IEEE Transactions on Cybernetics最新文献_第4页

T3DNet: Compressing Point Cloud Models for Lightweight 3-D Recognition T3DNet：压缩点云模型，实现轻量级三维识别

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2024-11-21 DOI: 10.1109/tcyb.2024.3487220

Zhiyuan Yang, Yunjiao Zhou, Lihua Xie, Jianfei Yang

引用次数: 0

Aeroengine Bearing Time-Varying Skidding Assessment With Prior Knowledge-Embedded Dual Feedback Spatial-Temporal GCN 利用先验知识嵌入式时空双反馈 GCN 评估航空发动机轴承时变防滑性能

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2024-11-20 DOI: 10.1109/tcyb.2024.3491634

Leiming Ma, Bin Jiang, Ningyun Lu, Qintao Guo, Zhisheng Ye

引用次数: 0

Visual-Inertial-Acoustic Sensor Fusion for Accurate Autonomous Localization of Underwater Vehicles 视觉-惯性-声学传感器融合用于水下航行器的精确自主定位

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2024-11-20 DOI: 10.1109/tcyb.2024.3488077

Yupei Huang, Peng Li, Shaoxuan Ma, Shuaizheng Yan, Min Tan, Junzhi Yu, Zhengxing Wu

引用次数: 0

Interval Secure Event-Triggered Mechanism for Load Frequency Control Active Defense Against DoS Attack 用于负载频率控制的间隔安全事件触发机制主动防御 DoS 攻击

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2024-11-19 DOI: 10.1109/tcyb.2024.3488208

Zihao Cheng, Songlin Hu, Dong Yue, Xuhui Bu, Xiaolong Ruan, Chenggang Xu

引用次数: 0

Bayesian Transfer Filtering Using Pseudo Marginal Measurement Likelihood 使用伪边际测量概率的贝叶斯转移滤波法

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2024-11-18 DOI: 10.1109/tcyb.2024.3490580

Shunyi Zhao, Tianyu Zhang, Yuriy S. Shmaliy, Xiaoli Luan, Fei Liu

引用次数: 0

Collaborative Multimodal Fusion Network for Multiagent Perception 用于多代理感知的协作式多模态融合网络

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2024-11-18 DOI: 10.1109/TCYB.2024.3491756

Lei Zhang;Binglu Wang;Yongqiang Zhao;Yuan Yuan;Tianfei Zhou;Zhijun Li

{"title":"Collaborative Multimodal Fusion Network for Multiagent Perception","authors":"Lei Zhang;Binglu Wang;Yongqiang Zhao;Yuan Yuan;Tianfei Zhou;Zhijun Li","doi":"10.1109/TCYB.2024.3491756","DOIUrl":"10.1109/TCYB.2024.3491756","url":null,"abstract":"With the increasing popularity of autonomous driving systems and their applications in complex transportation scenarios, collaborative perception among multiple intelligent agents has become an important research direction. Existing single-agent multimodal fusion approaches are limited by their inability to leverage additional sensory data from nearby agents. In this article, we present the collaborative multimodal fusion network (CMMFNet) for distributed perception in multiagent systems. CMMFNet first extracts modality-specific features from LiDAR point clouds and camera images for each agent using dual-stream neural networks. To overcome the ambiguity in-depth prediction, we introduce a collaborative depth supervision module that projects dense fused point clouds onto image planes to generate more accurate depth ground truths. We then present modality-aware fusion strategies to aggregate homogeneous features across agents while preserving their distinctive properties. To align heterogeneous LiDAR and camera features, we introduce a modality consistency learning method. Finally, a transformer-based fusion module dynamically captures cross-modal correlations to produce a unified representation. Comprehensive evaluations on two extensive multiagent perception datasets, OPV2V and V2XSet, affirm the superiority of CMMFNet in detection performance, establishing a new benchmark in the field.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 1","pages":"486-498"},"PeriodicalIF":9.4,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142670652","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

