中文版 | English

在结果中检索

CASIA OpenIR

浏览/检索结果: 共109条，第1-10条 帮助

限定条件    
已选(0)清除 条数/页：   排序方式：
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：21/8  |  提交时间：2024/03/18
Crowd density estimation  linear feature calibration  vision transformer  weakly-supervision learning  
Detection of Oscillations in Process Control Loops From Visual Image Space Using Deep Convolutional Networks 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2024, 卷号: 11, 期号: 4, 页码: 982-995
作者:  Tao Wang;  Qiming Chen;  Xun Lang;  Lei Xie;  Peng Li;  Hongye Su
Adobe PDF(3095Kb)  |  收藏  |  浏览/下载：28/9  |  提交时间：2024/03/18
Convolutional neural networks (CNNs)  deep learning  image processing  oscillation detection  process industries  
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：17/4  |  提交时间：2024/03/18
Data privacy  distributed machine learning  federated learning  software frameworks  
Depth-Guided Vision Transformer With Normalizing Flows for Monocular 3D Object Detection 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2024, 卷号: 11, 期号: 3, 页码: 673-689
作者:  Cong Pan;  Junran Peng;  Zhaoxiang Zhang
Adobe PDF(37784Kb)  |  收藏  |  浏览/下载：45/18  |  提交时间：2024/02/19
Monocular 3D object detection  normalizing flows  Swin Transformer  
MAUN: Memory-Augmented Deep Unfolding Network for Hyperspectral Image Reconstruction 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2024, 卷号: 11, 期号: 5, 页码: 1139-1150
作者:  Qian Hu;  Jiayi Ma;  Yuan Gao;  Junjun Jiang;  Yixuan Yuan
Adobe PDF(10937Kb)  |  收藏  |  浏览/下载：13/1  |  提交时间：2024/04/10
Compressive imaging  deep unfolding network hyperspectral image  
Visual Semantic Segmentation Based on Few/Zero-Shot Learning: An Overview 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2024, 卷号: 11, 期号: 5, 页码: 1106-1126
作者:  Wenqi Ren;  Yang Tang;  Qiyu Sun;  Chaoqiang Zhao;  Qing-Long Han
Adobe PDF(12695Kb)  |  收藏  |  浏览/下载：10/1  |  提交时间：2024/04/10
Computer vision  deep learning  few-shot learning  low-shot learning  semantic segmentation  zero-shot learning  
Intuitive Human-Robot-Environment Interaction With EMG Signals: A Review 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2024, 卷号: 11, 期号: 5, 页码: 1075-1091
作者:  Dezhen Xiong;  Daohui Zhang;  Yaqi Chu;  Yiwen Zhao;  Xingang Zhao
Adobe PDF(5061Kb)  |  收藏  |  浏览/下载：7/2  |  提交时间：2024/04/10
Electromyography  human-robot-environment interaction (HREI)  human-robot interaction (HRI)  semiautonomous  sensory feedback  
Dynamic Vision Enabled Contactless Cross-Domain Machine Fault Diagnosis With Neuromorphic Computing 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2024, 卷号: 11, 期号: 3, 页码: 788-790
作者:  Xinrui Chen;  Xiang Li;  Shupeng Yu;  Yaguo Lei;  Naipeng Li;  Bin Yang
Adobe PDF(24644Kb)  |  收藏  |  浏览/下载：65/20  |  提交时间：2024/02/19
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：218/152  |  提交时间：2024/01/02
Content-based Transformer  deep learning  feature aggregator  local attention  point cloud classification  
Reinforcement Learning in Process Industries: Review and Perspective 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2024, 卷号: 11, 期号: 2, 页码: 283-300
作者:  Oguzhan Dogru;  Junyao Xie;  Om Prakash;  Ranjith Chiplunkar;  Jansen Soesanto;  Hongtian Chen;  Kirubakaran Velswamy;  Fadi Ibrahim;  Biao Huang
Adobe PDF(1275Kb)  |  收藏  |  浏览/下载：37/13  |  提交时间：2024/01/23
Process control  process systems engineering  reinforcement learning