IEEE Transactions on Systems Man Cybernetics-Systems最新文献_第5页

IEEE Transactions on Systems, Man, and Cybernetics: Systems Publication Information IEEE系统、人与控制论汇刊：系统出版信息

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-03-01 Epub Date: 2026-04-16 DOI: 10.1109/TSMC.2026.3678687

引用次数: 0

Finite-Time Multilane Fusion Control for 2-D Plane Vehicle Platoon With Sensor and Actuator Faults 具有传感器和执行器故障的二维平面车辆排有限时间多车道融合控制

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-03-01 Epub Date: 2026-02-04 DOI: 10.1109/TSMC.2026.3657620

Man-Fei Lin;Zhan Shu;Cheng-Lin Liu

{"title":"Finite-Time Multilane Fusion Control for 2-D Plane Vehicle Platoon With Sensor and Actuator Faults","authors":"Man-Fei Lin;Zhan Shu;Cheng-Lin Liu","doi":"10.1109/TSMC.2026.3657620","DOIUrl":"https://doi.org/10.1109/TSMC.2026.3657620","url":null,"abstract":"This article focuses on the 2-D plane finite-time multilane fusion control problem with velocity sensor faults and actuator faults. First, considering the velocity sensor faults, radial basis function neural networks (RBFNNs) are introduced to approximate sensor fault functions. A finite-time fault-tolerant position controller is designed by employing a hyperbolic tangent function to address the partial failure of the position actuator. Second, in case of complete actuator failure, the backup actuator is activated without altering the controller structure. The majority of prescribed performance functions (PPFs) currently in use are unsuitable for addressing complete actuator failure. As tracking errors may exceed PPF constraints before the backup actuator is activated upon such faults, this could result in vehicle platoon instability. To address this issue, this article designs a modified PPF (MPPF) that is independent of the initial conditions and can adjust the performance boundary according to the error change at specific times by introducing a shifting function. When a complete actuator failure occurs, the MPPF can sacrifice part of the transient performance to enclose the increased tracking error within the range of the MPPF, thus maintaining the stability of the vehicle platoon. When the actuator is working normally or has a partial failure, it can restore the user-specified performance. Then, by constructing a finite-time angle sliding-mode surface, an angle controller is designed. The designed position and angle of finite-time controllers can ensure that the vehicle platoon achieves multilane fusion within a finite time. Finally, through simulation and comparative results, the effectiveness of the proposed MPPF and finite-time fault-tolerant algorithm is demonstrated.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 5","pages":"3315-3327"},"PeriodicalIF":8.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147685323","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

Perturbation-Based Pinning Control Strategy for Enhanced Synchronization in Complex Networks 基于微扰的复杂网络增强同步控制策略

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-03-01 Epub Date: 2026-03-05 DOI: 10.1109/TSMC.2026.3659710

Ziang Mao;Tianlong Fan;Linyuan Lü

{"title":"Perturbation-Based Pinning Control Strategy for Enhanced Synchronization in Complex Networks","authors":"Ziang Mao;Tianlong Fan;Linyuan Lü","doi":"10.1109/TSMC.2026.3659710","DOIUrl":"https://doi.org/10.1109/TSMC.2026.3659710","url":null,"abstract":"Synchronization is essential for the stability and coordinated operation of complex networked systems. Pinning control, which selectively controls a subset of nodes, provides a scalable solution to enhance network synchronizability. However, existing strategies face key limitations. Heuristic centrality-based methods lack a direct connection to synchronization dynamics, while spectral approaches, though effective, are computationally intensive. To address these challenges, we propose a perturbation-based optimized (PBO) strategy that dynamically evaluates each node’s spectral impact on the Laplacian matrix, achieving improved synchronizability with significantly reduced computational costs (with complexity <inline-formula> <tex-math>$O(kM)$ </tex-math></inline-formula>). Extensive experiments demonstrate that the proposed method outperforms traditional strategies in synchronizability, convergence rate, and pinning robustness to node failures. Notably, in all the empirical networks tested and some generated networks, PBO significantly outperforms the brute-force greedy (BFG) strategy, demonstrating its ability to avoid local optima and adapt to complex connectivity patterns. Our study establishes the theoretical relationship between network synchronizability and convergence rate, offering new insights into efficient synchronization strategies for large-scale complex networks.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 5","pages":"3328-3339"},"PeriodicalIF":8.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147685337","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 Segmentation-Driven Editing Method for Bolt Defect Augmentation and Detection 一种分段驱动的螺栓缺陷增强与检测编辑方法

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-03-01 Epub Date: 2026-02-06 DOI: 10.1109/TSMC.2026.3655553

Yangjie Xiao;Ke Zhang;Jiacun Wang;Xin Sheng;Yurong Guo;Meijuan Chen;Zehua Ren;Zhaoye Zheng;Zhenbing Zhao

