"Collaborative Control of Human–Machine Game in Lateral and Longitudinal Dimensions Considering Dynamic Allocation of Driving Authority","authors":"Xiang Chen;Wenlong Ding;Wanzhong Zhao;Chunyan Wang","doi":"10.1109/TSMC.2025.3549424","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3549424","url":null,"abstract":"In the process of human-machine collaborative driving, it is crucial to ensure that the driver and the machine operate the vehicle in a safe, stable, and efficient manner. However, most of the current studies focus on the lateral shared control under the condition of constant longitudinal speed, without considering the influence of longitudinal speed change on lateral control. Therefore, this article proposes a collaborative control framework of human-machine game in lateral and longitudinal dimensions considering dynamic allocation of driving authority to improve the collaborative performance of co-driving. First, a human-machine collaborative driving system model that adapts to the characteristics of co-driving mode is built as the basis of the shared control scheme. Then, the unconscious competitive relationship of human-machine is described as the game interaction relationship, with optimal control strategies for both sides being deduced theoretically at the game equilibrium. Additionally, a dynamic adjustment strategy of driving authority considering the longitudinal speed is established based on the assessment of lateral and longitudinal risks. Finally, the driver-in-the-loop test and co-simulation results show that the proposed control strategy has achieved good performance in terms of path tracking, driver’s driving burden, and vehicle stability.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 6","pages":"4309-4321"},"PeriodicalIF":8.6,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073231","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}

引用次数: 0

Nash Equilibrium Seeking for Nonzero-Sum Games of Switched Nonlinear Systems 切换非线性系统非零和博弈的纳什均衡寻求

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-03-26 DOI: 10.1109/TSMC.2025.3549599

Yan Zhang;Yuhang Meng;Fang Wang;Choon Ki Ahn;Zhengrong Xiang

引用次数: 0