• Journal of the military operations research society of Korea
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• v.32 no.2
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• pp.114-130
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• 2006

This paper deals with complexity theory to describe amphibious warfare situation using EINSTein (Enhanced ISAAC Neural Simulation Tool) simulation model. EINSTein model is an agent-based artificial "laboratory" for exploring self-organized emergent behavior in land combat. Many studies have shown that existing Lanchester equations used in most war simulation models does not describe changes of combat. Future warfare will be information warfare with various weapon system and complex combat units. We have compared and tested combat results with Lanchester models and EINSTein model. Furthermore, the EINSTein model has been applied and analyzed to amphibious warfare model such as amphibious assault and amphibious sudden attack. The results show that the EINSTein model has a possibility to apply and analyze amphibious warfare more properly than Lanchester models.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.6 no.4
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• pp.340-352
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• 1994

A numerical model of nonlinear stream function wave theory evolved from Dean's model (1965) is presented. The stream function theory has been evaluated to be an accurate and useful tool for engineering applications. Effects of damping coefficient employed in a linearized simultaneous equation and number of points in the numerical integration of model on numerical solutions are assessed. Most accurate wave characteristics calculated by the present model are tabulated using revised Dean's Table (Chaplin, 1980) input parameters. Since the well-known feature of nearly breaking waves that with increasing wave steepness the wave length as well as integral properties have a maximum prior to the limiting wave height is represented by the model, the accuracy of model can be proved.

• Journal of Korean Society of Transportation
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• v.19 no.2
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• pp.61-72
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• 2001

기존의 링크교통량으로부터 OD추정모형은 기존 OD에 대한 추정의 종속성이 커, 기존 OD나 관측링크교통량의 오차에 따라 추정결과가 일관적이지 않은 문제점을 가지고 있다. 또한 관측링크교통량의 정확도가 중요함에도 불구하고 차종구분 없이 링크교통량을 이용하여 정보의 손실을 초래하였고 결과적으로 OD 추정력을 저하시켰다. 그렇지만 다차종 링크교통량으로부터 다차종 OD를 구하는 연구는 거의 없었으며, 그 추정결과가 단일차종에 대한 추정결과와 어떻게 다른지에 대한 연구도 전무하였다. 본 연구의 목적은 기존의 OD 추정모형이 기존 OD에 대해 종속성을 가지며 차종구분 없이 모형을 구성함으로써 추정력의 저하를 초래하였음을 밝히고, 이에 대한 대안으로 종속성 문제를 완화하고 차종구분을 통해 OD 추정모형의 추정력을 증진시키자 하는 것이다. 이를 위해 유전알고리즘을 이용한 다차종 OD행렬 추정모형(GAMUC)을 구축하고, 이를 기존의 바이레벨 모형의 IEA 알고리즘 및 다차종으로 확장한 모형(IEAMUC)과 게임이론측면에서 검토하였으며, 사례네트워크에 대해 각 기법을 비교하였다. 본 연구는 유전알고리즘을 이용한 OD 추정기법을 축도로에 적용한 임용택 등(2000)과 이를 네트워크로 확장한 백승걸 등(2000)의 연구를 다차종으로 확장한 것이다. 사례분석 결과 기존 OD의 오차변화나 관측링크교통량의 오차변화 등에 있어 GAMUC가 IEA나 IEAMUC보다 추정력이 양호하여, 실제 OD를 알 수 없는 도시부 네트워크에서 GAMUC 모형의 적용력이 우수하였다. 또한 차종을 구분하지 않은 기존 모형은 실제 OD와는 전혀 다른 OD 구조를 도출할 수 있음을 보였으며, 단일 차종을 여러 차종으로 구분하여 OD를 추정하는 것이 더 양호한 추정력을 확보하는 것으로 나타났다.

• Journal of The Korean Association For Science Education
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• v.26 no.4
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• pp.475-491
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• 2006

In physics education, the research on students' conceptions has developed in the discussion on the nature and the difficulty of conceptual change. Recently, mental models have been a theoretical background in concrete arguments on "how students' conceptions are constructed or created." Mental models that integrate information in the presented problem and individual knowledge in their long-term memory have important information about not only expressed ideas but also in the thinking process behind the expressed ideas. The purpose of this study is to investigate the forming process and the characteristics of high school student's mental models about circular motion, and how they were changed by instruction. We used the think-aloud method based on the instrument for identifying student's mental models about circular motion, pretest of physics concept, mind map and interview for investigating student's characteristics. The results of the study showed that instructions based on the mental model theory facilitated scientific expressed model, but several factors that affected forming mental models like epistemological belief didn't change scientifically after 3 lessons.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.4D
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• pp.351-360
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• 2010

