IEEE Transactions on Cybernetics最新文献_第10页

Cross-Model Nested Fusion Network for Salient Object Detection in Optical Remote Sensing Images. 光学遥感图像中显著目标检测的跨模型嵌套融合网络。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-09-12 DOI: 10.1109/tcyb.2025.3571913

Mingzhu Xu,Sen Wang,Yupeng Hu,Haoyu Tang,Runmin Cong,Liqiang Nie

{"title":"Cross-Model Nested Fusion Network for Salient Object Detection in Optical Remote Sensing Images.","authors":"Mingzhu Xu,Sen Wang,Yupeng Hu,Haoyu Tang,Runmin Cong,Liqiang Nie","doi":"10.1109/tcyb.2025.3571913","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3571913","url":null,"abstract":"Recently, salient object detection (SOD) in optical remote sensing images, dubbed ORSI-SOD, has attracted increasing research interest. Although deep-based models have achieved impressive performance, several limitations remain: a single image contains multiple objects with varying scales, complex topological structures, and background interference. These unresolved issues render ORSI-SOD a challenging task. To address these challenges, we introduce a distinctive cross-model nested fusion network (CMNFNet), which leverages heterogeneous features to increase the performance of ORSI-SOD. Specifically, the proposed model comprises two heterogeneous encoders, a conventional CNN-based encoder that can model local features, and a specially designed graph convolutional network (GCN)-based encoder with local and global receptive fields that can model local and global features simultaneously. To effectively differentiate between multiple salient objects of different sizes or complex topological structures within an image, we project the image into two different graphs with different receptive fields and conduct message passing through two parallel graph convolutions. Finally, the heterogeneous features extracted from the two encoders are fused in the well-designed attention enhanced cross model nested fusion module (AECMNFM). This module is meticulously crafted to integrate features progressively, allowing the model to adaptively eliminate background interference while simultaneously refining the feature representations. We conducted comprehensive experimental analyzes on benchmark datasets. The results demonstrate the superiority of our CMNFNet over 16 state-of-the-art (SOTA) models.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"66 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145043837","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

Passivity-Based Asynchronous Control of 2-D Roesser Markovian Jump Systems and Stabilization Under DoS Attacks. 二维Roesser马尔可夫跳跃系统的无源异步控制及DoS攻击下的镇定。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-09-10 DOI: 10.1109/tcyb.2025.3600968

Yifang Zhang,Zheng-Guang Wu,Xinyu Lv,Yong Xu,James Lam,Ka-Wai Kwok

{"title":"Passivity-Based Asynchronous Control of 2-D Roesser Markovian Jump Systems and Stabilization Under DoS Attacks.","authors":"Yifang Zhang,Zheng-Guang Wu,Xinyu Lv,Yong Xu,James Lam,Ka-Wai Kwok","doi":"10.1109/tcyb.2025.3600968","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3600968","url":null,"abstract":"The passivity-based asynchronous control is tackled for 2-D Roesser Markovian jump systems (MJSs) and stabilization is guaranteed when 2-D MJSs are susceptible to Denial-of-Service (DoS) attacks. A novel jump model is proposed in this article, where the switching law of subsystems is regulated by the sum of the horizontal and vertical coordinates' values. This differs from the conventional jump model, which presumes that the transition probabilities are identical in both directions. The proposed jump model can avoid the mode ambiguity problem. Given the openness and sharing nature of communication networks, they are susceptible to malicious cyber-attacks that impair system performance. The concept of global time is introduced to help characterize the jump law and construct DoS attack model. Besides, a hidden Markov model (HMM) is utilized to manage the inevitable mismatched mode problem induced by any delay or data dropouts. With the above considerations, several conditions are established for ensuring passivity performance of 2-D MJSs and stabilization when facing DoS attacks. Several equivalent solvable conditions are derived via decoupling strategy and matrix inequality technique. Finally, two simulation examples are provided to demonstrate the validity of the established theoretical results.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"9 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145031727","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

Fixed-Time Leaderless Cluster Synchronization of Spatiotemporal Community Networks With Coopetition Interactions. 具有竞争相互作用的时空社区网络的固定时间无领导集群同步。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-09-09 DOI: 10.1109/tcyb.2025.3594209

