• Journal of KIISE:Software and Applications
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• v.29 no.9
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• pp.645-653
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• 2002

The unit commitment problem in a power system involves determining the start-up and shut-down schedules of many dynamos for a day or a week while satisfying the power demands and diverse constraints of the individual units in the system. It is very difficult to derive an economically optimal schedule due to its huge search space when the number of dynamos involved is large. Tabu search is a popular solution method used for various optimization problems because it is equipped with effective means of searching beyond local optima and also it can naturally incorporate and exploit domain knowledge specific to the target problem. When given a large-scaled problem with a number of complicated constraints, however, tabu search cannot easily find a good solution within a reasonable time. This paper shows that a large- scaled optimization problem such as the unit commitment problem can be solved efficiently by using a parallel tabu search. The parallel tabu search not only reduces the search time significantly but also finds a solution of better quality.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10b
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• pp.319-323
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• 2006

본 논문에서는 동작 모델과 작업 목표에 따라 지능 로봇 시스템의 작업 순서를 결정하는 작업 계획기의 설계와 데모시스템에 대해 설명한다. 블랙보드 중심의 분산 제어 구조에서 하나의 독립적인 지식원으로 동작하는 작업 계획기는 작업 관리기의 요청이 있을 때마다 지식베이스로부터 동작 모델과 월드 상태 정보를 제공받아 작업 목표 달성을 위한 작업 계획을 생성한다. 그리고 이렇게 생성된 작업 계획은 오류로 인해 재 계획이 필요할 때까지 작업 관리기를 통해 실행된다. 본 연구의 작업 계획기는 효율적인 작업 계획 생성을 위해 지역 상태공간 탐색법의 하나인 EHC+ 탐색법과 계획그래프-기반의 휴우리스틱 계산법을 적용한다. 본 논문에서는 작업 계획기의 효율성과 블랙보드와의 연계성을 시험하기 위한 데모 시스템에 대해 자세히 설명한다. 이를 통해 지식베이스, 작업 관리기, 컴포넌트 서비스기 등 로봇 제어 구조내의 다른 지식원들과의 인터페이스를 위한 메시지 설계도 소개한다.

• Journal of the Korean Geotechnical Society
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• v.21 no.4
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• pp.115-122
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• 2005

In this paper, a new method composed of discrete element method and genetic algorithm has been introduced to estimate the safety factor and search critical slip surface on slope stability analysis. In case of estimating the safety factor, conventional methods of slope analysis based on the limit equilibrium do not satisfy the overall equilibrium condition; they must make assumptions regarding the inclination and location of the interstice forces. An alternative slope analysis method based on the discrete element method, which can consider the compatibility condition between force and displacement, is presented. Real-coded genetic algorithm is applied to the search for the minimum factor of safety in proposed analysis method. This search method is shown to be more robust than simple optimization routines, which are apt to find local minimum. Examples are also shown to demonstrate the applicability of the proposed method.

• Land and Housing Review
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• v.13 no.3
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• pp.69-81
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• 2022

Unplanned land use, that is, unplanned development, causes various negative externalities. In the past, Korea has experienced significant socio-economic costs due to reckless development centered on the boundary between urban and non-urban areas.. Unplanned land use can be viewed as a result of the interaction of various factors.. Therefore, it is difficult to develop in areas where unplanned land use occurs intensively. It is necessary to strengthen legal and institutional measures so that negative externalities do not persis. In this study, we present a spatial analysis methodology to effectively find spatial clusters where unplanned land use is concentrated. By demonstrating and applying this to individual development activities that occurred in Asan City, we examine the usefulness of information to support decision making when establishing mid-to-long-term growth management strategies at the local government level.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.237-242
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• 2003

Ant Colony System (ACS) Algorithm is new meta heuristic for hard combinatorial optimization problem. It is a population based approach that uses exploitation of positive feedback as well as greedy search. It was first proposed for tackling the well known Traveling Salesman Problem (TSP). In this paper, we introduce ACS of new method that adds reinforcement value for each edge that visit to Local/Global updating rule. and the performance results under various conditions are conducted, and the comparision between the original ACS and the proposed method is shown. It turns out that our proposed method can compete with tile original ACS in terms of solution quality and computation speed to these problem.

