中文版 | English

在结果中检索

CASIA OpenIR

浏览/检索结果: 共17条，第1-10条 帮助

限定条件        
已选(0)清除 条数/页：   排序方式：
Knowledge Transfer Learning via Dual Density Sampling for Resource-Limited Domain Adaptation 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2023, 卷号: 10, 期号: 12, 页码: 2269-2291
作者:  Zefeng Zheng;  Luyao Teng;  Wei Zhang;  Naiqi Wu;  Shaohua Teng
Adobe PDF(15412Kb)  |  收藏  |  浏览/下载：133/25  |  提交时间：2023/10/31
Cross-domain risk  dual density sampling  intra-domain risk  maximum mean discrepancy  knowledge transfer learning  resource-limited domain adaptation  
Diverse Deep Matrix Factorization With Hypergraph Regularization for Multi-View Data Representation 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2023, 卷号: 10, 期号: 11, 页码: 2154-2167
作者:  Haonan Huang;  Guoxu Zhou;  Naiyao Liang;  Qibin Zhao;  Shengli Xie
Adobe PDF(3206Kb)  |  收藏  |  浏览/下载：145/42  |  提交时间：2023/09/22
Deep matrix factorization (DMF)  diversity  hypergraph regularization  multi-view data representation (MDR)  
GraphCA: Learning From Graph Counterfactual Augmentation for Knowledge Tracing 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2023, 卷号: 10, 期号: 11, 页码: 2108-2123
作者:  Xinhua Wang;  Shasha Zhao;  Lei Guo;  Lei Zhu;  Chaoran Cui;  Liancheng Xu
Adobe PDF(1627Kb)  |  收藏  |  浏览/下载：108/45  |  提交时间：2023/09/22
Contrastive learning  counterfactual representation  graph neural network  knowledge tracing  
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：136/56  |  提交时间：2023/09/22
Adaptive adjustment  consistency and specificity  domain adaptation  graph embedding  geometrical and semantic metrics  
A Data-Driven Rutting Depth Short-Time Prediction Model With Metaheuristic Optimization for Asphalt Pavements Based on RIOHTrack 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2023, 卷号: 10, 期号: 10, 页码: 1918-1932
作者:  Zhuoxuan Li;  Iakov Korovin;  Xinli Shi;  Sergey Gorbachev;  Nadezhda Gorbacheva;  Wei Huang;  Jinde Cao
Adobe PDF(3879Kb)  |  收藏  |  浏览/下载：113/37  |  提交时间：2023/09/07
Extreme learning machine algorithm with residual correction (RELM), metaheuristic optimization  oil-gas transportation  RIOHTrack  rutting depth  
Geometry Flow-Based Deep Riemannian Metric Learning 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2023, 卷号: 10, 期号: 9, 页码: 1882-1892
作者:  Yangyang Li;  Chaoqun Fei;  Chuanqing Wang;  Hongming Shan;  Ruqian Lu
Adobe PDF(1815Kb)  |  收藏  |  浏览/下载：139/45  |  提交时间：2023/08/10
Curvature regularization  deep metric learning (DML)  embedding learning  geometry flow  riemannian metric  
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：111/57  |  提交时间：2023/08/10
Bi-objective optimization  feature selection (FS)  genetic algorithm  high-dimensional data  length-adaptive  
A Simple Framework to Generalized Zero-Shot Learning for Fault Diagnosis of Industrial Processes 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2023, 卷号: 10, 期号: 6, 页码: 1504-1506
作者:  Jiacheng Huang;  Zuxin Li;  Zhe Zhou
Adobe PDF(526Kb)  |  收藏  |  浏览/下载：73/31  |  提交时间：2023/05/29
A Multi-Objective and Multi-Constraint Optimization Model for Cyber-Physical Power Systems Considering Renewable Energy and Electric Vehicles 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2023, 卷号: 10, 期号: 6, 页码: 1498-1500
作者:  Yu Zhang;  Minrui Fei;  Qing Sun;  Dajun Du;  Aleksandar Rakić;  Kang Li
Adobe PDF(1287Kb)  |  收藏  |  浏览/下载：103/35  |  提交时间：2023/05/29
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：130/42  |  提交时间：2023/05/29
Curse of dimensionality  data augmentation  data-driven modeling  industrial processes  machine learning