• The Journal of Engineering Geology
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• v.28 no.4
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• pp.715-726
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• 2018

Various methods, such as geophysical exploration, temperature measurement, and fiber optics, have been developed for detecting the seepage at a dam. In this study, in order to investigate the possibility of leakage detection using dielectric constant of FDR sensor, a physical model consisting of weak and no-weak zones is fabricated and the sensors for dielectric constant, temperature and pore water pressure measurements are installed. As a leakage happens, the dielectric constant changes more rapidly through a weak zone than no-weak zone. In addition, comparing three factors (dielectric constant, temperature, and pore water pressure), the response of dielectric constant to seepage is fast and it is easily recognized even at the end measurement point. Considering these features, it is concluded that it could be possible to cope with the leakage detection quickly and efficiently if the dielectric constant is measured at the downstream slope of a dam.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.749-754
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• 2005

본 연구에서는 한 개의 침투 조절 요소, 두 개의 직렬탱크 및 한 개의 병렬 탱크로 구성된 개선된 형태의 TANK 모형을 제시하였다. 침투는 강우의 형태로 유역에 공급되는 물의 분배를 결정하는 과정으로서, 이를 적절히 고려할 수 있는지의 여부가 강우-유출 모형의 유효성을 판단하는 기준이 된다고 해도 과언이 아니다. 따라서 본 연구에서는 구조가 비교적 단순하고 사용이 간편하여 기존에 널리 사용되어 오던 개념적 모형인 TANK 모형에 침투 조절 요소를 도입하여 보다 합리적으로 강우-침투-유출 과정을 모의하고 해석하고자 노력하였다. 이를 통해 단순히 시간의 함수가 아닌 토양 함수량의 함수로서 침투능의 변화를 고려할 수 있으며, 유역 유출의 각 성분(지표면 유출, 중간 유출, 지하수 유출)에 영향을 미치는 모형의 매개변수에 물리적 의미를 더욱 부여할 수 있다. 또한 침투 조절 요소의 매개변수 산정을 위해 선행 강우 지수(Antecedent Precipitation Index)를 이용하였으며, 이를 통해 토양 선행 함수 상태의 고려가 가능하다. 또한 본 연구에서는 모형의 매개변수 최적화를 위해 실수 코딩 유전 알고리즘(Real Coded Genetic Algorithm)을 사용하였으며, 모형의 적용성과 유효성 검증을 위해 IHP 연구 유역인 평창강 방림 유역을 대상유역으로 하여 이 유역의 실측 호우 사상을 사용하였다. 결과적으로 계산된 수문곡선은 관측치에 비교적 잘 일치하며, 단일 호우와 복합 호우 사상 모두에 대해 비교적 양호한 결과를 나타내었다.

• The Korean Journal of Applied Statistics
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• v.24 no.5
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• pp.861-869
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• 2011

One of the main objects of recent genetic studies is to understand genetic factors that induce complex diseases. If there are interactions between loci, it is difficult to find such associations through a single-locus analysis strategy. Thus we need to consider the gene-gene interactions and/or gene-environment interactions. The MDR(multifactor dimensionality reduction) method is being used frequently; however, it is not appropriate to detect interactions caused by a small fraction of the possible genotype pairs. In this study, we propose a relative risk interaction explorer that detects interactions through the calculation of the relative risks between the control and disease groups from each genetic combinations. For illustration, we apply this method to MDR open source data. We also compare the MDR and the proposed method using the simulated data eight genetic models.

• Journal of Animal Science and Technology
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• v.54 no.2
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• pp.71-75
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• 2012

The present study data were obtained from 36,894 cows in Korea Animal Improvement Association from 2001 to 2009 which was subjected for ultrasound measurements (eye muscle area, back-fat thickness, marbling score) and descent. Repeated record models were carried out using 7,913 of 36,894 of total animal traits. The ultrasound measured traits and performance test data were used to study the chest girth, body condition score, eye muscle area, back-fat thickness and marbling score with genetic correlation and parameters for the ultrasound measured traits using REMLF90 program. Genetic correlation of eye muscle area with back-fat thickness, marbling score and back-fat thickness with marbling score were noticed in repeated records animal model as 0.69, 0.54, and 0.59, whereas in multiple trait animal model method were 0.07, 0.66, and 0.39, respectively. Repeated records of animal models were used as positive correlation of traits. Multiple trait animal models were used as negative correlation of eye muscle area with marbling score. The analysis on repeat records of animal models using ultrasound measurements about Korean cattle showed positive effects for each traits. In comparison differences between the repeat records of animal models and multiple trait animal models was found with higher traits of her, the heritability and repeatability was found higher in repeat records animal models. In light of these assessments, carcass traits by ultrasound measurements are expected to help and improve an accurate analysis of each trait and if the research analysis using repeat records of animal models continue when we estimate genetic ability of these traits.

