• The Journal of Korean Association of Computer Education
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• v.6 no.1
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• pp.65-77
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• 2003

This paper presents a model for supporting the adaptive interaction between the computer and the learner. To design an adaptive interaction model, the strategy to support the adaptive interaction in the web-based educational system was established. And then. the necessary components for executing each strategy were selected. Also. the logical relations among the components were verified. To verify the effectiveness of the proposed adaptive interaction model, we applied the existing web-based courseware and the new courseware using the adaptive interaction models to two groups respectively, and then measured the performance of each group. Experimental results reveal that the adaptive interaction model actually has positive effects on the learning activities of the learner.

• Journal of The Korean Association of Information Education
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• v.15 no.2
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• pp.265-275
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• 2011

The purpose of this study was to identify the factors that determined the adoption of digital textbook among elementary and middle school teachers, and to propose and validate a revised Technology Acceptance Model. The study was grounded in the innovation diffusion theory and the attribute factors proposed in the theory were used in the model. More specifically, observability, compatibility, and subjective norms were proposed as external factors and usefulness, easy of use, intention to use were proposed as internal factors in the proposed model. It was found that (a) observability, compatibility, and subjective norms were the main external factors that influenced the teachers' intention to use digital textbook and (b) the revised TAM was validated.

• Proceedings of the Korea Water Resources Association Conference
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• 1993.07a
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• pp.327-334
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• 1993

일유출량을 모의하는 수문모형이 많이 개발되었지만 그 이용에 있어서는 입력자료와 매개변수가 다양하고 매개변수의 산정에 경험을 필요로 하는 경우가 많기 때문에 수문실무자들이 사용하기에는 다소 어려운 점이 있다. 본 연구에는 최소의 입력자료(강우량, 증발량)와 매개변수로 일자연유량을 모의할 수 있는 집중형 확정론적 모형을 개발하였는데, 관측된 수문곡선으로부터 입력매개변수를 도출할 수 있으며 그 종류가 작기때문에 실무에서 쉽게 이용할 수 있도록 하였다. 개발된 모형의 기본 개념은 강우-유출에 질량불변의 법칙을 적용하고, 선형저수지와 유사한 다중 감수 과정에 따른 수문곡선에 근거한다. 제안된 수문곡선은 차단을 제외한 강우량에 기인한 유량의 시간분포를 나타내며 기저유출과 장래에 손실될 증발산량을 포함한다. 제안된 수문곡선은 2개의 상승부와 3개의 감수부로 구성되며 수문곡선의 우측부는 개방되어 있다. 수문곡선의 상승부에는 감수계수의 개념을 역으로 적용하였으며, 계산을 단순화 하기 위하여 각 상승부 및 감수부의 구간은 정수값만을 갖는다고 가정하였다. 개발된 모형을 한강유역의 인도교지점(1918-1974), 영산강유역의 나주지점(1906-1990)의 일유출량을 모의하기 위하여 적용하였다. 모형의 적용결과 모의 기간중에 매개변수의 조정없이 전 기간에 걸쳐서 양호한 장기간의 일유출량 모의 결과를 얻을 수 있었다.

• The Korean Journal of Applied Statistics
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• v.26 no.6
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• pp.923-932
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• 2013

This paper proposes a method to predict the number of foodborne disease outbreaks by microbes. The weekly data of food poisoning occurrences by microbes in Korea contain many zero-valued observations and have dependency between outbreaks. In order to model both phenomena, the number of food poisonings is predicted by an autoregressive model and the probabilities of food poisoning occurrences by microbes (given the total of food poisonings) are estimated by the baseline category logit model. The predicted number of foodborne disease outbreaks by a microbe is obtained by multiplying the predicted number of foodborne disease outbreaks and the estimated probability of the food poisoning by the corresponding microbe. The mean squared error and the mean absolute value error are evaluated to compare the performances of the proposed method and the zero-inflated model.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.13-13
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• 2018

