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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：256/33  |  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：245/55  |  提交时间：2021/11/26
Disguised voice recognition  deep belief network  feature extraction  Gammatone frequency cepstrum coefficients (GFCC)  dropout  
Fault Information Recognition for On-board Equipment of High-speed Railway Based on Multi-neural Network Collaboration 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 6, 页码: 935-946
作者:  Lu-Jie Zhou;  Jian-Wu Dang;  Zhen-Hai Zhang
Adobe PDF(1263Kb)  |  收藏  |  浏览/下载：257/58  |  提交时间：2021/11/26
Train control system  Chinese named entity recognition (NER)  character feature  gating mechanism  bidirectional long short-term memory (BiLSTM)  
Supervised and Semi-supervised Methods for Abdominalm Organ Segmentation: A Review 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 6, 页码: 887-914
作者:  Isaac Baffour Senkyire;  Zhe Liu
Adobe PDF(1308Kb)  |  收藏  |  浏览/下载：254/53  |  提交时间：2021/11/26
Abdominal organ, supervised segmentation  semi-supervised segmentation  evaluation metrics  image segmentation  machine learning  
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：237/52  |  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：196/36  |  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：213/55  |  提交时间：2021/09/13
Action recognition  spatiotemporal relation  multi-branch fusion  long-term representation  video classification  
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：209/53  |  提交时间：2021/07/20
Self-supervised learning  contrastive learning  synthetic image  convolutional neural network  representation learning  
Skill Learning for Robotic Insertion Based on One-shot Demonstration and Reinforcement Learning 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 3, 页码: 457-467
作者:  Ying Li;  De Xu
Adobe PDF(1450Kb)  |  收藏  |  浏览/下载：221/53  |  提交时间：2021/05/24
Force Jacobian matrix  one-shot demonstration  dynamic exploration strategy  insertion skill learning  reinforcement  
Learning Deep RGBT Representations for Robust Person Re-identification 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 3, 页码: 443-456
作者:  Ai-Hua Zheng;  Zi-Han Chen;  Cheng-Long Li;  Jin Tang;  Bin Luo
Adobe PDF(1832Kb)  |  收藏  |  浏览/下载：296/67  |  提交时间：2021/05/24
Person re-identification (Re-ID)  thermal infrared  generative networks  attention  deep learning