Ieee-Caa Journal of Automatica Sinica最新文献_第7页

Automation 5.0: The Key to Systems Intelligence and Industry 5.0 自动化 5.0：系统智能和工业 5.0 的关键

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2024-07-19 DOI: 10.1109/JAS.2024.124635

Ljubo Vlacic;Hailong Huang;Mariagrazia Dotoli;Yutong Wang;Petros A. Ioannou;Lili Fan;Xingxia Wang;Raffaele Carli;Chen Lv;Lingxi Li;Xiaoxiang Na;Qing-Long Han;Fei-Yue Wang

{"title":"Automation 5.0: The Key to Systems Intelligence and Industry 5.0","authors":"Ljubo Vlacic;Hailong Huang;Mariagrazia Dotoli;Yutong Wang;Petros A. Ioannou;Lili Fan;Xingxia Wang;Raffaele Carli;Chen Lv;Lingxi Li;Xiaoxiang Na;Qing-Long Han;Fei-Yue Wang","doi":"10.1109/JAS.2024.124635","DOIUrl":"https://doi.org/10.1109/JAS.2024.124635","url":null,"abstract":"Automation has come a long way since the early days of mechanization, i.e., the process of working exclusively by hand or using animals to work with machinery. The rise of steam engines and water wheels represented the first generation of industry, which is now called Industry 1.0. Subsequently, Industry 2.0 witnessed the development of electric power and assembly lines. Later on, programmable logic controllers and Human Machine Interfaces (HMI) were the new productivity tools in Industry 3.0, which enabled precise and consistent production. In recent years, Industry 4.0 absorbed the latest technologies of Internet of Things (IoT), Artificial Intelligence (AI), and big data, making production processes integrated, interconnected, and smart. Nowadays, Industry 5.0 has been proposed, which emphasizes human-centric automation. Specifically, the new concept of automation in Industry 5.0, named Automation 5.0, is no longer about how to create machinery to replace humans. Instead, it aims to reach organic interactions and cooperation between humans and machines, meeting the goal of “6S” - Safety, Security, Sustainability, Sensitivity, Service, and Smartness [1]–[4] - and the overall objective of deploying automation for the better, human-friendly, and smarter industry.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"11 8","pages":"1723-1727"},"PeriodicalIF":15.3,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10605757","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141729900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Probabilistic Approach for Predicting Vessel Motion 预测船只运动的概率方法

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2024-07-19 DOI: 10.1109/JAS.2024.124536

Qi Hu;Jingyi Liu;Zongyu Zuo

引用次数: 0

A Novel Scalable Fault-Tolerant Control Design for DC Microgrids With Nonuniform Faults 针对具有非均匀故障的直流微电网的新型可扩展容错控制设计

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2024-07-19 DOI: 10.1109/JAS.2023.123918

Aimin Wang;Minrui Fei;Dajun Du;Yang Song

引用次数: 0

Optimal Positioning Strategy for Multi-Camera Zooming Drones 多摄像头变焦无人机的最佳定位策略

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2024-07-19 DOI: 10.1109/JAS.2024.124455

Manuel Vargas;Carlos Vivas;Teodoro Alamo

{"title":"Optimal Positioning Strategy for Multi-Camera Zooming Drones","authors":"Manuel Vargas;Carlos Vivas;Teodoro Alamo","doi":"10.1109/JAS.2024.124455","DOIUrl":"https://doi.org/10.1109/JAS.2024.124455","url":null,"abstract":"In the context of multiple-target tracking and surveillance applications, this paper investigates the challenge of determining the optimal positioning of a single autonomous aerial vehicle or agent equipped with multiple independently-steerable zooming cameras to effectively monitor a set of targets of interest. Each camera is dedicated to tracking a specific target or cluster of targets. The key innovation of this study, in comparison to existing approaches, lies in incorporating the zooming factor for the onboard cameras into the optimization problem. This enhancement offers greater flexibility during mission execution by allowing the autonomous agent to adjust the focal lengths of the on-board cameras, in exchange for varying real-world distances to the corresponding targets, thereby providing additional degrees of freedom to the optimization problem. The proposed optimization framework aims to strike a balance among various factors, including distance to the targets, verticality of viewpoints, and the required focal length for each camera. The primary focus of this paper is to establish the theoretical groundwork for addressing the non-convex nature of the optimization problem arising from these considerations. To this end, we develop an original convex approximation strategy. The paper also includes simulations of diverse scenarios, featuring varying numbers of onboard tracking cameras and target motion profiles, to validate the effectiveness of the proposed approach.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"11 8","pages":"1802-1818"},"PeriodicalIF":15.3,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141729935","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

