scholarly journals Testing Allele Transmission of an SNP Set Using a Family-Based Generalized Genetic Random Field Method

2016 ◽  
Vol 40 (4) ◽  
pp. 341-351 ◽  
Author(s):  
Ming Li ◽  
Jingyun Li ◽  
Zihuai He ◽  
Qing Lu ◽  
John S. Witte ◽  
...  
Keyword(s):  
Random Field ◽  
Field Method ◽  
Family Based ◽  
Allele Transmission

scholarly journals Detecting Rare Mutations with Heterogeneous Effects Using a Family-Based Genetic Random Field Method

Genetics ◽  
2018 ◽  
Vol 210 (2) ◽  
pp. 463-476 ◽  
Author(s):  
Ming Li ◽  
Zihuai He ◽  
Xiaoran Tong ◽  
John S. Witte ◽  
Qing Lu
Keyword(s):  
Random Field ◽  
Field Method ◽  
Rare Mutations ◽  
Heterogeneous Effects ◽  
Family Based

Open Domain New Word Detection Using Condition Random Field Method

2013 ◽  
Vol 24 (5) ◽  
pp. 1051-1060 ◽  
Author(s):  
Fei CHEN ◽  
Yi-Qun LIU ◽  
Chao WEI ◽  
Yun-Liang ZHANG ◽  
Min ZHANG ◽  
...  
Keyword(s):  
Random Field ◽  
Field Method ◽  
Open Domain ◽  
Word Detection

scholarly journals A Generalized Genetic Random Field Method for the Genetic Association Analysis of Sequencing Data

2014 ◽  
Vol 38 (3) ◽  
pp. 242-253 ◽  
Author(s):  
Ming Li ◽  
Zihuai He ◽  
Min Zhang ◽  
Xiaowei Zhan ◽  
Changshuai Wei ◽  
...  
Keyword(s):  
Random Field ◽  
Genetic Association ◽  
Association Analysis ◽  
Field Method ◽  
Sequencing Data ◽  
Genetic Association Analysis

Determination of contact deformation in flat junctions by the random field method

Wear ◽  
1988 ◽  
Vol 127 (1) ◽  
pp. 53-63
Author(s):  
N.K. Myshkin ◽  
N.F. Semeniuk ◽  
G.S. Kalda
Keyword(s):  
Random Field ◽  
Field Method ◽  
Contact Deformation

Bilayer Markov Random Field Method for Detecting Defects in Patterned Fabric

2021 ◽  
Author(s):  
Xingzhi Chang ◽  
Wei Liu ◽  
Chuan Zhu ◽  
Xiaohua Zou ◽  
Guan Gui
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Transition Probability ◽  
Estimation Method ◽  
Ground Truth ◽  
Recall Rate ◽  
Field Method ◽  
Markov Random ◽  
Block Level ◽  
Data Layer

Existing block-level defect detection method in patterned fabric causes a large number of false detections due to the lack of edge information. To solve this problem, in this paper, we propose a bilayer Markov random field (BMRF) method for inspecting defects in patterned fabric. First, the proposed method reduces samples of the original fabric image to obtain the constraint layer, which can locate the defective block roughly. Second, we interpolate samples into the image to supplement the local information to improve and optimize the imperfect boundary, to obtain a more detailed data layer. Moreover, this paper proposes a new potential function, which considers the differential characteristics of the image blocks in the same layer and the transition probability between different layers. Finally, this paper utilizes a parameter estimation method based on the expectation maximization to solve the parameters of the BMRF method. The proposed BMRF method is evaluated on databases of star-, box- and dot-patterned fabrics. By comparing the resultant and ground-truth images, the recall rate of the proposed method in the three patterned fabrics is 95.32%, 89.29% and 93.28%, respectively, which is comparable to the existing methods.

A Random Field Method for Time-Dependent Reliability Analysis With Random and Interval Variables

2016 ◽  
Author(s):  
Zhangli Hu ◽  
Xiaoping Du
Keyword(s):  
Random Field ◽  
Limit State ◽  
Gaussian Random Field ◽  
Field Method ◽  
Time Dependent ◽  
State Function ◽  
Good Efficiency ◽  
First Order ◽  
Interval Variables ◽  
Reliability Method

In many engineering applications, both random and interval variables exist. Some of the random variables may also vary over time. As a result, the reliability of a component not only decreases with time but also resides in an interval. Evaluating the time-dependent reliability bounds is a challenging task because of the intensive computational demand. This research develops a method that treats a time-dependent random response as a random field with respect to both intervals and time. Consequently, random field methodologies can be used to estimate the worse-case time-dependent reliability. The method employs the first-order reliability method, which results in a Gaussian random field for the response with respect to intervals and time. The Kriging method and Monte Carlo simulation are then used to estimate the worse-case reliability without calling the original limit-state function. Good efficiency and accuracy are demonstrated through examples.

scholarly journals Determining of the Joint Roughness Coefficient (JRC) of Rock Discontinuities Based on the Theory of Random Fields

Geosciences ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. 295 ◽  
Author(s):  
Elias Gravanis ◽  
Lysandros Pantelidis
Keyword(s):  
Random Field ◽  
Random Fields ◽  
Probabilistic Approach ◽  
Field Method ◽  
Rock Slope Stability ◽  
Roughness Coefficient ◽  
Rock Surface ◽  
Joint Roughness Coefficient ◽  
Joint Roughness ◽  
Pull Tests

This work intends to embed the estimation of the joint roughness coefficient (JRC) in the framework of random fields. The random field method is a probabilistic approach which involves modeling of the spatial variability of the pertinent physical quantities as a fundamental part of the (assumed) underlying probabilistic structure. Although this method is one of higher complexity in regard of the presumed background knowledge, it encodes naturally subtler information about the rock surface roughness. It is noted that, the proposed random field approach considers automatically the scale of the problem (no correction factor is needed), whilst the JRC estimates appear to be more stable (compared to those derived from Z2 or SF) in the sense that images of the same profile but of different quality give similar results for its roughness. The present work could also be useful in advanced probabilistic rock slope stability analysis based on random fields. In such a case, the required spatial correlation length θ can be obtained by the proposed θ = 145.5 σ/JRC relationship (σ = variance of the profile). The JRC can be obtained through tilt tests, push or pull tests, or matching roughness profiles, whilst σ can be obtained from inspection of the digitized profile.

Sign in / Sign up

Export Citation Format

Share Document