IEEE Transactions on Cybernetics最新文献_第2页

Toward Adaptive and Interpretable Process Monitoring: Incremental Variational Graph Attention Autoencoder With Probabilistic Inference. 面向自适应和可解释过程监控：带概率推理的增量变分图注意自编码器。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-07-23 DOI: 10.1109/tcyb.2025.3583035

Mingjie Lv,Yonggang Li,Huanzhi Gao,Bei Sun,Chunhua Yang,Weihua Gui

{"title":"Toward Adaptive and Interpretable Process Monitoring: Incremental Variational Graph Attention Autoencoder With Probabilistic Inference.","authors":"Mingjie Lv,Yonggang Li,Huanzhi Gao,Bei Sun,Chunhua Yang,Weihua Gui","doi":"10.1109/tcyb.2025.3583035","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3583035","url":null,"abstract":"Complex industrial processes exhibit typical nonstationarity due to frequently fluctuating material flows and complex control loops. This poses three challenges for trustworthy process monitoring, including data drift, coordination of old and new knowledge, and interpretability. In this study, the adaptive and interpretable process monitoring problem is formulated as an online updating strategy and the spatial topology structure representation learning process monitoring problem. An I-VGATEPi framework is proposed, which aims to effectively learn continuously from dynamically changing industrial data to make interpretable monitoring results. First, an incremental learning strategy based on the BSOM is presented, which can distinguish between real faults and time-varying changes. Once normal samples are encountered, the itself and downstream model are elegantly updated with a dynamic down-sampling replay strategy without leading to catastrophic forgetting. Subsequently, a VGATEPi is proposed, which endows interpretable spatial structural relationships through priors and effectively captures the variability of spatial latent representations suitable for nonstationary processes. Then, an incremental variational Bayesian inference is introduced to calculate the adaptive thresholds to adapt the system. In addition, an anomaly-AAGAL mechanism is provided to localize fault root causes and propagation paths. Finally, the effectiveness of the proposed method is validated through two industrial applications. The results demonstrate that the proposed method can significantly enhance the performance of process monitoring, especially for reducing the false alarm rate (FAR) in process monitoring schemes. Moreover, it offers interpretable causal relationships among faults.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"6 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144693384","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

Energy-Efficient Waypoint Tracking for Underwater Gliders: Theory and Experimental Results. 水下滑翔机的高效航路点跟踪：理论与实验结果。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-07-22 DOI: 10.1109/tcyb.2025.3578122

Anyan Jing,Jian Gao,Boxu Min,Jiarun Wang,Yimin Chen,Guang Pan,Chenguang Yang

引用次数: 0

Trajectory Tracking Control Employing Nonlinear Compensator and State Observer for Photothermal-Driven Liquid Crystal Elastomer Actuator. 基于非线性补偿和状态观测器的光热驱动液晶弹性体作动器轨迹跟踪控制。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-07-22 DOI: 10.1109/tcyb.2025.3586002

Jundong Wu,Xinyu Nie,Yawu Wang,Chun-Yi Su,Daiki Sato,Jinhua She

{"title":"Trajectory Tracking Control Employing Nonlinear Compensator and State Observer for Photothermal-Driven Liquid Crystal Elastomer Actuator.","authors":"Jundong Wu,Xinyu Nie,Yawu Wang,Chun-Yi Su,Daiki Sato,Jinhua She","doi":"10.1109/tcyb.2025.3586002","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3586002","url":null,"abstract":"The trajectory tracking control for the photothermal-driven liquid crystal elastomer (LCE) actuator presents a significant challenge due to its hysteresis nonlinear characteristic and its inherent complex deformation mechanism. To address this challenge, this article proposes a trajectory tracking control method for the LCE actuator utilizing a nonlinear compensator and a state observer. The proposed control is a multistep control, which includes temperature control from the input voltage to the LCE temperature and displacement control from the LCE temperature to the LCE displacement. In the proposed method, we design a non-Lipschitz continuous state-feedback controller to realize finite-time convergence control of the temperature. As for the displacement control, we design a state observer to estimate the change rate of the LCE displacement. Meanwhile, a nonlinear inverse compensator is designed to compensate for the hysteresis nonlinearity of the LCE dynamics, which simplifies the complex nonlinear control problem into a linear control problem. Hence, the pole placement method can be utilized to design a trajectory tracking controller to achieve the control objective. The proposed control method is validated by tracking control experiments with different target trajectories.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"107 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144684274","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

Active Learning for Object Detection With Vectorized Dual Pseudo Loss and Multiple Instance Offset Constraint. 基于向量化双伪损失和多实例偏移约束的目标检测主动学习。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-07-22 DOI: 10.1109/tcyb.2025.3584808