Fixed-Time Gradient Flows for Solving Constrained Optimization: A Unified Approach 解决约束优化的固定时间梯度流：统一方法

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2024-07-19 DOI: 10.1109/JAS.2023.124089

Xinli Shi;Xiangping Xu;Guanghui Wen;Jinde Cao

{"title":"Fixed-Time Gradient Flows for Solving Constrained Optimization: A Unified Approach","authors":"Xinli Shi;Xiangping Xu;Guanghui Wen;Jinde Cao","doi":"10.1109/JAS.2023.124089","DOIUrl":"10.1109/JAS.2023.124089","url":null,"abstract":"The accelerated method in solving optimization problems has always been an absorbing topic. Based on the fixed-time (FxT) stability of nonlinear dynamical systems, we provide a unified approach for designing FxT gradient flows (FxTGFs). First, a general class of nonlinear functions in designing FxTGFs is provided. A unified method for designing first-order FxTGFs is shown under Polyak-Ljasiewicz inequality assumption, a weaker condition than strong convexity. When there exist both bounded and vanishing disturbances in the gradient flow, a specific class of nonsmooth robust FxTGFs with disturbance rejection is presented. Under the strict convexity assumption, Newton-based FxTGFs is given and further extended to solve time-varying optimization. Besides, the proposed FxTGFs are further used for solving equation-constrained optimization. Moreover, an FxT proximal gradient flow with a wide range of parameters is provided for solving nonsmooth composite optimization. To show the effectiveness of various FxTGFs, the static regret analyses for several typical FxTGFs are also provided in detail. Finally, the proposed FxTGFs are applied to solve two network problems, i.e., the network consensus problem and solving a system linear equations, respectively, from the perspective of optimization. Particularly, by choosing component-wisely sign-preserving functions, these problems can be solved in a distributed way, which extends the existing results. The accelerated convergence and robustness of the proposed FxTGFs are validated in several numerical examples stemming from practical applications.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"11 8","pages":"1849-1864"},"PeriodicalIF":15.3,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135041883","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

Nonlinear Filtering With Sample-Based Approximation Under Constrained Communication: Progress, Insights and Trends 受限通信条件下基于采样逼近的非线性滤波：进展、见解和趋势

IF 11.8 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2024-06-12 DOI: 10.1109/JAS.2023.123588

Weihao Song;Zidong Wang;Zhongkui Li;Jianan Wang;Qing-Long Han

{"title":"Nonlinear Filtering With Sample-Based Approximation Under Constrained Communication: Progress, Insights and Trends","authors":"Weihao Song;Zidong Wang;Zhongkui Li;Jianan Wang;Qing-Long Han","doi":"10.1109/JAS.2023.123588","DOIUrl":"https://doi.org/10.1109/JAS.2023.123588","url":null,"abstract":"The nonlinear filtering problem has enduringly been an active research topic in both academia and industry due to its ever-growing theoretical importance and practical significance. The main objective of nonlinear filtering is to infer the states of a nonlinear dynamical system of interest based on the available noisy measurements. In recent years, the advance of network communication technology has not only popularized the networked systems with apparent advantages in terms of installation, cost and maintenance, but also brought about a series of challenges to the design of nonlinear filtering algorithms, among which the communication constraint has been recognized as a dominating concern. In this context, a great number of investigations have been launched towards the networked nonlinear filtering problem with communication constraints, and many sample-based nonlinear filters have been developed to deal with the highly nonlinear and/or non-Gaussian scenarios. The aim of this paper is to provide a timely survey about the recent advances on the sample-based networked nonlinear filtering problem from the perspective of communication constraints. More specifically, we first review three important families of sample-based filtering methods known as the unscented Kalman filter, particle filter, and maximum correntropy filter. Then, the latest developments are surveyed with stress on the topics regarding incomplete/imperfect information, limited resources and cyber security. Finally, several challenges and open problems are highlighted to shed some lights on the possible trends of future research in this realm.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"11 7","pages":"1539-1556"},"PeriodicalIF":11.8,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141315262","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