Tingting Shi,Cheng Hu,Haijun Jiang,Quanxin Zhu,Tingwen Huang

{"title":"Fixed-Time Leaderless Cluster Synchronization of Spatiotemporal Community Networks With Coopetition Interactions.","authors":"Tingting Shi,Cheng Hu,Haijun Jiang,Quanxin Zhu,Tingwen Huang","doi":"10.1109/tcyb.2025.3594209","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3594209","url":null,"abstract":"This article addresses the fixed-time leaderless cluster synchronization of spatiotemporal community networks (SCNs) characterized by nonidentical node dynamics and reaction-diffusion feature. First, a signed SCN with reaction-diffusion effect is formulated, where the sign-based coupling is introduced to capture the dynamics of coopetition interactions among different communities. Second, to ensure the invariance of the synchronous manifold, an improved interdegree balance condition is proposed as a prerequisite for achieving cluster synchronization of the community network. Third, based on the local state information from adjacent nodes within each community, a time-limited controller is designed to enhance intracommunity coordination while avoiding the adverse effects of intercommunity competition on synchronization. Subsequently, with the help of the matrix decomposition technique and a Lyapunov-like method, several flexible leaderless cluster synchronization criteria are derived by establishing a nontrivial integral inequality and key properties of the intracommunity Laplacian matrix. Finally, the theoretical results are substantiated through a numerical example.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"32 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145025760","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

Structure Learning of Deep Gaussian and Non-Gaussian Information Fusion Framework for Automated Predictive Data Analytics. 面向自动化预测数据分析的深度高斯和非高斯信息融合框架的结构学习。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-09-09 DOI: 10.1109/tcyb.2025.3603545

Zhiqiang Ge

{"title":"Structure Learning of Deep Gaussian and Non-Gaussian Information Fusion Framework for Automated Predictive Data Analytics.","authors":"Zhiqiang Ge","doi":"10.1109/tcyb.2025.3603545","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3603545","url":null,"abstract":"To combine the strengths of Gaussian and non-Gaussian latent variable models, a novel information fusion strategy has recently been proposed under the deep learning framework. Although promising results have been obtained, the critical structure learning problem remains unsolved, which seriously hinders the automation of data-driven modeling and analytics. In this article, the maximal information coefficient (MIC) method is introduced as a measurement of the AS between two latent variables, which has no restriction in the type of data distribution. Through an assessment on the necessity of adding a new hidden layer into the deep model in each step, an evaluation index is defined for automatic determination of the required hidden layers during the model training process. For time-varying industrial production environments, reconfiguration or updating of the model structure is frequently required. In this case, automated data-driven modeling and structure learning can significantly improve the efficiency of data analytics. Based on the study results obtained from two real industrial examples, the proposed structure learning algorithm is feasible, and the automated data analytics scheme has significantly improved the online prediction performance in time-varying industrial processes.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"31 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145025759","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

Delay-Tolerance-Region Estimation for Multiarea Networked LFC of Power Systems With Multisource-Induced Delays. 电力系统多源时延多区域网络化LFC的延迟容限域估计。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-09-09 DOI: 10.1109/tcyb.2025.3602631

Zhe-Li Yuan,Chuan-Ke Zhang,Xing-Chen Shangguan,Wei Yao,Li Jin,Yong He

{"title":"Delay-Tolerance-Region Estimation for Multiarea Networked LFC of Power Systems With Multisource-Induced Delays.","authors":"Zhe-Li Yuan,Chuan-Ke Zhang,Xing-Chen Shangguan,Wei Yao,Li Jin,Yong He","doi":"10.1109/tcyb.2025.3602631","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3602631","url":null,"abstract":"The frequency stability of multiarea power systems is guaranteed by networked load frequency control (LFC). Time delays due to occasional congestions/attacks in the LFC are often much longer than those from signal transmissions during normal communication, which invalidates the previous stability assessment methods. In this article, a novel stability analysis method for this scenario via a segmented delay description and a switched system is proposed. First, a two-piecewise function is used to describe multisource-induced delays, including large delays under occasional harsh network conditions and small delays under smooth network conditions; thus, a multiarea LFC model with multisource-induced delay is established. The stability criteria, which is based on switched system theory, reveals the relationship between delay characteristics and system stability. Finally, the proposed method is used to assess the delay tolerance of the LFC in traditional/deregulated environments. The results show that even with a large delay causing instability, as long as it meets certain frequency and duration constraints, the LFC remains stable. This novel discovery reflects the essential improvements of the proposed method and is useful for designing better control strategies.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"44 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145025761","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

Toward Foundational Model for Sleep Analysis Using a Multimodal Hybrid-Self-Supervised Learning Framework. 基于多模态混合自监督学习框架的睡眠分析基础模型。

IF 10.5 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-09-09 DOI: 10.1109/TCYB.2025.3603608

