IEEE Transactions on Cybernetics最新文献_第5页

Event-Based Prescribed-Time Output Regulation of Uncertain Nonlinear Multiagent Systems 不确定非线性多智能体系统基于事件的规定时间输出调节

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-01-22 DOI: 10.1109/tcyb.2024.3524199

Yancheng Yan, Tieshan Li, Hongjing Liang

引用次数: 0

An Impulsive Approach to State Estimation for Multirate Singularly Perturbed Complex Networks Under Bit Rate Constraints 比特率约束下多速率奇摄动复杂网络状态估计的脉冲方法

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-01-20 DOI: 10.1109/tcyb.2024.3524515

Yuru Guo, Zidong Wang, Jun-Yi Li, Yong Xu

引用次数: 0

Multisensor Transmission Scheduling for State Estimation Over Multihop Networks in a Cyber–Physical System Environment 网络物理系统环境下多跳网络状态估计的多传感器传输调度

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-01-17 DOI: 10.1109/tcyb.2024.3524560

Mengyao Mei, Dan Ye

引用次数: 0

Kriging Surrogate Model-Based Constraint Multiobjective Particle Swarm Optimization Algorithm 基于Kriging代理模型的约束多目标粒子群优化算法

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-01-16 DOI: 10.1109/tcyb.2024.3524457

Hui Wang, Tie Cai, Witold Pedrycz

引用次数: 0

Adaptive Quantized Iterative Learning Control Using Encoding–Decoding Strategy 基于编解码策略的自适应量化迭代学习控制

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-01-16 DOI: 10.1109/tcyb.2024.3524240

Taojun Liu, Dong Shen, Jinrong Wang

引用次数: 0

Prescribed-Time Stabilization of High-Order Polynomial Time-Varying Nonlinear Systems 高阶多项式时变非线性系统的规定时间镇定

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-01-16 DOI: 10.1109/tcyb.2024.3524366

Jiao-Jiao Li, Zong-Yao Sun, Changyun Wen, Chih-Chiang Chen

引用次数: 0

Hierarchical Containment Control With Bipartite Cluster Consensus for Heterogeneous Multiagent Systems Under Layer-Signed Digraph 层签名有向图下异构多智能体系统的二部聚类一致性层次包容控制

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-01-10 DOI: 10.1109/TCYB.2024.3506986

Dazhong Ma;Jingshu Sang;Lei Liu;Zhanshan Wang

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引用次数: 0

Adaptive Event-Triggered Lag Outer Synchronization for Coupled Neural Networks With Multistate or Multiderivative Couplings 多状态或多导数耦合神经网络的自适应事件触发滞后外同步

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-01-10 DOI: 10.1109/tcyb.2024.3519171

Jin-Liang Wang, Yan-Ran Zhu, Jian-Qiao Wang, Shun-Yan Ren, Tingwen Huang

引用次数: 0

FingHV: Efficient Sharing and Fine-Grained Scheduling of Virtualized HPU Resources FingHV：虚拟化HPU资源的高效共享和细粒度调度

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-01-10 DOI: 10.1109/TCYB.2024.3518569

Hui Wang;Zhiwen Yu;Zhuoli Ren;Yao Zhang;Jiaqi Liu;Liang Wang;Bin Guo

{"title":"FingHV: Efficient Sharing and Fine-Grained Scheduling of Virtualized HPU Resources","authors":"Hui Wang;Zhiwen Yu;Zhuoli Ren;Yao Zhang;Jiaqi Liu;Liang Wang;Bin Guo","doi":"10.1109/TCYB.2024.3518569","DOIUrl":"10.1109/TCYB.2024.3518569","url":null,"abstract":"While artificial intelligence (AI) technology has advanced in real-world applications, there is a strong motivation to develop hybrid systems where AI algorithms and humans collaborate, promoting more human-centered approaches in AI system design. This has led to the emergence of a novel human-machine computing (HMC) paradigm, which combines human cognitive abilities with machine computational power to create a collaborative computing framework that meets the demands of large-scale, complex tasks and enables human-machine symbiosis. Human processing units (HPUs) are crucial computing resources in HMC-oriented systems, and efficient HPU resource provisioning is key to boosting system performance. However, existing schemes often fail to assign tasks to the most suitable HPUs and optimize HPU utility, as they either cannot quantitatively measure skills or overlook utility concerns during task assignment and scheduling. To address these challenges, this article proposes a fine-grained HPU virtualization (FingHV) approach, which leverages virtualization techniques to improve flexibility, fairness, and utility in the provisioning process. The core idea is to use a tree-based skill model to precisely measure the levels and correlations of multiple skills within individual HPUs, and to apply a mixed time/event-based scheduling policy to maximize HPU utility. Specifically, we begin by proposing a hierarchical multiskill tree to model HPU skills and their correlations. Next, we formulate the HPU virtualization problem and present a fine-grained virtualization method, which includes a quality-driven HPU assignment process and a mixed time/event-based scheduling policy to improve resource-sharing efficiency. Finally, we evaluate FingHV on a synthetic dataset with varying task sizes and a real-world case. The results demonstrate that FingHV improves global matching quality by up to 39.7% and increases HPU utility by 11.2% compared to the baselines.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 2","pages":"600-614"},"PeriodicalIF":9.4,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142961510","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

Auxiliary Task-Based Deep Reinforcement Learning for Quantum Control 量子控制中基于辅助任务的深度强化学习

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-01-07 DOI: 10.1109/TCYB.2024.3521300

Shumin Zhou;Hailan Ma;Sen Kuang;Daoyi Dong

{"title":"Auxiliary Task-Based Deep Reinforcement Learning for Quantum Control","authors":"Shumin Zhou;Hailan Ma;Sen Kuang;Daoyi Dong","doi":"10.1109/TCYB.2024.3521300","DOIUrl":"10.1109/TCYB.2024.3521300","url":null,"abstract":"Due to its property of not requiring prior knowledge of the environment, reinforcement learning (RL) has significant potential for solving quantum control problems. In this work, we investigate the effectiveness of continuous control policies based on deep deterministic policy gradient. To achieve good control of quantum systems with high fidelity, we propose an auxiliary task-based deep RL (AT-DRL) for quantum control. In particular, we design an auxiliary task to predict the fidelity value, sharing partial parameters with the main network (from the main RL task). The auxiliary task learns synchronously with the main task, allowing one to extract intrinsic features of the environment, thus aiding the agent to achieve the desired state with high fidelity. To further enhance the control performance, we also design a guided reward function based on the fidelity of quantum states that enables gradual fidelity improvement. Numerical simulations demonstrate that the proposed AT-DRL can provide a good solution to the exploration of quantum dynamics. It not only achieves high task fidelities but also demonstrates fast learning rates. Moreover, AT-DRL has great potential in designing control pulses that achieve effective quantum state preparation.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 2","pages":"712-725"},"PeriodicalIF":9.4,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142936235","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