Ieee-Caa Journal of Automatica Sinica最新文献

Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-03-04 DOI: 10.1109/JAS.2025.125117

Xiaorui Li;Xiaojuan Ban;Haoran Qiao;Zhaolin Yuan;Hong-Ning Dai;Chao Yao;Yu Guo;Mohammad S. Obaidat;George Q. Huang

{"title":"Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects","authors":"Xiaorui Li;Xiaojuan Ban;Haoran Qiao;Zhaolin Yuan;Hong-Ning Dai;Chao Yao;Yu Guo;Mohammad S. Obaidat;George Q. Huang","doi":"10.1109/JAS.2025.125117","DOIUrl":"https://doi.org/10.1109/JAS.2025.125117","url":null,"abstract":"In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network (MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with 75% middle localization F1 score.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 3","pages":"528-538"},"PeriodicalIF":15.3,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553225","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

A Diffusion Model for Traffic Data Imputation

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-03-04 DOI: 10.1109/JAS.2024.124611

Bo Lu;Qinghai Miao;Yahui Liu;Tariku Sinshaw Tamir;Hongxia Zhao;Xiqiao Zhang;Yisheng Lv;Fei-Yue Wang

{"title":"A Diffusion Model for Traffic Data Imputation","authors":"Bo Lu;Qinghai Miao;Yahui Liu;Tariku Sinshaw Tamir;Hongxia Zhao;Xiqiao Zhang;Yisheng Lv;Fei-Yue Wang","doi":"10.1109/JAS.2024.124611","DOIUrl":"https://doi.org/10.1109/JAS.2024.124611","url":null,"abstract":"Imputation of missing data has long been an important topic and an essential application for intelligent transportation systems (ITS) in the real world. As a state-of-the-art generative model, the diffusion model has proven highly successful in image generation, speech generation, time series modelling etc. and now opens a new avenue for traffic data imputation. In this paper, we propose a conditional diffusion model, called the implicit-explicit diffusion model, for traffic data imputation. This model exploits both the implicit and explicit feature of the data simultaneously. More specifically, we design two types of feature extraction modules, one to capture the implicit dependencies hidden in the raw data at multiple time scales and the other to obtain the long-term temporal dependencies of the time series. This approach not only inherits the advantages of the diffusion model for estimating missing data, but also takes into account the multi-scale correlation inherent in traffic data. To illustrate the performance of the model, extensive experiments are conducted on three real-world time series datasets using different missing rates. The experimental results demonstrate that the model improves imputation accuracy and generalization capability.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 3","pages":"606-617"},"PeriodicalIF":15.3,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553224","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

Resilient Nonlinear MPC with a Dynamic Event-Triggered Strategy Under DoS Attacks

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-03-03 DOI: 10.1109/JAS.2024.124851

Shuang Shen;Runqi Chai;Yuanqing Xia;Senchun Chai

引用次数: 0

Spiking Reinforcement Learning Enhanced by Bioinspired Event Source of Multi-Dendrite Spiking Neuron and Dynamic Thresholds

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-03-03 DOI: 10.1109/JAS.2024.124551

Xingyue Liang;Qiaoyun Wu;Yun Zhou;Chunyu Tan;Hongfu Yin;Changyin Sun

{"title":"Spiking Reinforcement Learning Enhanced by Bioinspired Event Source of Multi-Dendrite Spiking Neuron and Dynamic Thresholds","authors":"Xingyue Liang;Qiaoyun Wu;Yun Zhou;Chunyu Tan;Hongfu Yin;Changyin Sun","doi":"10.1109/JAS.2024.124551","DOIUrl":"https://doi.org/10.1109/JAS.2024.124551","url":null,"abstract":"Deep reinforcement learning (DRL) achieves success through the representational capabilities of deep neural networks (DNNs). Compared to DNNs, spiking neural networks (SNNs), known for their binary spike information processing, exhibit more biological characteristics. However, the challenge of using SNNs to simulate more biologically characteristic neuronal dynamics to optimize decision-making tasks remains, directly related to the information integration and transmission in SNNs. Inspired by the advanced computational power of dendrites in biological neurons, we propose a multi-dendrite spiking neuron (MDSN) model based on Multi-compartment spiking neurons (MCN), expanding dendrite types from two to multiple and deriving the analytical solution of somatic membrane potential. We apply the MDSN to deep distributional reinforcement learning to enhance its performance in executing complex decision-making tasks. The proposed model can effectively and adaptively integrate and transmit meaningful information from different sources. Our model uses a bioinspired event-enhanced dendrite structure to emphasize features. Meanwhile, by utilizing dynamic membrane potential thresholds, it adaptively maintains the homeostasis of MDSN. Extensive experiments on Atari games show that the proposed model outperforms some state-of-the-art spiking distributional RL models by a significant margin.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 3","pages":"618-629"},"PeriodicalIF":15.3,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535451","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

