• Proceedings of the Computational Structural Engineering Institute Conference
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• 2009.04a
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• pp.97-100
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• 2009

신뢰성 분석은 불확실성으로 인한 제품의 성능 변동을 안전확률이나 파괴확률로 정량화 하여 설계에 이용하기 위해 연구되어 왔다. 불확실성은, 데이터의 양에 따라-물질의 본질적인 특성으로서의 많은 데이터가 주어진 경우의 물리적 불확실성과 부족한 데이터에서의 인식론적 불확실성으로 구분되고, 불확실성을 갖는 대상에 따라-입력변수 및 근사모델 불확실성으로 구분된다. 물리적 불확실성에 대한 연구는 많이 진행되어 왔지만, 실제 산업현장에는 부족한 데이터로 인한 인식론적 불확실성이 지배적이며 이에 대한 연구는 최근에서야 진행되고 있다. 불확실성을 고려하는 신뢰성 기반 설계에는 효율성을 위해 실제모델을 대체하는 근사모델이 이용되는데, 근사모델법 자체에 대한 연구는 많이 진행되어 왔으나, 근사모델 이기 때문에 존재하는 불확실성을 고려한 연구는 최근에서야 연구되기 시작하였다. 본 연구에서는 베이지안 접근법에 기반하여 입력변수 및 근사모델 불확실성을 통합 고려하는 새로운 신뢰성 분석 기법을 제시하고 수치예제를 통해 타당성을 증명한 후, 이를 공학문제에 적용한다.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.5
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• pp.381-389
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• 2017

The kernel density was determined based on sampling points obtained in a Markov chain simulation and was assumed to be an important sampling function. A Kriging metamodel was constructed in more detail in the vicinity of a limit state. The failure probability was calculated based on importance sampling, which was performed for the Kriging metamodel. A pre-existing method was modified to obtain more sampling points for a kernel density in the vicinity of a limit state. A stable numerical method was proposed to find a parameter of the kernel density. To assess the completeness of the Kriging metamodel, the possibility of changes in the calculated failure probability due to the uncertainty of the Kriging metamodel was calculated.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.6
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• pp.557-563
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• 2010

This paper addresses the issue of adaptive global optimization using Kriging metamodel known as EGO(Efficient Global Optimization). The algorithm adaptively chooses where to generate subsequent samples based on an explicit trade-off between reduction of global uncertainty and exploration of the region of the interest. A strategy that saves the computational cost by using expectations derived from probabilistic nature of approximate model is proposed. At every iteration, a candidate test point that seems to be feasible/inactive or has little possibility to improve for minimum is identified and excluded from updating approximate models. By doing that the computational cost is saved without loss of accuracy.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2018.05a
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• pp.41-42
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• 2018

This study is about the prediction of ship's roll motion characteristic which has been used for evaluating ship's seakeeping performance. In order to obtain the ship's roll RAO during voyage, this paper utilized machine learning-based surrogate model. By comparing the prediction result data of surrogate model with test data, we suggest the best approximation technique and data sampling interval of the surrogate model appropriate for predicting the ships' roll motion characteristic.

• Journal of the Korean Society for Railway
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• v.16 no.5
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• pp.372-378
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• 2013

This paper presents an approximate model for computing the peak demand power in a metro railway system. The peak demand of substations can be calculated using the current vector iteration method. But the existing method requires many repeated calculations to determine the peak demand power, which makes it difficult to apply to the real-time peak power control problem. In this paper, we assume that none of the conditions vary except source impedance and make an approximate model for rapid calculation based on changes in the impedance of the power substation. The proposed model result is approximately the same as the existing model, which is demonstrated through simulation.

