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Cooperative control of dual-arm robots in different human-robot collaborative tasks
期刊论文
ASSEMBLY AUTOMATION, 2019, 卷号: 40, 期号: 1, 页码: 95-104
作者:
Yu, Xinbo
;
Zhang, Shuang
;
Sun, Liang
;
Wang, Yu
;
Xue, Chengqian
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浏览/下载:2/0
  |  
提交时间:2020/04/07
Teleoperation
Impedance control
Baxter robot
Cooperative control
Human moving target estimation
Radial basis functions neural networks (RBFNNs)
SAR target configuration recognition based on the biologically inspired model
期刊论文
NEUROCOMPUTING, 2017, 卷号: 234, 页码: 185-191
作者:
Huang, Xiayuan
;
Nie, Xiangli
;
Wu, Wei
;
Qiao, Hong
;
Zhang, Bo
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浏览/下载:19/0
  |  
提交时间:2018/07/30
Biologically inspired model
SAR target configuration recognition
Episodic features
Semantic features
Aspect angle estimation
SAR target configuration recognition based on the biologically inspired model
期刊论文
NEUROCOMPUTING, 2017, 卷号: 234, 页码: 185-191
作者:
Huang, Xiayuan
;
Nie, Xiangli
;
Wu, Wei
;
Qiao, Hong
;
Zhang, Bo
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  |  
浏览/下载:28/0
  |  
提交时间:2017/05/05
Biologically inspired model
SAR target configuration recognition
Episodic features
Semantic features
Aspect angle estimation
基于混合高斯模型的日冕物质抛射探测方法
期刊论文
科学通报, 2016, 卷号: 61, 期号: 11, 页码: 1255-1264
作者:
曾丹丹
;
白先勇
;
强振平
;
李强
;
季凯帆
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浏览/下载:25/0
  |  
提交时间:2016/07/12
混合高斯模型
日冕物质抛射
探测
背景差分
Device-Free Mobile Target Tracking Using Passive Tags
期刊论文
international journal of distributed sensor networks, 2015
作者:
Ding, Han
;
Xi, Min
;
Li, Zhe
;
Zhao, Jizhong
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浏览/下载:23/0
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提交时间:2015/12/16
基于粒子滤波的目标跟踪技术研究
学位论文
博士: 中国科学院大学, 2014
宋策
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浏览/下载:101/0
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提交时间:2014/08/21
杂波环境下AUV纯方位目标跟踪方法研究
学位论文
硕士: 中国科学院沈阳自动化研究所, 2014
作者:
梅登峰
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浏览/下载:37/0
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提交时间:2014/07/18
自主水下机器人
容积卡尔曼滤波
概率数据关联滤波器
联合概率数据关联滤波器
广义概率数据关联滤波器 无人水面机器人
在线路径规划
速度避障法
混合整数线性规划
金字塔表观跟踪与半监督轨迹学习
学位论文
工学硕士, 中国科学院自动化研究所: 中国科学院研究生院, 2011
刘洋
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浏览/下载:19/0
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提交时间:2015/09/02
运动目标跟踪
金字塔表观建模
轨迹模式学习
半监督学习
轨迹描述子
object tracking
pyramidal appearance modeling
trajectory pattern learning
semi-supervised learning
trajectory descriptor
The new approach for infrared target tracking based on the particle filter algorithm (EI CONFERENCE)
会议论文
International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Infrared Imaging and Applications, May 24, 2011 - May 24, 2011, Beijing, China
Sun H.
;
Han H.-X.
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浏览/下载:50/0
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提交时间:2013/03/25
Target tracking on the complex background in the infrared image sequence is hot research field. It provides the important basis in some fields such as video monitoring
precision
and video compression human-computer interaction. As a typical algorithms in the target tracking framework based on filtering and data connection
the particle filter with non-parameter estimation characteristic have ability to deal with nonlinear and non-Gaussian problems so it were widely used. There are various forms of density in the particle filter algorithm to make it valid when target occlusion occurred or recover tracking back from failure in track procedure
but in order to capture the change of the state space
it need a certain amount of particles to ensure samples is enough
and this number will increase in accompany with dimension and increase exponentially
this led to the increased amount of calculation is presented. In this paper particle filter algorithm and the Mean shift will be combined. Aiming at deficiencies of the classic mean shift Tracking algorithm easily trapped into local minima and Unable to get global optimal under the complex background. From these two perspectives that "adaptive multiple information fusion" and "with particle filter framework combining"
we expand the classic Mean Shift tracking framework.Based on the previous perspective
we proposed an improved Mean Shift infrared target tracking algorithm based on multiple information fusion. In the analysis of the infrared characteristics of target basis
Algorithm firstly extracted target gray and edge character and Proposed to guide the above two characteristics by the moving of the target information thus we can get new sports guide grayscale characteristics and motion guide border feature. Then proposes a new adaptive fusion mechanism
used these two new information adaptive to integrate into the Mean Shift tracking framework. Finally we designed a kind of automatic target model updating strategy to further improve tracking performance. Experimental results show that this algorithm can compensate shortcoming of the particle filter has too much computation
and can effectively overcome the fault that mean shift is easy to fall into local extreme value instead of global maximum value.Last because of the gray and fusion target motion information
this approach also inhibit interference from the background
ultimately improve the stability and the real-time of the target track. 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).
复杂场景下目标的跟踪与姿态估计
学位论文
工学博士, 中国科学院自动化研究所: 中国科学院研究生院, 2010
作者:
张笑钦
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浏览/下载:179/0
  |  
提交时间:2015/09/02
图嵌入学习
粒子群优化
核贝叶斯
卡尔曼滤波
粒子滤波
目标跟踪
姿态估计
Graph embedding learning
particle swarm optimization
kernel Bayesian
Kalman filter
particle filter
object tracking
pose estimation
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