IEEE Transactions on Systems Man Cybernetics-Systems最新文献_第4页

Link-Based Attributed Graph Clustering via Approximate Generative Bayesian Learning 基于近似生成贝叶斯学习的链接属性图聚类

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-06-05 DOI: 10.1109/TSMC.2025.3572738

Yue Yang;Lun Hu;Guodong Li;Dongxu Li;Pengwei Hu;Xin Luo

{"title":"Link-Based Attributed Graph Clustering via Approximate Generative Bayesian Learning","authors":"Yue Yang;Lun Hu;Guodong Li;Dongxu Li;Pengwei Hu;Xin Luo","doi":"10.1109/TSMC.2025.3572738","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3572738","url":null,"abstract":"To understand the mechanisms of complex systems, attributed graphs (AGs) are recognized as a valuable model by their capability of describing nontrivial topological structures and rich node contents, and their emergence raises new challenges on the task of graph clustering. Although a variety of computational algorithms have been proposed to perform accurate clustering analysis on AGs, most of them are incapable of inferring the cluster labels of nodes through links, thus falling short of explaining node behaviors on how to formulate overlapping clusters. Moreover, the vast amount of links considerably decreases the computation efficiency if they are explicitly taken into account for AG clustering. To overcome this problem, we present a novel variational Bayesian learning model, which avoids generating a complete AG by only simulating the generative process of its skeleton with the prior knowledge on the cluster labels of links. When addressing the inference problem, we develop an efficient algorithm, namely, LCAAG, for determining the optimal cluster labels of nodes by estimating local community structures of links. The convergence of LCAAG has been proved theoretically. Compared with several state-of-the-art algorithms, LCAAG has demonstrated its promising performance in terms of both accuracy and scalability on five different scaled benchmark datasets. The source code and datasets are available at <uri>https://github.com/shallowdreamoon/LCAAG.git</uri>.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 8","pages":"5730-5743"},"PeriodicalIF":8.6,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144663704","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

Distributed Predictive Control for Multiagent Systems With Communication Delays via a Hybrid Data-Driven and Model-Based Approach 基于数据驱动和模型混合方法的通信延迟多智能体系统分布式预测控制

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-06-05 DOI: 10.1109/TSMC.2025.3571743

Yi Huang;Guo-Ping Liu;Yi Yu;Wenshan Hu

引用次数: 0

Video Object Detection Considering Dynamic Neighborhood Feature Multiplexing 考虑动态邻域特征复用的视频目标检测

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-06-05 DOI: 10.1109/TSMC.2025.3572123

Jiahui Yu;Yifan Chen;Xuna Wang;Long Chen;Hang Chen;Dalin Zhou;Yingke Xu;Zhaojie Ju

{"title":"Video Object Detection Considering Dynamic Neighborhood Feature Multiplexing","authors":"Jiahui Yu;Yifan Chen;Xuna Wang;Long Chen;Hang Chen;Dalin Zhou;Yingke Xu;Zhaojie Ju","doi":"10.1109/TSMC.2025.3572123","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3572123","url":null,"abstract":"Video object detection is essential for human-interaction applications, including bimanual manipulation sensing (BMS). The effects of video detection in practical applications still need to be improved, as they are restricted by long-range spatiotemporal dependency analysis. How do humans sense bimanual manipulation in videos, especially for deteriorated clips? We argue that humans analyze the current clips based on earlier memory, namely, long-term spatial and temporal dependencies (LTSTD). However, most existing methods have yet to report significant results, as the limited exploration of these dependencies limits them. Developing an easy-to-integrate module is generally preferred for future applications rather than designing a complex end-to-end framework. Therefore, we propose a dynamic neighborhood feature multiplexing mechanism for online video object detection in this article, which is better at learning LTSTD in flexible and robust ways, boosting existing detection results, called DNFM. Specifically, we develop dynamic memory enhancement neural networks for better long-term feature aggregation with negligible additional computation costs. We multiplex each frame feature to aggregate key enhanced representations under the guidance of dynamic memory recall. The DNFM contributes to various famous detectors in BMS and other challenging detection tasks, and particular attention has been devoted to “low-quality” frame detection. Experimental results show that, while achieving state-of-the-art detection performance, DNFM clearly illustrates the easy-to-integrate operation for boosting the video object detection results.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 8","pages":"5451-5463"},"PeriodicalIF":8.6,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657387","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

Workload Balancing for Photolithography Machines in Semiconductor Manufacturing via Estimation of Distribution Algorithm Integrating Kmeans Clustering 基于Kmeans聚类估计分布算法的半导体制造光刻机工作负载均衡

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-06-04 DOI: 10.1109/TSMC.2025.3572370

LiangChao Chen;Yan Qiao;NaiQi Wu;Mohammadhossein Ghahramani;YongHua Shao;SiJun Zhan