Cheol-Hui Lee, Hakseung Kim, Byung Chul Yoon, Dong-Joo Kim

{"title":"Toward Foundational Model for Sleep Analysis Using a Multimodal Hybrid-Self-Supervised Learning Framework.","authors":"Cheol-Hui Lee, Hakseung Kim, Byung Chul Yoon, Dong-Joo Kim","doi":"10.1109/TCYB.2025.3603608","DOIUrl":"https://doi.org/10.1109/TCYB.2025.3603608","url":null,"abstract":"<p><p>Sleep is essential for maintaining human health and quality of life. Analyzing physiological signals during sleep is critical in assessing sleep quality and diagnosing sleep disorders. However, manual diagnoses by clinicians are time-intensive and subjective. Despite advances in deep learning that have enhanced automation, these approaches remain heavily dependent on large-scale labeled datasets. This study introduces SynthSleepNet, a multimodal hybrid-self-supervised learning (SSL) framework designed for analyzing polysomnography (PSG) data. SynthSleepNet effectively integrates masked prediction and contrastive learning to leverage complementary features across multiple modalities, including electroencephalogram (EEG), electrooculography (EOG), electromyography (EMG), and electrocardiogram (ECG). This approach enables the model to learn highly expressive representations of PSG data. Furthermore, a TCM based on Mamba was developed to efficiently capture contextual information across signals. SynthSleepNet achieved superior performance compared to state-of-the-art methods across three downstream tasks: sleep-stage classification, apnea detection, and hypopnea detection, with accuracies of 89.89%, 99.75%, and 89.60%, respectively. The model demonstrated robust performance in a semi-SSL environment with limited labels, achieving accuracies of 87.98%, 99.37%, and 77.52% in the same tasks. These results underscore the potential of the model as a foundational tool for the comprehensive analysis of PSG data. SynthSleepNet demonstrates comprehensively superior performance across multiple downstream tasks compared to other methodologies, making it expected to set a new standard for sleep disorder monitoring and diagnostic systems. The source code is available at https://github.com/dlcjfgmlnasa/SynthSleepNet.</p>","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":10.5,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145029529","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 Distributed Projection Operator-Based Unknown Input Observer for Attack Estimation and Mitigation on DC Microgrids. 基于分布式投影算子的未知输入观测器在直流微电网攻击估计与缓解中的应用。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-09-09 DOI: 10.1109/tcyb.2025.3599202

Hamidreza Shafei,Majid Farhangi,Subrata K Sarker,Li Li,Ricardo P Aguilera,Hassan Haes Alhelou

{"title":"A Distributed Projection Operator-Based Unknown Input Observer for Attack Estimation and Mitigation on DC Microgrids.","authors":"Hamidreza Shafei,Majid Farhangi,Subrata K Sarker,Li Li,Ricardo P Aguilera,Hassan Haes Alhelou","doi":"10.1109/tcyb.2025.3599202","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3599202","url":null,"abstract":"Cyberattacks on transmitted signals are the most critical threats to modern microgrid (MG) systems and should be accurately addressed to ensure safe and reliable operation. This article investigates the cybersecurity of dc MGs against FDI attacks and develops a novel model-based observer system. The proposed projection operator (PO)-based UIO is uniquely designed to detect attacks on the transmitted data from other distributed generation units. For this purpose, a bank of PO observers is developed in each distributed generation unit to estimate all neighbor units' dynamic states. Cyberattack reconstruction compares the remotely observed state values with the measured ones. Afterwards, the detected attack signal values are utilized to restore the integrity of the compromised signals, effectively mitigating the harmful effects of cyberattacks. The most important feature of this scheme, which distinguishes it from similar methods, is that there is no need for a secure channel or additional information transfer in this method. Extensive real-time numerical simulations are performed to assess the practicality and efficiency of the proposed approach when subjected to various attack scenarios, demonstrating its advantages over other methods.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"128 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145025758","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

Limited Impulsive Control of Time-Delay Multiagent Systems With Packet Loss and Parameter Mismatch 具有丢包和参数不匹配的时滞多智能体系统的有限脉冲控制

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-09-08 DOI: 10.1109/tcyb.2025.3599493

Le You, Xiaowei Jiang, Chuan-Ke Zhang, Yan-Wu Wang, Huaicheng Yan

引用次数: 0

Integrated Fault Estimation and Fault-Tolerant Tracking Control for Unmanned Surface Vessels Under Connectivity-Hybrid Cyber-Attacks 连接-混合网络攻击下无人水面舰艇综合故障估计与容错跟踪控制

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-09-08 DOI: 10.1109/tcyb.2025.3598611

Chun Liu, Liang Xu, Dezhi Xu, Xiaofan Wang, Youmin Zhang

引用次数: 0

High-Order Control Barrier Function-Based Robust Safety-Critical Control With Sampled-Data Input 基于高阶控制屏障函数的采样数据输入鲁棒安全临界控制