• 한국정보교육학회:학술대회논문집
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• 2004.08a
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• pp.102-110
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• 2004

현행 7차 교육과정에서는 정보통신기술을 재량활동 시간을 이용하여 필수적으로 36시간 이상의 수업을 하도록 전장하고 있다. 이에 각 시 도에서는 자체적으로 교재를 개발하고 그에 따른 교사용 지도서도 나와 있다. 그러나 컴퓨터 내용학에 관한 교수 모형은 타 교과에서 만들어진 것을 차용하거나 컴퓨터 교과에 무리하게 적용한 것이 대부분이라 하겠다. 또한 컴퓨터 내용학보다는 소프트웨어를 다루는 기능 위주의 교육 내용 편성이 대부분이라 내용학을 다루는 것은 극히 일부라 하겠다. 이에 본 연구는 컴퓨터 내용학의 교수 모형을 제시하여 초등학교에서의 컴퓨터 내용학을 가르치는데 도움이 되고자 한다. 또한 현재 응용 소프트웨어 활용에 연연하고 있는 재량 활동 시간의 정보통신교육보다는 앞으로 정규교과목으로 지정될 것을 전제로 컴퓨터 내용학을 중심으로 한 교수 모형을 제안하고자 한다.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.80-84
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• 2005

본 연구에서는 지점강우를 이용하여 크리깅 기법을 통해 공간적으로 분포된 강우자료를 생성하였으며, HEC-GeoHMS 모형을 이용하여 대상유역의 지형자료, 수문매개변수를 격자크기에 따라 공간적으로 분포시켰다. 유출해석은 공간 분포된 강우의 적용을 용이하게 하기 위하여 준 분포형 접근방법인 Modified Clark 모형을 적용하였으며, 공간 평균된 강우는 Clark 모형을 적용하여 유출 모형을 수행하였다. 정규크리깅과 일반크리깅 기법에 의해 산정된 강우를 이용하여 격자크기에 따른 유출해석을 수행한 결과를 토대로 지점강우량을 공간분포 시킬 경우의 격자크기에 따른 유출 수문곡선의 변화를 파악하였다. 공간 분포된 강우와 공간 평균된 강우를 이용한 유출 모의 결과를 토대로 공간 분포 강우의 적용성을 평가하는데 있다.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.790-794
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• 2008

본 연구에서는 국내 분단위 강우자료(MMR)를 이용하여 시간해상도에 따른 강우의 공간상관구조 특성을 검토하였다. 이러한 특성을 파악하기 위해 이변량 혼합분포를 이용하여 강우를 모형화한 후 정규분포와 대수 정규분포를 고려하여 시간해상도별로 공간상관함수를 유도하고 그 변동특성을 파악하였다. 또한 분단위 강우 자료를 호우 발생 특성별(태풍, 장마, 대류성 강우)로 분류하여 이에 대한 공간상관함수를 각각 유도하였다. 이때 시간해상도를 고려하기 위한 대상 집성시간은 1, 2, 3, 5, 10, 30, 60분이고, 대상지점은 중부지역의 27개 우량관측소 지점을 이용하였다. 그 적용 결과 분단위 강우자료의 경우 무강우 자료의 영향이 상대적으로 매우 크게 나타나는 것을 확인할 수 있었다. 공간상관거리는 적용 분포형, 호우 발생 특성에 따라 차이가 있지만 1분의 경우 약 $9{\sim}15km$, 60분의 경우 약 $21{\sim}53km$인 것으로 파악되었다. 또한 강우의 집성시간이 길어질수록 공간상관특성이 상대적으로 뚜렷하게 나타나고 공간상관거리가 길어짐을 확인하였다. 본 연구의 결과는 분단위 강우자료의 관측소 밀도가 시단위 강우자료 관측소에 비해 상대적으로 매우 적음을 나타내며, 분단위 강우자료를 이용하여 지점빈도해석과 같은 공간적인 특성을 분석할 경우 적절한 개선방안이 제시되어야함을 의미하는 것이기도 하다.

• Journal of Korea Water Resources Association
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• v.40 no.12
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• pp.943-956
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• 2007

Among various statistics, the spatial correlation function, that is "correlogram", is frequently used to evaluate or design the rain gauge network and to model the rainfall field. The spatial correlation structure of rainfall has the significant variation due to many factors. Thus, the variation of spatial correlation structure of rainfall causes serious problems when deciding the spatial correlation function of rainfall within the basin. In this study, the spatial rainfall structure was modeled using bivariate mixed distributions to derive monthly spatial correlograms, based on Gaussian and lognormal distributions. This study derived the correlograms using hourly data of 28 rain gauge stations in the Keum river basin. From the results, we concluded as following; (1) Among three cases (Case A, Case B, Case C) considered, the Case A(+,+) seems to be the most relevant as it is not distorted much by zero measurements. (2) The spatial correlograms based on the lognormal distribution, which is theoretically as well as practically adequate, is better than that based on the Gaussian distribution. (3) The spatial correlation in July exponentially decrease more obviously than those in other months. (4) The spatial correlograms should be derived considering the temporal resolution(hourly, daily, etc) of interest.