{"title":"Workload Balancing for Photolithography Machines in Semiconductor Manufacturing via Estimation of Distribution Algorithm Integrating Kmeans Clustering","authors":"LiangChao Chen;Yan Qiao;NaiQi Wu;Mohammadhossein Ghahramani;YongHua Shao;SiJun Zhan","doi":"10.1109/TSMC.2025.3572370","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3572370","url":null,"abstract":"This work focuses on the scheduling of a photolithography area with multiple machine groups and each one consists of a predetermined number of photolithography machines (PMs). PMs belonging to the same machine group should have identical processing capacities. Additionally, all PMs are designated with downward processing compatibility. This means that the wafers requiring relatively low pattern precision can be processed by the PMs used to deal with high pattern precision. After executing a photolithography process, a circuit pattern is transferred from an auxiliary resource called a reticle onto the wafer surface. Moreover, when processing wafers with different reticle and processing environment requirements, the machine setup is necessary. With those complex processing requirements, the objective is to minimize the difference between the longest and shortest working time of PMs so as to balance the workloads among all PMs. To do so, a mixed-integer linear programming model is built and then solved by using CPLEX for the small-sized problem. For medium- and large-sized problems, a designed estimation of distribution algorithm integrating a Kmeans clustering is constructed to improve the productivity of the photolithography area. Comparison results show that the proposed method outperforms the compared algorithms regardless of problem sizes.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 8","pages":"5565-5580"},"PeriodicalIF":8.6,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657403","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

Deadlock Resolution in Networked Automated Manufacturing Systems With Communication Delays and Losses Using Petri Nets 基于Petri网的具有通信延迟和损耗的网络化自动化制造系统死锁解决

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-06-04 DOI: 10.1109/TSMC.2025.3571500

Zijian Zhang;Hesuan Hu

引用次数: 0

False Data Injection Attack Detection Under Uncertain Parameters in Cyber-Physical Systems 网络物理系统参数不确定条件下的虚假数据注入攻击检测

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-06-04 DOI: 10.1109/TSMC.2025.3572634

Yu Shi;Lingli Cheng;Xisheng Zhan;Huaicheng Yan

{"title":"False Data Injection Attack Detection Under Uncertain Parameters in Cyber-Physical Systems","authors":"Yu Shi;Lingli Cheng;Xisheng Zhan;Huaicheng Yan","doi":"10.1109/TSMC.2025.3572634","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3572634","url":null,"abstract":"The problem of detecting false data injection (FDI) attacks in a class of cyber-physical systems with discrete-time information under uncertain parameters is investigated in this article. In practical scenarios, it is noteworthy that accurately modeling the system matrix is often challenging, and traditional detection methods frequently result in false positives and missed detections. Therefore, this article introduces a novel approach for accurately detecting FDI attacks in the presence of parameter uncertainties. Assuming that system parameters reside within a convex polytope, three distinct FDI attack detection estimators with varying performance levels are proposed. Subsequently, a convex optimization problem is established to obtain the necessary gains for the detectors. The results demonstrate that the proposed detection method achieves higher accuracy under uncertain parameters. Furthermore, the method allows for flexible selection based on application-specific requirements, whether prioritizing accuracy or efficiency. Finally, the effectiveness and applicability of the proposed techniques are demonstrated through simulation examples using the F-404 aerospace engine system model.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 8","pages":"5696-5704"},"PeriodicalIF":8.6,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657411","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

Adaptive Fuzzy Secure Collision-Free Formation Control for Nonlinear MASs With DoS Attacks 具有DoS攻击的非线性质量的自适应模糊安全无碰撞编队控制

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-06-04 DOI: 10.1109/TSMC.2025.3572377

Jun Zhang;Jun Ning;Shaocheng Tong

引用次数: 0

New Criteria on Input-to-State Stability for Stochastic Nonlinear Delayed Systems With Multiple Impulses 多脉冲随机非线性时滞系统输入-状态稳定性新准则

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-06-03 DOI: 10.1109/TSMC.2025.3572289

Haofeng Xu;Quanxin Zhu;Wei Xing Zheng

引用次数: 0

Neural-Network-Based Distributed State Estimation Under Encoding-Decoding Schemes: Probabilistic-Constrained Cases 编解码方案下基于神经网络的分布式状态估计：概率约束情况

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-06-03 DOI: 10.1109/TSMC.2025.3571783

Yamei Ju;Yangkai Chen;Derui Ding;Guoliang Wei;Ying Sun

引用次数: 0

Distributed Privacy-Preserving Economic Dispatch of Isolated Microgrid Based on Event-Triggered Mechanism 基于事件触发机制的隔离微电网分布式隐私保护经济调度

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-06-03 DOI: 10.1109/TSMC.2025.3570718

Hongfei Bai;Shengxuan Weng;Dong Yue;Chunxia Dou

引用次数: 0