우리나라의 하천 홍수량 자료는 대부분 댐 상류나 홍수위험 지역 등 유역 내 하천관리가 필요한 주요 지점에서만 측정되고 있다. 그러나 매년 관측되는 강우량 자료에 비해 유출량 자료는 유역의 크기가 작아질수록 매우 제한적이며, 신뢰성 있는 홍수량자료의 구축이 어려운 실정이다. 이에 본 연구에서는 유역특성인자(유역면적, 유역경사)를 매개변수로 활용하여 권역별 설계홍수량 자료에 대한 지역화 분석을 수행하였으며, 미계측 유역에서 홍수량 추정이 가능하도록 모형을 개발 하였다. 모형에서 발생하는 불확실성을 고려하기 위하여 Bayesian GLM(generalized linear method)기법을 활용하였으며, 최종적으로 모형의 매개변수와 산정되는 홍수량 결과에 대한 불확실성 구간을 정량적으로 제시하였다. 제안된 모형을 통해 일부 유역을 미계측 유역으로 가정하여 홍수량을 추정하였으며, 통계적 지표를 활용하여 기수립된 설계홍수량 자료와의 비교를 통해 모형의 적합성을 평가하였다. 본 연구를 통해 제안된 모형은 검증과정과 도출된 결과를 통해 유역특성에 따른 재현기간별 홍수량을 효과적으로 재현하는데 유리할 뿐만 아니라, Bayesian 기법을 도입하여 매개변수와 도출된 결과에 대한 불확실성의 정량적인 평가가 가능한 장점을 확인하였다.

• Journal of Korean Society of Transportation
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• v.21 no.2
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• pp.107-118
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• 2003

Highway Capacity Manual (HCM) provides an analytical delay estimation model to assist the evaluation of traffic at a signalized intersection. The model revised and included in the HCM published in the year 2000 reflects the results of recent studies and is utilized in various fields of transportation studies. For the implementation of the model in the case of permitted left turns, the HCM supplement provides a computational procedure to adjust the saturation flow rate of permitted left toms. The model however, is originally designed for a protected movement and thus underestimates the delay of permitted left turns due to its difference right-of-way nature. This document describes (1) a review of the theoretical background of the HCM delay estimation model, (2) problems embedded in the model for the delay estimation of permitted left turns, (3) a proposed model developed in this study to improve the delay estimation for permitted left turns and (4) a set of verification tests. In order to reflect various traffic and control conditions in the test, simulation studies were performed to by using the field data based on 120 different permitted left-turn scenarios. Comparison studies conducted between sets of delays estimated by the HCM and the proposed models against a set of the CORSIM delays and showed that the proposed model improved the estimation of the permitted left-turn delays. The explanatory variable of the relationship between the HCM delay and the simulation delay was 0.47 and the one between the delay estimated by the proposed model and the simulation delay was 0.77.

• The Korean Journal of Applied Statistics
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• v.25 no.2
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• pp.351-362
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• 2012

Weather generators are statistical tools to produce synthetic sequences of daily weather variables. We propose the multisite weather generators with a spatio-temporal correlation based on hierarchical generalized linear models. We develop a computational algorithm to produce future weather variables that use three different types of green-house gases scenarios. We apply the proposed method to a daily time series of precipitation and average temperature for South Korea.

• Journal of Korean Society of Transportation
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• v.20 no.5
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• pp.193-206
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• 2002

A signal optimization model is proposed by applying the Cell-Transmission Model(CTM) as an embedded traffic flow model to estimate a system-optimal signal timing plan in a transportation network composed of signalized intersections. Beyond the existing signal-optimization models, the CTM provides appropriate theoretical and practical backgrounds to simulate oversaturation phenomena such as shockwave, queue length, and spillback. The model is formulated on the Mixed-Integer Programming(MIP) theory. The proposed model implies a system-optimal in a sense that traffic demand and signal system cooperate to minimize the traffic network cost: the demand departing from origins through route choice behavior until arriving at destinations and the signal system by calculating optimal signal timings considering the movement of these demand. The potential of model's practical application is demonstrated through a comparison study of two signal control strategies: optimal and fixed signal controls.

• Communications for Statistical Applications and Methods
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• v.18 no.5
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• pp.571-579
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• 2011

We propose a new bivariate zero-inflated negative binomial regression model to allow heterogeneous dispersions. To show the performance of our proposed model, Health Care data in Deb and Trivedi (1997) are used to compare it with the other bivariate zero-inflated negative binomial model proposed by Wang (2003) that has a common dispersion between the two response variables. This empirical study shows better results from the views of log-likelihood and AIC.

• Journal of the Korean Data and Information Science Society
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• v.24 no.1
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• pp.33-39
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• 2013

In the transformation of response variable in partial linear models outliers can cause a bad effect on estimating the transformation parameter, just as in the linear models. To solve this problem the processes of estimating transformation parameter and detecting outliers are needed, but have difficulties to be performed due to the arbitrariness of the nonparametric function included in the partial linear model. In this study, through the estimation of nonparametric function and outlier detection methods such as a sequential test and a maximum trimmed likelihood estimation, processes for transforming response variable robust to outliers in partial linear models are suggested. The proposed methods are verified and compared their effectiveness by simulation study and examples.