• Annual Conference of KIPS
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• 2000.04a
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• pp.715-718
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• 2000

본 논문은 WDM 광 통신망에서 망의 효율적인 설계에 필요한 알고리즘에 관한 것이다. WDM 광 통신망은 경로설정 및 파장할당이 중요한 변수로 s-d 노드 쌍간에 필요한 연결수요가 주어질 때 이 연결수요를 만족시켜주는 광 경로를 설정하고, 파장을 할당해 줌으로써 사용 파장수를 최소화하면서 파장을 재사용 할 수 있는 효율을 최대로 하는 것이 목적이다. WDM 방식을 이용하여 전기적 변환에 따른 지연 없이 전광통신망에서 여러 개의 파장을 다중화하여 동시에 자료를 전송함으로서 수Gbps, 혹은 그이상의 전송이 가능해질 것으로 기대되어 진다. 이들 설계 문제들은 일반적인 토폴로지 망에서 정적인 경로 설정 및 파장할당을 대상으로 하여 발견적인 알고리즘을 얻고자 하였다. 또한 이를 통해 LP모형으로 얻은 하한 한계 값과 휴리스틱 알고리즘의 최소파장수와 동일한 값이 나올 수 있는 알고리즘을 구하였다.

• Journal of the Korean Data and Information Science Society
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• v.28 no.5
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• pp.1125-1132
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• 2017

In a situation where the severity and frequency of drought events getting stronger and higher, many studies related to drought forecast have been conducted to improve the drought forecast accuracy. However it is difficult to predict drought events using a single model because of nonlinear and complicated characteristics of temporal behavior of drought events. In this study, in order to overcome the shortcomings of the single model approach, we first build various single models capable to explain the relationship between the meteorological drought index, Standardized Precipitation Index (SPI), and other independent variables such as world climate indices. Then, we developed a combined models using Stochastic Gradient Descent method among Ensemble Learnings.

• Communications for Statistical Applications and Methods
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• v.15 no.4
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• pp.615-631
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• 2008

Current credit evaluation models based on only financial data except non-financial data are used continuous data and produce credit scores for the ranking. In this work, some problems of the credit evaluation models based on transformed continuous financial data are discussed and we propose improved credit evaluation models based on categorized financial data. After analyzing and comparing goodness-of-fit tests of two models, the availability of the credit evaluation models for categorized financial data is explained.

• Journal of Animal Science and Technology
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• v.46 no.4
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• pp.509-516
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• 2004

This study is to investigate the amount of biased estimates for heritability and genetic correlation according to data structure on marbling scores in Korean cattle. Breeding population with 5 generations were simulated by way of selection for carcass weight, Longissimus muscle area and latent values of marbling scores and random mating. Latent variables of marbling scores were categorized into five by the thresholds of 0, I, 2, and 3 SD(DSI) or seven by the thresholds of -2, -1, 0,1I, 2, and 3 SD(DS2). Variance components and genetic pararneters(Heritabilities and Genetic correlations) were estimated by restricted maximum likelihood on multivariate linear mixed animal models and by Gibbs sampling algorithms on multivariate threshold mixed animal models in DS1 and DS2. Simulation was performed for 10 replicates and averages and empirical standard deviation were calculated. Using REML, heritabilitis of marbling score were under-estimated as 0.315 and 0.462 on DS1 and DS2, respectively, with comparison of the pararneter(0.500). Otherwise, using Gibbs sampling in the multivariate threshold animal models, these estimates did not significantly differ to the parameter. Residual correlations of marbling score to other traits were reduced with comparing the parameters when using REML algorithm with assuming linear and normal distribution. This would be due to loss of information and therefore, reduced variation on marbling score. As concluding, genetic variation of marbling would be well defined if liability concepts were adopted on marbling score and implemented threshold mixed model on genetic parameter estimation in Korean cattle.

• Journal of Wetlands Research
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• v.19 no.4
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• pp.423-432
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• 2017

This study used SSARR model to predict the flood hydrograph for the Miho stream in the Geum river basin. First, we performed the sensitivity analysis on the parameters of SSARR model to know the characteristics of the parameters and set the range. For the parameter calibration, optimization methods such as genetic algorithm, pattern search and SCE-UA were used. WSSR and SSR were applied as objective functions, and the results of optimization method and objective function were compared and analyzed. As a result of this study, flood prediction was most accurate when using pattern search as an optimization method and WSSR as an objective function. If the parameters are optimized based on the results of this study, it can be helpful for decision making such as flood prediction and flood warning.

