• Journal of Animal Science and Technology
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• v.44 no.2
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• pp.151-164
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• 2002

Genetic variance and covariance components of the linear traits and the ordered categorical traits, that are usually observed as dichotomous or polychotomous outcomes, were simultaneously estimated in a multivariate threshold animal model with concepts of arbitrary underlying liability scales with Bayesian inference via Gibbs sampling algorithms. A multivariate threshold animal model in this study can be allowed in any combination of missing traits with assuming correlation among the traits considered. Gibbs sampling algorithms as a hierarchical Bayesian inference were used to get reliable point estimates to which marginal posterior means of parameters were assumed. Main point of this study is that the underlying values for the observations on the categorical traits sampled at previous round of iteration and the observations on the continuous traits can be considered to sample the underlying values for categorical data and continuous data with missing at current cycle (see appendix). This study also showed that the underlying variables for missing categorical data should be generated with taking into account for the correlated traits to satisfy the fully conditional posterior distributions of parameters although some of papers (Wang et al., 1997; VanTassell et al., 1998) presented that only the residual effects of missing traits were generated in same situation. In present study, Gibbs samplers for making the fully Bayesian inferences for unknown parameters of interests are played rolls with methodologies to enable the any combinations of the linear and categorical traits with missing observations. Moreover, two kinds of constraints to guarantee identifiability for the arbitrary underlying variables are shown with keeping the fully conditional posterior distributions of those parameters. Numerical example for a threshold animal model included the maternal and permanent environmental effects on a multiple ordered categorical trait as calving ease, a binary trait as non-return rate, and the other normally distributed trait, birth weight, is provided with simulation study.

• Journal of the Korean Data and Information Science Society
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• v.25 no.4
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• pp.737-743
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• 2014

This experiment was conducted to compare heterogeneity for the variance in dairy cattle population and to induce homogeneity of variance using 502,228 performance test records of dairy cattle. The estimates of heritability for milk yields, fat yields and protein yields were 0.28, 0.26 and 0.24, respectively and the estimate of average breeding value by birth year was lower in HV (heterogenous variance) model than in animal model, collectively. The average breeding values of milk yields, fat yields and protein yields for 545 sire bulls applicable to the criteria of interbull MACE programme were 453.54kg, 10.75kg and 14.33kg, respectively and when the heterogeneity was adjusted they were 432.06kg, 10.15kg and 13.40kg, respectively, which were lower in all milk traits collectively. In animal model, coefficients of phenotypic correlation between dataset I and II were 0.839 in milk yields, 0.821 in fat yields, and 0.837 in protein yields, while in HV model, they were 0.841 in milk yields, 0.820 in fat yields, and 0.836 in protein yields, showing similar results in 2 models. When compared using animal model and HV model, the regression coefficient for ratio of number of daughters by calving year of milk yields increased from 15.157 to 16.105 and that of fat yields increased from =0.227 to =0.196, but that of protein yields decreased from 0.630 to 0.586.

• Proceedings of the Korea Society of Poultry Science Conference
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• 2003.11a
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• pp.127-128
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• 2003

This study was conducted to obtain for genetic information with 11, 538 progenies of 5 strains in Korean native chicken. The heritability estimates of the body weights at 150 and 270days of age were 0.25~0.61 and 0.32~0.59 in 5 strains, the heritabilities of the body weight at 270 days were relatively higher than that of the body weight at 150 days. The medium or low heritability estimates were observed in the ages at first egg as 0.15 ~0.31, and heritability estimates of the egg weights at the first egg and 270 days of age were 0.06~0.09 and 0.24~0.41. Medium or low heritability estimates were observed for the number of egg production as 0.09 ~0.26, respectively.

• Journal of Korea Society of Industrial Information Systems
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• v.2 no.2
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• pp.87-103
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• 1997

설비 배치는 기업의 장단기 생산능력 효율에 상당한 영향을 미치므로 제품 및 서비스의 생산에 있어 최대의 효율을 올릴 수 있도록 배치되어야 하나, 최적의 설비 배치안을 찾는다는 것은 매우 어려운 일이다. 이러한 설비 배치 문제는 이차할당문제(Quadratic Assignment Problem : QAP)로 모형화할 수 있으며, 이의 해결을 위해 일반적으로 휴리스틱 알고리즘은 전통적인 검색 기법에 비해 우위에 있는 것으로 알려지고 있다. 따라서 본 연구에서는 설비 배치 문제의 해결을 위하여 유전 알고리즘의 개발을 시도하였으며, 선행 연구들과의 비교 분석 결과 기존 연구들에 비해 더 우수한 해을 제시할 수 있었다.

