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

Fault-Tolerant Control for Autonomous Underwater Vehicles With Prescribed Tracking Accuracy 具有预定跟踪精度的自主水下航行器容错控制

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-09-18 DOI: 10.1109/TSMC.2025.3606857

Yifan Li;Xifeng Gao;Xingchen Li;Kai Zhang;Okyay Kaynak;Jiubin Tan

{"title":"Fault-Tolerant Control for Autonomous Underwater Vehicles With Prescribed Tracking Accuracy","authors":"Yifan Li;Xifeng Gao;Xingchen Li;Kai Zhang;Okyay Kaynak;Jiubin Tan","doi":"10.1109/TSMC.2025.3606857","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3606857","url":null,"abstract":"Autonomous underwater vehicles (AUVs) face significant challenges in trajectory tracking due to nonlinear dynamics, actuator faults, and environmental disturbances. To address these issues, this article proposes a novel fault-tolerant control strategy that ensures fixed-time trajectory tracking with prescribed accuracy for underactuated AUVs. The proposed approach integrates boundary functions with a constraint-handling mechanism, enabling guaranteed tracking performance within a fixed time horizon while satisfying output constraints. Unlike existing approaches, the controller does not rely on accurate system models, parameter estimation, or external observers, and avoids the computation of virtual control derivatives, resulting in reduced computational complexity. Moreover, the control scheme maintains robustness against time-varying actuator faults and environmental disturbances without auxiliary adaptation or learning mechanisms. Simulation results demonstrate the effectiveness and superior performance of the proposed approach compared with existing methods, validating its capability to maintain tracking accuracy and closed-loop stability under adverse operating conditions.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 11","pages":"8531-8543"},"PeriodicalIF":8.7,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145335300","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

Attention-Guided Multiscale Interaction Network for Face Super-Resolution 面向人脸超分辨率的注意引导多尺度交互网络

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-09-18 DOI: 10.1109/TSMC.2025.3605649

Xujie Wan;Wenjie Li;Guangwei Gao;Huimin Lu;Jian Yang;Chia-Wen Lin

{"title":"Attention-Guided Multiscale Interaction Network for Face Super-Resolution","authors":"Xujie Wan;Wenjie Li;Guangwei Gao;Huimin Lu;Jian Yang;Chia-Wen Lin","doi":"10.1109/TSMC.2025.3605649","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3605649","url":null,"abstract":"Recently, CNN and Transformer hybrid networks demonstrated excellent performance in face super-resolution (FSR) tasks. Because of numerous features at different scales in hybrid networks, how to fuse these multiscale features and promote their complementarity is crucial for enhancing FSR. However, existing hybrid network-based FSR methods ignore this, only simply combining the Transformer and CNN. To address this issue, we propose an attention-guided multiscale interaction network (AMINet), which incorporates local and global feature interactions, as well as encoder–decoder phase feature interactions. Specifically, we propose a local and global feature interaction (LGFI) module to promote the fusion of global features and the local features extracted from different receptive fields by our residual depth feature extraction (RDFE) module. Additionally, we propose a selective kernel attention fusion (SKAF) module to adaptively select fusions of different features within the LGFI and encoder–decoder phases. Our above design allows the free flow of multiscale features from within modules and between the encoder and decoder, which can promote the complementarity of different scale features to enhance FSR. Comprehensive experiments confirm that our method consistently performs well with less computational consumption and faster inference.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 11","pages":"8638-8650"},"PeriodicalIF":8.7,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145335295","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

Event-Triggered Dissipative Filtering for Stochastic Fuzzy Complex Networks Under Multiple Cyberattacks 多重网络攻击下随机模糊复杂网络的事件触发耗散滤波

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-09-16 DOI: 10.1109/TSMC.2025.3606937

Yinghong Zhao;Likui Wang;Hak-Keung Lam

{"title":"Event-Triggered Dissipative Filtering for Stochastic Fuzzy Complex Networks Under Multiple Cyberattacks","authors":"Yinghong Zhao;Likui Wang;Hak-Keung Lam","doi":"10.1109/TSMC.2025.3606937","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3606937","url":null,"abstract":"This article investigates the problem of event-based strictly dissipative filtering for stochastic fuzzy complex networks (SFCNs) subject to multiple cyberattacks, including deception attacks and denial-of-service (DoS) attacks. A resilient event-triggered mechanism (ETM) is proposed to reduce the communication burden while effectively countering these cyberattacks. The membership functions are assumed to be mismatched due to the impact of the network environment and sampling behavior. Furthermore, the system dynamics are modeled using Itô-type stochastic differential equations, which include deterministic fuzzy complex networks (CNs) as a special case. Unlike previous studies on stochastic systems, the auxiliary vector function method is employed to introduce more time-varying delay information into the piecewise Lyapunov–Krasovskii functional (LKF), thus reducing conservatism. Consequently, a series of delay-dependent sufficient conditions is derived to ensure the exponentially mean-square stability (EMSS) and strict dissipativity of the filtering error system. Finally, the effectiveness of the proposed method is demonstrated through an illustrative example.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 11","pages":"8612-8624"},"PeriodicalIF":8.7,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145335301","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

Conditions for Guaranteeing Nonovershooting Control of MIMO Nonlinear Systems With Application to Robot Manipulator 应用于机器人的MIMO非线性系统的非超调控制保证条件

