IEEE Transactions on Cybernetics最新文献_第10页

Adaptive Nussbaum Design for Nonholonomic Systems With Asymptotic Stabilization Against False Data Injection 非完整系统抗假数据注入渐近镇定的自适应Nussbaum设计

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-04-24 DOI: 10.1109/tcyb.2025.3558642

Guilong Liu, Yongliang Yang, Weinan Gao, Donald C. Wunsch

引用次数: 0

Adaptive Dictionary Learning for Multiview Subspace Clustering 多视图子空间聚类的自适应字典学习

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-04-24 DOI: 10.1109/TCYB.2025.3557917

Xu Chen;Zhiwen Yu;Ziwei Fan;Kaixiang Yang;C. L. Philip Chen

{"title":"Adaptive Dictionary Learning for Multiview Subspace Clustering","authors":"Xu Chen;Zhiwen Yu;Ziwei Fan;Kaixiang Yang;C. L. Philip Chen","doi":"10.1109/TCYB.2025.3557917","DOIUrl":"10.1109/TCYB.2025.3557917","url":null,"abstract":"Multiview Subspace Clustering (MvSC) has demonstrated impressive clustering performance on multiview data. Most existing methods rely on either raw features or reduced-redundancy data for subspace representation learning, followed by spectral clustering to derive the final results. However, these methods maintain a fixed feature space during subspace learning, which limits information propagation and compromises both representation quality and clustering performance. To address this issue, this article proposes an adaptive dictionary learning approach for MvSC (AMvSC), which seamlessly integrates redundancy reduction and representation learning within a unified framework to facilitate mutual information propagation. Specifically, an adaptive dictionary learning strategy is designed to automatically reduce redundancy and noise in the original feature space during the subspace representation learning process. This strategy ensures effective information exchange, thereby enhancing the quality of the learned representations. Additionally, low-rank constraints, combined with smoothness and diversity regularization, are applied to further refine the subspace representations and comprehensively capture complex correlations among samples. Finally, an alternating optimization algorithm is developed to iteratively update the unified learning model. Extensive experiments validate the effectiveness and superiority of the proposed method.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 6","pages":"2833-2843"},"PeriodicalIF":9.4,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873076","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

Reinforcement-Learning-Based Finite Time Fault Tolerant Control for a Manipulator With Actuator Faults 基于强化学习的机械臂故障有限时间容错控制

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-04-24 DOI: 10.1109/TCYB.2025.3557681

Pengxin Yang;Shuang Zhang;Xinbo Yu;Wei He

引用次数: 0

Fixed-Time Distributed Average Tracking for a Class of Nonlinear Multiagent Systems With Unity Relative Degree 一类具有统一关联度的非线性多智能体系统的固定时间分布平均跟踪

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-04-23 DOI: 10.1109/TCYB.2025.3559572

Qingpeng Liang;Deqing Huang;Lei Ma;Jiangping Hu;Linying Xiang;Yanzhi Wu

{"title":"Fixed-Time Distributed Average Tracking for a Class of Nonlinear Multiagent Systems With Unity Relative Degree","authors":"Qingpeng Liang;Deqing Huang;Lei Ma;Jiangping Hu;Linying Xiang;Yanzhi Wu","doi":"10.1109/TCYB.2025.3559572","DOIUrl":"10.1109/TCYB.2025.3559572","url":null,"abstract":"This article investigates the fixed-time distributed average tracking (DAT) problem for nonlinear multiagent systems with unity relative degree under external disturbances. A distributed control framework is developed to guarantee fixed-time convergence of all agents’ outputs to the target trajectory, which is defined as the average of multiple nonlinear reference signals. The approach consists of three main components. First, a steady-state generator is introduced to reconstruct the desired trajectory. Using this generator, a distributed observer is designed to estimate the target trajectory while ensuring robustness against initialization errors. Subsequently, an observer-based output-feedback controller is developed to guarantee the convergence of each agent’s output to its corresponding reference signal within a fixed time. Through rigorous theoretical analysis, it is proved that the proposed control architecture ensures fixed-time convergence to the target trajectory, effectively solving the fixed-time DAT problem. The effectiveness of the proposed method is validated through numerical simulations.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 6","pages":"2742-2753"},"PeriodicalIF":9.4,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143866640","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

tcyb-cover2-fill-3548830 tcyb - cover2 -填充- 3548830

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-04-23 DOI: 10.1109/TCYB.2025.3559260

引用次数: 0

Multiscale Skeleton-Based Temporal Action Segmentation Using Hierarchical Temporal Modeling and Prediction Ensemble 基于分层时间建模和预测集成的多尺度骨架时间动作分割

