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Computational Intelligence in Remote Sensing Image Registration: A survey 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 1, 页码: 1-17
作者:  Yue Wu;  Jun-Wei Liu;  Chen-Zhuo Zhu;  Zhuang-Fei Bai;  Qi-Guang Miao;  Wen-Ping Ma;  Mao-Guo Gong
浏览  |  Adobe PDF(995Kb)  |  收藏  |  浏览/下载:239/63  |  提交时间:2021/02/23
Computational intelligence  evolutionary computation  neural network  deep learning  remote sensing image registration  
A Comprehensive Review on Group Activity Recognition in Videos 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 3, 页码: 334-350
作者:  Li-Fang Wu;  Qi Wang;  Meng Jian;  Yu Qiao;  Bo-Xuan Zhao
Adobe PDF(1415Kb)  |  收藏  |  浏览/下载:262/61  |  提交时间:2021/05/24
Group activity recognition (GAR)  human activity recognition  scene understanding  video analysis  computer vision  
Dynamic Event-triggered Control and Estimation: A Survey 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 6, 页码: 857-886
作者:  Xiaohua Ge;  Qing-Long Han;  Xian-Ming Zhang;  Derui Ding
Adobe PDF(3887Kb)  |  收藏  |  浏览/下载:189/36  |  提交时间:2021/11/26
Networked systems  dynamic event-triggered control  dynamic event-triggered estimation  dynamic event-triggered mechanisms  vehicle active suspension system  water distribution and supply system  
Advances in Deep Learning Methods for Visual Tracking: Literature Review and Fundamentals 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 3, 页码: 311-333
作者:  Xiao-Qin Zhang;  Run-Hua Jiang;  Chen-Xiang Fan;  Tian-Yu Tong;  Tao Wang Peng-Cheng Huang
Adobe PDF(1787Kb)  |  收藏  |  浏览/下载:258/37  |  提交时间:2021/05/24
Deep learning  visual tracking  data-invariant  data-adaptive  general components  
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:206/47  |  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:210/50  |  提交时间:2021/09/13
On-board equipment  fault classification  capsule network  attention mechanism  focal loss  
Dynamic System Identification of Underwater Vehicles Using Multi-output Gaussian Processes 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 5, 页码: 681-693
作者:  Wilmer Ariza Ramirez;  Juš Kocijan;  Zhi Quan Leong;  Hung Duc Nguyen;  Shantha Gamini Jayasinghe
Adobe PDF(3231Kb)  |  收藏  |  浏览/下载:136/39  |  提交时间:2021/09/13
Dependent Gaussian processes  dynamic system identification  multi-output Gaussian processes  non-parametric identification  autonomous underwater vehicle (AUV)  
Fire Detection Method Based on Depthwise Separable Convolution and YOLOv3 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 2, 页码: 300-310
作者:  Yue-Yan Qin;  Jiang-Tao Cao;  Xiao-Fei Ji
Adobe PDF(2289Kb)  |  收藏  |  浏览/下载:166/49  |  提交时间:2021/04/22
Fire detection  depthwise separable convolution  fire classification  You Only Look Once version 3 (YOLOv3)  target regression  
Item Response Theory Based Ensemble in Machine Learning 期刊论文
International Journal of Automation and Computing, 2020, 卷号: 17, 期号: 5, 页码: 621-636
作者:  Ziheng Chen;  Hongshik Ahn
浏览  |  Adobe PDF(1163Kb)  |  收藏  |  浏览/下载:161/83  |  提交时间:2021/02/22
Classification  ensemble learning  item response theory  machine learning  expectation maximization (EM) algorithm.  
Hidden Markov Model Approach for Software Reliability Estimation with Logic Error 期刊论文
International Journal of Automation and Computing, 2020, 卷号: 17, 期号: 2, 页码: 305-320
作者:  R. Bharathi;  R. Selvarani
浏览  |  Adobe PDF(1248Kb)  |  收藏  |  浏览/下载:179/66  |  提交时间:2021/02/22
Hidden Markov model (HMM)  reliability  logic error  safety critical  software failure.