IEEE Transactions on Cybernetics最新文献_第4页

Self-Supervised Temperature Representation Learning for Fever Screening 用于发热筛查的自监督温度表征学习

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-05-28 DOI: 10.1109/tcyb.2025.3571015

Mengkai Yan, Jianjun Qian, Hang Shao, Lei Luo, Jian Yang

引用次数: 0

Secure Tracking Control of Cyber-Physical Systems Against Hybrid Attacks via FAS Terminal Sliding-Mode Predictive Control 基于FAS终端滑模预测控制的信息物理系统抗混合攻击安全跟踪控制

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-05-28 DOI: 10.1109/tcyb.2025.3569664

Da-Wei Zhang, Guo-Ping Liu

引用次数: 0

Adaptive Neural Network-Based Asynchronous Control for Switching Cyber–Physical Systems With Unknown Dead Zone 基于自适应神经网络的未知死区交换信息物理系统异步控制

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-05-26 DOI: 10.1109/tcyb.2025.3569806

Jun Cheng, Junhui Wu, Huaicheng Yan, Dan Zhang, Zheng-Guang Wu, Ying Zhai

引用次数: 0

Resilience Distributed MPC for Dynamically Coupled Multiple Cyber–Physical Systems Subject to Severe Attacks 受严重攻击的动态耦合多网络物理系统的弹性分布式MPC

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-05-26 DOI: 10.1109/tcyb.2025.3569377

Huan Yang, Li Dai, Yaling Ma, Zhiwen Qiang, Yuanqing Xia, Guo-Ping Liu

引用次数: 0

Low-Complexity Distributed Prescribed Performance Control of Unknown Nonlinear Multiagent Systems Under Switching Topologies 切换拓扑下未知非线性多智能体系统的低复杂度分布式预定性能控制

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-05-26 DOI: 10.1109/tcyb.2025.3569418

Hai-Xiu Xie, Jin-Xi Zhang, Tianyou Chai

引用次数: 0

An Interpretable Quantum Adjoint Convolutional Layer for Image Classification. 一种用于图像分类的可解释量子伴随卷积层。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-05-22 DOI: 10.1109/tcyb.2025.3567090

Shi Wang,Mengyi Wang,Ren-Xin Zhao,Licheng Liu,Yaonan Wang

{"title":"An Interpretable Quantum Adjoint Convolutional Layer for Image Classification.","authors":"Shi Wang,Mengyi Wang,Ren-Xin Zhao,Licheng Liu,Yaonan Wang","doi":"10.1109/tcyb.2025.3567090","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3567090","url":null,"abstract":"The interpretability of quantum machine learning (QML) refers to the capability to provide clear and understandable explanations for the predictions and decision-making processes of QML models. However, most quantum convolutional layers (QCLs) utilize closed-box structures that are inherently devoid of interpretability, leading to the opacity of principles and the suboptimal mapping of classical data. This significantly undermines the reliability of QML models. In addition, most of the current QML interpretability focuses on post hoc interpretability seriously neglecting the importance of exploring intrinsic causes. To tackle these challenges, we introduce the quantum adjoint convolution operation (QACO). It is an intrinsic interpretability scheme based on quantum evolution, as its quantum mapping precisely corresponds to the position and pixel values of the image and its principle is equivalent to the Frobenius inner product (FIP). Furthermore, we extend the QACO concept into the quantum adjoint convolutional layer (QACL) by integrating the quantum phase estimation (QPE) algorithm, enabling the parallel computation of all FIPs. Experimental results on PennyLane and TensorFlow platforms demonstrate that our method achieves a 6.3%, 3.4%, and 2.9% higher average test accuracy on Fashion MNIST, MNIST, and DermaMNIST datasets compared to classical and uninterpretable quantum counterparts, respectively, while maintaining 73.3% noise-robust accuracy under Gaussian noise, showcasing its superior generalizability and resilience in practical scenarios.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"31 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144122165","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

Robust Multiple Flat Projections Clustering With Truncated Distance Maximization Constraints. 截断距离最大化约束下的鲁棒多平面投影聚类。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-05-20 DOI: 10.1109/tcyb.2025.3553033

Jie Yang,Zhao Zhang,Xiaobo Chen,Zhongqi Xu,Liyong Fu,Qiaolin Ye

{"title":"Robust Multiple Flat Projections Clustering With Truncated Distance Maximization Constraints.","authors":"Jie Yang,Zhao Zhang,Xiaobo Chen,Zhongqi Xu,Liyong Fu,Qiaolin Ye","doi":"10.1109/tcyb.2025.3553033","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3553033","url":null,"abstract":"Recently, interest in flat-type projection clustering methods has grown as they improve learner's performance by exploring multiple projection subspaces. However, solvers used in previous representative works predominantly rely on greedy search strategies, which incur high computational costs and fail to consider interdependencies between projections. Moreover, these methods do not simultaneously guarantee the effective suppression of outliers and noisy data at cluster boundaries, ultimately compromising data discrimination. To address these limitations and discover a more effective subspace for each flat, we propose robust multiple flat projections clustering (RMFPC). This method computes within-and between-cluster distances using the L2,1-norm to enhance robustness against outliers. Furthermore, we propose a truncated distance maximization constraint (TDMC) to eliminate the influence of noisy data on cluster separability. The resulting objective is presented in a ratio form, which is not trivial. We provide a novel formulation to achieve a theoretically equivalent problem. Based on this reformulation, we develop an efficient non-greedy solution algorithm. In addition, a cluster center optimization mechanism is incorporated into the solution process to accurately estimate the distribution of each cluster center. The convergence analysis and proof of the proposed algorithm are provided. Experiments on both toy and real-world datasets demonstrate the effectiveness of the proposed method.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"62 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144103881","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

Resilient Collision-Free Distributed Optimal Coordination for Multiple Euler-Lagrangian Systems Under Unreliable Communication Topologies. 不可靠通信拓扑下多欧拉-拉格朗日系统弹性无碰撞分布式最优协调。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-05-20 DOI: 10.1109/tcyb.2025.3566155

Jia-Yuan Yin,Guang-Hong Yang,Huimin Wang

引用次数: 0

A Weight-Aware-Based Multisource Unsupervised Domain Adaptation Method for Human Motion Intention Recognition 基于权重感知的多源无监督域自适应人体运动意图识别方法

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-05-20 DOI: 10.1109/tcyb.2025.3565754

Xiao-Yin Liu, Guotao Li, Xiao-Hu Zhou, Xu Liang, Zeng-Guang Hou

引用次数: 0

Deep Reinforcement Learning for Wireless Scheduling in Distributed Networked Control 分布式网络控制中无线调度的深度强化学习

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-05-20 DOI: 10.1109/tcyb.2025.3566112

Gaoyang Pang, Kang Huang, Daniel E. Quevedo, Branka Vucetic, Yonghui Li, Wanchun Liu

引用次数: 0