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-04-23 DOI: 10.1109/TCYB.2025.3559660

Bowen Chen;Wei Nie;Haoyu Ji;Weihong Ren;Qiyi Tong;Zhiyong Wang;Honghai Liu

{"title":"Multiscale Skeleton-Based Temporal Action Segmentation Using Hierarchical Temporal Modeling and Prediction Ensemble","authors":"Bowen Chen;Wei Nie;Haoyu Ji;Weihong Ren;Qiyi Tong;Zhiyong Wang;Honghai Liu","doi":"10.1109/TCYB.2025.3559660","DOIUrl":"10.1109/TCYB.2025.3559660","url":null,"abstract":"Skeleton-based temporal action segmentation (TAS) decomposes untrimmed skeleton sequence into meaningful segments. The variance in temporal scale challenges the skeleton modeling network to seek a balance between over-segmentation and under-segmentation. Current methods often rely on parallel multiscale feature extractors and additional refinement modules to mitigate the multiscale issue, which brings significant computations and complexity. To address these issues, this article proposes multiscale skeleton-based TAS (MSTAS), consisting of temporal probability pyramid (TPP) and smoothed multiscale ensemble (SME). TPP represents each action as a collection of multiscale probability distributions using a U-shape hierarchical temporal pyramid. Subsequently, SME takes the average of distributions instead of deploying additional refinement stages to achieve action segmentation. Considering the over-confident issue that exists in each scale, SME incorporates a novel label smoothing phase to improve the probability distributions by dynamically calibrating the confidence of each scale. Experimental results on four public datasets show that the MSTAS achieves state-of-the-art performance with less computation overheads, such as +1.1% accuracy and +2.8% F1@0.5 on the challenging LARa dataset with 70% fewer parameters and 80% fewer GFLOPS. Benefiting from confidence calibration, the MSTAS efficiently utilizes more temporal scales while keeping better calibration for ambiguous action instances. Additionally, the U-shape pyramid demonstrates a strong compatibility with classical refinement module, enabling the efficient extraction of multiscale motion representations.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 6","pages":"2779-2791"},"PeriodicalIF":9.4,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143867010","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

IEEE Transactions on Cybernetics IEEE控制论汇刊

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-04-23 DOI: 10.1109/TCYB.2025.3559264

引用次数: 0

IEEE Transactions on Cybernetics IEEE控制论汇刊

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-04-23 DOI: 10.1109/TCYB.2025.3559262

引用次数: 0

Hyper-Heuristic Optimization Using Multifeature Fusion Estimator for PCB Assembly Lines With Linear-Aligned-Heads Surface Mounters 基于多特征融合估计器的线性对准头表面贴片PCB装配线超启发式优化

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-04-23 DOI: 10.1109/tcyb.2025.3556512

Guangyu Lu, Huijun Gao, Zhengkai Li, Xinghu Yu, Tong Wang, Jianbin Qiu, Juan J. Rodríguez-Andina

引用次数: 0

Fast UAV Object-Searching in Large-Scale and Complex Environments 大规模复杂环境下的无人机快速目标搜索

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-04-22 DOI: 10.1109/TCYB.2025.3556744

Hai Lin;Xinsong Yang;Guanghui Wen;Wei Xing Zheng

{"title":"Fast UAV Object-Searching in Large-Scale and Complex Environments","authors":"Hai Lin;Xinsong Yang;Guanghui Wen;Wei Xing Zheng","doi":"10.1109/TCYB.2025.3556744","DOIUrl":"10.1109/TCYB.2025.3556744","url":null,"abstract":"Autonomous object-searching is crucial for various applications of unmanned aerial vehicles (UAVs). Considering the fact that existing autonomous exploration methods either focus only on maximizing the exploration of unknown areas or suffer from insufficient searches due to repeated and unnecessary exploration, this article introduces an effective object-searching strategy for UAVs in large-scale and complex environments. A novel method is proposed to empower UAVs with the capability to conduct fast, secure, and efficient searches for interested objects in large-scale and complex environments. A Kalman filter-based YOLO algorithm is first proposed to achieve robust object position estimation in cluttered and occlusion-prone scenarios, and a mode-based method is then introduced to conduct a computationally efficient viewpoint generation. A hierarchical searching method is proposed, which not only can increase computational and search efficiency but also can leverage frontier data for search-planning, including coarse global searching paths and optimizing local refined searching trajectories. Experimental results in six different environments indicate that our proposed method outperforms existing techniques in terms of both reduced searching times and computing time. Moreover, the effectiveness of the proposed method is substantiated in various real-world scenarios.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 6","pages":"2993-3004"},"PeriodicalIF":9.4,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143862531","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