Ieee-Caa Journal of Automatica Sinica最新文献_第5页

Collision-Free Maneuvering for a UAV Swarm Based on Parallel Control 基于并行控制的无人机群无碰撞机动

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-03-31 DOI: 10.1109/JAS.2024.124674

Jiacheng Li;Wenhui Ma;YangWang Fang;Dengxiu Yu;C. L. Philip Chen

{"title":"Collision-Free Maneuvering for a UAV Swarm Based on Parallel Control","authors":"Jiacheng Li;Wenhui Ma;YangWang Fang;Dengxiu Yu;C. L. Philip Chen","doi":"10.1109/JAS.2024.124674","DOIUrl":"https://doi.org/10.1109/JAS.2024.124674","url":null,"abstract":"The maneuvering of a large-scale unmanned aerial vehicle (UAV) swarm, notable for flexible flight with collision-free, is still challenging due to the significant number of UAVs and the compact configuration of the swarm. In light of this problem, a novel parallel control method that utilizes space and time transformation is proposed. First, the swarm is decomposed based on a grouping-hierarchical strategy, while the distinct flight roles are assigned to each UAV. Then, to achieve the desired configuration (DCF) in the real world, a bijection transformation is conducted in the space domain, converting an arbitrarily general configuration (GCF) into a standard configuration (SCF) in the virtual space. Further, to improve the flexibility of the swarm, the time scaling transformation is adopted in the time domain, which ensures the desired prescribed-time convergence of the swarm independent of initial conditions. Finally, simulation results demonstrate that collision-free maneuvering, including formation changes and turning, can be effectively and rapidly achieved by the proposed parallel control method. Overall, this research contributes a viable solution for enhancing cooperation among large-scale UAV swarms.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 4","pages":"761-775"},"PeriodicalIF":15.3,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740190","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

Analysis of Students' Positive Emotion and Smile Intensity Using Sequence-Relative Key-Frame Labeling and Deep-Asymmetric Convolutional Neural Network 基于序列相关关键帧标记和深度不对称卷积神经网络的学生积极情绪和微笑强度分析

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-03-31 DOI: 10.1109/JAS.2024.125016

Zhenzhen Luo;Xiaolu Jin;Yong Luo;Qiangqiang Zhou;Xin Luo

{"title":"Analysis of Students' Positive Emotion and Smile Intensity Using Sequence-Relative Key-Frame Labeling and Deep-Asymmetric Convolutional Neural Network","authors":"Zhenzhen Luo;Xiaolu Jin;Yong Luo;Qiangqiang Zhou;Xin Luo","doi":"10.1109/JAS.2024.125016","DOIUrl":"https://doi.org/10.1109/JAS.2024.125016","url":null,"abstract":"Positive emotional experiences can improve learning efficiency and cognitive ability, stimulate students' interest in learning, and improve teacher-student relationships. However, positive emotions in the classroom are primarily identified through teachers' observations and postclass questionnaires or interviews. The expression intensity of students, which is extremely important for fine-grained emotion analysis, is not considered. Hence, a novel method based on smile intensity estimation using sequence-relative key-frame labeling is presented. This method aims to recognize the positive emotion levels of a student in an end-to-end framework. First, the intensity label is generated robustly for each frame in the expression sequence based on the relative key frames to address the lack of annotations for smile intensity. Then, a deep-asymmetric convolutional neural network learns the expression model through dual neural networks, to enhance the stability of the network model and avoid the extreme attention region learned. Further, dual neural networks and the dual attention mechanism are integrated using the intensity label based on the relative key frames as the supervised information. Thus, diverse features are effectively extracted and subtle appearance differences between different smiles are perceived based on different perspectives. Finally, comparative experiments for the convergence speed, model-training parameters, confusion matrix, and classification probability are performed. The proposed method was applied to a real classroom scene to analyze the emotions of students. Numerous experiments validated that the proposed method is promising for analyzing the differences in the positive emotion of students while learning in a classroom.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 4","pages":"806-820"},"PeriodicalIF":15.3,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740174","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