Discovering Latent Variables for the Tasks With Confounders in Multi-Agent Reinforcement Learning 在多代理强化学习中发现具有混杂因素的任务的潜在变量

IF 11.8 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2024-06-12 DOI: 10.1109/JAS.2024.124281

Kun Jiang;Wenzhang Liu;Yuanda Wang;Lu Dong;Changyin Sun

{"title":"Discovering Latent Variables for the Tasks With Confounders in Multi-Agent Reinforcement Learning","authors":"Kun Jiang;Wenzhang Liu;Yuanda Wang;Lu Dong;Changyin Sun","doi":"10.1109/JAS.2024.124281","DOIUrl":"https://doi.org/10.1109/JAS.2024.124281","url":null,"abstract":"Efficient exploration in complex coordination tasks has been considered a challenging problem in multi-agent reinforcement learning (MARL). It is significantly more difficult for those tasks with latent variables that agents cannot directly observe. However, most of the existing latent variable discovery methods lack a clear representation of latent variables and an effective evaluation of the influence of latent variables on the agent. In this paper, we propose a new MARL algorithm based on the soft actor-critic method for complex continuous control tasks with confounders. It is called the multi-agent soft actor-critic with latent variable (MASAC-LV) algorithm, which uses variational inference theory to infer the compact latent variables representation space from a large amount of offline experience. Besides, we derive the counterfactual policy whose input has no latent variables and quantify the difference between the actual policy and the counterfactual policy via a distance function. This quantified difference is considered an intrinsic motivation that gives additional rewards based on how much the latent variable affects each agent. The proposed algorithm is evaluated on two collaboration tasks with confounders, and the experimental results demonstrate the effectiveness of MASAC-LV compared to other baseline algorithms.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"11 7","pages":"1591-1604"},"PeriodicalIF":11.8,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141315240","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

Low-Rank Optimal Transport for Robust Domain Adaptation 稳健领域适应的低库最优传输

IF 11.8 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2024-06-12 DOI: 10.1109/JAS.2024.124344

Bingrong Xu;Jianhua Yin;Cheng Lian;Yixin Su;Zhigang Zeng

{"title":"Low-Rank Optimal Transport for Robust Domain Adaptation","authors":"Bingrong Xu;Jianhua Yin;Cheng Lian;Yixin Su;Zhigang Zeng","doi":"10.1109/JAS.2024.124344","DOIUrl":"https://doi.org/10.1109/JAS.2024.124344","url":null,"abstract":"When encountering the distribution shift between the source (training) and target (test) domains, domain adaptation attempts to adjust the classifiers to be capable of dealing with different domains. Previous domain adaptation research has achieved a lot of success both in theory and practice under the assumption that all the examples in the source domain are well-labeled and of high quality. However, the methods consistently lose robustness in noisy settings where data from the source domain have corrupted labels or features which is common in reality. Therefore, robust domain adaptation has been introduced to deal with such problems. In this paper, we attempt to solve two interrelated problems with robust domain adaptation: distribution shift across domains and sample noises of the source domain. To disentangle these challenges, an optimal transport approach with low-rank constraints is applied to guide the domain adaptation model training process to avoid noisy information influence. For the domain shift problem, the optimal transport mechanism can learn the joint data representations between the source and target domains using a measurement of discrepancy and preserve the discriminative information. The rank constraint on the transport matrix can help recover the corrupted subspace structures and eliminate the noise to some extent when dealing with corrupted source data. The solution to this relaxed and regularized optimal transport framework is a convex optimization problem that can be solved using the Augmented Lagrange Multiplier method, whose convergence can be mathematically proved. The effectiveness of the proposed method is evaluated through extensive experiments on both synthetic and real-world datasets.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"11 7","pages":"1667-1680"},"PeriodicalIF":11.8,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141315253","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

Secure Tracking Control via Fixed-Time Convergent Reinforcement Learning for a UAV CPS 通过固定时间聚合强化学习实现无人机 CPS 的安全跟踪控制

IF 11.8 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2024-06-12 DOI: 10.1109/JAS.2023.124149

Zhenyu Gong;Feisheng Yang

引用次数: 0

Distributed Optimal Variational GNE Seeking in Merely Monotone Games 单纯单调博弈中的分布式最优变式 GNE 寻求

IF 11.8 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2024-06-12 DOI: 10.1109/JAS.2024.124284

Wangli He;Yanzhen Wang

引用次数: 0