IEEE Transactions on Cybernetics最新文献_第10页

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 : 2025-03-21 DOI: 10.1109/TCYB.2025.3548832

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Connect. Support. Inspire. 连接。支持。激发灵感。

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-03-21 DOI: 10.1109/TCYB.2025.3552181

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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 : 2025-03-21 DOI: 10.1109/TCYB.2025.3548830

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Security Control of Safety-Critical Systems 安全关键系统的安全控制。

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-03-20 DOI: 10.1109/TCYB.2025.3545422

Yi Dong;Yiguang Hong;Jie Chen

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ERMAV: Efficient and Robust Graph Contrastive Learning via Multiadversarial Views Training ERMAV：基于多对抗视图训练的高效鲁棒图对比学习。

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-03-20 DOI: 10.1109/TCYB.2025.3548175

Wen Li;Wing W. Y. Ng;Hengyou Wang;Jianjun Zhang;Cankun Zhong;Liang Yang

{"title":"ERMAV: Efficient and Robust Graph Contrastive Learning via Multiadversarial Views Training","authors":"Wen Li;Wing W. Y. Ng;Hengyou Wang;Jianjun Zhang;Cankun Zhong;Liang Yang","doi":"10.1109/TCYB.2025.3548175","DOIUrl":"10.1109/TCYB.2025.3548175","url":null,"abstract":"Graph contrastive learning (GCL) is emerging as a pivotal technique in graph representation learning. However, recent research indicates that GCL is vulnerable to adversarial attacks, while existing robust GCL methods against adversarial attacks are inefficient and lack scalability due to the significant computational expenses of explicit adversarial attacks on the graph structure. To address the shortcomings of existing approaches, we propose an efficient and robust GCL via multiadversarial views training framework, called ERMAV. Specifically, the ERMAV generates two adversarial views by attacking both node attributes and latent representations on randomly sampled subgraphs. The method conducts explicit adversarial attacks on node attributes by attacking node attributes and implicit adversarial attacks on the graph structure by attacking latent representations, which avoids the costly computation of explicit graph structure attacks. Moreover, two efficient attack methods are developed to construct adversarial perturbations, which can dynamically generate different adversarial views to enhance sample diversity in the training phase. Furthermore, to validate the effectiveness and robustness of the proposed framework, extensive experiments of node classification on seven real-world datasets are conducted. Experimental results show that our ERMAV outperforms state-of-the-art GCL methods on the original graphs and is consistently more robust than existing robust GCL methods on a variety of attacked graphs. This demonstrates the strong robustness and great potential of our ERMAV in real-world applications.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 5","pages":"2188-2201"},"PeriodicalIF":9.4,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143669716","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}

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Event-Triggered Almost Output Regulation for Switched T-S Fuzzy Systems 切换T-S模糊系统的事件触发几乎输出调节。

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-03-20 DOI: 10.1109/TCYB.2025.3547288

Shuanghe Yu;Ying Zhao;Jingjie Xu

{"title":"Event-Triggered Almost Output Regulation for Switched T-S Fuzzy Systems","authors":"Shuanghe Yu;Ying Zhao;Jingjie Xu","doi":"10.1109/TCYB.2025.3547288","DOIUrl":"10.1109/TCYB.2025.3547288","url":null,"abstract":"This article investigates the event-triggered (ET) almost output regulation (ETAOR) issue for the switched T-S fuzzy (T-SF) systems with both output regulation (OR) characteristic and <inline-formula> <tex-math>$L_{2}$ </tex-math></inline-formula> gain characteristic considered. First, in order to conserve communication resources, an ET mechanism and an ET switched fuzzy feedback controller are devised. Then, the definition of the ETAOR issue for the switched T-SF systems is presented. Next, with the relaxed assumption of the same coordinate transformation, the ETAOR issue of the switched T-SF systems is transformed into the ET <inline-formula> <tex-math>$H_{infty } $ </tex-math></inline-formula> control problem of the switched T-SF systems. Further, by using the multiple Lyapunov functions approach, a solvability condition on the ETAOR issue is established for the switched T-SF systems with the average dwell-time related switching signals. Such condition is also suitable for nonswitched T-SF systems. In addition, Zeno behavior may be caused by the ET programme is excluded. Finally, the presented control method is applied to an aero-engine case study to corroborate its effectiveness.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 5","pages":"2223-2233"},"PeriodicalIF":9.4,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143669720","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 Scalable Test Problem Generator for Sequential Transfer Optimization 时序传输优化的可伸缩测试问题生成器。

