• Journal of the Korea Society of Computer and Information
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• 제14권6호
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• pp.135-142
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• 2009

Data mining is an emerging area of computational intelligence that offers new theories, techniques, and tools for analysis of large data sets. The major techniques used in data mining are mining association rules, classification and clustering. Since these techniques are used individually, it is necessary to develop the methodology for rule extraction using a process of integrating these techniques. Rule extraction techniques assist humans in analyzing of large data sets and to turn the meaningful information contained in the data sets into successful decision making. This paper proposes an autonomous method of rule extraction using clustering and rough set theory. The experiments are carried out on data sets of UCI KDD archive and present decision rules from the proposed method. These rules can be successfully used for making decisions.

• Journal of Korea Water Resources Association
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• 제47권5호
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• pp.469-482
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• 2014

This study developed a Bayesian spatial regional frequency analysis, which aimed to analyze spatial patterns of design rainfall by incorporating geographical information (e.g. latitude, longitude and altitude) and climate characteristics (e.g. annual maximum series) within a Bayesian framework. There are disadvantages to considering geographical characteristics and to increasing uncertainties associated with areal rainfall estimation on the existing regional frequency analysis. In this sense, this study estimated the parameters of Gumbel distribution which is a function of geographical and climate characteristics, and the estimated parameters were spatially interpolated to derive design rainfall over the entire Han-river watershed. The proposed Bayesian spatial regional frequency analysis model showed similar results compared to L-moment based regional frequency analysis, and even better performance in terms of quantifying uncertainty of design rainfall and considering geographical information as a predictor.

• Journal of Korea Water Resources Association
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• 제42권2호
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• pp.149-160
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• 2009

The objective of this study is to suggest parameter regionalization scheme which is integrated two multivariate statistical methods: principal components analysis(PCA) and hierarchical cluster analysis(HCA). This technique is to apply semi-distributed rainfall-runoff model on ungauged catchments. 7 catchment characteristics (area, mean altitude, mean slope, ratio of forest, water content at saturation, field capacity and wilting point) are estimated for 109 mid-sized sub-basins. The first two components from PCA results account for 82.11% of the total variance in the dataset. Component 1 is related to the location of the catchments relevant to the altitude and Component 2 is connected with the area of these. 103 ungauged catchments are clustered using HCA as the following 6 groups: Goesan 23, Andong 6, Imha 5, Hapcheon 21, Yongdam 4, Seomjin 44. SWAT model is used to simulate runoff and the parameters of the model on the 6 gauged basins are estimated. The model parameters were regionalized for Soyang, Chungju and Daecheong dam basins which are assumed as ungauged ones. The model efficiency coefficients of the simulated inflows for these three dams were at least 0.8. These results also mean that goodness of fit is high to the observed inflows. This research will contribute to estimate and analyze hydrologic components on the ungauged catchments.

• Journal of the Korean Association of Geographic Information Studies
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• 제8권2호
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• pp.55-67
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• 2005

This study presented the route location method by applying AHP and evaluating quantitatively. This study developed the program that can be easily applied to this kind of road design, and built the decision support system for route location. The study results are summarized as follows ; We could quantitatively evaluate the appropriateness of exiting routes by applying the AHP based on GIS. If we apply this to the roads that will be newly constructed, we can make the objective and reliable route location when making road plans and basic designs. We improved the technique of route location by applying the decision support system with third-dimensional data, which considers even the vertical alignment plan, to the existing decision support system with second-dimensional data. And, since we can set those data such as vertical slope, earth-volume, structure size, location and construction cost to independent variables, we can make road designs more scientifically and reasonably.

• The Korean Journal of Applied Statistics
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• 제30권1호
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• pp.135-145
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• 2017

Principal component analysis (PCA) describes the variation of multivariate data in terms of a set of uncorrelated variables. Since each principal component is a linear combination of all variables and the loadings are typically non-zero, it is difficult to interpret the derived principal components. Sparse principal component analysis (SPCA) is a specialized technique using the elastic net penalty function to produce sparse loadings in principal component analysis. When data are structured by groups of variables, it is desirable to select variables in a grouped manner. In this paper, we propose a new PCA method to improve variable selection performance when variables are grouped, which not only selects important groups but also removes unimportant variables within identified groups. To incorporate group information into model fitting, we consider a hierarchical lasso penalty instead of the elastic net penalty in SPCA. Real data analyses demonstrate the performance and usefulness of the proposed method.

• Tunnel and Underground Space
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• 제26권5호
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• pp.431-440
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• 2016

This study aims to suggest the detailed evaluation criteria based on performances for rock slopes. Using the previous research result, final evaluation items are proposed considering characteristics and similarities of each evaluation item. Weight for each evaluation item is deducted using AHP method, verification for suggested evaluation criteria is conducted based on correlation analysis. The research results as follows. All evaluation items have a high statistical correlation with final evaluation result(safety rating). Especially, items of the "rockfall", "ground deformation", "discontinuity characteristic", "instable lithology" were shown the highest in relative correlation coefficient(R), It is judged that items and weight presented in this study well reflect characteristics of rock slopes.

