• The Journal of the Korea Contents Association
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• v.10 no.5
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• pp.210-219
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• 2010

Consumers differ in both horizontally and vertically. Market segmentation aims to divide horizontally different (or heterogeneous) consumers into more similar (or homogeneous) small segments. A specific consumer, however, may differ in vertically. He (or she) may belong to a different market segment from another one where he (or she) belonged to before. In consumer panel data, the vertical difference can be observed by his (or her) choice among brand alternatives are changing over time. The consumer's vertical difference has been defined as 'dynamics'. In this research, we have developed a binary probit model with random-walk coefficients to capture the consumer's dynamics. With an application to a consumer panel data, we have examined how have the random-walk coefficients changed over time.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.496-499
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• 2008

현재 정보영재 교육에서 중요성이 증대되는 사고력 신장 교육에 적합한 교수-학습 프로그램이 부족한 실정이다. 따라서 이 논문에서는 정보영재의 교육과정에 실제적으로 적용할 수 있는 교수-학습 모형을 제시한다. 정보영재 사고력 신장을 위한 영역별 교육 내용에 공통적으로 포함되는 문제들을 분석하여 사고력 신장에 적합한 교육 프로그램 모형으로서 OR 트리에 기반을 둔 교수-학습 모형을 제시한다. 이 논문에서 제시된 OR 트리형 교수-학습 모형을 정보영재의 현장 지도에 도입하기 위하여 8-puzzle 문제를 예로 들어 적용 방안을 제시하며, 적용 결과를 분석하여 교육 프로그램 개발의 타당성을 검증한다. OR 트리형 교수-학습 모형에서는 정보영재의 사고력 신장을 위한 교육 과정에서 주요 내용이 되는 backtracking과 heuristic 개념을 배우며 트리의 탐색 방법들을 익히게 된다.

• Journal of Korea Water Resources Association
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• v.42 no.10
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• pp.891-896
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• 2009

The puff models have been developed to simulate the advection-diffusion processes of dredging suspended solids, either alone or in combination with Eulerian models. Computational efficiency and accuracy are of prime importance in designing these hybrid approaches to simulate a pollutant discharge, and we characterize two relatively simple Lagrangian techniques in this regard: forward Gaussian puff tracking (FGPT), and backward Gaussian puff tracking (BGPT). FGPT and BGPT offer dramatic savings in computational expense, but their applicability is limited by accuracy concerns in the presence of spatially variable flow or diffusivity fields or complex no-flux or open boundary conditions. For long simulations, particle and/or puff methods can transition to an Eulerian model if appropriate, since the relative computational expense of Lagrangian methods increases with time for continuous sources. Although we focus on simple Lagrangian models that are not suitable to all environmental applications, many of the implementation and computational efficiency concerns outlined herein would also be relevant to using higher order particle and puff methods to extend the near field.

• ETRI Journal
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• v.10 no.1
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• pp.39-52
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• 1988

This study suggests the diffusion models to predict the spread pattern of telecommunications services. The extended models containing both (either) price and (or) income varible are offered on the basis of Bass model. At the empirical test using Korean telephone data, the models with either price or income varible are the best forecasting model under apriori selected econometric criteria.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.479-482
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• 2008

사고력 신장 교육은 정보영재를 위한 교육과정 및 내용 중에서 매우 중요한 목표이다. 이 논문에서는 정보영재 교육에서 중요성이 증대되는 사고력 신장 교육에 적합한 교수-학습 프로그램을 제시하여 적용하고 분석하는 방안을 마련한다. 사고력 신장 교육을 구성하는 영역별 교육 내용에 포함되는 여러 가지의 문제들에 공통적으로 적용할 수 있는 모형으로서 AND/OR 트리에 기반을 둔 교수-학습 모형을 제시한다. 이 논문에서 제시된 AND/OR 트리형 교수-학습 모형을 정보영재의 현장 교육에 도입하기 위하여 tic-tac-toe 게임 문제를 예로 들어 적용 방안을 제시하며, 그 적용 결과를 분석하여 교육 프로그램 개발의 타당성을 검증한다. AND/OR 트리형 교수-학습 모형에서는 AND 트리와 OR 트리가 결합된 형태의 트리 구성과 그에 따른 트리 탐색을 주요 학습 내용으로 하는 고도의 사고력을 필요로 하는 학습 단계를 필요로 한다.

