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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：189/30  |  提交时间：2021/11/26
Light weight  image classification  channel attention  efficient convolution  object detection  
Improved Network for Face Recognition Based on Feature Super Resolution Method 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 6, 页码: 915-925
作者:  Ling-Yi Xu;  Zoran Gajic
Adobe PDF(1989Kb)  |  收藏  |  浏览/下载：174/41  |  提交时间：2021/11/26
Face recognition  feature super resolution  multiple-branch network  deep learning  convolutional neural networks  
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：219/48  |  提交时间：2021/11/26
Abdominal organ, supervised segmentation  semi-supervised segmentation  evaluation metrics  image segmentation  machine learning  
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：222/52  |  提交时间：2021/09/13
On-board equipment  fault classification  capsule network  attention mechanism  focal loss  
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：162/28  |  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  
A Fast Vision-inertial Odometer Based on Line Midpoint Descriptor 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 4, 页码: 667-669
作者:  Wen-Kuan Li;  Hao-Yuan Cai;  Sheng-Lin Zhao;  Ya-Qian Liu;  Chun-Xiu Liu
Adobe PDF(10350Kb)  |  收藏  |  浏览/下载：178/40  |  提交时间：2021/07/20
High efficiency  visual-inertial odometry (VIO)  non-linear optimization  points and lines  sliding window  
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：182/41  |  提交时间：2021/07/20
Self-supervised learning  contrastive learning  synthetic image  convolutional neural network  representation learning  
Ensuring the Correctness of Regular Expressions: A Review 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 4, 页码: 521-535
作者:  Li-Xiao Zheng;  Shuai Ma;  Zu-Xi Chen;  Xiang-Yu Luo
Adobe PDF(1076Kb)  |  收藏  |  浏览/下载：115/32  |  提交时间：2021/07/20
Regular expressions  correctness  string generation  learning  static checking  verification  visualization, repairing  
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：139/40  |  提交时间：2021/07/20
Evolutionary computation  complex network  propagation dynamics  social diffusion  evolution model  optimization algorithm  
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：183/45  |  提交时间：2021/05/24
Force Jacobian matrix  one-shot demonstration  dynamic exploration strategy  insertion skill learning  reinforcement