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A dual-mode real-time lip-sync system for a bionic dinosaur robot 会议论文
, Xi'an, China, 2018年11月30日-2018年12月2日
作者:  Yan, Shuaizheng;  Hao, Jiasheng;  Wu, Zhengxing
Adobe PDF(700Kb)  |  收藏  |  浏览/下载：59/15  |  提交时间：2023/06/12
Dinosaurs  Robots  Time-domain analysis  Frequency-domain analysis  Feature extraction  
Multitask Policy Adversarial Learning for Human-Level Control With Large State Spaces 期刊论文
IEEE Transactions on Industrial Informatics Information, 2019, 卷号: 15, 期号: 4, 页码: 2395-2404
作者:  Wang JP(王军平);  You Kang Shi;  Wen Sheng Zhang;  Ian Thomas;  Shi Hui Duan
Adobe PDF(2547Kb)  |  收藏  |  浏览/下载：112/39  |  提交时间：2023/05/05
A Robust Face Recognition Method Combining LBP with Multi-mirror Symmetry for Images with Various Face Interferences 期刊论文
International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 5, 页码: 671-682
作者:  Shui-Guang Tong;  Yuan-Yuan Huang;  Zhe-Ming Tong
浏览  |  Adobe PDF(1191Kb)  |  收藏  |  浏览/下载：143/54  |  提交时间：2021/02/22
Face recognition (FR)  local binary pattern (LBP)  facial symmetry  image interferences  multi-mirror average.  
New LMI Conditions for Reduced-order Observer of Lipschitz Discrete-time Systems: Numerical and Experimental Results 期刊论文
International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 5, 页码: 644-654
作者:  Noussaiba Gasmi;  Assem Thabet;  Mohamed Aoun
浏览  |  Adobe PDF(1366Kb)  |  收藏  |  浏览/下载：93/27  |  提交时间：2021/02/22
Reduced-order observer  discrete-time systems  Lipschitz systems  H∞  ARDUINO MEGA 2560 device.  
A Fault Tolerant Control Scheme Using the Feasible Constrained Control Allocation Strategy 期刊论文
International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 5, 页码: 628-643
作者:  Mehdi Naderi;  Tor Arne Johansen;  Ali Khaki Sedigh
浏览  |  Adobe PDF(1916Kb)  |  收藏  |  浏览/下载：99/43  |  提交时间：2021/02/22
Control allocation  feasibility  fault tolerant control  model predictive control  domain of attraction.  
Deep Learning Based Single Image Super-resolution: A Survey 期刊论文
International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 4, 页码: 413-426
作者:  Viet Khanh Ha;  Jin-Chang Ren;  Xin-Ying Xu;  Sophia Zhao;  Gang Xie;  Valentin Masero;  Amir Hussain
浏览  |  Adobe PDF(1389Kb)  |  收藏  |  浏览/下载：126/32  |  提交时间：2021/02/22
Image super-resolution  convolutional neural network  high-resolution image  low-resolution image  deep learning.  
A Hybrid Time Frequency Response and Fuzzy Decision Tree for Non-stationary Signal Analysis and Pattern Recognition 期刊论文
International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 3, 页码: 398-412
作者:  N. R. Nayak;  P. K. Dash;  R. Bisoi
浏览  |  Adobe PDF(1576Kb)  |  收藏  |  浏览/下载：130/42  |  提交时间：2021/02/22
Non-stationary signals  sparse S-transform (SST)  scaling method  fuzzy decision tree  pattern classification.  
Decomposition Based Fuzzy Model Predictive Control Approaches for Interconnected Nonlinear Systems 期刊论文
International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 3, 页码: 369-388
作者:  Latifa Dalhoumi;  Mohamed Chtourou;  Mohamed Djemel
浏览  |  Adobe PDF(2371Kb)  |  收藏  |  浏览/下载：111/41  |  提交时间：2021/02/22
Model predictive control  centralized control  decentralized control  Takagi-Sugeno (TS) fuzzy models  interconnected nonlinear systems  fuzzy model predictive control  parallel distributed compensation.  
Performance Evaluation and Improvement of Chipset Assembly & Test Production Line Based on Variability 期刊论文
International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 2, 页码: 186-198
作者:  Chang-Jun Li;  Zong-Shi Xie;  Xin-Ran Peng;  Bo Li
浏览  |  Adobe PDF(1239Kb)  |  收藏  |  浏览/下载：130/42  |  提交时间：2021/02/22
Performance evaluation and improvement  chipset assembly & test production line (CATPL)  parameters  Little′s law  variability.  
Potential Bands of Sentinel-2A Satellite for Classification Problems in Precision Agriculture 期刊论文
International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 1, 页码: 16-26
作者:  Tian-Xiang Zhang;  Jin-Ya Su;  Cun-Jia Liu;  Wen-Hua Chen
浏览  |  Adobe PDF(1123Kb)  |  收藏  |  浏览/下载：172/48  |  提交时间：2021/02/22
Sentinel-2A  remote sensing  image classification  supervised learning  precision agriculture.