• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2006년도 춘계학술대회
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• pp.167-175
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• 2006

Building accurate corporate bankruptcy prediction models has been one of the most important research issues in finance. Recently, support vector machines (SVMs) are popularly applied to bankruptcy prediction because of its many strong points. However, in order to use SVM, a modeler should determine several factors by heuristics, which hinders from obtaining accurate prediction results by using SVM. As a result, some researchers have tried to optimize these factors, especially the feature subset and kernel parameters of SVM But, there have been no studies that have attempted to determine appropriate instance subset of SVM, although it may improve the performance by eliminating distorted cases. Thus in the study, we propose the simultaneous optimization of the instance selection as well as the parameters of a kernel function of SVM by using genetic algorithms (GAs). Experimental results show that our model outperforms not only conventional SVM, but also prior approaches for optimizing SVM.

• Korean Journal of Geomatics
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• 제1권1호
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• pp.95-101
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• 2001

In a route plan, the route selection is a complicated problem to consider the spatial distribution and influence through overall related data and objective analysis on the social, economic and technical condition. The developed system in this study was compared and estimated by deciding a practical section for its validity and efficiency. Using Geographic Information System (GIS), the various information required for route selections in database was constructed, the characteristics of subject area by executing three-dimensional terrain analysis was grasped effectively, and the control point through buffering, overlay and location operation was extracted. An optimum route was selected by calculating the sum of alternatives to the sub-criteria weight, and from this result, there is a difference between real route and proposed route according to the prioritization of decision criteria based on the importance. This research could be constructed and applied geospatial information to the reasonable route plan and an optimum route selection efficiently using GIS. Therefore, the applications are presented by applying Analytic Hierarchy Process (AHP) to the decision-making of information needed in route selection.

• 한국공작기계학회논문집
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• 제18권1호
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• pp.59-67
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• 2009

The purpose of this study is to provide a decision support to select an appropriate layered manufacturing(LM) machine that suits the application of a part. Selection factors include concept model, form/fit/functional model, pattern model far molding, material property, build time and part cost that greatly affect the performance of LM machines. However, the selection of a LM is not an easy decision because they are uncertain and vague. For this reason, the aim of this research is to propose hybrid multiple attribute decision making approaches to effectively evaluate LM machines. In addition, because subjective considerations are relevant to selection decision, a fuzzy logic approach is adopted. The proposed selection procedure consists of several steps. First, we identify LM machines that the users consider After constructing the evaluation criteria, we calculate the weights of the criteria by applying the fuzzy Analytic Hierarchy Process(AHP) method. Finally, we construct the fuzzy Technique of Order Preference by Similarity to Ideal Solution(TOPSIS) method to achieve the ranking order of all machines providing the decision information for the selection of LM machines.

• 한국초전도ㆍ저온공학회논문지
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• 제24권3호
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• pp.19-23
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• 2022

We have been developing a magnetic separation device that can be used in low magnetic fields for paramagnetic materials. Magnetic separation of paramagnetic particles with a small particle size is desired for volume reduction of contaminated soil in Fukushima or separation of iron scale from water supply system in power plants. However, the implementation of the system has been difficult due to the needed magnetic fields is high for paramagnetic materials. This is because there was a problem in installing such a magnet in the site. Therefore, we have developed a magnetic separation system that combines a selection tube and magnetic separation that can separate small sized paramagnetic particles in a low magnetic field. The selection tube is a technique for classifying the suspended particles by utilizing the phenomenon that the suspended particles come to rest when the gravity acting on the particles and the drag force are balanced when the suspension is flowed upward. In the balanced condition, they can be captured with even small magnetic forces. In this study, we calculated the particle size of paramagnetic particles trapped in a selection tube in a high gradient magnetic field. As a result, the combination of the selection tube and HGMS (High Gradient Magnetic Separation-system) can separate small sized paramagnetic particles under low magnetic field with high efficiency, and this paper shows its potential application.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권8호
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• pp.3295-3311
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• 2020

With the wide application of hyperspectral images, it becomes more and more important to compress hyperspectral images. Conventional recursive least squares (CRLS) algorithm has great potentiality in lossless compression for hyperspectral images. The prediction accuracy of CRLS is closely related to the correlations between the reference bands and the current band, and the similarity between pixels in prediction context. According to this characteristic, we present an improved CRLS with adaptive band selection and adaptive predictor selection (CRLS-ABS-APS). Firstly, a spectral vector correlation coefficient-based k-means clustering algorithm is employed to generate clustering map. Afterwards, an adaptive band selection strategy based on inter-spectral correlation coefficient is adopted to select the reference bands for each band. Then, an adaptive predictor selection strategy based on clustering map is adopted to select the optimal CRLS predictor for each pixel. In addition, a double snake scan mode is used to further improve the similarity of prediction context, and a recursive average estimation method is used to accelerate the local average calculation. Finally, the prediction residuals are entropy encoded by arithmetic encoder. Experiments on the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) 2006 data set show that the CRLS-ABS-APS achieves average bit rates of 3.28 bpp, 5.55 bpp and 2.39 bpp on the three subsets, respectively. The results indicate that the CRLS-ABS-APS effectively improves the compression effect with lower computation complexity, and outperforms to the current state-of-the-art methods.

