Pub Date : 2026-02-09DOI: 10.1109/tase.2026.3659154
Xiao Jin, Yongxiong Wang, Shuai Huang, Nan Zhang, Han Chen, Hui Yang, Yiming Li
{"title":"OpenVL: Bridging 2D and 3D Worlds for Open-Vocabulary 3D Scene Understanding","authors":"Xiao Jin, Yongxiong Wang, Shuai Huang, Nan Zhang, Han Chen, Hui Yang, Yiming Li","doi":"10.1109/tase.2026.3659154","DOIUrl":"https://doi.org/10.1109/tase.2026.3659154","url":null,"abstract":"","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"25 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-09DOI: 10.1109/tase.2026.3662783
Zeli Zhao, Jinliang Ding, Jin-Xi Zhang, Tao Yang, Yang Shi
{"title":"Fully Distributed Sub-Optimal Coordination for Nonlinear Multi-Agent Systems","authors":"Zeli Zhao, Jinliang Ding, Jin-Xi Zhang, Tao Yang, Yang Shi","doi":"10.1109/tase.2026.3662783","DOIUrl":"https://doi.org/10.1109/tase.2026.3662783","url":null,"abstract":"","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"97 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146160389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-09DOI: 10.1109/tase.2026.3662198
Dingxin He, Haoping Wang, Yang Tian, Darwin G. Caldwell, Jesús Ortiz
{"title":"MIMO Model-Free Adaptive Practical Prescribed Performance Control for Mechatronic Systems With Mismatched Disturbance and Quantized Input","authors":"Dingxin He, Haoping Wang, Yang Tian, Darwin G. Caldwell, Jesús Ortiz","doi":"10.1109/tase.2026.3662198","DOIUrl":"https://doi.org/10.1109/tase.2026.3662198","url":null,"abstract":"","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"98 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146160388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-06DOI: 10.1109/tase.2026.3661045
Jiangming Xu, Xiang Zhang, Jun Cheng, Ruonan Liu, Weidong Zhang
{"title":"GA-Assisted Event-Triggered Fault Detection for Networked Systems Under DoS Attacks","authors":"Jiangming Xu, Xiang Zhang, Jun Cheng, Ruonan Liu, Weidong Zhang","doi":"10.1109/tase.2026.3661045","DOIUrl":"https://doi.org/10.1109/tase.2026.3661045","url":null,"abstract":"","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"35 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-06DOI: 10.1109/TASE.2026.3656787
Mohamed Zaery;Syed Muhammad Amrr;Abdullah Abushokor;S. M. Suhail Hussain;Mujahed Al-Dhaifallah;Leonid Fridman;Mohammad A. Abido
This paper proposes a robust distributed secondary control strategy for AC microgrids (MGs) that ensures voltage and frequency regulation within a predefined time limit, while effectively mitigating external disturbances. The proposed composite controller integrates the predefined time convergence approach with a fixed-time integral sliding mode control (ISMC) design. The ISMC enhances disturbance rejection, while the predefined time technique guarantees that all system trajectories reach their desired values within a user-specified timeframe, independent of initial conditions. This ensures accurate regulation of distributed generators’ voltages and frequencies, along with optimal active power sharing and equalized reactive power allocation. Theoretical analysis based on Lyapunov stability confirms the convergence and robustness of the proposed scheme. Multiple simulation and hardware-in-the-loop case studies validate the superior performance of the proposed method over existing time-based controllers, achieving up to 66% lower voltage ITSE and 91% lower frequency ITAE. This confirms its fast restoration capability and strong disturbance rejection across diverse operating conditions. Note to Practitioners—With the increasing penetration of renewable energy sources such as solar and wind, ensuring fast, reliable, and decentralized control in AC MGs has become essential for maintaining stability under uncertain and fluctuating operating conditions. The proposed strategy enables engineers to explicitly define the system’s response time, irrespective of initial conditions, while ensuring precise voltage and frequency regulation and maintaining optimal active and proportional reactive power sharing among multiple generators. This capability is particularly beneficial for real-time operation in isolated MGs, renewable-dominated systems, and mission-critical energy infrastructures. Additionally, the method is designed to remain robust under external disturbances, communication delays, and system noise, without requiring complex tuning or frequent recalibration. These attributes make the proposed controller a practical and effective solution for improving responsiveness, stability, and operational reliability in modern MG applications.