• Journal of Animal Science and Technology
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• v.48 no.6
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• pp.759-766
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• 2006

This study was conducted to investigate the genetic correlations among the traits used to select young bulls and proven bulls in Hanwoo Performance and Progeny Test Program in Korea. For the estimation of heritabilities and correlations among the growth traits of bulls and carcass traits of progeny steers, 2,532 records of performance tested bull calves and 1,819 records of progeny tested steers were collected from Livestock Improvement Main Center (LIMC), National Agricultural Cooperative Federation (NACF). Fixed effects of mixed model for each traits were selected by using stepwise regression analysis and prior values of variance components were estimated by MTDFREML. The prior values of variance components were estimated with pairwise 2 traits model followed by single trait analysis. The estimated heritability of backfat thickness(BF), dressing percentage(DP), loin-eye muscle area(LMA), marbling score(MS) and weight at 12 months(WT12) was 0.51, 0.32, 0.27, 0.33, 0.50 and 0.26, respectively. Genetic correlation of WT12 of bull calves with backfat thickness, carcass weight and loin-eye muscle area of steers was positive correlation as 0.05, 0.35 and 0.21, respectively. However genetic correlation of WT12 with DP and MS showed negative correlation as 󰠏0.09 and 󰠏0.27, respectively and these negative genetic correlations implies that bulls that may be superior in carcass traits can be lost at the first step of selection and current selection method should be modified to solve this problem.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.415-421
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• 1999

Altman의 연구(1965, 1977)나 Beaver의 연구(1986)와 같은 전통적 예측모형은 분석자의 판단에 따른 예측도가 높은 재무비율을 선정하여 다변량판별분석(MDA： multiple discriminant analysis), 로지스틱회귀분석 등과 같은 통계기법을 주로 이용해 왔으나 1980년 후반부터 인공지능 기법인 귀납적 학습방법, 인공신경망모형, 유전모형 둥이 부실기업예측에 응용되기 시작했다. 최근 연구에서는 인공신경망을 활용한 변수 및 모형개발에 관한 보고가 있다. 그러나 지금까지의 연구가 주로 기업의 재무적 비율지표를 고려한 모형에 치중되었으며 정성적 자료인 비재무지표에 대한 검증과 선정이 자의적으로 이루어져온 경향이었다. 또한 너무 많은 입력변수를 사용할 경우 다중공선성 문제를 유발시킬 위험을 내포하고 있다. 본 연구에서는 부실기업예측모형을 수립하기 위하여 정량적 요인인 재무적 지표변수와 정성적요인인 비재무적 지표변수를 모두 고려하였다. 재무적 지표변수는 상관분석 및 요인분석들을 통하여 유의한 변수들을 도출하였으며 비재무적 지표변수는 조직생태학내에서의 조직군내 조직사멸과 관련된 생태적 과정에 대한 요인들 중 조직군 내적요인으로 조직의 연령, 조직의 규모, 조직의 산업밀도를 도출하여 4개의 실험집단으로 분류하여 비재무적 지표변수를 보완하였다. 인공신경망은 다층퍼셉트론(multi-layer perceptrons)과 역방향 학습(back-propagation )알고리듬으로 입력변수와 출력변수, 그리고 하나의 은닉층을 가지는 3층 퍼셉트론(three layer perceptron)을 사용하였으며 은닉충의 노드(node)수는 3개를 사용하였다. 입력변수로 안정성, 활동성, 수익성, 성장성을 나타내는 재무적 지표변수와 조직규모, 조직연령, 그 조직이 속한 산업의 밀도를 비재무적 지표변수로 산정하여 로지스틱회귀 분석과 인공신경망 기법으로 검증하였다. 로지스틱회귀분석 결과에서는 재무적 지표변수 모형의 전체적 예측적중률이 87.50%인 반면에 재무/비재무적 지표모형은 90.18%로서 비재무적 지표변수 사용에 대한 개선의 효과가 나타났다. 표본기업들을 훈련과 시험용으로 구분하여 분석한 결과는 전체적으로 재무/비재무적 지표를 고려한 인공신경망기법의 예측적중률이 높은 것으로 나타났다. 즉, 로지스틱회귀분석의 재무적 지표모형은 훈련, 시험용이 84.45%, 85.10%인 반면, 재무/비재무적 지표모형은 84.45%, 85.08%로서 거의 동일한 예측적중률을 가졌으나 인공신경망기법 분석에서는 재무적 지표모형이 92.23%, 85.10%인 반면, 재무/비재무적 지표모형에서는 91.12%, 88.06%로서 향상된 예측적 중률을 나타내었다.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.415-421
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• 1999

