• Proceedings of the Korea Water Resources Association Conference
• /
• 2022.05a
• /
• pp.273-273
• /
• 2022

혼합층(Mixed layer)은 온도가 일정한 수심층으로, 해수표면에 작용하는 바람의 영향으로 인하여 해수가 위아래로 섞여 형성된다. 이러한 혼합층은 영양염의 순환과 산소의 공급 등과 함께 일차생산량을 결정하는 중요한 요인이 될 수 있으며 혼합층 두께의 변동은 양식 산업에 영향을 미칠 수 있다. 최근에는 기후변화로 인한 해수면 상승 및 해수온 상승 등이 지속되고 있으며, 이러한 현상은 해양생태계의 변화를 초래하여 수산업의 피해를 유발할 수 있다(강원연구원, 2017). 이에 국립수산과학원, 기상청, 국립해양조사원 등 유관기관에서는 정선해양 수온 관측 및 해수순환모델을 이용하여 혼합층의 분석을 수행하고 있으나 격자 구축 및 초기·경계장 설정의 한계가 존재하여 정밀하고 정확한 혼합층 분석에는 어려움이 있다. 이에 본 연구에서는 비정형격자를 사용하여 격자 구축에 제약이 없는 SCHISM (Semi-implicit Cross-scale Hydroscience Integrated System Model)을 이용하여 우리나라 연안해역의 계절변화 및 기후변동성에 따른 혼합층 두께의 변화를 검토하고자 한다. 연구대상지는 서해·동해·남해를 포함한 우리나라 전체 연안 해역(위도: 32°N ~ 39°N, 경도: 124°E ~ 132°E)으로 선정하였으며, 격자크기 100 ~ 3,000 m인 삼각격자로 격자를 구축하였다. 혼합층을 분석하기 위하여 수직격자 층은 50층으로 SZ(Sigma Z coordinate system)좌표계를 사용하였다. 초기·경계장은 FES(Finite Element Solution)2014, HYCOM(Hybrid Coordinate Ocean Model) 및 대기모델 결과를 이용하여 설정하였다. 수치모형 검증을 위하여 수온관측소에서 수심별 측정한 수온 값과 SCHISM 결과 값을 비교하였고, 상대오차가 약 10% 이내로 나타나 모형의 정확도를 확인하였다. 최종적으로 해수면 상승 및 해수온 상승 시나리오를 고려하여 계절별 연안해역의 혼합층 두께의 변화 양상에 대하여 검토하였다. 향후에는 보다 정밀한 대기모델과의 혼합모형 구축 및 다양한 수심 별 관측자료를 활용한다면 실무에서 적용 가능한 혼합층 분석 및 수산업 피해 발생 지역에 대한 피해저감 대책 수립이 가능할 것으로 판단된다.

• The Korean Journal of Applied Statistics
• /
• v.24 no.3
• /
• pp.547-557
• /
• 2011

In this paper we developed a statistical model for traffic volume data which collected from a spot of specific local state road. One peculiar property of daily traffic data is that it has bimodal shape which have two peaks on times of both going to office and coming back to home. So, various mixture models of circular distribution are suggested for bimodal traffic data and EM algorithms are applied to estimate the parameters of the suggested models. To compare the accuracy of the suggested models, classical regressions with dummy variables are also considered. The suggested models for traffic volumn data can be effectively used to estimate missing values due to measuring instrument disorder.

• The Korean Journal of Applied Statistics
• /
• v.30 no.6
• /
• pp.957-981
• /
• 2017

A generalized linear mixed model is an extension of a generalized linear model that allows random effect as well as provides flexibility in developing a suitable model when observations are correlated or when there are other underlying phenomena that contribute to resulting variability. We describe maximum likelihood estimation methods for logistic regression models that include random effects - the Laplace approximation, Gauss-Hermite quadrature, adaptive Gauss-Hermite quadrature, and pseudo-likelihood. Applications are provided with social science problems by analyzing the effect of mental health and life satisfaction on volunteer activities from Korean welfare panel data; in addition, we observe that the inclusion of random effects in the model leads to improved analyses with more reasonable inferences.