• Proceedings of the Korea Water Resources Association Conference
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• 한국수자원학회 2011년도 학술발표회
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• pp.455-455
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• 2011

개발로 인한 환경변화는 관련 분석모형을 통해 직접적으로 예측하기 하는 것이 가장 바람직하지만 데이터 취득의 어려움, 분석 방법론의 부재 등의 이유로 정량적 평가가 어려운 현실이다. 그렇기 때문에 수자원사업을 계획시 대부분 환경적인 영향을 매우 정성적인 형태로 평가하거나 수질과 같은 대표적인 항목에 대해서만 예측하는 수준이다. 기존의 연구 또한, 유역 또는 행정구역의 현재의 현 상황을 평가하기 위한 것이 주이며, 수자원사업과 관련성이 적은 항목도 일부 포함되어 있기 때문에 수자원사업의 특수성을 반영하기에 한계가 있다. 현 상황의 이러한 문제점을 인식하여 본 연구는 오늘날 대표적 의사결정 기법이라 할 수 있는 계층화분석과정(AHP)과 다속성효용이론(MAUT)을 활용하여 향후 수자원사업과 관련된 다기준의 사결정 과정에서의 환경성 평가방안을 제시하였다. 환경성 평가기준은 수질, 경관, 생태계 이렇게 세 가지 항목으로 구성하였고, 각 평가기준에 대한 수준을 직접적으로 대변 가능한 정량화 방안을 제시하였다. 그리고 앞서 정량화된 값을 표준화하기 위하여 MAUT 기법으로부터 평가기준별 효용함수를 도출하였다. 한편, 사업을 시행함에 따라 예상되는 환경성변화는 사업전 환경성과 사업 후 환경성을 비교하도록 하였고, 이때 해당사업의 특수성을 반영하고자 별도의 설문과정을 통해 평가기준별 가중치를 결정하였다. 본 연구는 환경성 검토시 생태학적, 물리적 분석에 기반을 둔 정량적 예측의 어려움을 보완하기 위해 정성적 예측을 추가적으로 제시하였고, 사업의 특수성과 평가항목이 갖는 일반성을 명확히 구분하여 의사결정 과정에서 주관적인 요소를 최소화하였다. 또한, 평가항목별 사업전후의 환경성을 비교, 검토함으로써 실제 사업추진 과정에서 개발로 인한 부정적 영향의 사전예방에 도움을 줄 수 있을 것으로 판단된다.

• Journal of the Korean Society for Geothermal and Hydrothermal Energy
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• 제17권4호
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• pp.1-14
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• 2021

Recently, various renewable systems have been developed and applied in Korea. However, there are many systems which were not utilized in real buildings or not widely spread in the market. Furthermore, the attention of most users focused on several certain system such as PV or solar system. Therefore, this research aims to find user's needs for renewable energy based on perception surveys to improve the acceptance of technology in line with the world's energy flow. The survey was conducted by classifying respondents by various criteria, and the results were analyzed using AHP technique and CVM technique, focusing on preference and acceptance. According to the results of the survey, it was found that the people felt the need for renewable energy but lacked knowledge about renewable energy compared to the various government-implemented renewable energy supply policies. Therefore, a government-level policy is needed so that the people can have universal knowledge about renewable energy system.

• Proceedings of the KAIS Fall Conference
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• 한국산학기술학회 2009년도 춘계학술발표논문집
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• pp.817-820
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• 2009

신종 및 재출현 전염병이 지속적으로 유행하고 해외 전염병의 국내유입가능성이 증대됨에 따라 국립검역소의 검역관리 사업의 효율성 평가는 매우 중요하다. 연구는 평가 구성요소 사이의 상대적 중요도, 즉 가중치(weight) 부여에 있어서 과학적 타당성을 인정받고 있는 계층분석절차(Analytical Hierarchy Process; AHP) 기법에 의한 이원비교방법을 사용하여 설정하였다. AHP기법은 복잡한 다기준 의사결정문제(multi-criteria decision making problem)를 계층화하여 단순화 체계화시킴으로써 그 영향도를 계량화하는데 탁월한 기법이라는 평가를 받고 있다. 실제 측정에 있어서는 본 모형을 이용하여 얻어졌다. 대영역(기관평가영역, 서비스 및 프로그램평가 영역), 중영역(투입, 과정, 결과, 검역업무, 검사업무, 위생관리업무, 병원체조사감시업무, 전염병예방홍보 교육업무)으로 구분하여 최종적으로 평가지표로 선정된 지표는 실제 국립검역소 사업에 적용하였으며, 이에 대한 최소한의 평가지표를 최종 선정하여 향후 국립검역소사업 평가체계를 보다 체계화 하였다.

• The Korean Journal of Applied Statistics
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• 제22권4호
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• pp.745-758
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• 2009

Cluster analysis is the automated search for groups of related observations in a data set. To group the observations into clusters many techniques has been proposed, and a variety measures aimed at validating the results of a cluster analysis have been suggested. In this paper, we compare complete linkage, Ward's method, K-means and model-based clustering and compute validity measures such as connectivity, Dunn Index and silhouette with simulated data from multivariate distributions. We also select a clustering algorithm and determine the number of clusters of Korean consumers based on Korean consumers' palatability scores for Hanwoo bull in BBQ cooking method.