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DLA+: A Light Aggregation Network for Object Classification and Detection 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 6, 页码: 963-972
作者:  Fu-Tian Wang;  Li Yang;  Jin Tang;  Si-Bao Chen;  Xin Wang
Adobe PDF(1212Kb)  |  收藏  |  浏览/下载：198/30  |  提交时间：2021/11/26
Light weight  image classification  channel attention  efficient convolution  object detection  
Research on Voiceprint Recognition of Camouflage Voice Based on Deep Belief Network 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 6, 页码: 947-962
作者:  Nan Jiang;  Ting Liu
Adobe PDF(1905Kb)  |  收藏  |  浏览/下载：206/46  |  提交时间：2021/11/26
Disguised voice recognition  deep belief network  feature extraction  Gammatone frequency cepstrum coefficients (GFCC)  dropout  
Mechanical Design and Dynamic Compliance Control of Lightweight Manipulator 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 6, 页码: 926-934
作者:  Shao-Lin Zhang;  Yue-Guang Ge;  Hai-Tao Wang;  Shuo Wang
Adobe PDF(1817Kb)  |  收藏  |  浏览/下载：212/49  |  提交时间：2021/11/26
Lightweight manipulator  mechanical design  dynamic trajectory planning  compliance control  robot control  
Fault Classification for On-board Equipment of High-speed Railway Based on Attention Capsule Network 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 5, 页码: 814-825
作者:  Lu-Jie Zhou;  Jian-Wu Dang;  Zhen-Hai Zhan
Adobe PDF(1208Kb)  |  收藏  |  浏览/下载：224/52  |  提交时间：2021/09/13
On-board equipment  fault classification  capsule network  attention mechanism  focal loss  
Behavior-based Autonomous Navigation and Formation Control of Mobile Robots in Unknown Cluttered Dynamic Environments with Dynamic Target Tracking 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 5, 页码: 766-786
作者:  Nacer Hacene;  Boubekeur Mendil
Adobe PDF(6433Kb)  |  收藏  |  浏览/下载：185/48  |  提交时间：2021/09/13
Behavior-based autonomous navigation  dynamic obstacles and walls  dynamic target tracking  formation control of multi-robot systems  dynamic environment  
A 2D Mapping Method Based on Virtual Laser Scans for Indoor Robots 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 5, 页码: 747-765
作者:  Xu-Yang Shao;  Guo-Hui Tian;  Ying Zhang
Adobe PDF(2637Kb)  |  收藏  |  浏览/下载：208/44  |  提交时间：2021/09/13
2D mapping  indoor robots  virtual laser  mapping auxiliary strategies  safe navigation  
STEP AP 242 Managed Model-based 3D Engineering: An Application Towards the Automation of Fixture Planning 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 5, 页码: 731-746
作者:  Remil George Thomas;  Deepak Lawrence K.;  Manu R.
Adobe PDF(1714Kb)  |  收藏  |  浏览/下载：165/29  |  提交时间：2021/09/13
Standard for the exchange of product model data (STEP) application protocol (AP) 242  product manufacturing information  computer-aided fixture design  computer aided design (CAD)/computer aided manufacturing (CAM), automation  computer integrated manufacturing  3D surface roughness  
STRNet: Triple-stream Spatiotemporal Relation Network for Action Recognition 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 5, 页码: 718-730
作者:  Zhi-Wei Xu;  Xiao-Jun Wu;  Josef Kittler
Adobe PDF(1129Kb)  |  收藏  |  浏览/下载：176/44  |  提交时间：2021/09/13
Action recognition  spatiotemporal relation  multi-branch fusion  long-term representation  video classification  
Delayed Teleoperation with Force Feedback of a Humanoid Robot 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 4, 页码: 605-618
作者:  Viviana Moya;  Emanuel Slawiñski;  Vicente Mut
Adobe PDF(2260Kb)  |  收藏  |  浏览/下载：134/44  |  提交时间：2021/07/20
Humanoid robot  bilateral teleoperation  time delay  force feedback  walking  manipulation  
Contrastive Self-supervised Representation Learning Using Synthetic Data 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 4, 页码: 556-567
作者:  Dong-Yu She;  Kun Xu
Adobe PDF(993Kb)  |  收藏  |  浏览/下载：183/41  |  提交时间：2021/07/20
Self-supervised learning  contrastive learning  synthetic image  convolutional neural network  representation learning