Jiachen Yang,Jiasai Wu,Shuai Xiao,Jiabao Wen,Qinggang Meng,Wen Lu,Xinbo Gao

{"title":"Active Learning for Object Detection With Vectorized Dual Pseudo Loss and Multiple Instance Offset Constraint.","authors":"Jiachen Yang,Jiasai Wu,Shuai Xiao,Jiabao Wen,Qinggang Meng,Wen Lu,Xinbo Gao","doi":"10.1109/tcyb.2025.3584808","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3584808","url":null,"abstract":"Existing active learning methods for object detection face challenges, such as the lack of ground truth labels for regression loss, insufficient representation of unlabeled instance samples information, and discrepancies in information quality between image-level and multiple anchor-level instances. To address these issues, we propose an active learning method for object detection with vectorized dual pseudo loss and multiple instance offset constraint. This method implements a two-stage framework. The first stage focuses on evaluating the information quality of detection images. We first pioneer a dual pseudo loss formulation that provides theoretically grounded regression loss estimation. The regression loss is calculated as the norm of the ODLV between the enhanced and original base box vector, further constrained by the cosine value of the angle between the anchor box feature and regressor parameters vector. The distance entropy from the base box feature vector to each category's feature prototype vector is used as a weighting factor for the regression and classification information quality of instance samples. Subsequently, the second stage employs diversity-driven sampling on high-information images, leveraging instance-level cosine similarity to effectively remove redundant images. The proposed method outperforms state-of-the-art active learning approaches for object detection on PASCAL VOC and MS COCO datasets. Additionally, the proposed dual pseudo regression loss robustly captures regression information quality, demonstrating its effectiveness for active learning in object detection.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"14 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144684296","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

Cooperative Fuzzy Event-Based Tracking Control of Heterogeneous Multiple Marine Vehicles With a Nonautonomous Leader. 具有非自治Leader的异构多船协同模糊事件跟踪控制。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-07-22 DOI: 10.1109/tcyb.2025.3585693

Shanling Dong,Enjun Liu,Yougang Bian,Zheng-Guang Wu,Meiqin Liu

引用次数: 0

Data-Driven Adaptive Control for Discrete-Time Linear Systems With Delayed Inputs. 时滞输入离散线性系统的数据驱动自适应控制。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-07-18 DOI: 10.1109/tcyb.2025.3582377

Ai-Guo Wu,Yuan Meng

引用次数: 0

Fixed-Time Optimal Consensus of Multiagent Systems Under Cyber-Attacks: A Hierarchical Control Approach. 网络攻击下多智能体系统的定时最优一致性：一种层次控制方法。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-07-18 DOI: 10.1109/tcyb.2025.3583368

Ruihong Li,Qintao Gan,Guoquan Ren,Huaiqin Wu,Jinde Cao

{"title":"Fixed-Time Optimal Consensus of Multiagent Systems Under Cyber-Attacks: A Hierarchical Control Approach.","authors":"Ruihong Li,Qintao Gan,Guoquan Ren,Huaiqin Wu,Jinde Cao","doi":"10.1109/tcyb.2025.3583368","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3583368","url":null,"abstract":"This article aims to address the fixed-time optimal leader-following consensus issue for unknown multiagent systems (MASs) under Denial of Service (DoS) and false data injection (FDI) attacks. A novel fixed-time stability theorem under DoS attacks is presented to simplify the stability conditions and decrease the computational complexity of the settling time. Simultaneously, the deep neural networks (DNNs) structure with the projection operator are adopted in real-time to approximate the unknown system dynamics. To achieve the optimal consensus under cyber-attacks, a hierarchical control approach is presented, which includes a reference signal generation layer and a tracking control layer. Specifically, the distributed and Luenberger-based observers are designed in the reference signal generation layer to solve the fixed-time state estimation issues of leader and followers under multiple malicious attacks, respectively. Then, the optimal control strategy based on the event-triggered mechanism (ETM) is designed in the tracking control layer to track the reference signal and minimize the cost consumption. Due to the difficulty in obtaining explicit expressions of the optimal control mechanisms, a critic-only reinforcement learning (RL)-based algorithm is presented for online learning the unknown weight within a fixed time. By rigorous proof, the developed observers can achieve the fixed-time state reconstruction and the optimal control policy can track observation states after a fixed time. Finally, simulation results about platooning control of automated vehicles are given to demonstrate the efficacy of the developed technique.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"672 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144661870","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

Game-Based Distributed Decision Optimization for Heterogeneous Multiagent Systems With Unknown Nonlinear Dynamics. 未知非线性动力学异构多智能体系统基于博弈的分布式决策优化。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-07-18 DOI: 10.1109/tcyb.2025.3581999