$mathrm{E}^{2}text{AG}$: Entropy-Regularized Ensemble Adaptive Graph for Industrial Soft Sensor Modeling $ mathm {E}^{2}text{AG}$：基于熵正则化集成自适应图的工业软传感器建模

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-03-31 DOI: 10.1109/JAS.2024.124884

Zhichao Chen;Licheng Pan;Yiran Ma;Zeyu Yang;Le Yao;Jinchuan Qian;Zhihuan Song

{"title":"$mathrm{E}^{2}text{AG}$: Entropy-Regularized Ensemble Adaptive Graph for Industrial Soft Sensor Modeling","authors":"Zhichao Chen;Licheng Pan;Yiran Ma;Zeyu Yang;Le Yao;Jinchuan Qian;Zhihuan Song","doi":"10.1109/JAS.2024.124884","DOIUrl":"https://doi.org/10.1109/JAS.2024.124884","url":null,"abstract":"Adaptive graph neural networks (AGNNs) have achieved remarkable success in industrial process soft sensing by incorporating explicit features that delineate the relationships between process variables. This article introduces a novel GNN framework, termed entropy-regularized ensemble adaptive graph <tex>$(mathbf{E}^{mathbf{2}}mathbf{AG})$</tex>, aimed at enhancing the predictive accuracy of AGNNs. Specifically, this work pioneers a novel AGNN learning approach based on mirror descent, which is central to ensuring the efficiency of the training procedure and consequently guarantees that the learned graph naturally adheres to the row-normalization requirement intrinsic to the message-passing of GNNs. Subsequently, motivated by multi-head self-attention mechanism, the training of ensembled AGNNs is rigorously examined within this framework, incorporating an entropy regularization term in the learning objective to ensure the diversity of the learned graph. After that, the architecture and training algorithm of the model are then concisely summarized. Finally, to ascertain the efficacy of the proposed <tex>$mathbf{E}^{mathbf{2}}mathbf{AG}$</tex> model, extensive experiments are conducted on real-world industrial datasets. The evaluation focuses on prediction accuracy, model efficacy, and sensitivity analysis, demonstrating the superiority of <tex>$mathbf{E}^{mathbf{2}}mathbf{AG}$</tex> in industrial soft sensing applications.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 4","pages":"745-760"},"PeriodicalIF":15.3,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740199","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

Robust Predefined-Time Control for Optimal Formation of Networked Mobile Vehicle Systems 网络移动车辆系统最优编队的鲁棒预定义时间控制

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-03-31 DOI: 10.1109/JAS.2023.124023

Jing-Zhe Xu;Zhi-Wei Liu;Dingxig He;Ming-Feng Ge;Ming Chi

引用次数: 0

A $k$-Winners-Take-All $(ktext{WTA})$ Network with Noise Characteristics Captured 一个具有噪声特征的$k$-赢家通吃$(ktext{WTA})$网络

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-03-31 DOI: 10.1109/JAS.2025.125153

