• Title/Summary/Keyword: 가중치해석

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System Decomposition Techniques in Multidisciplinary Design Optimization Problems Using Genetic Algorithms and Neural Networks (유전알고리즘 및 신경회로망을 이용한 다분야통합최적설계문제의 시스템분리기법 연구)

  • 김우석;이종수
    • Journal of the Computational Structural Engineering Institute of Korea
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    • v.12 no.4
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    • pp.619-627
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    • 1999
  • 다분야 통합 시스템의 설계문제는 다량의 설계변수와 구속조건으로 구성되며 다수의 공학적 현상으로 연관되어 있다. 다분야 통합 최적설계 문제를 효과적으로 다루기 위해서는 다양한 해석분야의 공학적 설계원리를 동시에 고려하여 균형 있고 유기적인 방법으로 최적의 설계를 결정하는 체계적인 설계자동화기술이 요구된다. 다분야 통합 설계문제를 위한 효율적인 설계방법론으로 분리기반 최적화 기법이 적용되는데 이 방법은 한 단위의 대규모 설계문제를 여러 개의 하부시스템으로 분리하여 독립적으로 최적화를 수행하고 각 하부 시스템으로부터의 설계해 사이의 중재 및 통합화를 거쳐 최종적으로 수렴된 최적설계를 찾는 방법이다. 본 논문에서는 분리기반 최적화기법을 다분야 통합최적 설계문제에 적용하는데 필요한 시스템분리기법을 유전알고리즘 및 다층 역전 파 신경회로망을 이용하여 정립하였다. 시스템분리기법을 검증하기 위해 최근 미국 Boeing사에서 개발중인 고속 민간항공기인 HSCT의 시뮬레이션기반 설계문제를 이용하였다. 대규모 설계시스템의 분리결과는 전체 설계문제의 특성을 파악하기 위한 자료로 활용되며 향후, 분리기반 최적화과정에서 최종적으로 통합된 최적설계를 탐색하는데 필요한 기반구조를 제공한다.

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Mineral Potential Mapping of Gagok Mine Using 3D Geological Modeling (3차원 지질모델링을 이용한 가곡광산 광상 포텐셜 지도 작성)

  • Park, Gyesoon;Cho, Seong-Jun;Oh, Hyun-Joo;Lee, Chang-Won
    • Journal of the Korean earth science society
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    • v.35 no.6
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    • pp.412-421
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    • 2014
  • In order to develop an effective mineral exploration technique, this study was carried out about the potential mapping of Gagok mine. The deposit model of Gagok mine is widely known. Based on the deposit model, we constructed mining indicator indices using related igneous rocks, faults, and carbonate rocks. By analyzing the spatial correlation between ore and indicator index structures, we decided the weighting values of indices according to the distance from the index structure. The 3D potential mapping was performed using 3D geological model and geological indices. The analyzed potential map verified that the locations and patterns of high potential regions of the results were well matched with those of the known ore bodies. Using the potential mapping results, we could effectively predict the location of a high potential area that has similar geological settings with ore.

Weight Determination of Landslide Factors Using Artificial Neural Networks (인공신경 망을 이용한 산사태 발생요인의 가중치 결정)

  • 류주형;이사로;원중선
    • Economic and Environmental Geology
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    • v.35 no.1
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    • pp.67-74
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    • 2002
  • The purpose of this study is to determine the weights of the factors for landslide susceptibility analysis using artificial neural network. Landslide locations were identified from interpretation of aerial photographs, field survey data, and topography. The landslide-related factors such as topographic slope, topographic curvature, soil drainage, soil effective thickness, soil texture, wood age and wood diameter were extracted from the spatial database in study area, Yongin. Using these factors, the weights of neural networks were calculated by backpropagation training algorithm and were used to determine the weight of landslide factors. Therefore, by interpreting the weights after training, the weight of each landslide factor can be ranked based on its contribution to the classification. The highest weight is topographic slope that is 5.33 and topographic curvature and soil texture are 1 and 1.17, respectively. Weight determination using backprogpagation algorithms can be used for overlay analysis of GIS so the factor that have low weight can be excluded in future analysis to save computation time.

Color Component Analysis For Image Retrieval (이미지 검색을 위한 색상 성분 분석)

  • Choi, Young-Kwan;Choi, Chul;Park, Jang-Chun
    • The KIPS Transactions:PartB
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    • v.11B no.4
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    • pp.403-410
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    • 2004
  • Recently, studies of image analysis, as the preprocessing stage for medical image analysis or image retrieval, are actively carried out. This paper intends to propose a way of utilizing color components for image retrieval. For image retrieval, it is based on color components, and for analysis of color, CLCM (Color Level Co-occurrence Matrix) and statistical techniques are used. CLCM proposed in this paper is to project color components on 3D space through geometric rotate transform and then, to interpret distribution that is made from the spatial relationship. CLCM is 2D histogram that is made in color model, which is created through geometric rotate transform of a color model. In order to analyze it, a statistical technique is used. Like CLCM, GLCM (Gray Level Co-occurrence Matrix)[1] and Invariant Moment [2,3] use 2D distribution chart, which use basic statistical techniques in order to interpret 2D data. However, even though GLCM and Invariant Moment are optimized in each domain, it is impossible to perfectly interpret irregular data available on the spatial coordinates. That is, GLCM and Invariant Moment use only the basic statistical techniques so reliability of the extracted features is low. In order to interpret the spatial relationship and weight of data, this study has used Principal Component Analysis [4,5] that is used in multivariate statistics. In order to increase accuracy of data, it has proposed a way to project color components on 3D space, to rotate it and then, to extract features of data from all angles.