Parallel Seeds: From Foundation Models to Foundation Intelligence for Agricultural Sustainability

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-03-03 DOI: 10.1109/JAS.2024.124914

Laiyi Fu;Shunkang Ling;Danyang Wu;Mengzhen Kang;Fei-Yue Wang;Hequan Sun

引用次数: 0

Consensus Control Strategy for the Treatment of Tumour with Neuroadaptive Cellular Immunotherapy

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-03-03 DOI: 10.1109/JAS.2024.124941

Jiayue Sun;Dongni Li;Huaguang Zhang;Lu Liu;Wenyue Zhao

{"title":"Consensus Control Strategy for the Treatment of Tumour with Neuroadaptive Cellular Immunotherapy","authors":"Jiayue Sun;Dongni Li;Huaguang Zhang;Lu Liu;Wenyue Zhao","doi":"10.1109/JAS.2024.124941","DOIUrl":"https://doi.org/10.1109/JAS.2024.124941","url":null,"abstract":"This paper presents a novel neuro-adaptive cellular immunotherapy control strategy that leverages the high efficiency and applicability of chimeric antigen receptor-engineered T (CAR-T) cells in treating cancer. The proposed real-time control strategy aims to maximize tumor regression while ensuring the safety of the treatment. A dynamic growth model of cancer cells under the influence of cellular immunotherapy is established for the first time, which aligns with clinical experimental results. Utilizing the backstepping method, a novel consensus reference model is designed to consider the characteristics of cancer cell changes during the treatment process and conform to clinical rules. The model is segmented and continuous, with cancer cells expected to decrease in a step-like manner. Furthermore, a prescribed performance mechanism is constructed to maintain the therapeutic effect of the proposed scheme while ensuring the transient performance of the system. Through the analysis of Lyapunov stability, all signals within the closed-loop system are proven to be semiglobally uniformly ultimately bounded (SGUUB). Simulation results demonstrate the effectiveness of the proposed control strategy, highlighting its potential for clinical application in cancer treatment.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 3","pages":"575-584"},"PeriodicalIF":15.3,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535489","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

A Verification Theorem for Feedback Nash Equilibrium in Multiple-Player Nonzero-Sum Impulse Game

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-03-03 DOI: 10.1109/JAS.2024.124752

Ruihai Li;Yaoyao Tan;Xiaojie Su;Jiangshuai Huang

引用次数: 0

When Embodied AI Meets Industry 5.0: Human-Centered Smart Manufacturing

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-03-03 DOI: 10.1109/JAS.2025.125327

Jing Xu;Qiyu Sun;Qing-Long Han;Yang Tang

{"title":"When Embodied AI Meets Industry 5.0: Human-Centered Smart Manufacturing","authors":"Jing Xu;Qiyu Sun;Qing-Long Han;Yang Tang","doi":"10.1109/JAS.2025.125327","DOIUrl":"https://doi.org/10.1109/JAS.2025.125327","url":null,"abstract":"As embodied intelligence (EI), large language models (LLMs), and cloud computing continue to advance, Industry 5.0 facilitates the development of industrial artificial intelligence (IndAI) through cyber-physical-social systems (CPSSs) with a human-centric focus. These technologies are organized by the system-wide approach of Industry 5.0, in order to empower the manufacturing industry to achieve broader societal goals of job creation, economic growth, and green production. This survey first provides a general framework of smart manufacturing in the context of Industry 5.0. Wherein, the embodied agents, like robots, sensors, and actuators, are the carriers for IndAI, facilitating the development of the self-learning intelligence in individual entities, the collaborative intelligence in production lines and factories (smart systems), and the swarm intelligence within industrial clusters (systems of smart systems). Through the framework of CPSSs, the key technologies and their possible applications for supporting the single-agent, multi-agent and swarm-agent embodied IndAI have been reviewed, such as the embodied perception, interaction, scheduling, multi-mode large language models, and collaborative training. Finally, to stimulate future research in this area, the open challenges and opportunities of applying Industry 5.0 to smart manufacturing are identified and discussed. The perspective of Industry 5.0-driven manufacturing industry aims to enhance operational productivity and efficiency by seamlessly integrating the virtual and physical worlds in a human-centered manner, thereby fostering an intelligent, sustainable, and resilient industrial landscape.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 3","pages":"485-501"},"PeriodicalIF":15.3,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535556","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