• Journal of Korea Technology Innovation Society
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• v.9 no.1
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• pp.84-102
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• 2006

This paper compares four models to value a biotechnology R&D project; option tree model, dynamic discounted cash flow(DCF) model, and option thinking DCF model with general DCF model. Real Options, especially 6-folded option tree model yields boner estimate of value than values using other methods. According to sensitivity analysis, sales of final products, number of investigational new drug developments(INDs) and success rates of each stage are key factors for the value of biotechnology R&D investment.

• Journal of agriculture & life science
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• v.44 no.2
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• pp.29-38
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• 2010

This study looked into estimation of variance component over swine's economic traits by multiple animal model and maternal effect model using on-farm test data of total 31,455 swine of Duroc, Landrace and Yorkshire species that were born between 2000 and 2008. Heritability by estimated additive genetic effect showed higher than one by maternal genetic effect using multivariate maternal animal model in each trait examined by each breed and most heritability when considering only additive genetic effect in multiple traits animal model was estimated to be higher than one by estimated additive genetic effect in multivariate maternal animal model. In correlation between breeding value by estimated maternal genetic effect and phenotypic value using multivariate maternal animal model, rank correlation and simple correlation of breeding value and phenotypic value by maternal genetic effect also showed low positive correlation or strong negative correlation, which can be considered that if correlation with phenotype were increased properly considering maternal genetic effect in each trait by each breed, even better improvement could be promoted.

• 종축개량
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• v.17 no.2
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• pp.57-60
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• 1995

카나다의 돼지개량에 대한 국가적 유전능력 평가는 산육능력(100kg의 등지방과 일령)과 모돈의 번식능력(총산자수)을 BLUP animal model(최선형 불변예상치 가축모형 : Best Linear Unbiased Predictor Animal Model)을 이용하여 정규적으로 평가하고 있다. 새로운 검정자료가 수집되어 질때마다 매번 BLUP평가가 이루어져 농장으로 제공된다. 현재의 유전능력 변화에 대한 추정가는 연간 등지방 두께 0.35mm와 100kg도달일령 1.5일이 향상되었다. 이것은 1985년 BLUP이 소개된 이전보다 등지방 $50\%$, 일령 20배 이상의 개량효과이다. 그 외에 모돈의 번식형질에 대한 개량은 계속적으로 연구가 진행되고 있으며 국가적 육종계획에는 도체와 육질에 대한 유전적 개량사업이 추진되고 있다.

• Journal of Navigation and Port Research
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• v.35 no.1
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• pp.83-91
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• 2011

The accuracy of forecasting is remarkably important to reduce total cost or to increase customer services, so it has been studied by many researchers. In this paper, the artificial neural network (ANN), one of the most popular nonlinear forecasting methods, is compared with autoregressive integrated moving average(ARIMA) model through performing a prediction of container traffic. It uses a hybrid methodology that combines both the linear ARIAM and the nonlinear ANN model to improve forecasting performance. Also, it compares the methodology with other models in performance for prediction. In designing network structure, this work specially applies the genetic algorithm which is known as the effectively optimal algorithm in the huge and complex sample space. It includes the time delayed neural network (TDNN) as well as multi-layer perceptron (MLP) which is the most popular neural network model. Experimental results indicate that both ANN and Hybrid models outperform ARIMA model.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.3
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• pp.119-130
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• 2010

The Genetic Algorithm has been frequently applied by many researchers as one of the population surface modelling tool in estimating the regional population based on the gridded spatial system. Taking the East-Hwasung area as the case, this study first builds a gridded population data based on the KLIS and eAIS databases as well as municipal population survey data, and then constructs the attribute values of the explanatory variables by way of GIS tools. The GA model is run to maximize its fitness function measuring the correlation coefficient between the observed and predicted values of the 70 population cells. It is shown that the GA output predicted reasonably consistent and meaningful coefficient estimates for the explanatory variables of the model.

• The Korean Journal of Applied Statistics
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• v.27 no.5
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• pp.693-703
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• 2014

When setting up an experiment for extrapolation at multiple points outside the design space, we often face a difficulty in which point we should emphasize even if the polynomial model under consideration is given. In this paper we propose various methods under two possible scenarios that deal with extrapolations. One considered in this paper is the situation when the model assumed can be extended beyond the design space. In this setting, the many classical methods(including various approaches the authors proposed before) were revisited in the context of extrapolation. But the real problem arises when there is an uncertainty concerning the validity of the assumed model. Therefore, the second scenario is to develop an appropriate procedure when we have limited information about model. Consequently, a hybrid approach is suggested to deal with this issue of how to handle the multiple extrapolating under model uncertainty. A search algorithm was implemented because the classical exchange algorithm was found difficult to handle the complexity of the problem.