• Asian-Australasian Journal of Animal Sciences
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• 제21권5호
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• pp.633-639
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• 2008

The genetic relationship of gestation length (GL) with birth and weaning weight (BW, WW) was investigated using data collected from the Hanwoo Experiment Station, National Institute of Animal Science, RDA, Republic of Korea. Analytical mixed models including birth year‐season, sex of calf, linear and quadratic covariates of age of dam (days) and linear covariate of age at weaning (days) as fixed effects were used. Corresponding restricted maximum likelihood (REML) and Bayesian estimates of variance components and heritability were obtained with two models; Model 1 included only direct genetic effect and Model 2 included direct genetic, maternal genetic and permanent environmental effect. All the genetic parameter estimates from REML were corresponding to the Bayesian estimates. Direct heritability estimates for GL, BW, and WW were 0.48, 0.33 and 0.25 by Model 1. From Model 2, direct and maternal heritability estimates were 0.38 and 0.03 for GL, 0.14 and 0.05 for BW, and 0.08 and 0.05 for WW. Genetic correlation estimates between direct and maternal effects were 0.05 for GL, 0.59 for BW, and 0.52 for WW. Estimates of direct genetic correlation between GL and BW (WW) were 0.44 (0.21). Positive genetic correlation of GL with BW and WW imply that selection for greater BW or WW would lead to prolonged gestation length.

• 인터넷정보학회논문지
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• 제15권6호
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• pp.117-124
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• 2014

모바일 기기를 사용한 실시간 비디오 영상처리분야의 중요 객체탐색 및 추적의 문제에 있어서 난제는 복잡한 배경속에서 전경을 구분해 내는 일이다. 본 논문에서는 기계학습을 위한 특성벡터 선정의 문제를 위한 문맥인식 모델을 제시하여 잡음제거를 위한 기계학습기반의 구분자를 구현하였다. 수학적으로 NP-hard로 알려진 가장 가까운 이웃을 사용한 문맥인식 특성벡터 선정 알고리즘의 구현에 있어서, 본 논문은 연산횟수를 줄인 유사방법론에 대해 자세히 거론하였다. 또한, 문맥인식 성격을 가미한 특성벡터 선정을 통해 얻어진 특성 공간에서의 향상된 분리성에 대해 주성분 분석을 통해 엄밀한 분석결과를 제시하였다. 전반적인 성능 향상의 정도를 계측하기 위해 다양한 기계학습 방법론, 예를 들어, 다층신경망, 지원벡터기계, 나이브베이지안, 회귀분석 등을 사용해 비교결과를 제시하였다. 본 논문에서 제시한 방법론의 성능과 계산상 자원사용에 대한 내용을 결론으로 서술하였다.

• Structural Engineering and Mechanics
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• 제54권2호
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• pp.363-378
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• 2015

Estimation of geotechnical properties is an essential but challenging task since they are major components governing the safety and reliability of the entire structural system. However, due to time and budget constraints, reliable geotechnical properties estimation using traditional site characterization approach is difficult. In view of this, an alternative efficient and cost effective approach to address the overall uncertainty is necessary to facilitate an economical, safe and reliable geotechnical design. In this paper a probabilistic approach is proposed for real-time updating by incorporating new geotechnical information from the underlying project site. The updated model obtained from the proposed method is advantageous because it incorporates information from both existing database and the site of concern. An application using real data from a site in Hong Kong will be presented to demonstrate the proposed method.

• 농업과학연구
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• 제39권3호
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• pp.357-364
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• 2012

Genomic breeding value (GEBV) has recently become available in the beef cattle industry. Genomic selection methods are exceptionally valuable for selecting traits, such as marbling, that are difficult to measure until later in life. One method to utilize information from sparse marker panels is the Bayesian model selection method with RJMCMC. The accuracy of prediction varies between a multiple SNP model with RJMCMC (0.47 to 0.73) and a least squares method (0.11 to 0.41) when using SNP information, while the accuracy of prediction increases in the multiple SNP (0.56 to 0.90) and least square methods (0.21 to 0.63) when including a polygenic effect. In the multiple SNP model with RJMCMC model selection method, the accuracy ($r^2$) of GEBV for marbling predicted based only on SNP effects was 0.47, while the $r^2$ of GEBV predicted by SNP plus polygenic effect was 0.56. The accuracies of GEBV predicted using only SNP information were 0.62, 0.68 and 0.73 for CWT, EMA and BF, respectively. However, when polygenic effects were included, the accuracies of GEBV were increased to 0.89, 0.90 and 0.89 for CWT, EMA and BF, respectively. Our data demonstrate that SNP information alone is missing genetic variation information that contributes to phenotypes for carcass traits, and that polygenic effects compensate genetic variation that whole genome SNP data do not explain. Overall, the multiple SNP model with the RJMCMC model selection method provides a better prediction of GEBV than does the least squares method (single marker regression).