Altman의 연구(1965, 1977)나 Beaver의 연구(1986)와 같은 전통적 예측모형은 분석자의 판단에 따른 예측도가 높은 재무비율을 선정하여 다변량판별분석(MDA:multiple discriminant analysis), 로지스틱회귀분석 등과 같은 통계기법을 주로 이용해 왔으나 1980년 후반부터 인공지능 기법인 귀납적 학습방법, 인공신경망모형, 유전모형 등이 부실기업예측에 응용되기 시작했다. 최근 연구에서는 인공신경망을 활용한 변수 및 모형개발에 관한 보고가 있다. 그러나 지금까지의 연구가 주로 기업의 재무적 비율지표를 고려한 모형에 치중되었으며 정성적 자료인 비재무지표에 대한 검증과 선정이 자의적으로 이루어져온 경향이었다. 또한 너무 많은 입력변수를 사용할 경우 다중공선성 문제를 유발시킬 위험을 내포하고 있다. 본 연구에서는 부실기업예측모형을 수립하기 위하여 정량적 요인인 재무적 지표변수와 정성적 요인인 비재무적 지표변수를 모두 고려하였다. 재무적 지표변수는 상관분석 및 요인분석들을 통하여 유의한 변수들을 도출하였으며 비재무적 지표변수는 조직생태학내에서의 조직군내 조직사멸과 관련된 생태적 과정에 대한 요인들 중 조직군 내적요인으로 조직의 연령, 조직의 규모, 조직의 산업밀도를 도출하여 4개의 실험집단으로 분류하여 비재무적 지표변수를 보완하였다. 인공신경망은 다층퍼셉트론(multi-layer perceptrons)과 역방향 학습(back-propagation)알고리듬으로 입력변수와 출력변수, 그리고 하나의 은닉층을 가지는 3층 퍼셉트론(three layer perceptron)을 사용하였으며 은닉층의 노드(node)수는 3개를 사용하였다. 입력변수로 안정성, 활동성, 수익성, 성장성을 나타내는 재무적 지표변수와 조직규모, 조직연령, 그 조직이 속한 산업의 밀도를 비재무적 지표변수로 산정하여 로지스틱회귀 분석과 인공신경망 기법으로 검증하였다. 로지스틱회귀분석 결과에서는 재무적 지표변수 모형의 전체적 예측적중률이 87.50%인 반면에 재무/비재무적 지표모형은 90.18%로서 비재무적 지표변수 사용에 대한 개선의 효과가 나타났다. 표본기업들을 훈련과 시험용으로 구분하여 분석한 결과는 전체적으로 재무/비재무적 지표를 고려한 인공신경망기법의 예측적중률이 높은 것으로 나타났다. 즉, 로지스틱회귀 분석의 재무적 지표모형은 훈련, 시험용이 84.45%, 85.10%인 반면, 재무/비재무적 지표모형은 84.45%, 85.08%로서 거의 동일한 예측적중률을 가졌으나 인공신경망기법 분석에서는 재무적 지표모형이 92.23%, 85.10%인 반면, 재무/비재무적 지표모형에서는 91.12%, 88.06%로서 향상된 예측적중률을 나타내었다.

• Journal of Environmental Impact Assessment
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• v.23 no.6
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• pp.432-444
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• 2014

When the water quality modelling is done with a manual calibration, it is possible that the researcher's opinion may affect the objectivity of the research. Hence, the role of the automatic calibration is highly important. This research applies a technique to automatically calibrate the water quality parameters by implementing an optimization method. This involves estimating the optimum water quality parameters targeting influential parameters towards the lake's BOD, DO, Phosphorus, Nitrogen and Phytoplankton. To accurately calculate the water temperature and hydraulic characteristics of a deep, stratifying lake, EFDC, a 3-dimensional hydraulic model which can be linked to the WASP7 was applied. With EFDC, the segment of the lake is formed and utilized as an input data of the WASP7. For the calibration of the water quality parameters of the WASP7, an influence coefficient algorithm and a genetic algorithm was applied. Of the five water quality variables for calibration, the normalized residuals of the observed and calculated values of DO, TN, CBOD were relatively small and the three water quality variables were calibrated properly. Yet the accuracy of the calibration of TP and Chl-a was relatively low.

• Journal of Animal Science and Technology
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• v.46 no.2
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• pp.137-144
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• 2004

Algorithms for estimating breeding values on several categorical data by using latent variables with threshold conception were developed and showed. Thresholds on each categorical trait were estimated by Newton’s method via gradients and Hessian matrix. This algorithm was developed by way of expansion of bivariate analysis provided by Quaas(2001). Breeding values on latent variables of categorical traits and observations on linear traits were estimated by preconditioned conjugate gradient(PCG) method, which was known having a property of fast convergence. Example was shown by simulated data with two linear traits and a categorical trait with four categories(CE=calving ease) and a dichotomous trait(SB=Still Birth) in threshold animal mixed model(TAMM). Breeding value estimates in TAMM were compared to those in linear animal mixed model (LAMM). As results, correlation estimates of breeding values to parameters were 0.91${\sim}$0.92 on CE and 0.87${\sim}$0.89 on SB in TAMM and 0.72~0.84 on CE and 0.59~0.70 on SB in LAMM. As conclusion, PCG method for estimating breeding values on several categorical traits with linear traits were feasible in TAMM.

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1385-1395
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• 2014

Economic traits of livestock are affected by environmental factors and genetic factors. In addition, it is not affected by one gene, but is affected by interaction of genes. We used a linear regression model in order to adjust environmental factors. And, in order to identify gene-gene interaction effect, we applied data mining techniques such as neural network, logistic regression, CART and C5.0 using five-SNPs (single nucleotide polymorphism) of FASN (fatty acid synthase). We divided total data into training (60%) and testing (40%) data, and applied the model which was designed by training data to testing data. By the comparison of prediction accuracy, C5.0 was identified as the best model. It were selected superior genotype using the decision tree.