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-03-20 DOI: 10.1109/TCYB.2025.3547565

Xiaoming Xue;Cuie Yang;Liang Feng;Kai Zhang;Linqi Song;Kay Chen Tan

{"title":"A Scalable Test Problem Generator for Sequential Transfer Optimization","authors":"Xiaoming Xue;Cuie Yang;Liang Feng;Kai Zhang;Linqi Song;Kay Chen Tan","doi":"10.1109/TCYB.2025.3547565","DOIUrl":"10.1109/TCYB.2025.3547565","url":null,"abstract":"Despite the increasing interest in sequential transfer optimization (STO), a comprehensive benchmark suite for systematically comparing various STO algorithms remains underexplored. Existing test problems, which are often manually configured and lack scalability, can result in biased and nongeneralizable algorithm performance. In light of the above, we first introduce four concepts for characterizing STO problems (STOPs) in this study and present an important feature, namely similarity distribution, to quantitatively delineate the relationship between the optimal solutions of source and target tasks. Subsequently, we present general design guidelines for STOPs and introduce a problem generator that demonstrates strong scalability. Specifically, the similarity distribution of a problem can be easily customized through a novel inverse generation strategy, allowing for a continuous spectrum that captures the diverse similarity relationships present in real-world scenarios. Lastly, a benchmark suite comprising 12 STOPs, characterized by a range of customized similarity relationships, has been developed using the proposed generator and will serve as a platform for examining various STO algorithms. For instance, biased transferability representation, irregular mapping learning behaviors, and performance improvements unrelated to search experience are significant empirical findings that previous benchmarks failed to reveal, yet can be effectively identified through our test problems. The source code of the proposed problem generator is available at <uri>https://github.com/XmingHsueh/STOP-G</uri>.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 5","pages":"2110-2123"},"PeriodicalIF":9.4,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143669711","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 State Space Model for Multiobject Full 3-D Information Estimation From RGB-D Images RGB-D图像多目标全三维信息估计的状态空间模型

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-03-19 DOI: 10.1109/TCYB.2025.3548788

Jiaming Zhou;Qing Zhu;Yaonan Wang;Mingtao Feng;Jian Liu;Jianan Huang;Ajmal Mian

{"title":"A State Space Model for Multiobject Full 3-D Information Estimation From RGB-D Images","authors":"Jiaming Zhou;Qing Zhu;Yaonan Wang;Mingtao Feng;Jian Liu;Jianan Huang;Ajmal Mian","doi":"10.1109/TCYB.2025.3548788","DOIUrl":"10.1109/TCYB.2025.3548788","url":null,"abstract":"Visual understanding of 3-D objects is essential for robotic manipulation, autonomous navigation, and augmented reality. However, existing methods struggle to perform this task efficiently and accurately in an end-to-end manner. We propose a single-shot method based on the state space model (SSM) to predict the full 3-D information (pose, size, shape) of multiple 3-D objects from a single RGB-D image in an end-to-end manner. Our method first encodes long-range semantic information from RGB and depth images separately and then combines them into an integrated latent representation that is processed by a modified SSM to infer the full 3-D information in two separate task heads within a unified model. A heatmap/detection head predicts object centers, and a 3-D information head predicts a matrix detailing the pose, size and latent code of shape for each detected object. We also propose a shape autoencoder based on the SSM, which learns canonical shape codes derived from a large database of 3-D point cloud shapes. The end-to-end framework, modified SSM block and SSM-based shape autoencoder form major contributions of this work. Our design includes different scan strategies tailored to different input data representations, such as RGB-D images and point clouds. Extensive evaluations on the REAL275, CAMERA25, and Wild6D datasets show that our method achieves state-of-the-art performance. On the large-scale Wild6D dataset, our model significantly outperforms the nearest competitor, achieving 2.6% and 5.1% improvements on the IOU-50 and 5°10 cm metrics, respectively.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 5","pages":"2248-2260"},"PeriodicalIF":9.4,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143661347","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 Subspace Search-Based Evolutionary Algorithm for Large-Scale Constrained Multiobjective Optimization and Application 一种基于子空间搜索的大规模约束多目标优化进化算法及其应用

