• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.6
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• pp.955-963
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• 2019

Currently, the establishment and management of civil defense shelters are under the initiative of the government and local governments to protect the lives of citizens. In the future, there is a need for efficient civil defense shelters operation through the expansion of general shelters, including designated dedicated shelters. Therefore, it is more efficient to consider the distribution of residents and the location of access to shelters, not the quantitative operation considering only the number of residents. This study uses genetic algorithms and Huff gravity model based on census output data, building data, and road network information to understand the distribution of inhabitants more precisely than existing administrative district data. In addition, the spatial- database was used for efficient data management and fast processing, and if this study is improved, it can be used as a basis for the selection and improvement of general shelters positioning for a wider area.

• Journal of KIISE:Software and Applications
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• v.31 no.4
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• pp.525-536
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• 2004

Gene expression profile is numerical data of gene expression level from organism, measured on the microarray. Generally, each specific tissue indicates different expression levels in related genes, so that we can classify disease with gene expression profile. Because all genes are not related to disease, it is needed to select related genes that is called feature selection, and it is needed to classify selected genes properly. This paper Proposes GA based method for searching optimal ensemble of feature-classifier pairs that are composed with seven feature selection methods based on correlation, similarity, and information theory, and six representative classifiers. In experimental results with leave-one-out cross validation on two gene expression Profiles related to cancers, we can find ensembles that produce much superior to all individual feature-classifier fairs for Lymphoma dataset and Colon dataset.

• Tunnel and Underground Space
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• v.15 no.1 s.54
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• pp.47-54
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• 2005

One of the standard procedures of discontinuity survey is the joint set identification from the population of field orientation data. Discontinuity set identification is fundamental to rock engineering tasks such as rock mass classification, discrete element analysis, key block analysis. and discrete fracture network modeling. Conventionally, manual method using contour plot had been widely used for this task, but this method has some short-comings such as yielding subjective identification results, manual operations, and so on. In this study, the method of discontinuity set identification using genetic algorithm was introduced, but slightly modified to handle the orientation data. Finally, based on the genetic algorithm, we developed a FORTRAN program, Genetic Algorithm based Clustering(GAC) and applied it to two different discontinuity data sets. Genetic Algorithm based Clustering(GAC) was proved to be a fast and efficient method for the discontinuity set identification task. In addition, fitness function based on variance showed more efficient performance in finding the optimal number of clusters when compared with Davis - Bouldin index.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.8
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• pp.1765-1772
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• 2015

In the underwater acoustic communication channels, multipath reflection become the cause of obstacle. Generally, equalizer has been applied to overcome these problems. In this paper, the method was proposed to optimize tap-length of decision feedback equalizer using genetic algorithm. After inputting feed-forward filter length and feed-back filter length as genetic information of the genetic algorithm, it optimize tap-length using BER(bit error rate) calculation in accordance with object function. The object function consist of decision feedback equalizer and BER calculation. For the purpose of BER calculation in the object function, the method was proposed to optimize the tap-length of decision feedback equalizer with genetic algorithm using preamble signals. As a result of experiments, the optimized BER is 0.0355 for signals which were received through a 25m receiver and which were applied to calculate BER merely using preamble signals in object function. When all data were used to calculate BER in object function, the optimized BER is 0.0215.

• International Journal of Highway Engineering
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• v.7 no.4 s.26
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• pp.41-47
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• 2005

Traffic data such as traffic volume, speed, and vehicle Class are very important basic data for the plan and design of highway. Based on traffic data, the future service level of a specific highway and geometry of newly constructed or expended road is predicted and determined. The Ministry of Construction & Transportation has simultaneously surveyed coverage count and permanent count at highways since 1985. Traffic volume survey sections were determined at jointed nodes of highways and jointed nodes of highways and other roads such as freeway and local highway. Volume survey was performed at these sections. The premise to decide these sections is assumed that links between jointed nodes of main highways exhibit similar traffic characteristics. Recently, due to the change of highway geometries such as construction of detour road and installations of traffic facilities such as installation of media, traffic characteristics of the existing traffic volume survey sections was changed. To reflect these changes, traffic characteristics at homogeneous road sections was widely evaluated and analyzed. Using Genetic Algorithms, a model was developed for the evaluation of traffic characteristics at homogeneous road sections. Traffic volume survey sections were then determined through the application of the developed model for current traffic system.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.6
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• pp.44-55
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• 2009

Diagnosis of diseases using gene expression data obtained from microarray chip is an active research area recently. It has been done by general machine learning algorithms, because it is difficult to analyze directly. However, recent research results about the analysis based on the interaction between genes is essential for the gene expression analysis, which means the analysis using the traditional machine learning algorithms has limitations. In this paper, we classify the gene expression data using the hyper-network model that considers the higher-order correlations between the features, and then compares the classification accuracies. And also, we present the new hypo-network model that improve the disadvantage of existing model, and compare the processing performances of the existing hypo-network model based on general sequential processor and the improved hypo-network model implemented on parallel processors. In the experimental results, we show that the performance of our model shows improved and competitive classification performance than traditional machine learning methods, as well as, the existing hypo-network model. We show that the performance is maximized when the hypernetwork model is implemented on our parallel processors.