• 응용통계연구
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• 제34권3호
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• pp.491-505
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• 2021

본 연구에서는, 고차원상황(p ≫ n)에서의 회귀분석 모형을 고려하여 다양한 베이지안 회귀분석 방법들을 비교하였다. Spike and slab 사전분포는 고차원 베이지안 회귀분석에서 가장 많이 사용되는 사전분포 중 하나이지만, 탐험해야 하는 모형 공간이 너무 크기 때문에 유한 표본에서 좋지 않은 성능을 보일 수 있다는 문제가 있다. 이에 대한 대안으로, horseshoe 사전분포를 비롯한 다양한 연속 수축사전분포들이 제안되어 사용되고 있다. 비록 위 사전분포들 각각에 대해서는 많은 연구들이 진행되고 있지만, 이들에 대한 포괄적인 비교연구는 매우 드물게 진행되고 있다. 따라서 본 연구에서는, spike and slab 사전분포와 다양한 연속수축사 전분포들을 다양한 상황에서 비교하는 연구를 진행 하였다. 각 방법의 성능은 회귀계수 추정 측면과 변수선택 측면을 나누어 비교하였다. 최종적으로, 본 연구에서 진행된 시뮬레이션 연구에 기반하여, 사용시 몇 가지 주의점과 제안들을 제시하였다.

• 한국GIS학회:학술대회논문집
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• 한국GIS학회 2003년도 공동 춘계학술대회 논문집
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• pp.65-70
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• 2003

The weight-of-evidence model one of the Bayesian probability model was applied to the task of evaluating landslide susceptibility using GIS. Using the location of the landslides and spatial database such as topography, soil, forest, geology, land use and lineament, the weight-of-evidence model was applied to calculate each factor's rating at Boun area in Korea where suffered substantial landslide damage fellowing heavy rain in 1998, The factors are slope, aspect and curvature from the topographic database, soil texture, soil material, soil drainage, soil effective thickness, and topographic type from the soil database, forest type, timber diameter, timber age and forest density from the forest map, lithology from the geological database, land use from Landsat TM satellite image and lineament from IRS satellite image. Tests of conditional independence were performed for the selection of the factors, allowing the 43 combinations of factors to be analyzed. For the analysis, the contrast value, W$\^$＋/and W$\^$-/, as each factor's rating, were overlaid to map laudslide susceptibility. The results of the analysis were validated using the observed landslide locations, and among the combinations, the combination of slope, curvature, topographic, timber diameter, geology and lineament show the best results. The results can be used for hazard prevention and planning land use and construction

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2005년도 BIOINFO 2005
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• pp.357-360
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• 2005

In this paper we consider the well-known semiparametric proportional hazards (PH) models for survival analysis. These models are usually used with few covariates and many observations (subjects). But, for a typical setting of gene expression data from DNA microarray, we need to consider the case where the number of covariates p exceeds the number of samples n. For a given vector of response values which are times to event (death or censored times) and p gene expressions (covariates), we address the issue of how to reduce the dimension by selecting the significant genes. This approach enable us to estimate the survival curve when n < < p. In our approach, rather than fixing the number of selected genes, we will assign a prior distribution to this number. The approach creates additional flexibility by allowing the imposition of constraints, such as bounding the dimension via a prior, which in effect works as a penalty. To implement our methodology, we use a Markov Chain Monte Carlo (MCMC) method. We demonstrate the use of the methodology to diffuse large B-cell lymphoma (DLBCL) complementary DNA(cDNA) data.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1524-1529
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• 2005

Particle filter is a kind of conditional density propagation model. Its similar characteristics to both selection and mutation operator of evolutionary strategy (ES) due to its Bayesian inference rule structure, shows better performance than any other tracking algorithms. When a new object is entering the region of interest, particle filter sets which have been swarming around the existing objects have to move and track the new one instantaneously. Moreover, there is another problem that it could not track multiple objects well if they were moving away from each other after having been overlapped. To resolve reinitialization problem, we use competitive-AVQ algorithm of neural network. And we regard interfarme difference (IFD) of background images as potential field and give priority to the particles according to this IFD to track multiple objects independently. In this paper, we showed that the possibility of real-time object tracking as intelligent interfaces by simulating the deformable contour particle filters.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.402-404
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• 2003

We present two methods for the automatic selection of the threshold values in unsupervised change detection. Both methods consist of the same two procedures: 1) to determine the parameters of Gaussian mixtures from a difference image or ratio image, 2) to determine threshold values using the Bayesian rule for minimum error. In the first method, the Expectation-Maximization algorithm is applied for estimating the parameters of the Gaussian mixtures. The second method is based on the iterative thresholding that successively employs thresholding and estimation of the model parameters. The effectiveness and applicability of the methods proposed here are illustrated by an experiment on the multi-temporal KOMPAT-1 EOC images.

• 디지털융복합연구
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• 제12권6호
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• pp.325-332
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• 2014

일반적으로 자동분류는 학습문서의 개수에 영향을 받는다고 알려져 있지만 실제로 학습문서의 수가 텍스트 자동분류에 어떻게 영향을 주는지 입증한 연구는 거의 없었다. 본 연구에서는 학습문서 수가 자동분류에 어떤 영향을 주는지 알아보기 위해 최근에 개발된 편차기반 분류방법을 중심으로 다른 분류 알고리즘과 비교하는데 초점을 두었다. 실험결과, 편차기반 분류모델은 학습문서의 수가 총 21개(7개 장르)인 상황에서 정확도가 0.8로 베이지안이나 지지벡터기계보다 우수하게 나타났다. 이것은 편차기반 분류모델이 장르내의 주제정보를 이용하여 학습하기 때문에 학습문서의 수가 적더라도 다른 학습방법보다 좋은 자질 선택 능력을 갖는다는 것을 입증한 것이다.