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Deep Space Probing for Point Cloud Analysis 会议论文
, 线上, 2021-1
作者:  Yirong, Yang;  Bin Fan;  Yongcheng Liu;  Hua Lin;  Jiyong Zhang;  Xin Liu;  Xinyu Cai;  Shiming Xiang;  Chunhong Pan
Adobe PDF(4906Kb)  |  收藏  |  浏览/下载：255/57  |  提交时间：2021/05/27
Efficient cooperative structured control for a multi-joint biomimetic robotic fish 期刊论文
IEEE/ASME Transactions on Mechatronics, 2020, 卷号: 26, 期号: 5, 页码: 2506-2516
作者:  Yan Shuaizheng;  Wu Zhengxing;  Wang Jian;  Tan Min;  Yu Junzhi
Adobe PDF(2394Kb)  |  收藏  |  浏览/下载：69/22  |  提交时间：2023/05/31
Optimal Elevator Group Control via Deep Asynchronous Actor-Critic Learning 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2020, 卷号: 31, 期号: 12, 页码: 5245-5256
作者:  Wei, Qinglai;  Wang, Lingxiao;  Liu, Yu;  Polycarpou, Marios M.
Adobe PDF(4019Kb)  |  收藏  |  浏览/下载：315/75  |  提交时间：2021/03/08
Elevators  Optimal control  Backpropagation  Machine learning  Neural networks  Learning (artificial intelligence)  Actor  –critic  adaptive dynamic programming  deep learning (DL)  elevator group control (EGC)  optimal control  reinforcement learning (RL)  
Distractor-aware discrimination learning for online multiple object tracking 期刊论文
PATTERN RECOGNITION, 2020, 期号: 107, 页码: 10
作者:  Zhou, Zongwei;  Luo, Wenhan;  Wang, Qiang;  Xing, Junliang;  Hu, Weiming
Adobe PDF(840Kb)  |  收藏  |  浏览/下载：359/105  |  提交时间：2020/08/31
Multi-object tracking  Distractor-aware discrimination learning  Relational attention learning  
Non-Negative Iterative Convex Refinement Approach for Accurate and Robust Reconstruction in Cerenkov Luminescence Tomography 期刊论文
IEEE TRANSACTIONS ON MEDICAL IMAGING, 2020, 卷号: 39, 期号: 10, 页码: 3207-3217
作者:  Cai, Meishan;  Zhang, Zeyu;  Shi, Xiaojing;  Yang, Junying;  Hu, Zhenhua;  Tian, Jie
Adobe PDF(2176Kb)  |  收藏  |  浏览/下载：302/62  |  提交时间：2021/01/07
Image reconstruction  Imaging  Mathematical model  Shape  Slabs  Iterative methods  Luminescence  Cerenkov luminescence tomography  sparse reconstruction  inverse problem  tumor  
Adaptive Fuzzy Nonsmooth Backstepping Output-Feedback Control for Hypersonic Vehicles With Finite-Time Convergence 期刊论文
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2020, 卷号: 28, 期号: 10, 页码: 2320-2334
作者:  Sun, Jinlin;  Yi, Jianqiang;  Pu, Zhiqiang;  Liu, Zhen
Adobe PDF(3972Kb)  |  收藏  |  浏览/下载：188/0  |  提交时间：2021/01/07
Backstepping  Adaptive fuzzy control  Fuzzy logic  finite-time control  fixed-time state observer  
Reaction-Wheel-Based Roll Stabilization for a Robotic Fish Using Neural Network Sliding Mode Control 期刊论文
IEEE-ASME TRANSACTIONS ON MECHATRONICS, 2020, 卷号: 25, 期号: 4, 页码: 1904-1911
作者:  Zhang, Pengfei;  Wu, Zhengxing;  Dong, Huijie;  Tan, Min;  Yu, Junzhi
Adobe PDF(486Kb)  |  收藏  |  浏览/下载：256/34  |  提交时间：2020/09/28
Mobile robots  Wheels  Rotors  Robot sensing systems  Stability analysis  Mechatronics  Neural network feedforward control  reaction wheel  robotic fish  roll stability  sliding mode control  
Active Impact Motion for a Quadruped Robot 会议论文
, Hong Kong, China, 20-21 August 2020
作者:  Shang LL(尚琳琳);  Wang Wei;  Yi Jianqianng
Adobe PDF(1147Kb)  |  收藏  |  浏览/下载：140/49  |  提交时间：2022/06/23
Improved Block Sparse Bayesian Learning Method Using K-Nearest Neighbor Strategy for Accurate Tumor Morphology Reconstruction in Bioluminescence Tomography 期刊论文
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2020, 卷号: 67, 期号: 7, 页码: 2023-2032
作者:  Yin, Lin;  Wang, Kun;  Tong, Tong;  An, Yu;  Meng, Hui;  Yang, Xin;  Tian, Jie
浏览  |  Adobe PDF(646Kb)  |  收藏  |  浏览/下载：317/56  |  提交时间：2020/08/03
Image reconstruction  Bayes methods  Tumors  Inverse problems  Light sources  Tomography  Morphology  Bioluminescence tomography (BLT)  block sparse Bayesian learning  morphology recovery  
NIR-II/NIR-I Fluorescence Molecular Tomography of Heterogeneous Mice Based on Gaussian Weighted Neighborhood Fused Lasso Method 期刊论文
IEEE TRANSACTIONS ON MEDICAL IMAGING, 2020, 卷号: 39, 期号: 6, 页码: 2213-2222
作者:  Cai, Meishan;  Zhang, Zeyu;  Shi, Xiaojing;  Hu, Zhenhua;  Tian, Jie
Adobe PDF(2134Kb)  |  收藏  |  浏览/下载：327/69  |  提交时间：2020/08/03
Fluorescence molecular tomography  NIR-II  NIR-I  GWNFL method