{"title":"Robust Predefined-Time Frequency and Voltage Control for AC Microgrid Under Disturbances","authors":"Mohamed Zaery;Syed Muhammad Amrr;Abdullah Abushokor;S. M. Suhail Hussain;Mujahed Al-Dhaifallah;Leonid Fridman;Mohammad A. Abido","doi":"10.1109/TASE.2026.3656787","DOIUrl":"10.1109/TASE.2026.3656787","url":null,"abstract":"This paper proposes a robust distributed secondary control strategy for AC microgrids (MGs) that ensures voltage and frequency regulation within a predefined time limit, while effectively mitigating external disturbances. The proposed composite controller integrates the predefined time convergence approach with a fixed-time integral sliding mode control (ISMC) design. The ISMC enhances disturbance rejection, while the predefined time technique guarantees that all system trajectories reach their desired values within a user-specified timeframe, independent of initial conditions. This ensures accurate regulation of distributed generators’ voltages and frequencies, along with optimal active power sharing and equalized reactive power allocation. Theoretical analysis based on Lyapunov stability confirms the convergence and robustness of the proposed scheme. Multiple simulation and hardware-in-the-loop case studies validate the superior performance of the proposed method over existing time-based controllers, achieving up to 66% lower voltage ITSE and 91% lower frequency ITAE. This confirms its fast restoration capability and strong disturbance rejection across diverse operating conditions. Note to Practitioners—With the increasing penetration of renewable energy sources such as solar and wind, ensuring fast, reliable, and decentralized control in AC MGs has become essential for maintaining stability under uncertain and fluctuating operating conditions. The proposed strategy enables engineers to explicitly define the system’s response time, irrespective of initial conditions, while ensuring precise voltage and frequency regulation and maintaining optimal active and proportional reactive power sharing among multiple generators. This capability is particularly beneficial for real-time operation in isolated MGs, renewable-dominated systems, and mission-critical energy infrastructures. Additionally, the method is designed to remain robust under external disturbances, communication delays, and system noise, without requiring complex tuning or frequent recalibration. These attributes make the proposed controller a practical and effective solution for improving responsiveness, stability, and operational reliability in modern MG applications.","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"23 ","pages":"4198-4212"},"PeriodicalIF":6.4,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-06DOI: 10.1109/tase.2026.3662192
Ying Jing, Hong Zheng, Yuchuan Ji
{"title":"Prior-Guided and Gaussian Mixture-Refined Network for Industrial Anomaly Detection and Localization","authors":"Ying Jing, Hong Zheng, Yuchuan Ji","doi":"10.1109/tase.2026.3662192","DOIUrl":"https://doi.org/10.1109/tase.2026.3662192","url":null,"abstract":"","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"27 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-06DOI: 10.1109/tase.2026.3662003
Ming Sun, Yu Wang, Bo Yang, Li He, Hong Zhang
{"title":"Accurate and Robust UWB Localization with Incomplete Measurements based on Multi-Modal Diffusion Model","authors":"Ming Sun, Yu Wang, Bo Yang, Li He, Hong Zhang","doi":"10.1109/tase.2026.3662003","DOIUrl":"https://doi.org/10.1109/tase.2026.3662003","url":null,"abstract":"","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"1 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-06DOI: 10.1109/TASE.2026.3661872
Rui Liu;Cong Wu;Yifan Zhang;Haiying Song;Fei Yuan;Wei Dai;Wen Jung Li;Jun Liu
Accurate and robust cell detection in microscopic images is a fundamental yet challenging task due to diverse imaging conditions, dense cell distributions, and morphological variability. In this study, we propose FACellDet, a novel dual-branch hierarchical encoder-decoder framework that integrates adaptive feature alignment and a new supervisory signal to enhance cell detection. Specifically, a Coordinate-Attention-based Feature Alignment (CAFA) module is introduced to address spatial misalignment during multi-scale feature fusion, substantially improving cell detection precision. Furthermore, we design a Focal Attenuated Distance (FAD) map as an intermediate representation, providing highly discriminative and spatially informative cues, particularly in crowded regions. FACellDet features a dual-branch architecture, with the main branch predicting FAD maps for cell