• Journal of Korea Multimedia Society
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• v.7 no.11
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• pp.1531-1539
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• 2004

In this paper, we propose an efficient method for locating human lips. Based on Point Distribution Model and Principle Component Analysis, a lip shape model is built. Lip boundary model is represented based on the concatenated gray level distribution model. We calculate the distribution of shape parameters using Gaussian mixture. The problem to locate lip is simplified as the minimization problem of matching object function. The Down Hill Simplex Algorithm is used for the minimization with Gaussian Mixture for setting initial condition and refining estimate of lip shape parameter, which can refrain iteration from converging to local minima. The experiments have been performed for many images, and show very encouraging result.

• Journal of the Korean Geographical Society
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• v.39 no.2
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• pp.212-228
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• 2004

This paper is concerned with an important aspect of regional income convergence, ${\beta}$-convergence, which refers to the negative relationship between initial income levels and income growth rates of regions over a period of time. The common research framework on ${\beta}$-convergence which is based on OLS regression models has two drawbacks. First, it ignores spatially autocorrelated residuals. Second, it does not provide any way of exploring spatial heterogeneity across regions in terms of ${\beta}$-convergence. Given that empirical studies on ${\beta}$-convergence need to be edified by spatial data analysis, this paper aims to: (1) provide a critical review of empirical studies on ${\beta}$-convergence from a spatial perspective; (2) investigate spatio-temporal income dynamics across the U.S. labor market areas for the last 30 years (1969-1999) by fitting spatial regression models and applying bivariate ESDA techniques. The major findings are as follows. First, the hypothesis of ${\beta}$-convergence was only partially evidenced, and the trend substantively varied across sub-periods. Second, a SAR model indicated that ${\beta}$-coefficient for the entire period was not significant at the 99％ confidence level, which may lead to a conclusion that there is no statistical evidence of regional income convergence in the US over the last three decades. Third, the results from bivariate ESDA techniques and a GWR model report that there was a substantive level of spatial heterogeneity in the catch-up process, and suggested possible spatial regimes. It was also observed that the sub-periods showed a substantial level of spatio-temporal heterogeneity in ${\beta}$-convergence: the catch-up scenario in a spatial sense was least pronounced during the 1980s.

• Journal of Korean Association for Spatial Structures
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• v.8 no.3
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• pp.75-82
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• 2008

In this study, the modified genetic algorithm, D-GA, is proposed. D-GA is a hybrid genetic algorithm combined a simple genetic algorithm and the local search algorithm using direction vectors. Also, two types of direction vectors, learning direction vector and random direction vector, are defined without the sensitivity analysis. The accuracy of D-GA is compared with that of simple genetic algorithm. It is demonstrated that the proposed approach can be an effective optimization technique through a minimum weight structural optimization of ten bar truss.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.4
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• pp.207-210
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• 2012

Genetic algorithms(GA) are well known and very popular stochastic optimization algorithm. Although, GA is very powerful method to find the global optimum, it has some drawbacks, for example, premature convergence to local optima, slow convergence speed to global optimum. To enhance the performance of GA, this paper proposes an adaptive partitioning-based genetic algorithm. The partitioning method, which enables GA to find a solution very effectively, adaptively divides the search space into promising sub-spaces to reduce the complexity of optimization. This partitioning method is more effective as the complexity of the search space is increasing. The validity of the proposed method is confirmed by applying it to several bench mark test function examples and the optimization of fuzzy controller for the control of an inverted pendulum.

• Korea Real Estate Review
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• v.28 no.1
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• pp.23-37
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• 2018

The genetic algorithm (GA) and branch and bound (B&B) methods are the useful methods of searching the optimal project combination (combinatorial optimization) to maximize the project effect considering the budget constraint and the balance of regional development with regard to the Urban Regeneration New Deal policy, the core real estate policy of the Moon Jae-in government. The Ministry of Land, Infrastructure, and Transport (MOLIT) will choose 13 central-city-area-type projects, 2 economic-base-type projects, and 10 public-company-proposal-type projects among the numerous projects from 16 local governments while each government can apply only 4 projects, respectively, for the 2017 Urban Regeneration New Deal project. If MOLIT selects only those projects with a project effect maximization purpose, there will be unselected regions, which will harm the balance of regional development. For this reason, an optimization model is proposed herein, and a combinatorial optimization method using the GA and B&B methods should be sought to satisfy the various constraints with the object function. Going forward, it is expected that both these methods will present rational decision-making criteria if the central government allocates a special-purpose-limited budget to many local governments.