{"title":"A Segmentation-Driven Editing Method for Bolt Defect Augmentation and Detection","authors":"Yangjie Xiao;Ke Zhang;Jiacun Wang;Xin Sheng;Yurong Guo;Meijuan Chen;Zehua Ren;Zhaoye Zheng;Zhenbing Zhao","doi":"10.1109/TSMC.2026.3655553","DOIUrl":"https://doi.org/10.1109/TSMC.2026.3655553","url":null,"abstract":"Bolt defect detection (BDD) is critical to ensure the safety of transmission lines. However, the scarcity of defect images and imbalanced data distributions significantly limit detection performance. To address this issue, we propose a segmentation-driven bolt defect editing (SBDE) method for defect generation and data augmentation. The proposed framework comprises three main components. First, a bolt attribute segmentation (BAS) model is constructed, which integrates contrast-based enhancement and frequency-domain filtering to improve edge feature representation. A multipart-aware optimization strategy is introduced to handle the noncontiguous structures of bolt attributes, enabling the generation of high-quality structural masks for subsequent editing. Second, a bolt attribute editing module based on image inpainting is designed, which removes specific bolt attributes by applying mask boundary optimization and context-aware reconstruction, thereby transforming normal bolts into defective ones. Finally, an editing recovery augmentation strategy is proposed to restore the edited bolts into the original inspection images, constructing complete defect samples for expanding the detection dataset. Extensive experiments were conducted on a self-built bolt dataset and the public MVTec AD dataset. The results show that the proposed method significantly outperforms existing state-of-the-art image editing approaches in generation quality and effectively improves defect classification and detection performance across multiple mainstream models, thereby validating the effectiveness and practical potential of the proposed approach. The code of the project is available at <uri>https://github.com/Jay-xyj/SBDE</uri>","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 5","pages":"3113-3127"},"PeriodicalIF":8.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147685506","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

GroupEx: Toward Group-Level Explanations of Graph Neural Networks GroupEx：迈向图神经网络的组级解释

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-03-01 Epub Date: 2026-02-04 DOI: 10.1109/TSMC.2026.3655491

Sayan Saha;Sanghamitra Bandyopadhyay

{"title":"GroupEx: Toward Group-Level Explanations of Graph Neural Networks","authors":"Sayan Saha;Sanghamitra Bandyopadhyay","doi":"10.1109/TSMC.2026.3655491","DOIUrl":"https://doi.org/10.1109/TSMC.2026.3655491","url":null,"abstract":"The increasing adoption of graph neural networks (GNNs) in critical real-world applications brings with it the need for interpretability and explainability of these models. The existing paradigms of GNN explainability typically operate at the extremes. Instance-level methods offer a microscopic view focused on individual predictions, whereas model-level approaches provide a macroscopic summary of the classifier behavior on a target class. We introduce the group-level paradigm, which views explanations at the right resolution, grounded in the intuition that groups of instances embedded close together in the latent space share a common rationale for belonging to the same target class. We show that the instance-level and model-level paradigms are the special cases of this broader framework. To realize this paradigm, we propose GroupEx, a novel method for interpreting GNNs at the group level. GroupEx has the capability to identify the subgroups of graphs within a target class using a group identity (GI) score. It employs a group extractor that can extract a subgroup of graphs that share a common explanation for their class identity by clustering graph embeddings in the latent space of the classifier. A specially devised explanation generator can then discover a group-level explanation by optimizing a novel isomorphism objective, which ensures that the discovered explanation commonly explains the class identity of all the instances in the group. Empirical results on real-world and synthetic datasets demonstrate that GroupEx provides deeper insights into GNN decision-making than the existing state-of-the-art explainability methods, enabling a more structured and interpretable understanding of model predictions.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 5","pages":"3025-3034"},"PeriodicalIF":8.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147685516","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

Edge–Cloud Cooperation-Driven Sustainable Smart Optimization Strategy for Additive Manufacturing 边缘云合作驱动的增材制造可持续智能优化策略

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-03-01 Epub Date: 2026-02-11 DOI: 10.1109/TSMC.2026.3657115

Shuaiyin Ma;Junchi Lv;Yuming Huang;Yanping Chen;Zhiqiang Yan;Maoyuan Li;Jiewu Leng

{"title":"Edge–Cloud Cooperation-Driven Sustainable Smart Optimization Strategy for Additive Manufacturing","authors":"Shuaiyin Ma;Junchi Lv;Yuming Huang;Yanping Chen;Zhiqiang Yan;Maoyuan Li;Jiewu Leng","doi":"10.1109/TSMC.2026.3657115","DOIUrl":"https://doi.org/10.1109/TSMC.2026.3657115","url":null,"abstract":"Additive manufacturing (AM) is widely used in fields, such as aerospace and medical treatment. However, the massive heterogeneous data generated during its production process face challenges, such as high transmission latency and large energy consumption. This article proposes a sustainable intelligent optimization strategy based on edge–cloud collaboration to enhance the intelligence and sustainability of AM. First, a hybrid model that integrates the local feature extraction of convolutional neural network (CNN) and the global dependency modeling of transformer (CNN–transformer) is designed to accurately predict the key process parameters of AM. Second, a multiobjective optimization model for surface roughness, processing time, and energy consumption is constructed. Combined with the improved Pareto set learning (PSL) algorithm, the collaborative optimization of economic and environmental sustainability is achieved. Finally, verification is carried out on selective laser melting (SLM) technology. The experimental results show that the prediction error of the CNN–transformer is lower than that of traditional models. It can reduce energy consumption and processing time while ensuring surface quality, thus providing a systematic solution for green intelligent manufacturing from Industry 4.0 to Industry 5.0.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 5","pages":"3174-3185"},"PeriodicalIF":8.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147685530","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

TechRxiv: Share Your Preprint Research With the World! techxiv：与世界分享你的预印本研究！

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-02-23 DOI: 10.1109/TSMC.2026.3661668

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IEEE Systems, Man, and Cybernetics Society Information IEEE系统、人与控制论学会信息

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-02-23 DOI: 10.1109/TSMC.2026.3661664

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IEEE Systems, Man, and Cybernetics Society Information IEEE系统、人与控制论学会信息

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-02-23 DOI: 10.1109/TSMC.2026.3661676

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IEEE Transactions on Systems, Man, and Cybernetics: Systems Information for Authors IEEE系统、人与控制论汇刊：作者的系统信息

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-02-23 DOI: 10.1109/TSMC.2026.3661674

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