Ieee-Caa Journal of Automatica Sinica最新文献_第9页

A Transfer Learning Framework for Deep Multi-Agent Reinforcement Learning 深度多代理强化学习的迁移学习框架

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2024-10-08 DOI: 10.1109/JAS.2023.124173

Yi Liu;Xiang Wu;Yuming Bo;Jiacun Wang;Lifeng Ma

引用次数: 0

Prediction-Based State Estimation and Compensation Control for Networked Systems with Communication Constraints and DoS Attacks 具有通信限制和 DoS 攻击的网络系统的基于预测的状态估计和补偿控制

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2024-10-08 DOI: 10.1109/JAS.2024.124605

Zhong-Hua Pang;Qian Cao;Haibin Guo;Zhe Dong

引用次数: 0

Image Enhancement via Associated Perturbation Removal and Texture Reconstruction Learning 通过相关扰动消除和纹理重构学习增强图像效果

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2024-10-08 DOI: 10.1109/JAS.2024.124521

Kui Jiang;Ruoxi Wang;Yi Xiao;Junjun Jiang;Xin Xu;Tao Lu

{"title":"Image Enhancement via Associated Perturbation Removal and Texture Reconstruction Learning","authors":"Kui Jiang;Ruoxi Wang;Yi Xiao;Junjun Jiang;Xin Xu;Tao Lu","doi":"10.1109/JAS.2024.124521","DOIUrl":"https://doi.org/10.1109/JAS.2024.124521","url":null,"abstract":"Degradation under challenging conditions such as rain, haze, and low light not only diminishes content visibility, but also results in additional degradation side effects, including detail occlusion and color distortion. However, current technologies have barely explored the correlation between perturbation removal and background restoration, consequently struggling to generate high-naturalness content in challenging scenarios. In this paper, we rethink the image enhancement task from the perspective of joint optimization: Perturbation removal and texture reconstruction. To this end, we advise an efficient yet effective image enhancement model, termed the perturbation-guided texture reconstruction network (PerTeRNet). It contains two sub-networks designed for the perturbation elimination and texture reconstruction tasks, respectively. To facilitate texture recovery, we develop a novel perturbation-guided texture enhancement module (PerTEM) to connect these two tasks, where informative background features are extracted from the input with the guidance of predicted perturbation priors. To alleviate the learning burden and computational cost, we suggest performing perturbation removal in a sub-space and exploiting super-resolution to infer high-frequency background details. Our PerTeRNet has demonstrated significant superiority over typical methods in both quantitative and qualitative measures, as evidenced by extensive experimental results on popular image enhancement and joint detection tasks. The source code is available at https://github.com/kuijiang94/PerTeRNet.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"11 11","pages":"2253-2269"},"PeriodicalIF":15.3,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397482","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

Revisiting the LQR Problem of Singular Systems 重新审视奇异系统的 LQR 问题

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2024-10-08 DOI: 10.1109/JAS.2024.124665

Komeil Nosrati;Juri Belikov;Aleksei Tepljakov;Eduard Petlenkov

{"title":"Revisiting the LQR Problem of Singular Systems","authors":"Komeil Nosrati;Juri Belikov;Aleksei Tepljakov;Eduard Petlenkov","doi":"10.1109/JAS.2024.124665","DOIUrl":"https://doi.org/10.1109/JAS.2024.124665","url":null,"abstract":"In the development of linear quadratic regulator (LQR) algorithms, the Riccati equation approach offers two important characteristics —it is recursive and readily meets the existence condition. However, these attributes are applicable only to transformed singular systems, and the efficiency of the regulator may be undermined if constraints are violated in nonsingular versions. To address this gap, we introduce a direct approach to the LQR problem for linear singular systems, avoiding the need for any transformations and eliminating the need for regularity assumptions. To achieve this goal, we begin by formulating a quadratic cost function to derive the LQR algorithm through a penalized and weighted regression framework and then connect it to a constrained minimization problem using the Bellman's criterion. Then, we employ a dynamic programming strategy in a backward approach within a finite horizon to develop an LQR algorithm for the original system. To accomplish this, we address the stability and convergence analysis under the reachability and observability assumptions of a hypothetical system constructed by the pencil of augmented matrices and connected using the Hamiltonian diagonalization technique.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"11 11","pages":"2236-2252"},"PeriodicalIF":15.3,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397481","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

