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A Two-layer Encoding Learning Swarm Optimizer based on Frequent Itemsets for Sparse Large-scale Multi-objective Optimization 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2024, 卷号: 11, 期号: 6, 页码: 1342-1357
作者:  Sheng Qi;  Rui Wang;  Tao Zhang;  Xu Yang;  Ruiqing Sun;  Ling Wang
Adobe PDF(3038Kb)  |  收藏  |  浏览/下载：4/0  |  提交时间：2024/05/22
Evolutionary algorithms  learning swarm optimization  sparse large-scale optimization  sparse large-scale multi-objective problems  two-layer encoding  
Computational Experiments for Complex Social Systems: Integrated Design of Experiment System 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2024, 卷号: 11, 期号: 5, 页码: 1175-1189
作者:  Xiao Xue;  Xiangning Yu;  Deyu Zhou;  Xiao Wang;  Chongke Bi;  Shufang Wang;  Fei-Yue Wang
Adobe PDF(11890Kb)  |  收藏  |  浏览/下载：32/4  |  提交时间：2024/04/10
Artificial society  computational experiments  model integration  operation engine  technology integration  
Evolutionary Optimization Methods for High-Dimensional Expensive Problems: A Survey 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2024, 卷号: 11, 期号: 5, 页码: 1092-1105
作者:  MengChu Zhou;  Meiji Cui;  Dian Xu;  Shuwei Zhu;  Ziyan Zhao;  Abdullah Abusorrah
Adobe PDF(2100Kb)  |  收藏  |  浏览/下载：16/3  |  提交时间：2024/04/10
Evolutionary algorithm (EA)  high-dimensional expensive problems (HEPs)  industrial applications  surrogate-assisted optimization  
Computational Experiments for Complex Social Systems: Experiment Design and Generative Explanation 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2024, 卷号: 11, 期号: 4, 页码: 1022-1038
作者:  Xiao Xue;  Deyu Zhou;  Xiangning Yu;  Gang Wang;  Juanjuan Li;  Xia Xie;  Lizhen Cui;  Fei-Yue Wang
Adobe PDF(7239Kb)  |  收藏  |  浏览/下载：36/8  |  提交时间：2024/03/18
Agent-based modeling  computational experiments  cyber-physical-social systems (CPSS)  generative deduction  generative experiments  meta model  
Adaptive Trajectory Tracking Control for Nonholonomic Wheeled Mobile Robots: A Barrier Function Sliding Mode Approach 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2024, 卷号: 11, 期号: 4, 页码: 1007-1021
作者:  Yunjun Zheng;  Jinchuan Zheng;  Ke Shao;  Han Zhao;  Hao Xie;  Hai Wang
Adobe PDF(32944Kb)  |  收藏  |  浏览/下载：27/3  |  提交时间：2024/03/18
Adaptive sliding mode  barrier function  nonholonomic wheeled mobile robot (NWMR)  trajectory tracking control  
PAPS: Progressive Attention-Based Pan-sharpening 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2024, 卷号: 11, 期号: 2, 页码: 391-404
作者:  Yanan Jia;  Qiming Hu;  Renwei Dian;  Jiayi Ma;  Xiaojie Guo
Adobe PDF(10173Kb)  |  收藏  |  浏览/下载：48/10  |  提交时间：2024/01/23
High-resolution multispectral image  image fusion  pan-sharpening  progressive enhancement  
The TAO of Blockchain Intelligence for Intelligent Web 3.0 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2023, 卷号: 10, 期号: 12, 页码: 2183-2186
作者:  Juanjuan Li;  Fei-Yue Wang
Adobe PDF(146Kb)  |  收藏  |  浏览/下载：88/35  |  提交时间：2023/10/31
A Brief Overview of ChatGPT: The History, Status Quo and Potential Future Development 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2023, 卷号: 10, 期号: 5, 页码: 1122-1136
作者:  Tianyu Wu;  Shizhu He;  Jingping Liu;  Siqi Sun;  Kang Liu;  Qing-Long Han;  Yang Tang
Adobe PDF(4650Kb)  |  收藏  |  浏览/下载：790/653  |  提交时间：2023/04/26
AIGC  ChatGPT  GPT-3  GPT-4  human feedback  large language models  
Cryptocurrency Transaction Network Embedding From Static and Dynamic Perspectives: An Overview 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2023, 卷号: 10, 期号: 5, 页码: 1105-1121
作者:  Yue Zhou;  Xin Luo;  MengChu Zhou
Adobe PDF(1723Kb)  |  收藏  |  浏览/下载：129/62  |  提交时间：2023/04/26
Big data analysis.  cryptocurrency transaction network embedding (CTNE)  dynamic network  network embedding  network representation  static network  
DAO to Hanoi via DeSci: AI Paradigm Shift from AlphaGo to ChatGPT 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2023, 卷号: 10, 期号: 4, 页码: 877-897
作者:  Miao, Qinghai (proxy) (contact);  Zheng, Wenbo;  吕, 宜生;  Huang, Min;  Ding, Wenwen;  Wang, Fei-Yue
Adobe PDF(4968Kb)  |  收藏  |  浏览/下载：246/67  |  提交时间：2023/03/22
ChatGPT, decentralized science (DeSci)  decentralized autonomous organization (DAO)  machine learning  paradigm shift