中文版 | English

在结果中检索

CASIA OpenIR

浏览/检索结果: 共21条，第1-10条 帮助

限定条件        
已选(0)清除 条数/页：   排序方式：
Magnetic Field-Based Reward Shaping for Goal-Conditioned Reinforcement Learning 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2023, 卷号: 10, 期号: 12, 页码: 2233-2247
作者:  Hongyu Ding;  Yuanze Tang;  Qing Wu;  Bo Wang;  Chunlin Chen;  Zhi Wang
Adobe PDF(5205Kb)  |  收藏  |  浏览/下载：126/40  |  提交时间：2023/10/31
Dynamic environments  goal-conditioned reinforcement learning  magnetic field  reward shaping  
Adaptive Graph Embedding With Consistency and Specificity for Domain Adaptation 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2023, 卷号: 10, 期号: 11, 页码: 2094-2107
作者:  Shaohua Teng;  Zefeng Zheng;  Naiqi Wu;  Luyao Teng;  Wei Zhang
Adobe PDF(1786Kb)  |  收藏  |  浏览/下载：160/68  |  提交时间：2023/09/22
Adaptive adjustment  consistency and specificity  domain adaptation  graph embedding  geometrical and semantic metrics  
A Novel Competition-Based Coordination Model With Dynamic Feedback for Multi-Robot Systems 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2023, 卷号: 10, 期号: 10, 页码: 2029-2031
作者:  Bo Peng;  Xuerui Zhang;  Mingsheng Shang
Adobe PDF(9603Kb)  |  收藏  |  浏览/下载：77/30  |  提交时间：2023/09/07
Dynamic Event-Triggered Fixed-Time Consensus Control and Its Applications to Magnetic Map Construction 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2023, 卷号: 10, 期号: 10, 页码: 2000-2013
作者:  Jiayu Chai;  Qiang Lu;  Xudong Tao;  Dongliang Peng;  Botao Zhang
Adobe PDF(5376Kb)  |  收藏  |  浏览/下载：141/47  |  提交时间：2023/09/07
Dynamic event-triggered rule  fixed-time consensus control  fixed-time stability condition  magnetic map construction  multi-agent system  
A Length-Adaptive Non-Dominated Sorting Genetic Algorithm for Bi-Objective High-Dimensional Feature Selection 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2023, 卷号: 10, 期号: 9, 页码: 1834-1844
作者:  Yanlu Gong;  Junhai Zhou;  Quanwang Wu;  MengChu Zhou;  Junhao Wen
Adobe PDF(1308Kb)  |  收藏  |  浏览/下载：130/63  |  提交时间：2023/08/10
Bi-objective optimization  feature selection (FS)  genetic algorithm  high-dimensional data  length-adaptive  
How Generative Adversarial Networks Promote the Development of Intelligent Transportation Systems: A Survey 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2023, 卷号: 10, 期号: 9, 页码: 1781-1796
作者:  Hongyi Lin;  Yang Liu;  Shen Li;  Xiaobo Qu
Adobe PDF(1497Kb)  |  收藏  |  浏览/下载：237/143  |  提交时间：2023/08/10
Autonomous driving  generative adversarial network (GAN)  intelligent transportation system (ITS)  traffic anomaly inspection  traffic flow  
New Control Paradigm for Industry 5.0: From Big Models to Foundation Control and Management 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2023, 卷号: 10, 期号: 8, 页码: 1643-1646
作者:  Fei-Yue Wang
Adobe PDF(966Kb)  |  收藏  |  浏览/下载：66/22  |  提交时间：2023/07/20
Pavement Cracks Coupled With Shadows: A New Shadow-Crack Dataset and A Shadow-Removal-Oriented Crack Detection Approach 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2023, 卷号: 10, 期号: 7, 页码: 1593-1607
作者:  Lili Fan;  Shen Li;  Ying Li;  Bai Li;  Dongpu Cao;  Fei-Yue Wang
Adobe PDF(23502Kb)  |  收藏  |  浏览/下载：123/16  |  提交时间：2023/06/14
Automatic pavement crack detection  data augmentation compensation  deep learning  residual feature augmentation  shadow removal  shadow-crack dataset  
Estimating the State of Health for Lithium-ion Batteries: A Particle Swarm Optimization-Assisted Deep Domain Adaptation Approach 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2023, 卷号: 10, 期号: 7, 页码: 1530-1543
作者:  Guijun Ma;  Zidong Wang;  Weibo Liu;  Jingzhong Fang;  Yong Zhang;  Han Ding;  Ye Yuan
Adobe PDF(4250Kb)  |  收藏  |  浏览/下载：169/70  |  提交时间：2023/06/14
Deep transfer learning  domain adaptation  hyperparameter selection  lithium-ion batteries (LIBs)  particle swarm optimization  state of health estimation (SOH)  
Augmented Industrial Data-Driven Modeling Under the Curse of Dimensionality 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2023, 卷号: 10, 期号: 6, 页码: 1445-1461
作者:  Xiaoyu Jiang;  Xiangyin Kong;  Zhiqiang Ge
Adobe PDF(25936Kb)  |  收藏  |  浏览/下载：135/43  |  提交时间：2023/05/29
Curse of dimensionality  data augmentation  data-driven modeling  industrial processes  machine learning