• Journal of Korea Water Resources Association
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• v.42 no.3
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• pp.201-212
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• 2009

The Tank model and the PRMS(Precipitation Runoff Modeling-modular System) model have been adopted to simulate runoff data from 1981 to 2001 year in the Seomgin-dam basin. However, the simulated runoff by each single model showed some deviations compared with the observed runoff, respectively. In this study a genetic algorithm combination runoff model has been proposed to minimize deviations between simulated runoff and observed runoff that should yield from single model such as Tank model or PRMS model. The proposed combination runoff model combining the simulated respective output of the Tank model and the PRMS model is to produce the optimum combination ratio of each single model applying to the genetic algorithm which may yield the minimum deviations between simulated runoff and observed one. The proposed combination runoff model has been applied to the Seomgin-dam basin. It has also been shown that the combination model by introducing optimal combination ratio should yield less deviations than single model such as the Tank model or the PRMS model.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.231-239
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• 2015

A Poisson model is the first choice for counts data. Quasi Poisson or negative binomial models are usually used in cases of over (or under) dispersed data. However, these models might be unsuitable if the data consist of excessive number of zeros (zero inflated data). For zero inflated counts data, Zero Inflated Poisson (ZIP) or Zero Inflated Negative Binomial (ZINB) models are recommended to address the issue. In this paper, we further considered a situation where zero inflated data are spatially correlated. A mixed effect model with random effects that account for spatial autocorrelation is used to fit the data.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.541-562
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• 2000

The generalized linear mixed model framework is for handling count-type categorical data as well as for clustered or overdispersed non-Gaussian data, or for non-linear model data. In this study, we review its general formulation and estimation methods, based on quasi-likelihood and Monte-Carlo techniques. The current research areas and topics for further development are also mentioned.

• Journal of Korean Society of Transportation
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• v.30 no.2
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• pp.101-116
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• 2012

Accident prediction models have been utilized to predict accident possibilities in existing or projected freeways and to evaluate programs or policies for improving safety. In this study, a traffic accident prediction model for freeways was developed for the above purposes. When selecting variables for the model, the highest priority was on the ease of both collecting data and applying them into the model. The dependent variable was set as the number of total accidents and the number of accidents including casualties in the unit of IC(or JCT). As a result, two models were developed; the overall accident model and the casualty-related accident model. The error structure adjusted to each model was the negative binomial distribution and the Poisson distribution, respectively. Among the two models, a more appropriate model was selected by statistical estimation. Major nine national freeways were selected and five-year dada of 2003~2007 were utilized. Explanatory variables should take on either a predictable value such as traffic volumes or a fixed value with respect to geometric conditions. As a result of the Maximum Likelihood estimation, significant variables of the overall accident model were found to be the link length between ICs(or JCTs), the daily volumes(AADT), and the ratio of bus volume to the number of curved segments between ICs(or JCTs). For the casualty-related accident model, the link length between ICs(or JCTs), the daily volumes(AADT), and the ratio of bus volumes had a significant impact on the accident. The likelihood ratio test was conducted to verify the spatial and temporal transferability for estimated parameters of each model. It was found that the overall accident model could be transferred only to the road with four or more than six lanes. On the other hand, the casualty-related accident model was transferrable to every road and every time period. In conclusion, the model developed in this study was able to be extended to various applications to establish future plans and evaluate policies.

• Journal of The Korean Association For Science Education
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• v.34 no.6
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• pp.523-534
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• 2014

This study aimed to develop collective intelligence (CI) based instructional models for teaching socioscientific issues on the basis of intimate collaboration with science teachers, and to investigate the participating teachers' perceptions on the effectiveness of the instructional models. Adapting the ADDIE model, we suggested three types of SSI instructional models (i.e. generative model, exploratory model, and decision-making model). Generative models emphasized the process of brainstorming ideas or possible solutions for SSI. Exploratory models focused on providing students opportunities to explore various SSI cases and diverse perspectives to understand its controversial nature and complexity. Decision-making models encouraged students to negotiate or develop a group-consensus on SSI through the dialogical process. After implementing the instructional models in the science classroom, the teachers reported that CI-based SSI instructional models contributed to encouraging students' active participation and collaboration as well as to improving the quality of their argument or discourses on SSI. They also supported the importance of developing collective consciousness on the issues in the beginning of the SSI class, providing independent time and space for reflecting on their personal values and opinions with scientific evidence, and formulating an atmosphere where they freely exchanged opinions and feedback for constructing better collective ideas.