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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：219/47  |  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)  
Global FLS-based Consensus of Stochastic Uncertain Nonlinear Multi-agent Systems 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 5, 页码: 826-837
作者:  Jia-Xi Chen;  Jun-Min Li
Adobe PDF(2352Kb)  |  收藏  |  浏览/下载：210/48  |  提交时间：2021/09/13
Fuzzy logic systems  stochastic nonlinear  multi-agent systems (MAS)  consensus algorithm  adaptive 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：231/53  |  提交时间：2021/09/13
On-board equipment  fault classification  capsule network  attention mechanism  focal loss  
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：184/46  |  提交时间：2021/09/13
Action recognition  spatiotemporal relation  multi-branch fusion  long-term representation  video classification  
Why and When Can Deep-but Not Shallow-networks Avoid the Curse of Dimensionality: A Review 期刊论文
International Journal of Automation and Computing, 2017, 卷号: 14, 期号: 5, 页码: 503-519
作者:  Tomaso Poggio;  Hrushikesh Mhaskar;  Lorenzo Rosasco;  Brando Miranda;  Qianli Liao
浏览  |  Adobe PDF(1711Kb)  |  收藏  |  浏览/下载：195/31  |  提交时间：2021/02/23
Deep learning  fine-grained image classification  semantic segmentation  convolutional neural network (CNN)  recurrent neural network (RNN).  
A Survey on Deep Learning-based Fine-grained Object Classification and Semantic Segmentation 期刊论文
International Journal of Automation and Computing, 2017, 卷号: 14, 期号: 2, 页码: 119-135
作者:  Bo Zhao;  Jiashi Feng;  Xiao Wu;  Shuicheng Yan
浏览  |  Adobe PDF(5409Kb)  |  收藏  |  浏览/下载：179/40  |  提交时间：2021/02/23
Deep learning  fine-grained image classi¯cation  semantic segmentation  convolutional neural network (CNN)  recurrent neural network (RNN).  
An Energy-based Nonlinear Coupling Control for Offshore Ship-mounted Cranes 期刊论文
International Journal of Automation and Computing, 2018, 卷号: 15, 期号: 5, 页码: 570-581
作者:  Yu-Zhe Qian;  Yong-Chun Fang;  Tong Yang
浏览  |  Adobe PDF(1113Kb)  |  收藏  |  浏览/下载：133/50  |  提交时间：2021/02/23
Energy-based control  offshore ship-mounted cranes  Lyapunov methods  underactuated  nonlinear control systems.  
PSO Optimal Control of Model-free Adaptive Control for PVC Polymerization Process 期刊论文
International Journal of Automation and Computing, 2018, 卷号: 15, 期号: 4, 页码: 482-491
作者:  Shu-Zhi Gao;  Xiao-Feng Wu;  Liang-Liang Luan;  Jie-Sheng Wang;  Gui-Cheng Wang
Adobe PDF(1024Kb)  |  收藏  |  浏览/下载：143/44  |  提交时间：2021/02/23
Polyvinyl chloride (PVC)  polymerization temperature  model-free adaptive control  particle swarm optimization (PSO) algorithm.  
A Survey of Scene Understanding by Event Reasoning in Autonomous Driving 期刊论文
International Journal of Automation and Computing, 2018, 卷号: 15, 期号: 3, 页码: 249-266
作者:  Jian-Ru Xue;  Jian-Wu Fang;  Pu Zhang
Adobe PDF(2242Kb)  |  收藏  |  浏览/下载：116/38  |  提交时间：2021/02/23
Autonomous vehicle  scene understanding  event reasoning  intention prediction  scene representation.  
Feedforward Control for Wind Turbine Load Reduction with Pseudo-LIDAR Measurement 期刊论文
International Journal of Automation and Computing, 2018, 卷号: 15, 期号: 2, 页码: 142-155
作者:  Jie Bao;  Hong Yue;  William E. Leithead;  Ji-Qiang Wang
浏览  |  Adobe PDF(7211Kb)  |  收藏  |  浏览/下载：116/41  |  提交时间：2021/02/23
Wind turbine control  light detection and ranging (LIDAR) measurement  feedforward control  load reduction  gain scheduling  disturbance rejection.