• Proceedings of the Korean Society of Computer Information Conference
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• 2010.07a
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• pp.103-107
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• 2010

게임이나 네비게이션 시스템, 관광경로 설계에 있어서 경로찾기는 매우 중요한 부분 중의 하나이다. 일반적으로 TSP(Traveling Salesman Problem), RPP(Rural Postman Problem), CPP(Chinese Postman Problem)와 같은 경로찾기 문제들은 일반적인 알고리즘으로 최적해를 구할 수 없다. 문제크기가 커질수록 해집합이 폭발적으로 커짐으로써 전체 해집합을 탐색하는데 많은 비용이 든다. 따라서, 이러한 문제들은 유전알고리즘이나 Simulated Annealing과 같은 휴리스틱 알고리즘을 이용하여 근사최적 경로를 찾는다. 본 논문에서는 이와 같은 경로찾기 문제의 근사 최적해를 구하기 위한 시뮬레이션 시스템을 설계하고 구현하였다. 본 연구에서 구현한 시뮬레이션 시스템에는 유전알고리즘 엔진(GA 엔진)과 사용자 인터페이스를 제공한다. 사용자 인터페이스는 유전알고리즘에 사용될 파라미터를 설정하는 부분이며, GA 엔진은 유전알고리즘의 연산자들을 제공하는 부분이다. 본 논문에서 구현한 시뮬레이션 시스템은 게임과 같은 경로찾기 등에 활용될 수 있다.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.116-118
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• 1999

일반적으로 컴퓨터를 이용한 디자인 지원 시스템은 많은 양의 수집된 데이터에 기반하기 때문에 계속해서 변화하는 유행에 대처할 수 없을 뿐만 아니라 개인의 취향을 반영하기도 어렵다. 이같은 문제점을 해결하기 위해 이 논문에서는 대화형 유전자 알고리즘(Interactive Genetic Algorithm : IGA)을 이용한 디자인 지원 시스템을 제안한다. IGA는 상호작용에서 얻어지는 사용자의 평가를 적합도 함수로 사용하는 유전자 알고리즘의 하나로, 일반적인 적합도 함수를 결정하기 어려운 디자인이나 예술 등의 문제 해결에 사용될 수 있다. 이때, 몇 개의 부분적인 곡선으로 의상의 디자인을 표현하였던 기존의 방법들과는 달리 전체 디자인을 목과 몸통, 팔과 소매, 치마와 허리선의 세 가지 부분 디자인으로 나누어 표현하는 문제영역 지식을 활용함으로써 좀 더 현실적인 여성복을 디자인할 수 있었다. 시스템의 인터페이스를 보완하기 위해 OpenGL이 사용되었으며, 실험 결과 지식기반 인코딩을 통한 IGA 의상디자인 시스템이 유용함을 알 수 있었다.

• Journal of KIISE:Software and Applications
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• v.27 no.9
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• pp.942-951
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• 2000

일반적으로 컴퓨터를 이용한 디자인 지원 시스템은 다지이너의 행동과 같은 데이터를 통계적으로 분석하여 일련의 디자인 행위를 추출해 내는 인공지능의 접근 방식을 사용하여 왔다. 그러나 이는 많은 양의 수집된 데이터를 기반한 방법이기 때문에 계속해서 변화하는 유행에 대처할 수 없을 뿐만 아니라 개인의 취향을 반영하기도 어렵다. 이같은 문제점을 해결하기 위해 이 논문에서는 대화형 유전자 알고리즘(Interactive Genetic Algorithm : IGA)을 이용하여 명시적으로 표현하기 어려운 취향을 적절히 반영하는 디자인 지원 시스템을 제안한다. IGA는 상호작용에서 얻어지는 사용자의 평가를 적합도 함수로 사용하는 유전자 알고리즘의 하나로, 일반적인 적합도 함수를 결정하기 어려운 디자인이나 예술 등의 문제 해결에 사용될 수 있다. 한편, 몇 개의 부분적인 곡선으로 의상의 디자인을 표현하였던 기존의 방법들과는 달리 전체 디자인을 목과 몸통, 팔과 소매, 치마와 허리선의 세 가지 부분 디자인으로 나누어 표현함으로써 좀더 현실적인 여성복을 디자인할 수 있었다. 시스템의 인터페이스를 보완하기 위해 OpenGL을 이용한 3차원 모델이 사용되었으며, 사용자들을 대상으로 한 실험 결과 제안한 의상 디자인 지원 시스템이 효과적임을 알 수 있었다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.04a
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• pp.309-312
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• 2005

로봇 매니퓰레이터의 제어를 위해서는 정확한 값의 역기구학 값을 구해야한다 하지만 일반적으로 역기구학의 경우 그 계산 과정이 매우 복잡하여 실시간으로 처리하기 어렵다는 문제점이 있다. 본 논문에서는 로봇 매니퓰레이터를 퍼지 역기구학 맵핑 기법을 기반으로 제어를 한 후, 정기구학을 적합도 함수로 사용하는 유전자 알고리즘을 이용하여, 좀더 빠르고, 높은 정확도를 가지는 제어를 구현하고자 한다.