• Journal of the Korean Data and Information Science Society
• /
• v.26 no.2
• /
• pp.313-322
• /
• 2015

A mixture distribution of a discrete uniform or degenerated distribution and two binomial distribution is proposed and a method of obtaining the maximum likelihood estimates of the parameters is provided. For the proposed model simulation studies were conducted to see performance of the maximum likelihood estimates and a mixture of a degenerated distribution and two binomial distributions was applied to fit a lecture evaluation data to show a good result.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2010.05a
• /
• pp.198-201
• /
• 2010

지형이 불규칙한 자연하천에 대해 2차원 격자를 구성할 경우, 사각형 격자만을 사용한다면 지류와 본류의 합류부분에서 격자의 처리가 어려운 문제가 발생할 수 있으며, 삼각형 격자만을 사용하여 지형을 처리한다면 격자수가 많아져 계산시간이 다소 많이 소요되는 어려움이 존재할 수 있다. 혼합격자의 적용이 가능하다면 이러한 어려움은 어느정도 극복할 수 있다. 본 연구에서는 1차정확도 기법인 HLLC 기법을 적용하고, 지형이 복잡한 자연하천에 대한 격자처리의 유연성을 위해 삼각형 및 사각형 격자 그리고 이 두 격자가 혼용된 혼합격자의 적용이 가능한 2차원 유한체적모형을 개발하였다. 그리고 개발모형을 수리모형 실험을 통해 얻어진 실험자료가 존재하는 실험하도 및 실제 자연하천에서의 댐 붕괴에 대해 적용하여 결과를 비교하였다.

• The Korean Journal of Applied Statistics
• /
• v.25 no.2
• /
• pp.279-291
• /
• 2012

When fitting a Cox proportional hazards model with missing covariates, it is inefficient to exclude observations with missing values in the analysis. Furthermore, if the missing-data mechanism is not Missing Completely At Random(MCAR), it may lead to biased parameter estimation. Many approaches have been suggested to handle the Cox proportional hazards model when covariates are sometimes missing, but they are based on the selection model. This paper suggest an approach to handle Cox proportional hazards model with missing covariates by using the pattern-mixture model (Little, 1993). The pattern-mixture model is expressed by the joint distribution of survival time and the missing-data mechanism. In the pattern-mixture model, many models can be considered by setting up various restrictions, and different results under various restrictions indicate the sensitivity of the model due to missing covariates. A simulation study was conducted to show the sensitivity of parameter estimation under different restrictions in a pattern-mixture model. The proposed approach was also applied to mouse leukemia data.

• Journal of the Korean Data and Information Science Society
• /
• v.25 no.5
• /
• pp.1025-1038
• /
• 2014

Linear mixed model has often been utilized for genetic association analysis with family-based samples. The correlation matrix for family-based samples is constructed with kinship coefficient and assumes that parental phenotypes are independent and the amount of correlations between parent and offspring is same as that of correlations between siblings. However, for instance, there are positive correlations between parental heights, which indicates that the assumption for correlation matrix is often violated. The statistical validity and power are affected by the appropriateness of assumed variance covariance matrix, and in this thesis, we provide the linear mixed model with flexible variance covariance matrix. Our results show that the proposed method is usually more efficient than existing approaches, and its application to genome-wide association study of body mass index illustrates the practical value in real data analysis.

• Nuclear Engineering and Technology
• /
• v.25 no.2
• /
• pp.215-221
• /
• 1993

A required initial condition for a steam explosion to occur following core meltdown accidents of a nuclear power plant is the formation of a coarse mixture of molten fuel and water. The extent of a premixing is the measure of efficiency of steam explosion that may follow. A simple one-dimensional, transient model and the flooding criteria have been applied to evaluate the fuel/coolant mixing limit. Also, both instant breakup and dynamic breakup models for the mixing process have been separately used here and compared each other. The results indicate that fuel temperature, ambient pressure, mixing diameter, water depth, and pouring diameter are the important parameters affecting the mixing behavior.

• The Korean Journal of Applied Statistics
• /
• v.32 no.2
• /
• pp.199-211
• /
• 2019

This paper is concerned with issues in the finite mixture of regression modeling as well as the simultaneous selection of the number of mixing components and relevant predictors. We propose a penalized likelihood method for both mixture components and regression coefficients that enable the simultaneous identification of significant variables and the determination of important mixture components in mixture of regression models. To avoid over-fitting and bias problems, we applied smoothly clipped absolute deviation (SCAD) penalties on the logarithm of component probabilities suggested by Huang et al. (Statistical Sinica, 27, 147-169, 2013) as well as several well-known penalty functions for coefficients in regression models. Simulation studies reveal that our method is satisfactory with well-known penalties such as SCAD, MCP, and adaptive lasso.

• The Korean Journal of Applied Statistics
• /
• v.26 no.5
• /
• pp.821-834
• /
• 2013

Law et al. (2004) proposed a normal distribution based salient mixture model for variable selection in clustering. However, this model has substantial problems such as the unidentifiability of components an the inaccurate selection of informative variables in the case of a small cluster size. We propose an alternative method to overcome problems and demonstrate a good performance through experiments on simulated data and real data.