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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：275/35  |  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：254/56  |  提交时间：2021/11/26
Disguised voice recognition  deep belief network  feature extraction  Gammatone frequency cepstrum coefficients (GFCC)  dropout  
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：274/66  |  提交时间：2021/09/13
On-board equipment  fault classification  capsule network  attention mechanism  focal loss  
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：249/55  |  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：203/39  |  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：220/58  |  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：210/54  |  提交时间：2021/07/20
Self-supervised learning  contrastive learning  synthetic image  convolutional neural network  representation learning  
Evolutionary Computation in Social Propagation over Complex Networks: A Survey 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 4, 页码: 503-520
作者:  Tian-Fang Zhao;  Wei-Neng Chen;  Xin-Xin Ma;  Xiao-Kun Wu
Adobe PDF(1283Kb)  |  收藏  |  浏览/下载：185/53  |  提交时间：2021/07/20
Evolutionary computation  complex network  propagation dynamics  social diffusion  evolution model  optimization algorithm  
EDT Method for Multiple Labelled Objects Subject to Tied Distances 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 3, 页码: 468-479
作者:  Andre Marasca;  Andre Backes;  Fabio Favarim;  Marcelo Teixeira;  Dalcimar Casanova
Adobe PDF(2313Kb)  |  收藏  |  浏览/下载：289/65  |  提交时间：2021/05/24
Euclidean distance transform (EDT)  multiple-labelled objects  tied distances  fractal analysis  texture analysis  
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：299/69  |  提交时间：2021/05/24
Person re-identification (Re-ID)  thermal infrared  generative networks  attention  deep learning