• The Korean Journal of Applied Statistics
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• v.33 no.1
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• pp.47-59
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• 2020

In this paper, we present the longitudinal Markov binary regression model with t-link function when its transition order is known or unknown. It is assumed that logit or probit models are considered in binary regression models. Here, t-link function can be used for more flexibility instead of the probit model since the t distribution approaches to normal distribution as the degree of freedom goes to infinity. A Markov regression model is considered because of the longitudinal data of each individual data set. We propose Bayesian method to determine the transition order of Markov regression model. In particular, we use the deviance information criterion (DIC) (Spiegelhalter et al., 2002) of possible models in order to determine the transition order of the Markov binary regression model if the transition order is known; however, we compute and compare their posterior probabilities if unknown. In order to overcome the complicated Bayesian computation, our proposed model is reconstructed by the ideas of Albert and Chib (1993), Kuo and Mallick (1998), and Erkanli et al. (2001). Our proposed method is applied to the simulated data and real data examined by Sommer et al. (1984). Markov chain Monte Carlo methods to determine the optimal model are used assuming that the transition order of the Markov regression model are known or unknown. Gelman and Rubin's method (1992) is also employed to check the convergence of the Metropolis Hastings algorithm.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.307-332
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• 2022

One of the most intensively conducted research areas in business application study is a bankruptcy prediction model, a representative classification problem related to loan lending, investment decision making, and profitability to financial institutions. Many research demonstrated outstanding performance for bankruptcy prediction models using artificial intelligence techniques. However, since most machine learning algorithms are "black-box," AI has been identified as a prominent research topic for providing users with an explanation. Although there are many different approaches for explanations, this study focuses on explaining a bankruptcy prediction model using a counterfactual example. Users can obtain desired output from the model by using a counterfactual-based explanation, which provides an alternative case. This study introduces a counterfactual generation technique based on a genetic algorithm (GA) that leverages both domain knowledge (i.e., causal feasibility) and feature importance from a black-box model along with other critical counterfactual variables, including proximity, distribution, and sparsity. The proposed method was evaluated quantitatively and qualitatively to measure the quality and the validity.

• Journal of Korea Water Resources Association
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• v.41 no.12
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• pp.1211-1218
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• 2008

This study is for design of the detention system distributed in a watershed by the Multi-Objective Genetic Algorithms(MOGAs). A new model is developed to determine optimal size and location of detention. The developed model has two primary interfaced components such as a rainfall runoff model to simulate water surface elevation(or flowrate) and MOGAs to get the optimal solution. The objective functions used in this model depend on the peak flow and storage of detention. With various constraints such as structural limitations, capacities of storage and operational targets. The developed model is applied at Gwanyang basin within Anyang watershed. The simulation results show the maximum outlet reduction is occurred at detention facilities located in upper reach of watershed in the peak discharge rates. It is also reviewed the simultaneous construction of an off-line detention and an on-line detention. The methodologies obtained from this study will be used to control the flood discharges and to reduce flood damage in urbanized watershed.

• Journal of Global Scholars of Marketing Science
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• v.7
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• pp.127-146
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• 2001

Attitude toward the advertising is widely used in studies of traditional mass media advertising. As internet marketing becomes more important, attitude toward the website will gain parallel status in evaluating effectiveness. And also, studies on relationship among attitude toward the website, attitude toward the advertising, attention to the commercial, attitude toward the brand, & purchase intention was needed. This study sets a hypothetical model about the relationship among attitude toward the website, attitude toward the advertising, attention to the commercial, attitude toward the brand, & purchase intention. And to testify this model, 10 hypotheses were set. The results of the analysis, all variables were related significantly.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.1455-1459
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• 2006

우리나라 수자원 관리에서 여름 유량은 이수 및 치수 측면에서 매우 중요한 역할을 한다. 이러한 점에서 여름유량의 예측 가능성을 검토하는 것은 수자원 관리에 유연성을 주는 동시에 상대적으로 위험도를 저감시킬 수 있는 역할을 할 수 있다. 따라서 본 연구의 목적은 여름 계절 유량을 대상으로 기상인자와의 상관성 분석을 통해 유량 예측을 위한 수문기상정보(hydroclimatics)를 전 지구적으로 검토하고 최종적으로 불확실성을 고려할 수 있는 Ensemble예측을 실시하고자 한다. Ensemble예측은 설정 가능한 입력 자료를 통하여 다수의 출력자료를 얻는 방법론으로서 불확실성이 큰 기상 및 수문기상자료 분석에 주로 이용되고 있다. 본 연구에서는 해수면온도(sea surface temperature), 해수면기압(sea level pressure)과 방출장파복사에너지(outgoing longwave radiation)를 주요 기상인자로 고려하였으며 예측모형으로서는 Cross Ensemble(out of bagging)방법에 근거한 Support Vector Machine 모형을 이용하였다. 분석결과 주요 기상인자와 50%이상의 상관관계를 보이고 있으며 다소 합리적인 예측 결과를 제시하여 주고 있어 수자원관리를 위한 보조수단으로 이용이 가능할 것으로 사료된다.