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A Weakly-Supervised Crowd Density Estimation Method Based on Two-Stage Linear Feature Calibration 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2024, 卷号: 11, 期号: 4, 页码: 965-981
作者:  Yong-Chao Li;  Rui-Sheng Jia;  Ying-Xiang Hu;  Hong-Mei Sun
Adobe PDF(10448Kb)  |  收藏  |  浏览/下载：52/24  |  提交时间：2024/03/18
Crowd density estimation  linear feature calibration  vision transformer  weakly-supervision learning  
Label Recovery and Trajectory Designable Network for Transfer Fault Diagnosis of Machines With Incorrect Annotation 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2024, 卷号: 11, 期号: 4, 页码: 932-945
作者:  Bin Yang;  Yaguo Lei;  Xiang Li;  Naipeng Li;  Asoke K. Nandi
Adobe PDF(18822Kb)  |  收藏  |  浏览/下载：43/8  |  提交时间：2024/03/18
Deep transfer learning  domain adaptation  incorrect label annotation  intelligent fault diagnosis  rotating machines  
A Tutorial on Federated Learning from Theory to Practice: Foundations, Software Frameworks, Exemplary Use Cases, and Selected Trends 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2024, 卷号: 11, 期号: 4, 页码: 824-850
作者:  M. Victoria Luzón;  Nuria Rodríguez-Barroso;  Alberto Argente-Garrido;  Daniel Jiménez-López;  Jose M. Moyano;  Javier Del Ser;  Weiping Ding;  Francisco Herrera
Adobe PDF(4602Kb)  |  收藏  |  浏览/下载：41/13  |  提交时间：2024/03/18
Data privacy  distributed machine learning  federated learning  software frameworks  
More than Lightening: A Self-supervised Low-light Image Enhancement Method Capable for Multiple Degradations 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2024, 卷号: 11, 期号: 3, 页码: 622-637
作者:  Han Xu;  Jiayi Ma;  Yixuan Yuan;  Hao Zhang;  Xin Tian;  Xiaojie Guo
Adobe PDF(25490Kb)  |  收藏  |  浏览/下载：56/9  |  提交时间：2024/02/19
Color correction  low-light image enhancement  self-supervised learning  
Point Cloud Classification Using Content-Based Transformer via Clustering in Feature Space 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2024, 卷号: 11, 期号: 1, 页码: 231-239
作者:  Yahui Liu;  Bin Tian;  Yisheng Lv;  Lingxi Li;  Fei-Yue Wang
Adobe PDF(8541Kb)  |  收藏  |  浏览/下载：251/161  |  提交时间：2024/01/02
Content-based Transformer  deep learning  feature aggregator  local attention  point cloud classification  
Steps Toward Industry 5.0: Building “6S” Parallel Industries With Cyber-Physical-Social Intelligence 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2023, 卷号: 10, 期号: 8, 页码: 1692-1703
作者:  Xingxia Wang;  Jing Yang;  Yutong Wang;  Qinghai Miao;  Fei-Yue Wang;  Aijun Zhao;  Jian-Ling Deng;  Lingxi Li;  Xiaoxiang Na;  Ljubo Vlacic
Adobe PDF(15811Kb)  |  收藏  |  浏览/下载：130/26  |  提交时间：2023/07/20
ACP  artificial intelligence  CPS  CPSS  Industry 4.0  Industry 5.0  parallel industries  parallel intelligence  
Point Cloud Classification Using Content-based Transformer via Clustering in Feature Space 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2023, 页码: 1-9
作者:  Yahui Liu;  Bin Tian;  Yisheng Lv;  Lingxi Li;  Feiyue Wang
Adobe PDF(4216Kb)  |  收藏  |  浏览/下载：213/72  |  提交时间：2023/05/19
SuperFusion: A Versatile Image Registration and Fusion Network with Semantic Awareness 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2022, 卷号: 9, 期号: 12, 页码: 2121-2137
作者:  Linfeng Tang;  Yuxin Deng;  Yong Ma;  Jun Huang;  Jiayi Ma
Adobe PDF(7619Kb)  |  收藏  |  浏览/下载：251/93  |  提交时间：2022/12/02
Global spatial attention  image fusion  image registration  mutual promotion  semantic awareness  
Parallel Manufacturing for Industrial Metaverses: A New Paradigm in Smart Manufacturing 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2022, 卷号: 9, 期号: 12, 页码: 2063-2070
作者:  Jing Yang;  Xiaoxing Wang;  Yandong Zhao
Adobe PDF(2163Kb)  |  收藏  |  浏览/下载：166/49  |  提交时间：2022/12/02
Parallel Manufacturing  Digital Workers  CPSS  Smart Manufacturing  Industrial Metaverses  
Visuals to Text: A Comprehensive Review on Automatic Image Captioning 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2022, 卷号: 9, 期号: 8, 页码: 1339-1365
作者:  Yue Ming;  Nannan Hu;  Chunxiao Fan;  Fan Feng;  Jiangwan Zhou;  Hui Yu
Adobe PDF(56128Kb)  |  收藏  |  浏览/下载：170/21  |  提交时间：2022/08/01
Artificial intelligence  attention mechanism  encoder-decoder framework  image captioning  multi-modal understanding  training strategies