Probabilistic Automata-Based Method for Enhancing Performance of Deep Reinforcement Learning Systems 基于概率自动机的深度强化学习系统性能提升方法

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2024-10-08 DOI: 10.1109/JAS.2024.124818

Min Yang;Guanjun Liu;Ziyuan Zhou;Jiacun Wang

{"title":"Probabilistic Automata-Based Method for Enhancing Performance of Deep Reinforcement Learning Systems","authors":"Min Yang;Guanjun Liu;Ziyuan Zhou;Jiacun Wang","doi":"10.1109/JAS.2024.124818","DOIUrl":"https://doi.org/10.1109/JAS.2024.124818","url":null,"abstract":"Deep reinforcement learning (DRL) has demonstrated significant potential in industrial manufacturing domains such as workshop scheduling and energy system management. However, due to the model's inherent uncertainty, rigorous validation is requisite for its application in real-world tasks. Specific tests may reveal inadequacies in the performance of pre-trained DRL models, while the “black-box” nature of DRL poses a challenge for testing model behavior. We propose a novel performance improvement framework based on probabilistic automata, which aims to proactively identify and correct critical vulnerabilities of DRL systems, so that the performance of DRL models in real tasks can be improved with minimal model modifications. First, a probabilistic automaton is constructed from the historical trajectory of the DRL system by abstracting the state to generate probabilistic decision-making units (PDMUs), and a reverse breadth-first search (BFS) method is used to identify the key PDMU-action pairs that have the greatest impact on adverse outcomes. This process relies only on the state-action sequence and final result of each trajectory. Then, under the key PDMU, we search for the new action that has the greatest impact on favorable results. Finally, the key PDMU, undesirable action and new action are encapsulated as monitors to guide the DRL system to obtain more favorable results through real-time monitoring and correction mechanisms. Evaluations in two standard reinforcement learning environments and three actual job scheduling scenarios confirmed the effectiveness of the method, providing certain guarantees for the deployment of DRL models in real-world applications.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"11 11","pages":"2327-2339"},"PeriodicalIF":15.3,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397369","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

Boosting Adaptive Weighted Broad Learning System for Multi-Label Learning 用于多标签学习的提升式自适应加权广义学习系统

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2024-10-08 DOI: 10.1109/JAS.2024.124557

Yuanxin Lin;Zhiwen Yu;Kaixiang Yang;Ziwei Fan;C. L. Philip Chen

引用次数: 0

A State-Migration Particle Swarm Optimizer for Adaptive Latent Factor Analysis of High-Dimensional and Incomplete Data 用于高维不完整数据自适应潜因分析的状态迁移粒子群优化器

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2024-10-08 DOI: 10.1109/JAS.2024.124575

Jiufang Chen;Kechen Liu;Xin Luo;Ye Yuan;Khaled Sedraoui;Yusuf Al-Turki;MengChu Zhou

{"title":"A State-Migration Particle Swarm Optimizer for Adaptive Latent Factor Analysis of High-Dimensional and Incomplete Data","authors":"Jiufang Chen;Kechen Liu;Xin Luo;Ye Yuan;Khaled Sedraoui;Yusuf Al-Turki;MengChu Zhou","doi":"10.1109/JAS.2024.124575","DOIUrl":"https://doi.org/10.1109/JAS.2024.124575","url":null,"abstract":"High-dimensional and incomplete (HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis (LFA) model is capable of conducting efficient representation learning to an HDI matrix, whose hyper-parameter adaptation can be implemented through a particle swarm optimizer (PSO) to meet scalable requirements. However, conventional PSO is limited by its premature issues, which leads to the accuracy loss of a resultant LFA model. To address this thorny issue, this study merges the information of each particle's state migration into its evolution process following the principle of a generalized momentum method for improving its search ability, thereby building a state-migration particle swarm optimizer (SPSO), whose theoretical convergence is rigorously proved in this study. It is then incorporated into an LFA model for implementing efficient hyper-parameter adaptation without accuracy loss. Experiments on six HDI matrices indicate that an SPSO-incorporated LFA model outperforms state-of-the-art LFA models in terms of prediction accuracy for missing data of an HDI matrix with competitive computational efficiency. Hence, SPSO's use ensures efficient and reliable hyper-parameter adaptation in an LFA model, thus ensuring practicality and accurate representation learning for HDI matrices.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"11 11","pages":"2220-2235"},"PeriodicalIF":15.3,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397487","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

