机构

• 长春光学精密机械与物... [1]
• 数学与系统科学研究院 [1]
• 自动化研究所 [1]

采集方式

• OAI收割 [3]

内容类型

• 期刊论文 [2]
• 会议论文 [1]

发表日期

• 2021 [2]
• 2011 [1]

学科主题

筛选

浏览/检索结果: 共3条，第1-3条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 Active Disturbance Rejection Control for a Fluid-Driven Hand Rehabilitation Device 期刊论文  OAI收割 IEEE-ASME TRANSACTIONS ON MECHATRONICS, 2021, 卷号: 26, 期号: 2, 页码: 841-853 作者:  Li, Houcheng;  Cheng, Long;  Li, Zhengwei;  Xue, Wenchao   |  收藏  |  浏览/下载：25/0  |  提交时间：2021/06/07 Actuators  Exoskeletons  Performance evaluation  Training  IEEE transactions  Mechatronics  Active disturbance rejection control  extended state observer  extension motion  hand rehabilitation  parameter selection  transient  steady-state performance   Active Disturbance Rejection Control for a Fluid-Driven Hand Rehabilitation Device 期刊论文  OAI收割 IEEE-ASME TRANSACTIONS ON MECHATRONICS, 2021, 卷号: 26, 期号: 2, 页码: 841-853 作者:  Li, Houcheng;  Cheng, Long;  Li, Zhengwei;  Xue, Wenchao   |  收藏  |  浏览/下载：26/0  |  提交时间：2021/10/26 Actuators  Exoskeletons  Performance evaluation  Training  IEEE transactions  Mechatronics  Active disturbance rejection control  extended state observer  extension motion  hand rehabilitation  parameter selection  transient  steady-state performance   Efficient human action recognition using accumulated motion image and support vector machines (EI CONFERENCE) 会议论文  OAI收割 International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2011, November 19, 2011 - November 23, 2011, Suzhou, China 作者:  Zhang X.;  Zhang J.;  Zhang J.;  Zhang X.;  Zhang X. 收藏  |  浏览/下载：67/0  |  提交时间：2013/03/25 Vision-based human action recognition provides an advanced interface  and research in this field of human action recognition has been actively carried out. This paper describes a scheme for recognizing human actions from a video sequences. The proposed method is an extension of the Motion History Image(MHI) method based on the ordinal measure of accumulated motion  which is robust to variations of appearances. We define the accumulated motion image(AMI) using image differences firstly. Then the AMI of the video sequencesis resized to a MN regulation following the standard of training phases. Finally  we employ Support Vector Machine(SVM) as a classifier to distinguish the current activity in target video sequences. In a word  our proposed algorithm not only outperforms the state of art on public available KTH data set and Weizmann data set  but also proves practical to some real world applications  in addition  this method is computationally simple and able to achieve a satisfactory accuracy.