Ieee-Caa Journal of Automatica Sinica最新文献

Machine Learning-Based Prediction of Depressive Disorders via Various Data Modalities: A Survey 基于机器学习的抑郁症预测：通过各种数据模式：一项调查

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-07-02 DOI: 10.1109/JAS.2025.125393

Qiong Li;Xiaotong Liu;Xuecai Hu;Md Atiqur Rahman Ahad;Min Ren;Li Yao;Yongzhen Huang

{"title":"Machine Learning-Based Prediction of Depressive Disorders via Various Data Modalities: A Survey","authors":"Qiong Li;Xiaotong Liu;Xuecai Hu;Md Atiqur Rahman Ahad;Min Ren;Li Yao;Yongzhen Huang","doi":"10.1109/JAS.2025.125393","DOIUrl":"https://doi.org/10.1109/JAS.2025.125393","url":null,"abstract":"Depression, a pervasive mental health disorder, has substantial impacts on both individuals and society. The conventional approach to predicting depression necessitates substantial collaboration between health care professionals and patients, leaving room for the influence of subjective factors. Consequently, it is imperative to develop a more efficient and accessible prediction methodology for depression. In recent years, numerous investigations have delved into depression prediction techniques, employing diverse data modalities and yielding notable advancements. Given the rapid progression of this domain, the present article comprehensively reviews major breakthroughs in depression prediction, encompassing multiple data modalities such as electrophysiological signals, brain imaging, audiovisual data, and text. By integrating depression prediction methods from various data modalities, it offers a comparative assessment of their advantages and limitations, providing a well-rounded perspective on how different modalities can complement each other for more accurate and holistic depression prediction. The survey begins by examining commonly used datasets, evaluation metrics, and methodological frameworks. For each data modality, it systematically analyzes traditional machine learning methods alongside the increasingly prevalent deep learning approaches, providing a comparative assessment of detection frameworks, feature representations, context modeling, and training strategies. Finally, the survey culminates with the identification of prospective avenues that warrant further exploration. It provides researchers with valuable insights and practical guidance to advance the field of depression prediction.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 7","pages":"1320-1349"},"PeriodicalIF":15.3,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536529","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

Nash Bargaining Solution-Based Multi-Objective Model Predictive Control for Constrained Interactive Robots 基于Nash议价解的约束交互机器人多目标模型预测控制

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-07-02 DOI: 10.1109/JAS.2024.124398

Minglei Zhu;Jun Qi

引用次数: 0

Hybrid Event-Triggered Control with Stability Analysis 混合事件触发控制与稳定性分析

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-07-02 DOI: 10.1109/JAS.2024.125067

Ding Wang;Lingzhi Hu;Junfei Qiao

{"title":"Hybrid Event-Triggered Control with Stability Analysis","authors":"Ding Wang;Lingzhi Hu;Junfei Qiao","doi":"10.1109/JAS.2024.125067","DOIUrl":"https://doi.org/10.1109/JAS.2024.125067","url":null,"abstract":"In this paper, a novel hybrid event-triggered control (ETC) method is developed based on the online action-critic technique, which aims at tackling the optimal regulation problem of discrete-time nonlinear systems. In order to ensure the normal execution of the online learning algorithm, a stability criterion condition is created to obtain the initial admissible control policy by using an offline iterative method under the time-triggered control framework. Subsequently, a general triggering condition is designed based on the uniform ultimate boundedness of the controlled system. In order to determine a constant interval which can ensure the system stability, another triggering condition is introduced and the asymptotic stability of the closed-loop system satisfying this condition is analyzed from the perspective of the input-to-state stability. The designed online hybrid ETC method not only further improves control efficiency, but also avoids the continuous judgment of the corresponding triggering condition. In addition, the event-based control law can approach the optimal control input within a finite approximation error. Finally, two experimental examples with physical background are conducted to indicate the present results.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 7","pages":"1464-1474"},"PeriodicalIF":15.3,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536560","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

