IEEE Transactions on Cybernetics最新文献_第3页

A Dynamic Systems Approach to Modeling Human-Machine Rhythm Interaction.

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-03-25 DOI: 10.1109/TCYB.2025.3547216

Zhongju Yuan, Wannes Van Ransbeeck, Geraint A Wiggins, Dick Botteldooren

{"title":"A Dynamic Systems Approach to Modeling Human-Machine Rhythm Interaction.","authors":"Zhongju Yuan, Wannes Van Ransbeeck, Geraint A Wiggins, Dick Botteldooren","doi":"10.1109/TCYB.2025.3547216","DOIUrl":"https://doi.org/10.1109/TCYB.2025.3547216","url":null,"abstract":"Rhythm is an inherent aspect of human behavior, present from infancy and embedded in cultural practices. At the core of rhythm perception lies meter anticipation, a spontaneous process in the human brain that typically occurs before actual beats. This anticipation can be framed as a time series prediction problem. From the perspective of human embodied system behavior, although many models have been developed for time series prediction, most prioritize accuracy over biological realism, contrasting with the natural imprecision of human internal clocks. Neuroscientific evidence, such as infants' natural meter synchronization, underscores the need for biologically plausible models. Therefore, we propose a neuron oscillator-based dynamic system that simulates human behavior during meter perception. The model introduces two tunable parameters for local and global adjustments, fine-tuning the oscillation combinations to emulate human-like rhythmic behavior. The experiments are conducted under three common scenarios encountered during human-machine interaction, demonstrating that the proposed model can exhibit human-like reactions. Additionally, experiments involving human-machine and interhuman interactions show that the model successfully replicates real-world rhythmic behavior, advancing toward more natural and synchronized human-machine rhythm interaction.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":9.4,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143709394","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

Prescribed-Time Bipartite Synchronization for General Linear Multiagent Systems: An Adaptive Dynamic Output-Feedback Strategy.

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-03-24 DOI: 10.1109/TCYB.2025.3546386

Yuan Zhou, Yu Zhao, Guofeng Zhang, Heung-Wing Joseph Lee

{"title":"Prescribed-Time Bipartite Synchronization for General Linear Multiagent Systems: An Adaptive Dynamic Output-Feedback Strategy.","authors":"Yuan Zhou, Yu Zhao, Guofeng Zhang, Heung-Wing Joseph Lee","doi":"10.1109/TCYB.2025.3546386","DOIUrl":"https://doi.org/10.1109/TCYB.2025.3546386","url":null,"abstract":"Achieving prescribed-time synchronization with output-feedback measurements in general linear multiagent systems is challenging, as it necessitates the simultaneous achievement of state synchronization and observer estimation within a prescribed time. This article focuses on general linear dynamics and aims to solve the prescribed-time bipartite synchronization (PT-BS) problem over cooperative-antagonistic networks. First, a couple of time-varying Riccati equations (TVREs) is introduced, which transforms the prescribed-time synchronization problem into a dynamic parameter design issue. By using the solutions of TVREs to design output feedback gains, a class of time-varying-gain prescribed-time observers and observer-based protocols are proposed. Then, since the proposed PT-BS observers require knowledge of some global information (i.e., the minimum eigenvalue of the topology-relevant Laplacian matrix), two adaptive strategies are presented to solve the output-feedback PT-BS problems in a fully distributed manner: an edge-based adaptive strategy and a node-based adaptive strategy. It successfully achieves state synchronization, observer estimation, and adaptive gain convergence within the prescribed settling time. Finally, a simulation example demonstrates the effectiveness of the theoretical results.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":9.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143700358","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

Secure Consensus for Switched Multiagent Systems Under DoS Attacks: Hybrid Event-Triggered and Impulsive Control Approach DoS 攻击下交换式多代理系统的安全共识：事件触发和脉冲控制混合方法

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-03-22 DOI: 10.1109/tcyb.2025.3546923

Xin Wang, Zhuocheng Yin, Yan Lei, Tingwen Huang, Jürgen Kurths

引用次数: 0

IEEE Transactions on Cybernetics 电气和电子工程师学会控制论论文集

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-03-21 DOI: 10.1109/TCYB.2025.3548834

引用次数: 0

IEEE Transactions on Cybernetics

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

引用次数: 0

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

引用次数: 0

tcyb-cover2-fill-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

引用次数: 0

Security Control of Safety-Critical Systems.

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

引用次数: 0

Event-Triggered Almost Output Regulation for Switched T-S Fuzzy Systems.

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

引用次数: 0

ERMAV: Efficient and Robust Graph Contrastive Learning via Multiadversarial Views Training.

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":"PP ","pages":""},"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}

引用次数: 0