• 한국융합학회논문지
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• 제10권11호
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• pp.149-159
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• 2019

식스시그마는 시장과 고객의 패러다임과 트렌드의 변화에 맞추어 모든 사업의 프로세스와 전략을 개선하는 경영 혁신운동이다. 식스시그마 프로젝트 선정에 관한 기존의 연구는 있으나 불완전한 정보환경 하에서 프로젝트 선정을 위한 연구는 거의 없다. 본 연구의 목적은 불완전한 정보 하에서 올바른 프로젝트 선정을 위해 통합 MCDM 기법을 적용 방법을 제안하는 것이다. 식스시그마 프로젝트 선정을 위해 4단계인 1) 평가기준 간 가중치 결정 2) 팀 멤버 간 전문역량의 상대적 중요도 결정 3) 프로젝트 선호도 척도 산정 4) 최종 프로젝트 우선순위 결정 등을 위해 그룹 Fuzzy AHP, 불완전한 정보환경 하에서의 비퍼지화 TrFN 변환, GRA의 통합기법을 제안하였다. 본 연구에서 제안한 식스시그마 프로젝트 선정단계의 적용방안에 대한 이해를 돕기 위해 수치예가 제시되었다.

• 응용통계연구
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• 제31권6호
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• pp.733-749
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• 2018

어떠한 연구에서 관심의 대상이 되는 관찰치가 부분적으로 관측 가능할 때 표본선택의 문제가 일어난다. 이러한 자료를 분석하기 위해 헤크만은 표본선택 모형을 개발하였고 이변량 정규분표의 가정 하에 최대우도방법을 사용하여 모수를 추정하였다. 최근 이항자료와 포아송 자료에 대한 표본선택모형이 제안되었다. 이를 분포조정에 기초하여 과대산포 자료에 대한 모형으로 확장하고자 한다. 표본선택이 없는 과대산포 자료는 흔히 음이항 분포로 분석되어진다. 따라서 음이항 분포를 이용하고 분포조정을 도입한 과대산포 자료에 대한 새로운 모형을 제시하고자 한다. 실제 자료를 이용하여 분석을 하였다. 모의실험 결과 프로파일 우도함수를 이용하여 모수에 대해 추정한 결과는 안정적이다.

• 에너지공학
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• 제23권4호
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• pp.123-129
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• 2014

국내에서도 에너지절약성과 보증계약 방식 위주의 역량강화를 위한 측정 검증(M&V)이 중요한 과제로 부각됨에 따라 선진국과 같은 M&V 방법의 선택과 적용이 필요하다. 국내 ESCO 산업의 역량강화를 촉진하기 위해 에너지절약성과에 대한 M&V를 위해서, 적합한 M&V 선택 및 적용방안의 제시가 요청되고 있다. 본고에서는 IPMVP, 미국 및 일본 사례를 참조하여 국내의 M&V 적용목표 및 적용방향을 설정하고, 적용절차 및 유의사항을 설명한 뒤 M&V 방안(A, B, C, D)의 선택 및 검증방안을 제시하였다. 향후 연구과제로 앞에 제시된 M&V 선택 및 적용방안을 기초로 한 M&V 계획서 및 결과서 작성을 위한 가이드라인 제시방안이 마련될 필요가 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권2호
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• pp.335-352
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• 2023

When solving multi-objective optimization problems, the blindness of the evolution direction of the population gradually emerges with the increase in the number of objectives, and there are also problems of convergence and diversity that are difficult to balance. The many- objective optimization problem makes some classic multi-objective optimization algorithms face challenges due to the huge objective space. The sine cosine algorithm is a new type of natural simulation optimization algorithm, which uses the sine and cosine mathematical model to solve the optimization problem. In this paper, a knee-driven many-objective sine-cosine algorithm (MaSCA-KD) is proposed. First, the Latin hypercube population initialization strategy is used to generate the initial population, in order to ensure that the population is evenly distributed in the decision space. Secondly, special points in the population, such as nadir point and knee points, are adopted to increase selection pressure and guide population evolution. In the process of environmental selection, the diversity of the population is promoted through diversity criteria. Through the above strategies, the balance of population convergence and diversity is achieved. Experimental research on the WFG series of benchmark problems shows that the MaSCA-KD algorithm has a certain degree of competitiveness compared with the existing algorithms. The algorithm has good performance and can be used as an alternative tool for many-objective optimization problems.

• 한국정보과학회논문지:시스템및이론
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• 제37권3호
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• pp.127-137
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• 2010

다양한 과학 분야에서 대규모의 계산집중적인 어플리케이션들이 많은 그리드 자원을 활용해감에 따라 그 실행 관리와 제어의 어려움도 증가하였다. 어플리케이션의 반복되는 실행으로 축적된 작업 이력을 참조하여 어플리케이션의 특성을 파악하고 그리드 자원 선택 정책을 결정하였다. 본 논문은 그리드 컴퓨팅 환경과 이를 활용한 어플리케이션의 이력을 분석하기 위해 통계적 기법인 PBDF(Plackett-Burman with Fold-Over)계획법을 적용하였다. PBDF는 그리드 환경과 어플리케이션에서 주요한 요인들을 파악하고, 그것들이 얼마만큼 영향을 미치는 가를 수치화한다. 영향력 큰 요인은 작업 이력에서 참조 프로파일을 찾고 적절한 자원을 선택하는데 사용하였다. 응용의 수행 결과를 다시 작업 이력에 포함시키고 인자의 신뢰도를 조정하였다. 본 연구는 항공우주 연구 그리드의 작업 이력을 분석하여 적응형 자원 선택 알고리즘을 제안하였다. 주요한 요인들의 영향력을 계산하고 자원 선택 정책에 반영하는 실험을 하였다. 또한, 수행이 끝난 후 인자의 신뢰도를 평가해 그리드 환경 변화에 적응하는 알고리즘의 유효성을 검증하였다. 오류가 빈번한 그리드 환경에서 자원 선택 기법을 평가하기 위해 다양한 시나리오에서 그 적응력을 실험하였다.