Hierarchical Secure Steering Control of In-Wheel Motor Driven Electric Vehicle Under Cyber-Physical Constraints 网络物理约束下轮毂电机驱动电动汽车分层安全转向控制

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-07-01 DOI: 10.1109/JAS.2023.124092

Zifan Gao;Dawei Zhang;Shuqian Zhu

引用次数: 0

Prescribed Performance Control of Nonlinear Systems With Unknown Sign-Switching Virtual Control Coefficients 未知符号切换虚拟控制系数非线性系统的规定性能控制

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-07-01 DOI: 10.1109/JAS.2025.125135

Jin-Zi Yang;Jin-Xi Zhang;Tianyou Chai

{"title":"Prescribed Performance Control of Nonlinear Systems With Unknown Sign-Switching Virtual Control Coefficients","authors":"Jin-Zi Yang;Jin-Xi Zhang;Tianyou Chai","doi":"10.1109/JAS.2025.125135","DOIUrl":"https://doi.org/10.1109/JAS.2025.125135","url":null,"abstract":"The problem of high-performance tracking control for the lower-triangular systems with unknown sign-switching virtual control coefficients as well as unmatched disturbances is investigated in this paper. Instead of the online estimation algorithm, the sliding mode method and the Nussbaum gain technique, a group of orientation functions are employed to handle the unknown sign-switching virtual control coefficients. The control law is combined with the orientation functions and the barrier functions lumped in a recursive manner. It achieves output tracking with the preassigned rate, overshoot, and accuracy. In contrast with the existing solutions, it is effective for the nearly model-free case, with the requirement for information of neither the system nonlinearities nor their bounding functions of the plant, nor the bounds of the disturbances. In addition, our controller exhibits significant simplicity, without parameter identification, disturbance estimation, function approximation, derivative calculation, dynamic surfaces, or command filtering. Two simulation examples are conducted to substantiate the efficacy and advantages of our approach.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 7","pages":"1381-1390"},"PeriodicalIF":15.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536553","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

Federated Experiments: Generative Causal Inference Powered by LLM-Based Agents Simulation and RAG-Based Domain Docking 联邦实验：基于llm的agent仿真和基于rag的领域对接驱动的生成因果推理

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-07-01 DOI: 10.1109/JAS.2024.124671

De-Yu Zhou;Xiao Xue;Qun Ma;Chao Guo;Li-Zhen Cui;Yong-Lin Tian;Jing Yang;Fei-Yue Wang

引用次数: 0

A Learning-Based Passive Resilient Controller for Cyber-Physical Systems: Countering Stealthy Deception Attacks and Complete Loss of Actuators Control Authority 一种基于学习的网络物理系统被动弹性控制器：对抗隐形欺骗攻击和执行器控制权限完全丧失

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-07-01 DOI: 10.1109/JAS.2024.124683

Liang Xin;Zhi-Qiang Long

{"title":"A Learning-Based Passive Resilient Controller for Cyber-Physical Systems: Countering Stealthy Deception Attacks and Complete Loss of Actuators Control Authority","authors":"Liang Xin;Zhi-Qiang Long","doi":"10.1109/JAS.2024.124683","DOIUrl":"https://doi.org/10.1109/JAS.2024.124683","url":null,"abstract":"Cyber-physical systems (CPSs) are increasingly vulnerable to cyber-attacks due to their integral connection between cyberspace and the physical world, which is augmented by Internet connectivity. This vulnerability necessitates a heightened focus on developing resilient control mechanisms for CPSs. However, current observer-based active compensation resilient controllers exhibit poor performance against stealthy deception attacks (SDAs) due to the difficulty in accurately reconstructing system states because of the stealthy nature of these attacks. Moreover, some non-active compensation approaches are insufficient when there is a complete loss of actuator control authority. To address these issues, we introduce a novel learning-based passive resilient controller (LPRC). Our approach, unlike observer-based state reconstruction, shows enhanced effectiveness in countering SDAs. We developed a safety state set, represented by an ellipsoid, to ensure CPS stability under SDA conditions, maintaining system trajectories within this set. Additionally, by employing deep reinforcement learning (DRL), the LPRC acquires the capacity to adapt and diverse evolving attack strategies. To empirically substantiate our methodology, various attack methods were compared with current passive and active compensation resilient control methods to evaluate their performance.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 7","pages":"1368-1380"},"PeriodicalIF":15.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536562","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