• The Korean Journal of Applied Statistics
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• v.33 no.3
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• pp.335-345
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• 2020

This paper deals with the problem of estimating the log-transformed linear regression model to fit actual battle data from the Ardennes Campaign of World War II into the Lanchester model. The problem of determining a global solution for parameters and multicollinearity problems are identified and modified by examining the results of previous studies on data. The least squares method requires attention because a local solution can be found rather than a global solution if considering a specific constraint or a limited candidate group. The method of exploring this multicollinearity problem can be confirmed by a statistic known as a variance inflation factor. Therefore, the Lanchester model is simplified to avoid these problems, and the combat power attrition rate model was proposed which is statistically significant and easy to explain. When fitting the model, the dependence problem between the data has occurred due to autocorrelation. Matters that might be underestimated or overestimated were resolved by the Cochrane-Orcutt method as well as guaranteeing independence and normality.

• The Korean Journal of Applied Statistics
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• v.33 no.1
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• pp.25-46
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• 2020

This paper presents ordinal probit semiparametric regression models using Bayesian Spectral Analysis Regression (BSAR) method. Ordinal probit regression is a way of modeling ordinal responses - usually more than two categories - by connecting the probability of falling into each category explained by a combination of available covariates using a probit (an inverse function of normal cumulative distribution function) link. The Bayesian probit model facilitates posterior sampling by bringing a latent variable following normal distribution, therefore, the responses are categorized by the cut-off points according to values of latent variables. In this paper, we extend the latent variable approach to a semiparametric model for the Bayesian ordinal probit regression with nonparametric functions using a spectral representation of Gaussian processes based BSAR method. The latent variable is decomposed into a parametric component and a nonparametric component with or without a shape constraint for modeling ordinal responses and predicting outcomes more flexibly. We illustrate the proposed methods with simulation studies in comparison with existing methods and real data analysis applied to a Korean National Health and Nutrition Examination Survey (KNHANES) 2016 for investigating nonparametric relationship between smoking behavior and coffee intake.

• The Journal of the Korea Contents Association
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• v.18 no.5
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• pp.265-279
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• 2018

This study conducted an experiment using data mining techniques to develop prediction models of worker job turnover. The experiment used data from the '2015 Graduate Occupational Mobility Survey' by the Korea Employment Information Service. We developed the prediction models using a decision tree, Bayes net, and artificial neural network. We found that the decision tree-based prediction model reported the best accuracy. We also found that the six influential factors affecting employees' turnover intention are type of working time, job status, full-time or not full-time, regular working hours per week, regular working days per week, and personal development opportunities. From the decision tree-based prediction model, we derived 12 rules of employee turnover for all job types. Using the derived rules, we proposed helpful directions for enhancing workers' job tenure. In addition, we analyzed the influential factors affecting employees' job turnover intention according to four job types and derived rules for each: office (ten rules), culture and art (nine rules), construction (four rules), and information technology (six rules). Using the derived rules, we proposed customized directions for improving the job tenure for each group.

• Geotechnical Engineering
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• v.14 no.5
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• pp.39-52
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• 1998

The pupose of the present paper is to estimate the effects of constraint condition of pile head, ground condition(dry unit weight. $\gamma_4$) and embedded pile lengths on the behavior of single pile which is embedded in normally consolidated clay. BBperiment functions can be quantified to these effects obtained from the results of model teats. The ground of model tests is normally consolidated( NC ) clay under three kinds of effective vertical stress. The results of the model tests using the steel pile of two different embedded pile length and of free-head and fired-head show that the lateral load-deflection relationship is to be elasto plastic behavior below $\gamma_d/\gamma_{dmax}$: 0.84 and that the reduction of lateral load of beyond maximum lateral load($Q_{max}$) at each model test is significantly time-dependent. In this study, it is shown that the displacement relationship can be fitted to exponential function of time by model best results. The effect of ground conditions on the ultimate and yield lateral load is fitted to exponential function including the ratio of dry unit weight to maximum dry unit weight. When tests by results are compared with those from Broms and Budhu et at., the predicted results are over-estimated about 27-87 ayo. In effectivity of constraint condition of pile head on the lateral load-deflection response, the $Q_{fixed}/Q_{gree}-y/D$ relationship is highly non-linear and fitted to parabolic function.

• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.895-906
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• 2015

Diffusion is a mathematical tool to explain the fluctuation of financial assets and the movement of particles in a micro time scale. There are ongoing statistical trials to develop an estimation method for diffusion models based on likelihood. When we estimate diffusion models by applying the maximum likelihood estimation method on data observed at discrete time points, we need to know the transition density of the diffusion. In order to approximate the transition densities of diffusion models, we suggests the method to approximate the path integral of the random process with normal random variables, and compare the numerical properties of the method with other approximation methods.

• Korea journal of population studies
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• v.25 no.2
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• pp.41-68
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• 2002

This study explores school to work transition of female youth. Particularly, the analyses focus on a transition to the first job from the graduation, and exits of irregular employees from their first occupational status. Data used for the analysis are “The 4th Survey on Women's Employment”, collected by KWDI in 2001. The results show that it takes 1.54 years on average for transition. Significant factors that influence the probability of transition to the first job include economic situation and satisfaction level of major at college education. The general high school educated are less likely to move into the labor market. Only a half percent of irregular employees at their first jobs exits to regular employees or non-economically active status, and education levels and age cohorts have clear impacts of those exits. Majors in college education and holding irregular jobs before the graduation significantly affect the probability of being regular employees, while industry influences the exits to be non-economically active status.