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检索条件"机构=Key Laboratory of Imaging Processing and Intelligence Control"
1043 条 记 录,以下是531-540 订阅
排序:
Unsupervised Time Series Anomaly Detection under Data Contamination  12
Unsupervised Time Series Anomaly Detection under Data Contam...
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12th International Conference on Information Technology in Medicine and Education, ITME 2022
作者: Lin, Xiaohui Li, Zuoyong Huang, Xunhua Chen, Xinwei Fan, Haoyi Fuzhou University College of Computer and Data Science Fuzhou350108 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Fuzhou350121 China Harbin University of Science and Technology School of Computer Science and Technology Harbin150080 China Minjiang University Fujian Engineering and Research Center of New Chinese Lacquer Materials Fuzhou350121 China Zhengzhou University School of Computer and Artificial Intelligence Zhengzhou450001 China
Unsupervised learning utilizes unlabeled data to alleviate the reliance on large amounts of labeled data, and it has made great progress in time series anomaly detection. However, there are still some thorny issues un... 详细信息
来源: 评论
RegFormer: An Efficient Projection-Aware Transformer Network for Large-Scale Point Cloud Registration
arXiv
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arXiv 2023年
作者: Liu, Jiuming Wang, Guangming Liu, Zhe Jiang, Chaokang Pollefeys, Marc Wang, Hesheng Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai Jiao Tong University China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China China University of Mining and Technology China ETH Zürich Switzerland Microsoft United States
Although point cloud registration has achieved remarkable advances in object-level and indoor scenes, large-scale registration methods are rarely explored. Challenges mainly arise from the huge point number, complex d... 详细信息
来源: 评论
Vehicle detection based on point cloud intensity and distance clustering  5
Vehicle detection based on point cloud intensity and distanc...
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2020 5th International Seminar on Computer Technology, Mechanical and Electrical Engineering, ISCME 2020
作者: Wang, Zhao Wang, Xing Fang, Bin Yu, Kun Ma, Jie School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China National Key Laboratory of Multispectral Information Processing Technology Huazhong University of Science and Technology Wuhan430074 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China
In the intelligent transportation system, vehicle detection is one of the essential technologies in obstacle avoidance and navigation, however the existing vehicle detection methods cannot meet the actual needs. This ... 详细信息
来源: 评论
Research on Robust Measurement Method of Heart Rate Using Remote Photoplethysmography Based on Adversarial Learning Network with High and Low Frequency Features
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IEEE Transactions on Circuits and Systems for Video Technology 2025年 第6期35卷 5208-5222页
作者: Zhai, Dezhao Chen, Wei Ding, Yinghao Yu, Ming Li, Qinwei Wu, Hang Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology China People's Liberation Army Systems Engineering Institute Academy of Military Sciences Tianjin300161 China Civil Aviation University of China Tianjin Key Laboratory for Advanced Signal Processing Tianjin300300 China Nankai University School of Artificial Intelligence Tianjin300381 China
Remote Photoplethysmography (rPPG) is a non-contact method for measuring heart rate (HR) through facial video, breaking the constraints of contact measurements and offering broad application prospects. However, in rea... 详细信息
来源: 评论
Adversarial attacks and defenses in physiological computing:a systematic review
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National Science Open 2023年 第1期2卷 62-90页
作者: Dongrui Wu Jiaxin Xu Weili Fang Yi Zhang Liuqing Yang Xiaodong Xu Hanbin Luo Xiang Yu Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Zhejiang Lab Hangzhou 311121China School of Civil and Hydraulic Engineering Huazhong University of Science and TechnologyWuhan 430074China College of Public Administration Huazhong University of Science and TechnologyWuhan 430074China Electrical Engineering and Computer Science Department University of MichiganAnn Arbor MI 48109USA School of Management and Sino-European Institute for Intellectual Property Huazhong University of Science and TechnologyWuhan 430074China
Physiological computing uses human physiological data as system inputs in real *** includes,or significantly overlaps with,brain-computer interfaces,affective computing,adaptive automation,health informatics,and physi... 详细信息
来源: 评论
EMPIRICAL STUDIES ON THE PROPERTIES OF LINEAR REGIONS IN DEEP NEURAL NETWORKS  8
EMPIRICAL STUDIES ON THE PROPERTIES OF LINEAR REGIONS IN DEE...
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8th International Conference on Learning Representations, ICLR 2020
作者: Zhang, Xiao Wu, Dongrui Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China
A deep neural network (DNN) with piecewise linear activations can partition the input space into numerous small linear regions, where different linear functions are fitted. It is believed that the number of these regi... 详细信息
来源: 评论
control Selection Based on State Change of Chaotic System  8
Control Selection Based on State Change of Chaotic System
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8th IEEE International Conference on Computer and Communications, ICCC 2022
作者: Zhou, Jiaxi Zhang, Wei Feng, Yuming Onasanya, B.O. School of Electronics and Information Engineering Chongqing Three Gorges University Chongqing Wanzhou404100 China Chongqing Three Gorges University Key Laboratory of Intelligent Information Processing and Control Chongqing Wanzhou404100 China School of Three Gorges Artificial Intelligence Chongqing Three Gorges University Chongqing Wanzhou404100 China University of Ibadan Department of Mathematics Oyo State Ibadan200284 Nigeria
Considering that the system may need to meet more needs in the control process, that is, to accelerate or slow down the control intensity according to the system state, the control mode that combines the control cycle... 详细信息
来源: 评论
Cooperative Label-Free Moving Target Fencing for Second-Order Multi-Agent Systems with Rigid Formation
arXiv
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arXiv 2023年
作者: Hu, Bin-Bin Zhang, Hai-Tao Shi, Yang School of Artificial Intelligence Automation Key Laboratory of Image Processing Intelligent Control State Key Lab of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Mechanical Engineering University of Victoria VictoriaBCV8W 2Y2 Canada
This paper proposes a label-free controller for a second-order multi-agent system to cooperatively fence a moving target of variational velocity into a convex hull formed by the agents whereas maintaining a rigid form... 详细信息
来源: 评论
T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play BCIs
arXiv
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arXiv 2024年
作者: Li, Siyang Wang, Ziwei Luo, Hanbin Ding, Lieyun Wu, Dongrui Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China Henan Key Laboratory of Brain Science and Brain Computer Interface Technology School of Electrical and Information Engineering Zhengzhou University China School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan430074 China
Objective: An electroencephalogram (EEG)-based brain-computer interface (BCI) enables direct communication between the human brain and a computer. Due to individual differences and non-stationarity of EEG signals, suc... 详细信息
来源: 评论
Learning from Noisy Labels with Coarse-to-Fine Sample Credibility Modeling
arXiv
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arXiv 2022年
作者: Zhang, Boshen Li, Yuxi Tu, Yuanpeng Peng, Jinlong Wang, Yabiao Wu, Cunlin Xiao, Yang Zhao, Cairong YouTu Lab Tencent Shanghai China Tongji University Shanghai China Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology China
Training deep neural network (DNN) with noisy labels is practically challenging since inaccurate labels severely degrade the generalization ability of DNN. Previous efforts tend to handle part or full data in a unifie... 详细信息
来源: 评论