• 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.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.1262-1270
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• 2006

사실적인 물 애니메이션을 위한 격자 기반 시뮬레이션 기법은 자연스러운 물의 움직임뿐만 아니라 부드러운 물의 표면을 잘 표현해주는 장점이 있다. 이러한 격자 기반 방법과 함께 상대적으로 적은 계산으로 안정적인 결과를 산출해주는 입자 기반의 액체 시뮬레이션 기법이 최근 애니메이션 분야에 적용되기 시작했고, 그로 인하여 입자로 이루어진 시뮬레이션 데이터에 특화된 효과적인 렌더링 기술의 개발이 요구되고 있다. 본 논문에서는 주로 3차원 스캔 데이터와 같이 물체 표면을 샘플링 하여 얻어진 점 집합에 대한 렌더링 기법을 확장하여, 위상 변화가 크고 점 집합에 의해 내부까지 표현되는 물 데이터의 특성에 적합한 렌더링 기법을 제안한다. 본 기법에서는 시뮬레이션을 통하여 얻은 입자 데이터로부터 물의 표면을 표현해주는 새로운 점 집합을 생성하고, 시뮬레이션 된 데이터의 특성을 잘 반영하도록 각 점에 대한 법선 벡터와 반지름을 결정한다. 특히 가공된 점 집합 데이터에 대하여 확장된 점 집합 렌더링 기법을 적용함으로써 입자 데이터가 표현해주는 세밀한 부분들을 보존하면서, 부드러운 물의 표면을 가시화할 수 있도록 하였다.

• Journal of the Korea Society for Simulation
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• v.30 no.1
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• pp.75-85
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• 2021

In this paper, we present a deep neural network-based prediction model that processes and analyzes the corporate credit and personal credit information of individual business owners as a new method to predict the default rate of individual business more accurately. In modeling research in various fields, feature selection techniques have been actively studied as a method for improving performance, especially in predictive models including many features. In this paper, after statistical verification of macroeconomic indicators (macro variables) and credit information (micro variables), which are input variables used in the default rate prediction model, additionally, through the credit information feature selection method, the final feature set that improves prediction performance was identified. The proposed credit information feature selection method as an iterative & hybrid method that combines the filter-based and wrapper-based method builds submodels, constructs subsets by extracting important variables of the maximum performance submodels, and determines the final feature set through prediction performance analysis of the subset and the subset combined set.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.32 no.2
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• pp.125-132
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• 2019

This paper presents a seismic reliability analysis method for an offshore wind turbine with a twisted tripod support structure under earthquake loading. A three dimensional dynamic finite element model is proposed to consider the nonlinearity of the ground-pile interactions and the geometrical characteristics of the twisted tripod support structure where out-of-plane displacement occurs even under in-plane lateral loadings. For the evaluation of seismic reliability, the failure probability was calculated for the maximum horizontal displacement of the pile head, which is calculated from time history analysis using artificial earthquakes for the design return periods. The application of the subset simulation method using the Markov Chain Monte Carlo(MCMC) sampling is proposed for efficient reliability analysis considering the limit state equation evaluation by the nonlinear time history analysis. The proposed method can be applied to the reliability evaluation and design criteria development of the offshore wind turbine with twisted tripod support structure in which two dimensional models and static analysis can not produce accurate results.

• Journal of Digital Convergence
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• v.12 no.6
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• pp.223-229
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• 2014

In an attempt to retrieve useful information for an efficient decision in the large knowledge system, it is generally necessary and important for a refined feature selection. Rough set has difficulty in generating optimal reducts and classifying boundary objects. In this paper, we propose quick reduction algorithm generating optimal features by rough entropy analysis for condition and decision attributes to improve these restrictions. We define a new conditional information entropy for efficient feature extraction and describe procedure of feature selection to classify the significance of features. Through the simulation of 5 datasets from UCI storage, we compare our feature selection approach based on rough set theory with the other selection theories. As the result, our modeling method is more efficient than the previous theories in classification accuracy for feature selection.

• Journal of Korea Game Society
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• v.14 no.6
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• pp.29-38
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• 2014

In the large scale indoor 3D game scenes, the data amount of potentially visible set (PVS) which pre-computes the information of occlusion culling can be huge. However, the large part of them can be represented as zero. In this paper, the effective hardware structure is designed, which compresses PVS data as the way of zero run length encoding (ZRLE) during building the scene trees of 3D games in mobile environments. The compression ratio of the proposed structure and the rendering speed (frame per second: FPS) according to both PVS culling and frustum culling are analyzed under 3D game simulations.

• Journal of the Korea Society for Simulation
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• v.22 no.4
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• pp.139-147
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• 2013

This paper proposes a novel method for feature selection for mass spectrometric proteomic data based on Random Forest. The method includes an effective preprocessing step to filter a large amount of redundant features with high correlation and applies a tournament strategy to get an optimal feature subset. Experiments on three public datasets, Ovarian 4-3-02, Ovarian 7-8-02 and Prostate shows that the new method achieves high performance comparing with widely used methods and balanced rate of specificity and sensitivity.

• Journal of the Korean Society of Safety
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• v.32 no.3
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• pp.54-59
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• 2017

This paper presents an efficient finite analysis model and a simulation-based reliability analysis method for stowage device system failure of a container crane with respect to lateral load. A quasi-static analysis model is introduced to simulate the nonlinear resistance characteristics and failure of tie-down and stowage pin, which are the main structural stowage devices of a crane. As a reliability analysis method, a subset simulation method is applied considering the uncertainties of later load and mechanical characteristic parameters of stowage devices. An efficient Markov chain Monte Carlo (MCMC) method is applied to sample random variables. Analysis result shows that the proposed model is able to estimate the probability of failure of crane system effectively which cannot be calculated practically by crude Monte Carlo simulation method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.8
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• pp.1433-1445
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• 1994

In this paper, an coherent optical correlator system based on the off-axis projection synthetic discriminant funtion (OA-pSDF) was analyzed and implemented optically. The filter was synthesized by combining conventional pSDF with single reference plane wave multiplexing. Synthesized pSDF were transformed to binary phase only filters (BPOFs) and fabricated as computer generated holograms(CGHs), which was used in the real time optical correlator system instead of using expensive spatial light modulators(SLMs). From the characteristic test, it was found that OA-pSDF showed distortion invariance and good performances in discrminating subset images. The proposed OA-pSDF BPOF could overcome the limitations of conventional BPOFs : that is distortion variance such as acale and rotation, especially out of plane variance.

• Journal of the Korea Society for Simulation
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• v.20 no.2
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• pp.77-85
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• 2011

It is believed that the anomalies or diseases of human organs are identified by the analysis of the patterns. This paper proposes a new classification technique for the identification of cancer disease using the proteome patterns obtained from two-dimensional polyacrylamide gel electrophoresis(2-D PAGE). In the new classification method, three different classification methods such as support vector machine(SVM), multi-layer perceptron(MLP) and k-nearest neighbor(k-NN) are extended by multi-boosting method in an array of subclassifiers and the results of each subclassifier are merged by ensemble method. Genetic algorithm was applied to obtain optimal feature set in each subclassifier. We applied our method to empirical data set from cancer research and the method showed the better accuracy and more stable performance than single classifier.