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-09-16 DOI: 10.1109/TSMC.2025.3607885

Fujin Jia;Quanxin Zhu

引用次数: 0

CoMix: Collaborative Mixed Learning via Style Fuzzy Normalization for Visible–Infrared Person Re-Identification 基于风格模糊归一化的可见光-红外人再识别协同混合学习

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-09-15 DOI: 10.1109/TSMC.2025.3604832

Haojie Liu;Jianyang Gu;Zhiyong Li;Mingyu Wang;Q. M. Jonathan Wu;Wei Jiang

{"title":"CoMix: Collaborative Mixed Learning via Style Fuzzy Normalization for Visible–Infrared Person Re-Identification","authors":"Haojie Liu;Jianyang Gu;Zhiyong Li;Mingyu Wang;Q. M. Jonathan Wu;Wei Jiang","doi":"10.1109/TSMC.2025.3604832","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3604832","url":null,"abstract":"Visible–infrared person re-identification (VI-ReID) focuses on accurately matching individuals across different imaging modalities. Existing studies focus on generating modality-consistent images at the pixel level through the use of generative adversarial networks (GANs) to mitigate the impact of modality discrepancies. However, these methods face significant challenges in overcoming the limitation that synthesized samples from different modalities may suffer from semantic distortion. In this work, we propose an online one-stage style fuzzy normalization (SFN) method to generate modality-fuzzy features in the latent space while regularizing the model’s predictions. Specifically, SFN adaptively mixes the feature statistics of two random modality instances of the same identity in a single forward pass during training. In this process, to enhance the richness of modality interaction information, we design a novel causality balance loss, which enforces the generated fuzzy features to be independent of their initial modality while simultaneously encouraging them to align more closely with the other modality. Furthermore, we introduce an identity-aware consistency loss to regularize the predictions between the original and SFN-generated features to ensure semantic consistency. In contrast to prior work, SFN is a plug-and-play module that does not rely on any generative-based models, making it highly adaptable to various network architectures. Extensive experiments were performed on three public cross-modality datasets to ensure fair and reliable comparisons. The empirical results demonstrate the clear superiority of our method over previous state-of-the-art methods.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 11","pages":"8572-8586"},"PeriodicalIF":8.7,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145335290","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

Control of Fully Actuated Systems With Perturbed Input Matrices and Partial Knowledge of Uncertainty Bounds 具有摄动输入矩阵和不确定性界部分知识的全驱动系统控制

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-09-12 DOI: 10.1109/TSMC.2025.3605860

Shiyu Zhang;Guangren Duan

引用次数: 0

State Observer-Based Predictor Feedback Control for Discrete-Time Linear Systems With State and Input Delays via Fundamental Matrices 基于状态观测器的状态和输入时滞离散线性系统的预测反馈控制

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-09-12 DOI: 10.1109/TSMC.2025.3605976

Jie Zhang;Ying Zhang;Wen-Nian Qi

引用次数: 0

Disturbance Decoupling of Probabilistic Boolean Networks 概率布尔网络的干扰解耦

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-09-10 DOI: 10.1109/TSMC.2025.3605616

Yifeng Li;Ying Cheng;Haitao Li;Zhichun Yang;Tingwen Huang

引用次数: 0

Event-Based Adaptive PI-Funnel Global Tracking Control for Uncertain Nonlinear Systems 不确定非线性系统的事件自适应pi -漏斗全局跟踪控制

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-09-10 DOI: 10.1109/TSMC.2025.3605859

Cui-Hua Zhang;Ze-Yun Hu;Yu-Jia Li;Ying Zhang;Chang-Chun Hua;Yue-Ying Liu

引用次数: 0

Modeling Opinion Evolution and Conformity Behavior in Large-Scale Social Network Group Decision-Making 大规模社会网络群体决策中的意见演化与从众行为建模

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-09-10 DOI: 10.1109/TSMC.2025.3606507

Xiujuan Ma;Xinwang Liu;Zaiwu Gong;Fang Liu

{"title":"Modeling Opinion Evolution and Conformity Behavior in Large-Scale Social Network Group Decision-Making","authors":"Xiujuan Ma;Xinwang Liu;Zaiwu Gong;Fang Liu","doi":"10.1109/TSMC.2025.3606507","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3606507","url":null,"abstract":"Individual interactions and conformity play a crucial role in shaping group opinions and influencing the decision-making process. This article introduces a novel opinion evolution model to simulate the impact of conformity on group opinion formation, focusing on weight allocation, relationship propagation, and evolution analysis. In the weight allocation phase, individual weights are evaluated using network structure and the PageRank algorithm. For relationship propagation, indirect trust relationships are computed via a weighted average method, leading to a more precise social network model. In the evolution analysis, an improved Hegselmann–Krause (HK) model demonstrates evolutionary stability. Two types of conformity behavior are simulated: active conformity behavior within clusters and passive conformity behavior via opinion leaders across clusters. Experimental studies on public health policy validate the effectiveness of the proposed model. The results show that this model more accurately captures the complex behavioral patterns of individuals in large-scale social networks and exhibits strong evolutionary stability.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 11","pages":"8558-8571"},"PeriodicalIF":8.7,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145335309","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