Two-Stage Approach for Targeted Knowledge Transfer in Self-Knowledge Distillation 自我知识提炼中的两阶段定向知识转移法

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2024-10-08 DOI: 10.1109/JAS.2024.124629

Zimo Yin;Jian Pu;Yijie Zhou;Xiangyang Xue

{"title":"Two-Stage Approach for Targeted Knowledge Transfer in Self-Knowledge Distillation","authors":"Zimo Yin;Jian Pu;Yijie Zhou;Xiangyang Xue","doi":"10.1109/JAS.2024.124629","DOIUrl":"https://doi.org/10.1109/JAS.2024.124629","url":null,"abstract":"Knowledge distillation (KD) enhances student network generalization by transferring dark knowledge from a complex teacher network. To optimize computational expenditure and memory utilization, self-knowledge distillation (SKD) extracts dark knowledge from the model itself rather than an external teacher network. However, previous SKD methods performed distillation indiscriminately on full datasets, overlooking the analysis of representative samples. In this work, we present a novel two-stage approach to providing targeted knowledge on specific samples, named two-stage approach self-knowledge distillation (TOAST). We first soften the hard targets using class medoids generated based on logit vectors per class. Then, we iteratively distill the under-trained data with past predictions of half the batch size. The two-stage knowledge is linearly combined, efficiently enhancing model performance. Extensive experiments conducted on five backbone architectures show our method is model-agnostic and achieves the best generalization performance. Besides, TOAST is strongly compatible with existing augmentation-based regularization methods. Our method also obtains a speedup of up to 2.95x compared with a recent state-of-the-art method.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"11 11","pages":"2270-2283"},"PeriodicalIF":15.3,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397459","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

Multi-USV Formation Collision Avoidance via Deep Reinforcement Learning and COLREGs 通过深度强化学习和 COLREGs 避免多 USV 编队碰撞

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2024-10-08 DOI: 10.1109/JAS.2023.123846

Cheng-Cheng Wang;Yu-Long Wang;Li Jia

引用次数: 0

Privacy Preserving Distributed Bandit Residual Feedback Online Optimization Over Time-Varying Unbalanced Graphs 时变不平衡图上的隐私保护分布式 Bandit 残差反馈在线优化

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2024-10-08 DOI: 10.1109/JAS.2024.124656

Zhongyuan Zhao;Zhiqiang Yang;Luyao Jiang;Ju Yang;Quanbo Ge

{"title":"Privacy Preserving Distributed Bandit Residual Feedback Online Optimization Over Time-Varying Unbalanced Graphs","authors":"Zhongyuan Zhao;Zhiqiang Yang;Luyao Jiang;Ju Yang;Quanbo Ge","doi":"10.1109/JAS.2024.124656","DOIUrl":"https://doi.org/10.1109/JAS.2024.124656","url":null,"abstract":"This paper considers the distributed online optimization (DOO) problem over time-varying unbalanced networks, where gradient information is explicitly unknown. To address this issue, a privacy-preserving distributed online one-point residual feedback (OPRF) optimization algorithm is proposed. This algorithm updates decision variables by leveraging one-point residual feedback to estimate the true gradient information. It can achieve the same performance as the two-point feedback scheme while only requiring a single function value query per iteration. Additionally, it effectively eliminates the effect of time-varying unbalanced graphs by dynamically constructing row stochastic matrices. Furthermore, compared to other distributed optimization algorithms that only consider explicitly unknown cost functions, this paper also addresses the issue of privacy information leakage of nodes. Theoretical analysis demonstrate that the method attains sublinear regret while protecting the privacy information of agents. Finally, numerical experiments on distributed collaborative localization problem and federated learning confirm the effectiveness of the algorithm.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"11 11","pages":"2284-2297"},"PeriodicalIF":15.3,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397368","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