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-03-19 DOI: 10.1109/TCYB.2025.3548414

Xuanxuan Ban;Jing Liang;Kunjie Yu;Kangjia Qiao;Ponnuthurai Nagaratnam Suganthan;Yaonan Wang

{"title":"A Subspace Search-Based Evolutionary Algorithm for Large-Scale Constrained Multiobjective Optimization and Application","authors":"Xuanxuan Ban;Jing Liang;Kunjie Yu;Kangjia Qiao;Ponnuthurai Nagaratnam Suganthan;Yaonan Wang","doi":"10.1109/TCYB.2025.3548414","DOIUrl":"10.1109/TCYB.2025.3548414","url":null,"abstract":"Large-scale constrained multiobjective optimization problems (LSCMOPs) exist widely in science and technology. LSCMOPs pose great challenges to algorithms due to the need to optimize multiple conflicting objectives and satisfy multiple constraints in a large search space. To better address such problems, this article proposes a dynamic subspace search-based evolutionary algorithm for solving LSCMOPs. The main idea is to initially allow the population to search in a low-dimensional subspace to increase convergence, then the searched subspace is gradually expanded to encourage the population to further search the full decision space. Specifically, the contribution of each decision variable to the evolution is first calculated using the proposed decision variable analysis method. Then, a probability-based offspring generation strategy is developed to encourage the population to preferentially search in a low-dimensional subspace composed of decision variables with high contribution degrees, thus speeding up the early convergence. With the continuous progress of evolution, the subspace is gradually expanded to ensure that the population can better explore the entire space. The performance of the proposed algorithm is evaluated on a variety of test problems with 100–1000 decision variables. Experimental results on four test suits and three real-world instances show that the proposed algorithm is efficient in solving LSCMOPs.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 5","pages":"2486-2499"},"PeriodicalIF":9.4,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143661348","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

Unsupervised Feature Selection for High-Order Embedding Learning and Sparse Learning 高阶嵌入学习和稀疏学习的无监督特征选择

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-03-19 DOI: 10.1109/TCYB.2025.3546658

Zebiao Hu;Jian Wang;Jacek Mańdziuk;Zhongxin Ren;Nikhil R. Pal

{"title":"Unsupervised Feature Selection for High-Order Embedding Learning and Sparse Learning","authors":"Zebiao Hu;Jian Wang;Jacek Mańdziuk;Zhongxin Ren;Nikhil R. Pal","doi":"10.1109/TCYB.2025.3546658","DOIUrl":"10.1109/TCYB.2025.3546658","url":null,"abstract":"The majority of the unsupervised feature selection methods usually explore the first-order similarity of the data while ignoring the high-order similarity of the instances, which makes it easy to construct a suboptimal similarity graph. Furthermore, such methods, often are not suitable for performing feature selection due to their high complexity, especially when the dimensionality of the data is high. To address the above issues, a novel method, termed as unsupervised feature selection for high-order embedding learning and sparse learning (UFSHS), is proposed to select useful features. More concretely, UFSHS first takes advantage of the high-order similarity of the original input to construct an optimal similarity graph that accurately reveals the essential geometric structure of high-dimensional data. Furthermore, it constructs a unified framework, integrating high-order embedding learning and sparse learning, to learn an appropriate projection matrix with row sparsity, which helps to select an optimal subset of features. Moreover, we design a novel alternative optimization method that provides different optimization strategies according to the relationship between the number of instances and the dimensionality, respectively, which significantly reduces the computational complexity of the model. Even more amazingly, the proposed optimization strategy is shown to be applicable to ridge regression, broad learning systems and fuzzy systems. Extensive experiments are conducted on nine public datasets to illustrate the superiority and efficiency of our UFSHS.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 5","pages":"2355-2368"},"PeriodicalIF":9.4,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143661503","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