Jiexing Li;Yulin Cao;Zhengtai Xie;Long Jin

{"title":"A $k$-Winners-Take-All $(ktext{WTA})$ Network with Noise Characteristics Captured","authors":"Jiexing Li;Yulin Cao;Zhengtai Xie;Long Jin","doi":"10.1109/JAS.2025.125153","DOIUrl":"https://doi.org/10.1109/JAS.2025.125153","url":null,"abstract":"Competition-based <tex>$k-mathbf{winners}-mathbf{take}-mathbf{all} (k mathbf{WTA})$</tex> networks play a crucial role in multi-agent systems. However, existing <tex>$k mathbf{WTA}$</tex> networks either neglect the impact of noise or only consider simple forms, such as constant noise. In practice, noises often exhibit time-varying and nonlinear characteristics, which can be modeled using nonlinear functions and approximated by high-order polynomials. Such noises pose significant challenges for current <tex>$k mathbf{WTA}$</tex> networks, limiting their practical applications. To address this, a <tex>$k mathbf{WTA}$</tex>. network with noise characteristics captured <tex>$(k mathbf{WTA}-mathbf{NCC})$</tex> is proposed in this article. Theoretical analyses demonstrate that the residual error of the proposed <tex>$kmathbf{WTA}- mathbf{NCC}$</tex> network converges to zero globally, while simulation results confirm its robustness against polynomial noises. Additionally, a <tex>$k mathbf{WTA}$</tex> coordination model is constructed by integrating the proposed network with a consensus estimator to achieve multi-agent tracking tasks. Finally, simulations and physical experiments are conducted further to demonstrate the validity and practicality of the <tex>$k$</tex> WTA coordination model.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 4","pages":"734-744"},"PeriodicalIF":15.3,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740306","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 Medical Devices and Instruments: Integrating Edge and Cloud Intelligence for Smart Treatment and Health Systems 并行医疗设备和仪器：为智能治疗和健康系统集成边缘和云智能

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-03-31 DOI: 10.1109/JAS.2024.124614

Fei Lin;Tommy Gao;Dali Sun;Qinghua Ni;Xianting Ding;Jing Wang;David Wenzhong Gao;Fei-Yue Wang

引用次数: 0

Path Following Control of Uncertain and Underactuated Autonomous Surface Vessels 不确定欠驱动自主水面舰艇路径跟踪控制

IF 19.2 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-03-24 DOI: 10.1109/JAS.2024.124713

Yang Wu;Yueying Wang;Zhiguang Feng;Xiangpeng Xie

引用次数: 0

Online Estimation of DC-link Capacitor Parameters of Three-Level NPC Converters Using Inherent Signals Analysis 基于固有信号分析的三电平NPC变换器直流电容参数在线估计

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-03-24 DOI: 10.1109/JAS.2025.125159

Ricardo Lucio De Araujo Ribeiro;Reuben Palmer Rezende De Sousa;Alexandre Cunha Oliveira;Antonio Marcus Nogueira Lima;Qing-Long Han

{"title":"Online Estimation of DC-link Capacitor Parameters of Three-Level NPC Converters Using Inherent Signals Analysis","authors":"Ricardo Lucio De Araujo Ribeiro;Reuben Palmer Rezende De Sousa;Alexandre Cunha Oliveira;Antonio Marcus Nogueira Lima;Qing-Long Han","doi":"10.1109/JAS.2025.125159","DOIUrl":"https://doi.org/10.1109/JAS.2025.125159","url":null,"abstract":"This paper presents a method for estimating the parameters of DC-link capacitors in three-level NPC voltage source inverters (3L-NPC-VSI) used in grid-tied systems. The technique uses the signals generated by the intermodulation caused by the PWM strategy and converter topology interaction to estimate the capacitor parameters of the converter DC-link. It utilizes an observer-based structure consisting of a recursive noninteger sliding discrete Fourier transform (rnSDFT) and an RLS filter improved with a forgetting factor (oSDFT-RLS) to accurately estimate the capacitance and equivalent series resistance (ESR). Importantly, this method does not require additional sensors beyond those already installed in off-the-shelf 3L-NPC-VSI systems, ensuring its noninvasiveness. Furthermore, the oSDFT-RLS estimates capacitor parameters in the time-frequency domain, enabling the tracking of capacitor degradation and predicting potential faults. Experimental results from the laboratory setup demonstrate the effectiveness of the proposed condition monitoring method.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 7","pages":"1434-1444"},"PeriodicalIF":15.3,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536430","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

Multi-UAV Cooperative Pursuit Strategy with Limited Visual Field in Urban Airspace: A Multi-Agent Reinforcement Learning Approach 城市空域有限视野下多无人机协同追击策略：多智能体强化学习方法

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-03-24 DOI: 10.1109/JAS.2024.124965