Hao Wang,Hao Luo,Yuchen Jiang,Shimeng Wu

{"title":"Game-Based Distributed Decision Optimization for Heterogeneous Multiagent Systems With Unknown Nonlinear Dynamics.","authors":"Hao Wang,Hao Luo,Yuchen Jiang,Shimeng Wu","doi":"10.1109/tcyb.2025.3581999","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3581999","url":null,"abstract":"This article proposes a game-based distributed decision optimization method for heterogeneous multiagent systems with unknown nonlinear dynamics. Due to the information exchange between agents in the network, the unknown nonlinear dynamics lead to the degradation of local and all-agent control performance, which causes the strategies of all agents to deviate from the Nash equilibrium under a given goal. To address this problem, an adaptive distributed algorithm is designed to seek Nash equilibrium by combining two optimization levels. Specifically, the decision layer uses a distributed consensus algorithm to achieve benefit evaluation and a gradient algorithm to generate reference signals. Then, the control layer uses the virtual reference signal from the decision layer and the neural network estimation information to design an adaptive control algorithm. The proposed method performs real-time adaptive optimization of the strategies and control performance of the decision and control layers, ensuring the successful implementation of the distributed Nash equilibrium search. The convergence of the proposed algorithm is proved in the Lyapunov sense. Finally, simulation examples demonstrate the performance and effectiveness of the proposed method.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"109 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144661868","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 Improved Performance of Inner Convex Approximation for Suboptimal Nonlinear MPC. 次优非线性MPC的内凸逼近性能改进研究。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-07-17 DOI: 10.1109/tcyb.2025.3583588

Jinxian Wu,Li Dai,Songshi Dou,Yunshan Deng,Yuanqing Xia

{"title":"Toward Improved Performance of Inner Convex Approximation for Suboptimal Nonlinear MPC.","authors":"Jinxian Wu,Li Dai,Songshi Dou,Yunshan Deng,Yuanqing Xia","doi":"10.1109/tcyb.2025.3583588","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3583588","url":null,"abstract":"Inner convex approximation is a compelling method that enables the real-time implementation of suboptimal nonlinear model predictive controls (MPCs). However, it suffers from a slow convergence rate, which prevents suboptimal MPC from achieving better performance within a specific sample time. To address this issue, we first reformulate the conventional inner convex approximation procedure as a root-finding problem for a nonlinear equation. Then, under mild assumptions, a comprehensive functional analysis is performed on the derived nonlinear equation, focusing on its continuity, differentiability, and the invertibility of the Jacobian matrix. Building on this analysis, we propose an improved algorithm that applies Broyden's method to accelerate the root-finding procedure of this derived nonlinear equation, thereby enhancing the convergence rate of the conventional inner convex approximation method. We also provide a detailed analysis of the proposed algorithm's convergence properties and computational complexity, showing that it achieves a locally superlinear convergence rate without devoting much additional computational effort. Simulation experiments are performed in an obstacle avoidance scenario, and the results are compared to the conventional inner convex approximation method to assess the effectiveness and advantages of the proposed approach.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"73 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144652873","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 Adaptive Accelerated Nash Equilibrium Seeking for Noncooperative Games: A Differentially Private Method. 非合作博弈的分布式自适应加速纳什均衡寻求：一种差分私有方法。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-07-17 DOI: 10.1109/tcyb.2025.3579593

Ruixu Hu,Wenying Xu,Li Sun,Jinde Cao

{"title":"Distributed Adaptive Accelerated Nash Equilibrium Seeking for Noncooperative Games: A Differentially Private Method.","authors":"Ruixu Hu,Wenying Xu,Li Sun,Jinde Cao","doi":"10.1109/tcyb.2025.3579593","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3579593","url":null,"abstract":"This article is concerned with a distributed algorithm for seeking the Nash equilibrium in noncooperative games with partial-decision information, which simultaneously addresses the protection of individual privacy and ensures fast algorithmic convergence. First, a differential privacy mechanism is used in the fully distributed consensus-based projected pseudo-gradient algorithm to obfuscate shared messages over the communication network and quantify the algorithm's privacy level. To achieve fast convergence, a novel relaxed inertial method is designed, consisting of two steps with independently designed parameters: 1) a relaxation step and 2) an inertia step. The adaptive inertia coefficient in the inertia step is designed based on the iteration error of the players' estimated decisions and a decaying sequence, with the only requirement being the non-negativity of its internal parameters. Compared to existing approaches, our algorithm exhibits high flexibility in parameter selection. Furthermore, we analyze the algorithm's convergence and differential privacy under both linearly decaying and fixed stepsizes within a unified framework, providing sufficient conditions that are independent of the number of players. Finally, numerical simulations validate the algorithm's potential, demonstrating significant improvements in convergence rate, accuracy, and privacy level.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"52 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144652721","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