CASIA OpenIR

浏览/检索结果: 共5条,第1-5条 帮助

限定条件        
已选(0)清除 条数/页:   排序方式:
Two-particle Debris Flow Simulation Based on SPH 期刊论文
Computer Animation and Virtual Worlds, 2024, 卷号: 35, 期号: 3, 页码: 1-17
作者:  Zhang JX(张佳岫);  Yang M(杨猛);  Xiaomin Li;  Qunou Jiang;  Heng Zhang;  Meng WL(孟维亮)
Adobe PDF(4962Kb)  |  收藏  |  浏览/下载:40/17  |  提交时间:2024/06/04
debris flow  GPU acceleration  natural disaster simulation  SPH  
A Graded Assessment System for Parkinsons Upper-Limb Bradykinesia Based on a Temporal Convolutional Network Model 期刊论文
IEEE SENSORS JOURNAL, 2023, 卷号: 23, 期号: 23, 页码: 29283-29292
作者:  Tong, Lina;  Liu, Dai-Song;  Peng, Liang;  Hao, Hong-Lin;  Wang, Chen
Adobe PDF(9425Kb)  |  收藏  |  浏览/下载:78/9  |  提交时间:2024/02/21
Bradykinesia grade  inertial sensors  Parkinson's disease (PD)  temporal convolutional network (TCN)  wearable device  
Performance Improvement of a High-Speed Swimming Robot for Fish-Like Leaping 期刊论文
IEEE ROBOTICS AND AUTOMATION LETTERS, 2022, 卷号: 7, 期号: 2, 页码: 1936-1943
作者:  Chen, Di;  Wu, Zhengxing;  Zhang, Pengfei;  Tan, Min;  Yu, Junzhi
Adobe PDF(2838Kb)  |  收藏  |  浏览/下载:395/78  |  提交时间:2022/06/06
Biologically-inspired robots  mechanism design  compliant joints and mechanisms  high-speed swimming  fish-like leaping motion  
AutoDet: Pyramid Network Architecture Search for Object Detection 期刊论文
INTERNATIONAL JOURNAL OF COMPUTER VISION, 2021, 期号: 4, 页码: 1087-1105
作者:  Li, Zhihang;  Xi, Teng;  Zhang, Gang;  Liu, Jingtuo;  He, Ran
Adobe PDF(2604Kb)  |  收藏  |  浏览/下载:306/50  |  提交时间:2021/03/01
Object detection  Neural architecture search  Feature pyramids  
Deep Self-Evolution Clustering 期刊论文
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2020, 卷号: 42, 期号: 4, 页码: 809-823
作者:  Chang, Jianlong;  Meng, Gaofeng;  Wang, Lingfeng;  Xiang, Shiming;  Pan, Chunhong
浏览  |  Adobe PDF(4817Kb)  |  收藏  |  浏览/下载:447/97  |  提交时间:2020/06/02
Task analysis  Unsupervised learning  Training  Clustering methods  Pattern analysis  Clustering  deep self-evolution clustering  self-evolution clustering training  deep unsupervised learning