Zhe Peng;Guohua Wu;Biao Luo;Ling Wang

{"title":"Multi-UAV Cooperative Pursuit Strategy with Limited Visual Field in Urban Airspace: A Multi-Agent Reinforcement Learning Approach","authors":"Zhe Peng;Guohua Wu;Biao Luo;Ling Wang","doi":"10.1109/JAS.2024.124965","DOIUrl":"https://doi.org/10.1109/JAS.2024.124965","url":null,"abstract":"The application of multiple unmanned aerial vehicles (UAVs) for the pursuit and capture of unauthorized UAVs has emerged as a novel approach to ensuring the safety of urban airspace. However, pursuit UAVs necessitate the utilization of their own sensors to proactively gather information from the unauthorized UAV. Considering the restricted sensing range of sensors, this paper proposes a multi-UAV with limited visual field pursuit-evasion (MUV-PE) problem. Each pursuer has a visual field characterized by limited perception distance and viewing angle, potentially obstructed by buildings. Only when the unauthorized UAV, i.e., the evader, enters the visual field of any pursuer can its position be acquired. The objective of the pursuers is to capture the evader as soon as possible without collision. To address this problem, we propose the normalizing flow actor with graph attention critic (NAGC) algorithm, a multi-agent reinforcement learning (MARL) approach. NAGC executes normalizing flows to augment the flexibility of policy network, enabling the agent to sample actions from more intricate distributions rather than common distributions. To enhance the capability of simultaneously comprehending spatial relationships among multiple UAVs and environmental obstacles, NAGC integrates the “obstacle-target” graph attention networks, significantly aiding pursuers in supporting search or pursuit activities. Extensive experiments conducted in a high-precision simulator validate the promising performance of the NAGC algorithm.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 7","pages":"1350-1367"},"PeriodicalIF":15.3,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536308","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

LTDNet: A Lightweight Text Detector for Real-Time Arbitrary-Shape Traffic Text Detection LTDNet：用于实时任意形状交通文本检测的轻量级文本检测器

IF 19.2 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-03-24 DOI: 10.1109/JAS.2024.125022

Runmin Wang;Yanbin Zhu;Ziyu Zhu;Lingxin Cui;Zukun Wan;Anna Zhu;Yajun Ding;Shengyou Qian;Changxin Gao;Nong Sang

{"title":"LTDNet: A Lightweight Text Detector for Real-Time Arbitrary-Shape Traffic Text Detection","authors":"Runmin Wang;Yanbin Zhu;Ziyu Zhu;Lingxin Cui;Zukun Wan;Anna Zhu;Yajun Ding;Shengyou Qian;Changxin Gao;Nong Sang","doi":"10.1109/JAS.2024.125022","DOIUrl":"https://doi.org/10.1109/JAS.2024.125022","url":null,"abstract":"Traffic text detection plays a vital role in understanding traffic scenes. Traffic text, a distinct subset of natural scene text, faces specific challenges not found in natural scene text detection, including false alarms from non-traffic text sources, such as roadside advertisements and building signs. Existing state-of-the-art methods employ increasingly complex detection frameworks to pursue higher accuracy, leading to challenges with real-time performance. In response to this issue, we propose a real-time and efficient traffic text detector named LTDNet, which strikes a balance between accuracy and real-time capabilities. LTDNet integrates three essential techniques to address these challenges effectively. First, a cascaded multilevel feature fusion network is employed to mitigate the limitations of lightweight backbone networks, thereby enhancing detection accuracy. Second, a lightweight feature attention module is introduced to enhance inference speed without compromising accuracy. Finally, a novel point-to-edge distance vector loss function is proposed to precisely localize text instance boundaries within traffic contexts. The superiority of our method is validated through extensive experiments on five publicly available datasets, demonstrating its state-of-the-art performance. The code will be released at https://github.com/runminwang/LTDNet.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 8","pages":"1648-1660"},"PeriodicalIF":19.2,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144880488","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