• Journal of the Korea Convergence Society
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• v.12 no.3
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• pp.187-196
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• 2021

The paper deals with comparative study for characteristics of approximation of design space according to various approximate models and sensitivity analysis using orthogonal array experiments in structure design of active type DSF which was developed for float-over installation of offshore plant. This study aims to propose the orthogonal array experiments based design methodology which is able to efficiently explore an optimum design case and to generate the accurate approximate model. Thickness sizes of main structure member were applied to the design factors, and output responses were considered structure weight and strength performances. Quantitative effects on the output responses for each design factor were evaluated using the orthogonal array experiment. Best design case was also identified to improve the structure design with weight minimization. From the orthogonal array experiment results, various approximate models such as response surface model, Kriging model, Chebyshev orthogonal polynomial model, and radial basis function based neural network model were generated. The experiment results from orthogonal array method were validated by the approximate modeling results. It was found that the radial basis function based neural network model among the approximate models was able to approximate the design space of the active type DSF with the highest accuracy.

• Journal of Radiation Protection and Research
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• v.15 no.2
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• pp.51-56
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• 1990

The dispersing model of radioactive plume in the atmosphere was assumed to form finite ellipseshaped volumes rather than a single plume and gamma absorbed doses from the plume were computed using the proposed model. The results obtained were compared with those computed by the Gaussian plume and the circular approximation models. The results computed by the proposed ellipse-shaped approximation model were close to those by the Gaussian plume model. and more accurate than those by the circular approximation model. The computing time for the proposed approximation model was one fortieth of that for the Gaussian plume model.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.04a
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• pp.534-537
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• 2010

반투명 재질의 효과적인 표현을 위해서 일반적으로 사용되는 빛의 확산 모델은 일반적 확산 모델(Standard Diffusion model: SDA)이다. 그러나 일반적 확산 모델은 흡수 변수 (absorption coefficient) ${\sigma}_a$가 감소한 산란 변수(reduced scattering coefficient) ${\sigma}_s$ 보다 상대적으로 큰 재질에 대해서는 부정확한 한계를 가지고 있다. 이러한 한계를 극복하기 위하여 다양한 모델이 제시되었다. $P_N$ 근사화 이론은 이러한 일반적 확산 모델이 가지고 있는 한계를 잘 극복한다. 우리는 일반적 확산 모델을 기반으로 하고 $P_3$ 근사화 이론의 변수들을 이용하는 hybrid-P3 근사화 방법을 이용하여 흡수 변수가 감소한 산란 변수보다 상대적으로 큰 재질을 그래픽 공간 상에서 효과적으로 표현하는 방법을 제시한다. 또한 그 재질의 광학적 특성을 추정하기 위하여 우리가 제안하는 모델에 적합한 측정장비를 개발하다.

• Computational Structural Engineering
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• v.11 no.3
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• pp.241-252
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• 1998

본 논문은 신경망 근사 해석 모델의 원형을 스터브 거더의 거동 해석에 적용하고, 이 과정 중에 발생한 문제점을 파악하여 해결책을 제시함으로써, 앞서 개발한 원형 모델을 스터브 거더 시스템에 적합하도록 발전시키는데 목적이 있다. 스터브 거더의 해석 변수는 주어진 시간 내에 시뮬레이션이 가능하게 7개, 해석 결과값은 탄성 처짐뿐만 아니라 응력까지 고려하여 총 4개의 결과값을 동시에 고려하고, 학습 패턴 수는 총 143개를 사용하였다. 근사해석의 정확도를 향상시키고 학습의 수렴성을 보장하기 위하여 다양한 시뮬레이션을 수행하여 은닉층 뉴런 수, 학습 패턴 그리고 최대 에러의 관계를 규명하고, 이 결과를 바탕으로 신경망 근사 해석 모델 개발 단계를 수정하여 제안하였다.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.25 no.3
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• pp.237-243
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• 2012

The thickness optimization of the gearbox housing for 5MW wind turbine is carried out with the help of the efficient structure analysis model and the approximation model of objective function. Wind turbine gearbox is a complex structural system composed of a number of gear trains, shafts, bearing and gearbox housing, requiring a tremendous number of elements for the structural analysis and design. In this paper, an effective analysis and design model considering the tooth stiffness of helical gears is proposed. It enables to significantly reduce the total element number and the analysis time. Through the numerical optimization of housing thickness making use of the effective gearbox model and the approximate model of objective function, the total weight of the gearbox housing is minimized. It has been observed from the numerical experiment that the approximation model is reliable and the optimization result is acceptable and verified analysis.