Cyber-Attacks with Resource Constraints on Discrete Event Systems Under Supervisory Control

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-03-03 DOI: 10.1109/JAS.2024.124596

Zhaoyang He;Naiqi Wu;Rong Su;Zhiwu Li

{"title":"Cyber-Attacks with Resource Constraints on Discrete Event Systems Under Supervisory Control","authors":"Zhaoyang He;Naiqi Wu;Rong Su;Zhiwu Li","doi":"10.1109/JAS.2024.124596","DOIUrl":"https://doi.org/10.1109/JAS.2024.124596","url":null,"abstract":"With the development of cyber-physical systems, system security faces more risks from cyber-attacks. In this work, we study the problem that an external attacker implements covert sensor and actuator attacks with resource constraints (the total resource consumption of the attacks is not greater than a given initial resource of the attacker) to mislead a discrete event system under supervisory control to reach unsafe states. We consider that the attacker can implement two types of attacks: One by modifying the sensor readings observed by a supervisor and the other by enabling the actuator commands disabled by the supervisor. Each attack has its corresponding resource consumption and remains covert. To solve this problem, we first introduce a notion of combined-attackability to determine whether a closed-loop system may reach an unsafe state after receiving attacks with resource constraints. We develop an algorithm to construct a corrupted supervisor under attacks, provide a verification method for combined-attackability in polynomial time based on a plant, a corrupted supervisor, and an attacker's initial resource, and propose a corresponding attack synthesis algorithm. The effectiveness of the proposed method is illustrated by an example.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 3","pages":"585-595"},"PeriodicalIF":15.3,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535445","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 Control for Unstable Networked Plants in the Presence of Actuator and Sensor Limitations Using Robust Right Coprime Factorization

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-03-03 DOI: 10.1109/JAS.2024.124854

Yuanhong Xu;Mingcong Deng

{"title":"Nonlinear Control for Unstable Networked Plants in the Presence of Actuator and Sensor Limitations Using Robust Right Coprime Factorization","authors":"Yuanhong Xu;Mingcong Deng","doi":"10.1109/JAS.2024.124854","DOIUrl":"https://doi.org/10.1109/JAS.2024.124854","url":null,"abstract":"In this paper, a nonlinear control approach for an unstable networked plant in the presence of actuator and sensor limitations using robust right coprime factorization is proposed. The actuator is limited by upper and lower constraints and the sensor in the feedback loop is subjected to network-induced unknown time-varying delay and noise. With this nonlinear control method, we first employ right coprime factorization based on isomorphism and operator theory to factorize the plant, so that bounded input bounded output (BIBO) stability can be guaranteed. Next, continuous-time generalized predictive control (CGPC) is utilized for the unstable operator of the right coprime factorized plant to guarantee inner stability and enables the closed-loop dynamics of the system with predictive characteristics. Meanwhile, a second-DoF (degrees of freedom) switched controller that satisfies a perturbed Bezout identity and a robustness condition is designed. By using the CGPC controller that possesses predictive behavior and the second-DoF switched stabilizer, the overall stability of the plant subjected to actuator limitations is guaranteed. To address sensor limitations that exist in networked plants in the form of delay and noise which often cause system performance degradation, we implement an identity operator definition in the feedback loop to compensate for these adverse effects. Further, a pre-operator is designed to ensure that the plant output tracks the reference input. Finally, the effectiveness of the proposed design scheme is demonstrated by simulations.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 3","pages":"516-527"},"PeriodicalIF":15.3,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535454","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