IEEE Transactions on Systems Man Cybernetics-Systems最新文献

IEEE Systems, Man, and Cybernetics Society Information IEEE系统、人与控制论学会信息

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-04-16 DOI: 10.1109/TSMC.2025.3561478

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IEEE Systems, Man, and Cybernetics Society Information IEEE系统、人与控制论学会信息

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-04-16 DOI: 10.1109/TSMC.2025.3561484

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Together, we are advancing technology 我们共同推动技术进步

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-04-16 DOI: 10.1109/TSMC.2025.3561493

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IEEE Transactions on Systems, Man, and Cybernetics: Systems Information for Authors IEEE系统、人与控制论汇刊：作者的系统信息

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-04-16 DOI: 10.1109/TSMC.2025.3561480

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TechRxiv: Share Your Preprint Research With the World! techxiv：与世界分享你的预印本研究！

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-04-16 DOI: 10.1109/TSMC.2025.3561489

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IEEE Transactions on Systems, Man, and Cybernetics: Systems Publication Information IEEE系统、人与控制论汇刊：系统出版信息

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-04-16 DOI: 10.1109/TSMC.2025.3561476

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IEEE Transactions on Systems, Man, and Cybernetics: Systems Information for Authors IEEE系统、人与控制论汇刊：作者的系统信息

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-04-16 DOI: 10.1109/TSMC.2025.3561486

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IEEE Transactions on Systems, Man, and Cybernetics: Systems Publication Information IEEE系统、人与控制论汇刊：系统出版信息

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-04-16 DOI: 10.1109/TSMC.2025.3561482

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Night-Time Traffic Light Recognition Based on Enhancement-Guided Object Detection 基于增强制导目标检测的夜间交通灯识别

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-04-02 DOI: 10.1109/TSMC.2025.3552621

Zikai Yao;Qiang Liu;Zhangzhen Zhao;Yuliang Qin;Jinglong Zhu;Tianzhi Xia;Bo Li;Lipo Wang

{"title":"Night-Time Traffic Light Recognition Based on Enhancement-Guided Object Detection","authors":"Zikai Yao;Qiang Liu;Zhangzhen Zhao;Yuliang Qin;Jinglong Zhu;Tianzhi Xia;Bo Li;Lipo Wang","doi":"10.1109/TSMC.2025.3552621","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3552621","url":null,"abstract":"Traffic light recognition is crucial for autonomous driving. While significant progress has been made in favorable conditions, recognition performance in night-time scenes remains a challenge. One straightforward approach is to apply enhancement methods that improve degraded images prior to object detection. However, since most enhancement methods are tailored for human perception; they may not consistently improve recognition accuracy for machine learning techniques. To address this, we propose an enhancement-guided framework for night-time traffic light recognition, called EG-TLR. EG-TLR consists of a residual denoising module (RDM) and a mixed attention traffic light detection module (MATLDM). The RDM reduces noise in degraded night-time images while preserving essential traffic light features by extracting sparsity information and performing context aggregation. The MATLDM improves feature extraction and recognition performance in complex night-time scenes by incorporating a shadow detection layer (SDL) and a mixed attention module (MAM). Moreover, to address the lack of a dedicated night-time traffic light dataset, we construct the Night-TL dataset utilizing publicly available images. Extensive experiments on Night-TL and LISA datasets demonstrate that EG-TLR achieves an AP50 of 79.83% and an AP50:95 of 36.62%, with an inference speed of 5.9 ms and 16.9 GFLOPs, outperforming other state-of-the-art methods. Furthermore, ablation studies and visualization results validate the effectiveness of our proposed method. The Night-TL dataset can be downloaded from: <uri>https://github.com/feiqinaqian/Night-TL-dataset</uri>.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 6","pages":"4410-4422"},"PeriodicalIF":8.6,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073330","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}

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Fixed-Time Tracking Control of 3-D Collaborative Double Boom Cranes With Obstacle Avoidance and Prescribed Performance 具有避障和规定性能的三维协同双臂起重机的固定时间跟踪控制

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-04-02 DOI: 10.1109/TSMC.2025.3551601

Zhuoqing Liu;Tong Yang;Lu Zhou;Yongchun Fang;Ning Sun

{"title":"Fixed-Time Tracking Control of 3-D Collaborative Double Boom Cranes With Obstacle Avoidance and Prescribed Performance","authors":"Zhuoqing Liu;Tong Yang;Lu Zhou;Yongchun Fang;Ning Sun","doi":"10.1109/TSMC.2025.3551601","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3551601","url":null,"abstract":"Collaborative double boom cranes (CDBCs) play a crucial role in modern industries, offering superior hoisting capabilities and adaptability. However, the intricate dynamic characteristics of CDBCs, combined with demanding working requirements, pose significant challenges for control safety and efficiency. The existing control methods for CDBCs primarily focus on two-dimensional space and lack theoretical guarantees for rapid error convergence, which limits working efficiency. Furthermore, safety concerns arise when collision-free reference trajectories are unavailable or incomplete during dynamic operations. To this end, this article proposes a novel fixed-time tracking control method with obstacle avoidance and prescribed performance for three-dimensional (3-D) CDBCs. As the first collision-free tracking method for 3-D CDBCs, the proposed method stands as a noteworthy contribution aimed at improving safety, accuracy, and efficiency. By simultaneously considering pitch and rotation motions, the proposed method expands the working space and efficiency of 3-D CDBCs. Elaborately designed sliding surfaces ensure fixed-time convergence, thereby improving response speed. For safe complete trajectory tracking, the proposed method can limit transient tracking errors within a prescribed performance function, preventing collisions from unexpected errors. In scenarios with unavailable or incomplete safe reference trajectories, autonomous obstacle avoidance is achieved through potential function design, thereby enhancing operation safety. Additionally, a thorough closed-loop stability analysis is provided based on Lyapunov methods. Finally, experimental results on a built CDBC prototype offer validation for the tracking and obstacle avoidance performance of the proposed method under various working conditions.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 6","pages":"4175-4187"},"PeriodicalIF":8.6,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073435","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