Granular Computing for Machine Learning: Pursuing New Development Horizons 机器学习的粒度计算：追求新的发展视野

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2024-11-18 DOI: 10.1109/TCYB.2024.3487934

Witold Pedrycz

{"title":"Granular Computing for Machine Learning: Pursuing New Development Horizons","authors":"Witold Pedrycz","doi":"10.1109/TCYB.2024.3487934","DOIUrl":"10.1109/TCYB.2024.3487934","url":null,"abstract":"Undoubtedly, machine learning (ML) has demonstrated a wealth of far-reaching successes present both at the level of fundamental developments, design methodologies and numerous application areas, quite often encountered in domains requiring a high level of autonomous behavior. Over the passage of time, there are growing challenges of privacy and security, interpretability, explainability, confidence (credibility), and computational sustainability, among others. In this study, we advocate that these quests could be addressed by casting them both conceptually and algorithmically in the unified environment augmented by the principles of granular computing. It is demonstrated that the level of abstraction, delivered by granular computing plays a pivotal role in the interpretation by quantifying the level of credibility of ML constructs. The study also highlights the principles of granular computing and elaborates on its landscape. The original idea of a comprehensive and unified framework of data-knowledge environment of ML is introduced along with a detailed discussion on how data and knowledge are used in a seamless fashion by invoking granular embedding and producing relevant loss functions. Key categories of knowledge-data integration realized at the levels of data and model (involving symbolic/qualitative models and physics-oriented models) and investigated.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 1","pages":"460-471"},"PeriodicalIF":9.4,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142670651","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 Secure Control for Nonlinear Descriptor Multiagent Systems With Unknown Inputs Under Denial-of-Service Attacks 拒绝服务攻击下具有未知输入的非线性描述符多代理系统的分布式安全控制

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2024-11-18 DOI: 10.1109/TCYB.2024.3486562

Tianbiao Shi;Fanglai Zhu

{"title":"Distributed Secure Control for Nonlinear Descriptor Multiagent Systems With Unknown Inputs Under Denial-of-Service Attacks","authors":"Tianbiao Shi;Fanglai Zhu","doi":"10.1109/TCYB.2024.3486562","DOIUrl":"10.1109/TCYB.2024.3486562","url":null,"abstract":"This article investigates the secure control problem for a class of Lipschitz nonlinear descriptor multiagent systems (MASs) with unknown inputs under Denial-of-Service (DoS) attacks. In order to address the presence of unknown state variables and external disturbances in both the state and output equations, a local unknown input observer (UIO) is developed for each follower agent. The proposed UIO is capable of simultaneously estimating the system state, measurement noise and unknown inputs through an interval observer. With regards to DoS attacks, we consider two types: those that maintain connectivity and those that paralyze it by disrupting the structure of the information communication topology graph. By utilizing the proposed UIO, a distributed compensation controller is designed to achieve asymptotic consensus for leader-following MASs under DoS attacks. Additionally, a comprehensive stability analysis of the closed-loop system is provided, taking into account switching systems. Finally, two simulation examples are presented to validate the effectiveness of the proposed UIO-based distributed secure control scheme.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 1","pages":"472-485"},"PeriodicalIF":9.4,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142670657","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

Safe Reinforcement Learning: Optimal Formation Control With Collision Avoidance of Multiple Satellite Systems 安全强化学习：避免多卫星系统碰撞的最佳编队控制

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2024-11-18 DOI: 10.1109/TCYB.2024.3491582

Hui Yu;Liqian Dou;Xiuyun Zhang;Jinna Li;Qun Zong

引用次数: 0

A Hierarchical Surrogate-Assisted Differential Evolution With Core Space Localization 具有核心空间定位功能的分层代理辅助差分进化论

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2024-11-18 DOI: 10.1109/tcyb.2024.3489885

Laiqi Yu, Zhenyu Meng, Haibin Zhu

引用次数: 0