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A non-iterative posterior sampling algorithm for Laplace linear regression model
期刊论文
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2017, 卷号: 46, 期号: 3, 页码: 2488-2503
作者:
Yang, Fengkai
;
Yuan, Haijing
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  |  
浏览/下载:4/0
  |  
提交时间:2019/12/12
EM algorithm
Gibbs sampling
Inverse Bayes formulae
Laplace linear
regression
Sampling/important resampling
Efficient estimation of the functional reliability of a passive system by means of an improved Line Sampling method
期刊论文
ANNALS OF NUCLEAR ENERGY, 2013, 卷号: 55, 期号: [db:dc_citation_issue], 页码: 9-17
作者:
Wang, Baosheng
;
Wang, Dongqing
;
Jiang, Jin
;
Zhang, Jianmin
收藏
  |  
浏览/下载:6/0
  |  
提交时间:2019/12/10
Passive safety system
Uncertainty
Important direction
Functional failure reliability
Line Sampling
Markov Chain
基于自适应光子发射的渐进式光子映射
期刊论文
Computer Engineering and Design, 2012, 卷号: 33, 期号: 1, 页码: 219-223
李睿
;
陈彦云
;
刘学慧
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  |  
浏览/下载:18/0
  |  
提交时间:2012/11/12
progressive photon mapping
adaptive photon tracing
visual importance
important sampling
global illumination
Important Sampling for Structural Reliability Based on Radial Basis Function Neural Network
会议论文
作者:
Gao, Lihong
;
Xu, Gening
;
Yang, Ping
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  |  
浏览/下载:0/0
  |  
提交时间:2019/11/15
Structure reliability
Important sampling(IS)
Radial basis function neural network(RBFNN)
Important sampling for structural reliability based on radial basis function neural network
会议论文
Sanya, China, July 29, 2011 - July 31, 2011
作者:
Gao, Lihong
;
Xu, Gening
;
Yang, Ping
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  |  
浏览/下载:0/0
  |  
提交时间:2020/11/15
Computer simulation
Finite element method
Image segmentation
Neural networks
Numerical methods
Radial basis function networks
Random variables
Rectangular waveguides
Structural analysis
Calculation procedure
Design points
Finite element solver
Implicit function
Important sampling(IS)
Input random variables
Limit state functions
Radial basis function neural networks
Radial basis functions neural networks
Reliability Index
Structural reliability
Structural reliability analysis
Structure reliability
Structure response
Geometrical modulation transfer function of different active pixel of CMOS APS (EI CONFERENCE)
会议论文
2nd International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test and Measurement Technology and Equipment, November 2, 2005 - November 5, 2005, Zian, China
Li J.
;
Liu J.
;
Hao Z.
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浏览/下载:20/0
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提交时间:2013/03/25
The geometrical Modulation Transfer Function (MTF) of CMOS APS (active pixel sensor) is analyzed in this paper. Advanced APS have been designed and fabricated where different pixel shapes such as square
rectangle and L shape
were placed
because the amplifier circuit and other function circuits inter pixel of APS take up some pixel area. MTF is an important figure of merit in focal plane array imaging sensors. Research on analyzing the MTF for the proper pixel shape is currently in progress for a centroidal configuration of a target position. MTF will give us a more complete understanding of the tradeoffs opposed by the different pixel designs and by the signal processing conditions. Based on image sensor sampling and reconstructing model
the MTF expression of any active pixel shape has been deduced in this paper. According to actual pixel shape
three different active area pixels were analyzed
they were square
rectangle
and L shape
their Fill Factor (FF) is 30%
44% and 55%
respectively. Results of simulation experiments indicate that different pixel geometrical characteristics contribute significantly to the figures of their MTF. Different geometrical shape of active sensitive area of pixel and different station in pixel would influence MTF figures. The analysis results are important in designing better APS pixel and more important in analyzing imaging system performance of APS subpixel precision system.
Improved adaptive particle filters for mobile robot indoor localizations using laser range finder
会议论文
6th World Congress on Intelligent Control and Automation, Dalian, PEOPLES R CHINA, 2006-01-01
作者:
Yan, Fei
;
Wang, Wei
;
Wang, Sheng
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  |  
浏览/下载:3/0
  |  
提交时间:2019/12/27
global localization
adaptive particle filters
sequential important sampling
autonomous mobile robot
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