detection, while the auxiliary branch generates density maps to estimate cell counts for suppressing false detections. Extensive experiments on diverse cell types and imaging modalities from multiple public and in-house datasets demonstrate that our approach outperforms state-of-the-art methods in detection accuracy, while maintaining strong adaptability and robustness across challenging biomedical imaging scenarios. These results underscore the potential of FACellDet as an accurate and generalizable solution for automated cell detection in heterogeneous microscopic cell images, thereby facilitating reliable cell analysis to accelerate biomedical research and clinical workflows. Note to Practitioners—This work addresses the need for accurate and efficient cell detection and counting in biomedical images, where manual methods are time-consuming and error-prone, and existing automated approaches often struggle with dense or diverse cells. FACellDet offers a practical deep learning solution adaptable to various cell types and imaging conditions, improving both detection accuracy and robustness through innovative feature alignment and enhanced supervisory signals. This system can streamline laboratory workflows and support high-throughput research and clinical diagnostics. While FACellDet demonstrates strong performance across challenging datasets, its current deployment requires adequate computational resources. Future development could focus on creating lightweight versions and integrating the framework with automated imaging systems, further broadening its accessibility and impact in routine biomedical practice.
{"title":"Feature-Aligned Cell Detection for Heterogeneous Microscopic Images With Focal Attenuated Distance Transform","authors":"Rui Liu;Cong Wu;Yifan Zhang;Haiying Song;Fei Yuan;Wei Dai;Wen Jung Li;Jun Liu","doi":"10.1109/TASE.2026.3661872","DOIUrl":"10.1109/TASE.2026.3661872","url":null,"abstract":"Accurate and robust cell detection in microscopic images is a fundamental yet challenging task due to diverse imaging conditions, dense cell distributions, and morphological variability. In this study, we propose FACellDet, a novel dual-branch hierarchical encoder-decoder framework that integrates adaptive feature alignment and a new supervisory signal to enhance cell detection. Specifically, a Coordinate-Attention-based Feature Alignment (CAFA) module is introduced to address spatial misalignment during multi-scale feature fusion, substantially improving cell detection precision. Furthermore, we design a Focal Attenuated Distance (FAD) map as an intermediate representation, providing highly discriminative and spatially informative cues, particularly in crowded regions. FACellDet features a dual-branch architecture, with the main branch predicting FAD maps for cell detection, while the auxiliary branch generates density maps to estimate cell counts for suppressing false detections. Extensive experiments on diverse cell types and imaging modalities from multiple public and in-house datasets demonstrate that our approach outperforms state-of-the-art methods in detection accuracy, while maintaining strong adaptability and robustness across challenging biomedical imaging scenarios. These results underscore the potential of FACellDet as an accurate and generalizable solution for automated cell detection in heterogeneous microscopic cell images, thereby facilitating reliable cell analysis to accelerate biomedical research and clinical workflows. Note to Practitioners—This work addresses the need for accurate and efficient cell detection and counting in biomedical images, where manual methods are time-consuming and error-prone, and existing automated approaches often struggle with dense or diverse cells. FACellDet offers a practical deep learning solution adaptable to various cell types and imaging conditions, improving both detection accuracy and robustness through innovative feature alignment and enhanced supervisory signals. This system can streamline laboratory workflows and support high-throughput research and clinical diagnostics. While FACellDet demonstrates strong performance across challenging datasets, its current deployment requires adequate computational resources. Future development could focus on creating lightweight versions and integrating the framework with automated imaging systems, further broadening its accessibility and impact in routine biomedical practice.","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"23 ","pages":"4375-4387"},"PeriodicalIF":6.4,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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