DoS Attack Schedules for Remote State Estimation in CPSs with Two-Hop Relay Networks Under Round-Robin Protocol 轮循协议下两跳中继网络cps远程状态估计的DoS攻击调度

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-07-01 DOI: 10.1109/JAS.2024.124755

Shuo Zhang;Lei Miao;Xudong Zhao

引用次数: 0

A Robust Large-Scale Multiagent Deep Reinforcement Learning Method for Coordinated Automatic Generation Control of Integrated Energy Systems in a Performance-Based Frequency Regulation Market 基于性能的频率调节市场中集成能源系统协调自动发电控制的鲁棒大规模多智能体深度强化学习方法

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-07-01 DOI: 10.1109/JAS.2024.124482

Jiawen Li;Tao Zhou

{"title":"A Robust Large-Scale Multiagent Deep Reinforcement Learning Method for Coordinated Automatic Generation Control of Integrated Energy Systems in a Performance-Based Frequency Regulation Market","authors":"Jiawen Li;Tao Zhou","doi":"10.1109/JAS.2024.124482","DOIUrl":"https://doi.org/10.1109/JAS.2024.124482","url":null,"abstract":"To enhance the frequency stability and lower the regulation mileage payment of a multiarea integrated energy system (IES) that supports the power Internet of Things (IoT), this paper proposes a data-driven cooperative method for automatic generation control (AGC). The method consists of adaptive fractional-order proportional-integral (FOPI) controllers and a novel efficient integration exploration multiagent twin delayed deep deterministic policy gradient (EIE-MATD3) algorithm. The FOPI controllers are designed for each area based on the performance-based frequency regulation market mechanism. The EIE-MATD3 algorithm is used to tune the coefficients of the FOPI controllers in real time using centralized training and decentralized execution. The algorithm incorporates imitation learning and efficient integration exploration to obtain a more robust coordinated control strategy. An experiment on the four-area China Southern Grid (CSG) real-time digital system shows that the proposed method can improve the control performance and reduce the regulation mileage payment of each area in the IES.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 7","pages":"1475-1488"},"PeriodicalIF":15.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536436","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

System Identification in the Network Era: A Survey of Data Issues and Innovative Approaches 网络时代的系统识别：数据问题与创新方法综述

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-07-01 DOI: 10.1109/JAS.2024.125109

Qing-Guo Wang;Liang Zhang

{"title":"System Identification in the Network Era: A Survey of Data Issues and Innovative Approaches","authors":"Qing-Guo Wang;Liang Zhang","doi":"10.1109/JAS.2024.125109","DOIUrl":"https://doi.org/10.1109/JAS.2024.125109","url":null,"abstract":"System identification is a data-driven modeling technique that originates from the control field. It constructs models from data to mimic the behavior of dynamic systems. However, in the network era, scenarios such as sensor malfunctions, packet loss, cyber-attacks, and big data affect the quality, integrity, and security of the data. These data issues pose significant challenges to traditional system identification methods. This paper presents a comprehensive survey of the emergent challenges and advances in system identification in the network era. It explores cutting-edge methodologies to address data issues such as data loss, outliers, noise and nonlinear system identification for complex systems. To tackle the data loss, the methods based on imputation and likelihood-based inference (e.g., expectation maximization) have been employed. For outliers and noise, methods like robust regression (e.g., least median of squares, least trimmed squares) and low-rank matrix decomposition show progress in maintaining data integrity. Nonlinear system identification has advanced through kernel-based methods and neural networks, which can model complex data patterns. Finally, this paper provides valuable insights into potential directions for future research.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 7","pages":"1305-1319"},"PeriodicalIF":15.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536309","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