In 2010, the number of registered vehicles reached almost at 17.48 millions in Korea. This dramatic increase of vehicles influenced to increase the number of traffic accidents which is one of the serious social problems and also to soar the personal and economic losses in Korea. Through this research, an enhanced intersection hazard prediction model by combining Genetic Algorithm and Artificial Neural Network will be developed in order to obtain the important data for developing the countermeasures of traffic accidents and eventually to reduce the traffic accidents in Korea. Firstly, this research has investigated the influencing factors of road geometric features on the traffic volume of each approaching for the intersections where traffic accidents and congestions frequently take place and, a linear regression model of traffic accidents and traffic conflicts were developed by examining the relationship between traffic accidents and traffic conflicts through the statistical significance tests. Secondly, this research also developed an intersection hazard prediction model by combining Genetic Algorithm and Artificial Neural Network through applying the intersection traffic volume, the road geometric features and the specific variables of traffic conflicts. Lastly, this research found out that the developed model is better than the existed forecasting models in terms of the reliability and accuracy by comparing the actual number of traffic accidents and the predicted number of accidents from the developed model. In conclusion, it is expect that the cost/effectiveness of any traffic safety improvement projects can be maximized if this developed intersection hazard prediction model by combining Genetic Algorithm and Artificial Neural Network use practically at field in the future.

• Journal of the Korea Society for Simulation
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• v.2 no.1
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• pp.67-77
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• 1993

• Information and Communications Magazine
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• v.26 no.7
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• pp.10-16
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• 2009

본고에서는 무선 통신 망에서 게임 이론이 적용된 주요 물리 계층 설계 기술을 소개한다. 셀룰러 망과 ad-hoc 망에서 일반적으로 적용할 수 있는 비협력 게임 이론 모형을 제시하고 서로 다른 목적을 위해 제안된 게임 전략과 유틸리티 함수를 소개함으로써 게임 이론으로 다양한 분산 알고리즘을 도출할 수 있음을 보인다.

• The Korean Journal of Financial Management
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• v.24 no.3
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• pp.133-152
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• 2007

In this paper, we examine a portfolio selection model in which a safety-first investor maximizes expected return subject to a downside risk constraint. We use the Value-at-Risk as the downside risk measure. We exploit the fact that returns are fat-tailed, and use a semi-parametric method suggested by Jansen, Koedijk and de Vries(2000). We find a more realistic asset allocation than the one suggested by the literature based on the traditional mean-variance framework. For the robustness check, we provide empirical analyses using empirical quantiles. The results highlight that for optimal portfolio selection involving downside risks that are far in the tails of the distribution, our mean-VaR model with a fat-tailed distribution is superior.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.2
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• pp.102.2-102.2
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• 2012

이 연구의 목적은 거시적인 관점에서 NOS의 중요한 요소들을 강조한 Flow map(oh, 2011)를 이용한 명시적이고 암시적인 교육의 본성교육이 얼마나 이루어졌는가를 알아본다. 과학의 본성은 지식의 내용뿐만 아니라 지식이 어떻게 형성되었는가에 있다. 이러한 과학의 본성을 이해한다는 것이 현대의 과학의 중요한 목적중의 하나인 과학적 소양을 얻는 것이다. 이를 구체적으로 실현하는 교수 모형으로 가장 중요한 이론의 생성과 실험에 의한 확증과 이론의 확증을 미시적인 관점으로 연구를 전개하였다. 따라서 먼저 Kuhn(1996)의 과학철학 이론과 NOS를 통한 Flow map 개발에 이론적 배경이 된 과학의 본성의 정의, 과학의 본성에 대해 합의된 핵심 요소들, Kuhn(1996)의 과학철학 이론을 바탕으로 한 Oh(2011)가 제안한 Flow map의 구조를 알아본다. 연구방법은 귀추(abduction)를 통한 가설의 생성과정과 확증을 보여주는 Oh(2012)의 자연과학의 추리과정을 이용하여 충북대학교 천문우주학과에서 자연과학캠프에 참여한 고등학생들을 대상으로, "Galileo의 흑점발견과정"으로부터 현대의 태양의 흑점 모형을 이해하도록 한다.

• Water for future
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• v.29 no.4
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• pp.109-118
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• 1996

The neural network is a mathematical model of theorized brain activity which attempts to exploit the parallel local processing and distributed storage properties. The neural metwork is a good model to be applied for the classification problem, large combinatorial optimization and nonlinear mapping. A multi-layer neural network is constructed to predict rainfall. The network learns continuourvalued input and output data. Application of neural network to 1-hour real data in Seoul metropolitan area and the Soyang River basin shows slightly good predictions. Therefore, when good data is available, the neural network is expected to predict the complicated rainfall successfully.