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Adaptive Fault-tolerant Control for Trajectory Tracking and Rectification of Directional Drilling 期刊论文
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS, 2022, 卷号: 20, 期号: 1, 页码: 334-348
作者:  Zhang, Chi;  Zou, Wei;  Cheng, Ningbo;  Gao, Junshan
Adobe PDF(3031Kb)  |  收藏  |  浏览/下载：208/29  |  提交时间：2022/03/17
Fault-tolerant control (FTC)  integral sliding mode control (ISMC)  neural network (NN)  nonlinear control system  reinforcement learning (RL)  
A Calibration and 3-D Measurement Method for an Active Vision System With Symmetric Yawing Cameras 期刊论文
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2021, 卷号: 70, 页码: 13
作者:  Xu, De;  Zhang, Di;  Liu, Xilong;  Ma, Liping
Adobe PDF(2707Kb)  |  收藏  |  浏览/下载：286/53  |  提交时间：2021/11/03
3-D measurement  active vision  calibration  stereo vision  symmetric yawing system  
Combining Prior Knowledge With CNN for Weak Scratch Inspection of Optical Components 期刊论文
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2021, 卷号: 70, 期号: 1, 页码: 11
作者:  Hou, Wei;  Tao, Xian;  Xu, De
Adobe PDF(6798Kb)  |  收藏  |  浏览/下载：371/130  |  提交时间：2021/03/08
Convolutional neural network (CNN)  direction-sensitive convolution (DSC)  local maximum index (LMI)  optical component  weak scratch inspection  
A novel hardware-oriented ultra-high-speed object detection algorithm based on convolutional neural network 期刊论文
JOURNAL OF REAL-TIME IMAGE PROCESSING, 2019, 期号: 17, 页码: 1703-1714
作者:  Li, Jianquan;  Long, Xianlei;  Hu, Shenhua;  Hu, Yiming;  Gu, Qingyi;  Xu, De
Adobe PDF(1879Kb)  |  收藏  |  浏览/下载：284/43  |  提交时间：2020/08/03
FPGA implementation  High-speed vision  Fast-object detection  Convolutional neural network  
Robotic Skill Learning for Precision Assembly With Microscopic Vision and Force Feedback 期刊论文
IEEE-ASME TRANSACTIONS ON MECHATRONICS, 2019, 卷号: 24, 期号: 3, 页码: 1117-1128
作者:  Qin, Fangbo;  Xu, De;  Zhang, Dapeng;  Li, Ying
浏览  |  Adobe PDF(2296Kb)  |  收藏  |  浏览/下载：524/198  |  提交时间：2019/05/06
Force control  learning from demonstration  microscopic vision  precision assembly  robotic skill learning