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Underground Comprehensive Pipe Gallery Illegal Intrusion Wireless Sensing System
会议论文
2018 IEEE INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING, COMMUNICATIONS AND COMPUTING (ICSPCC), 2018-01-01
作者:
Zhuang, Xueyong[1]
;
Chen, Siwen[2]
;
Wu, Jie[3]
;
Wang, Chao[4]
收藏
  |  
浏览/下载:2/0
  |  
提交时间:2019/04/22
underground comprehensive pipe gallery illegal intrusion wireless sensing system
channel state information
human actions recognition
Wi-Fi
Human Actions Recognition Using Improved MHI and 2-D Gabor Filter Based on Energy Blocks
会议论文
作者:
Junfeng Sun
;
Hongji Xu
;
Yingming Zhou
;
Lingling Pan
;
Feifei Li
收藏
  |  
浏览/下载:7/0
  |  
提交时间:2019/12/31
human actions recognition
motion-history image (MHI)
2-D Gabor feature
adaboost
An improved probabilistic method for screening safety-related human actions in nuclear power plants
期刊论文
Nuclear Engineering and Design, 2018, 卷号: 340, 页码: 415-420
作者:
Zhang, Li
;
Wang, Chunbo
;
Zio, Enrico
;
Zou, Yanhua*
;
Jiang, Jianjun
收藏
  |  
浏览/下载:11/0
  |  
提交时间:2019/12/27
ASG
Auxiliary feedwater system
CDF
Core damage frequency
EPRI
Electric Power Research Institute
FV
Fussell-Vesely
HEP
Human error probability
HFE
Human factors engineering
HRA
Human reliability analysis
HRAW
Human action risk achievement worth
IAEA
International Atomic Energy Agency
LERF
Large early release frequency
NEI
Nuclear Energy Institute
NNSA
National Nuclear Safety Administration of China
NPP
Nuclear power plant
NRC
Nuclear Regulatory Commission
ONR
Office for Nuclear Regulation
PSA
Probabilistic safety assessment
RAW
Risk achievement worth
RHR
Residual heat removal system
RRW
Risk reduction worth
SDP
Significance determination process
SGTR
Steam generator tube rupture
Nuclear power plant (NPP)
Human actions
Probabilistic screening method
Human action risk achievement worth (HRAW)
Human factors engineering (HFE)
Online Data Segmentation Based on Clustering Algorithm and Autoregressive Model for Human Actions Recognition
会议论文
2017 4TH INTERNATIONAL CONFERENCE ON INFORMATION, CYBERNETICS AND COMPUTATIONAL SOCIAL SYSTEMS (ICCSS), 2017-01-01
作者:
Jiang, M.
;
Liu, X. L.
;
Zhang, Z.
;
Zhao, Y.
;
Zhang, R.
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  |  
浏览/下载:6/0
  |  
提交时间:2019/12/03
Online data segmentation
human actions recognition
inertial sensors
Autoregressive model
3D human activity recognition using skeletal data from RGBD sensors
会议论文
作者:
Ling, Jiaxu
;
Tian, Lihua
;
Li, Chen
收藏
  |  
浏览/下载:2/0
  |  
提交时间:2019/12/02
Direction features
Fusion features
Human actions
Human activity recognition
Human postures
Microsoft kinect
Multi-class support vector machines
Rgb-d sensors
Efficient Search and Localization of Human Actions in Video Databases
期刊论文
ieee transactions on circuits and systems for video technology, 2014, 卷号: 24, 期号: 3, 页码: 504-512
作者:
Shao, Ling
;
Jones, Simon
;
Li, Xuelong
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  |  
浏览/下载:18/0
  |  
提交时间:2015/03/18
Human actions
relevance feedback
spatio-temporal localization
video retrieval
Latent semantic learning with structured sparse representation for human action recognition
期刊论文
模式识别, 2013
Lu, Zhiwu
;
Peng, Yuxin
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  |  
浏览/下载:5/0
  |  
提交时间:2015/11/10
Human action recognition
Latent semantic learning
Spectral embedding
Structured sparse representation
L-1-norm hypergraph regularization
DIMENSIONALITY REDUCTION
RECOGNIZING ACTIONS
VOCABULARIES
FRAMEWORK
FEATURES
SET
Efficient human action recognition using accumulated motion image and support vector machines (EI CONFERENCE)
会议论文
International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2011, November 19, 2011 - November 23, 2011, Suzhou, China
Cao W.
;
Zhang X.
;
Cao S.
;
Zhang J.
;
Wang M.
;
Han G.
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  |  
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.
多任务环境中基于情感的拟人Agent面部运动的实现
期刊论文
2010, 2010
杨斌
;
周芳芳
;
贾培发
;
YANG Bin
;
ZHOU Fangfang
;
JIA Peifa
收藏
  |  
浏览/下载:3/0
多任务环境中基于情感的拟人Agent面部运动的实现
期刊论文
2010, 2010
杨斌
;
周芳芳
;
贾培发
;
YANG Bin
;
ZHOU Fangfang
;
JIA Peifa
收藏
  |  
浏览/下载:2/0
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