Ieee-Caa Journal of Automatica Sinica最新文献

A Multi-Type Feature Fusion Network Based on Importance Weighting for Occluded Human Pose Estimation

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-04-01 DOI: 10.1109/JAS.2024.124953

Jiahong Jiang;Nan Xia;Siyao Zhou

{"title":"A Multi-Type Feature Fusion Network Based on Importance Weighting for Occluded Human Pose Estimation","authors":"Jiahong Jiang;Nan Xia;Siyao Zhou","doi":"10.1109/JAS.2024.124953","DOIUrl":"https://doi.org/10.1109/JAS.2024.124953","url":null,"abstract":"Human pose estimation is a challenging task in computer vision. Most algorithms perform well in regular scenes, but lack good performance in occlusion scenarios. Therefore, we propose a multi-type feature fusion network based on importance weighting, which consists of three modules. In the first module, we propose a multi-resolution backbone with two feature enhancement sub-modules, which can extract features from different scales and enhance the feature expression ability. In the second module, we enhance the expressiveness of keypoint features by suppressing obstacle features and compensating for the unique and shared attributes of keypoints and topology. In the third module, we perform importance weighting on the adjacency matrix to enable it to describe the correlation among nodes, thereby improving the feature extraction ability. We conduct comparative experiments on the keypoint detection datasets of common objects in Context 2017 (COCO2017), COCO-Whole-body and CrowdPose, achieving the accuracy of 78.9%, 67.1% and 77.6%, respectively. Additionally, a series of ablation experiments are designed to show the performance of our work. Finally, we present the visualization of different scenarios to verify the effectiveness of our work.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 4","pages":"789-805"},"PeriodicalIF":15.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748740","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-Spacecraft Formation Control Under False Data Injection Attack: A Cross Layer Fuzzy Game Approach

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-04-01 DOI: 10.1109/JAS.2024.124872

Yifan Zhong;Yuan Yuan;Huanhuan Yuan;Mengbi Wang;Huaping Liu

引用次数: 0

Dissecting and Mitigating Semantic Discrepancy in Stable Diffusion for Image-to-Image Translation

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-04-01 DOI: 10.1109/JAS.2024.124800

Yifan Yuan;Guanqun Yang;James Z. Wang;Hui Zhang;Hongming Shan;Fei-Yue Wang;Junping Zhang

{"title":"Dissecting and Mitigating Semantic Discrepancy in Stable Diffusion for Image-to-Image Translation","authors":"Yifan Yuan;Guanqun Yang;James Z. Wang;Hui Zhang;Hongming Shan;Fei-Yue Wang;Junping Zhang","doi":"10.1109/JAS.2024.124800","DOIUrl":"https://doi.org/10.1109/JAS.2024.124800","url":null,"abstract":"Finding suitable initial noise that retains the original image's information is crucial for image-to-image (I2I) translation using text-to-image (T2I) diffusion models. A common approach is to add random noise directly to the original image, as in SDEdit. However, we have observed that this can result in “semantic discrepancy” issues, wherein T2I diffusion models mis-interpret the semantic relationships and generate content not present in the original image. We identify that the noise introduced by SDEdit disrupts the semantic integrity of the image, leading to unintended associations between unrelated regions after U-Net upsampling. Building on the widely-used latent diffusion model, Stable Diffusion, we propose a training-free, plug-and-play method to alleviate semantic discrepancy and enhance the fidelity of the translated image. By leveraging the deterministic nature of denoising diffusion implicit models (DDIMs) inversion, we correct the erroneous features and correlations from the original generative process with accurate ones from DDIM inversion. This approach alleviates semantic discrepancy and surpasses recent DDIM-inversion-based methods such as PnP with fewer priors, achieving a speedup of 11.2 times in experiments conducted on COCO, ImageNet, and ImageNet-R datasets across multiple I2I translation tasks. The codes are available at https://github.com/Sherlockyyf/Semantic_Discrepancy.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 4","pages":"705-718"},"PeriodicalIF":15.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748843","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

Collision-Free Maneuvering for a UAV Swarm Based on Parallel Control

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

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

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

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

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

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

Necessary and Sufficient Conditions for Controllability and Essential Controllability of Directed Circle and Tree Graphs

IF 15.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2025-03-07 DOI: 10.1109/JAS.2024.124866

Jijun Qu;Zhijian Ji;Jirong Wang;Yungang Liu

引用次数: 0