• Journal of the Korean Statistical Society
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• 제28권2호
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• pp.135-150
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• 1999

Latin hypercube sampling(LHS) introduced by McKay et al. (1979) is a widely used method for Monte Carlo integration. Stratified Latin hypercube sampling(SLHS) proposed by Choi and Lee(1993) improves LHS by combining it with stratified sampling. In this article it is shown that SLHS yields an asymptotically more accurate than both stratified sampling and LHS.

• 응용통계연구
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• 제8권2호
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• pp.99-108
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• 1995

본 논문에서는 컴퓨터 모델을 이용하여 복잡한 시스템을 모형화할 때 결과값의 분포를 보다 정확히 추정하기 위한 입력변수의 추출방법으로서 McKay 등(1979)이 제안한 Latin Hypercube 추출법을 개선한 이단계 Latin Hypercube 추출법을 제시하고 모의 실험을 통하여 새로운 표본추출법이 기존의 표본추출법들보다 더 효율적임을 보였다.

• 한국지진공학회논문집
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• 제22권2호
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• pp.63-75
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• 2018

This paper proposes extension of Latin Hypercube Sampling (LHS) to avoid the necessity of using intervals with the same probability area where intervals with different probability areas are used. This method is called Weighted Latin Hypercube Sampling (WLHS). This paper describes equations and detail procedure necessary to apply weight function to WLHS. WLHS is verified through numerical examples by comparing the estimated distribution parameters with those from other methods such as Random Sampling and Latin Hypercube Sampling. WLHS provides more flexible way on selecting samples than LHS. Accuracy of WLHS estimation on distribution parameters is depending on the selection of weight function. The proposed WLHS is applied to seismically isolated structures in nuclear power plants. In this application, clearance-to-stops (CSs) calculated using LHS proposed by Huang et al. [1] and WLHS proposed in this paper, respectively, are compared to investigate the effect of choosing different sampling techniques.

• Transactions on Electrical and Electronic Materials
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• 제7권4호
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• pp.210-214
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• 2006

In this paper, two sampling methods which are Latin hypercube sampling (LHS) and simple random sampling were. compared to improve the modeling speed of neural network model. Sampling method was used to generate initial weights and bias set. Electrical characteristic data for $HfO_2$ thin film was used as modeling data. 10 initial parameter sets which are initial weights and bias sets were generated using LHS and simple random sampling, respectively. Modeling was performed with generated initial parameters and measured epoch number. The other network parameters were fixed. The iterative 20 minimum epoch numbers for LHS and simple random sampling were analyzed by nonparametric method because of their nonnormality.

• Journal of Electrical Engineering and Technology
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• 제9권6호
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• pp.1832-1842
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• 2014

A novel probabilistic small signal stability analysis (PSSSA) method considering load correlation is proposed in this paper. The superiority Latin hypercube sampling (LHS) technique combined with Monte Carlo simulation (MCS) is utilized to investigate the probabilistic small signal stability of power system in presence of load correlation. LHS helps to reduce the sampling size, meanwhile guarantees the accuracy and robustness of the solutions. The correlation coefficient matrix is adopted to represent the correlations between loads. Simulation results of the two-area, four-machine system prove that the proposed method is an efficient and robust sampling method. Simulation results of the 16-machine, 68-bus test system indicate that load correlation has a significant impact on the probabilistic analysis result of the critical oscillation mode under a certain degree of load uncertainty.

• 전산구조공학
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• 제10권4호
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• pp.143-154
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• 1997

본 연구에서는 지하암반구조물의 구조해석시 불연속암반체의 물성변이를 고려할 수 있는 확률론적 해석기법을 개발하였다. 수치해석적 접근은 몬테칼로모사기법의 단점을 보완한 LHS기법을 사용하였고, 불연속면의 영향은 단층, 벽개 등과 같이 불연속성이 뚜렷한 지역에서 적용성이 높은 절리유한요소모델을 사용하였다. 재료특성에 대한 확률변수는 불연속면의 수직강성과 전단강성을 다확률변수로 사용하였으며, 이들은 확률공간에서 정규분포를 갖는 경우에 대하여 고려하였다. 본 연구에서 개발된 수치해석프로그램은 검증예제를 통하여 타당성을 확인하였으며, 가상의 불연속면군이 존재하는 지하원형공동에 대한 해석을 통하여 프로그램의 적용성을 확인하였다.

• International Journal of Fluid Machinery and Systems
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• 제10권3호
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• pp.240-253
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• 2017

Latin hypercube sampling is widely used design-of-experiment technique to select design points for simulation which are then used to construct a surrogate model. The exploration/exploitation properties of surrogate models depend on the size and distribution of design points in the chosen design space. The present study aimed at evaluating the performance characteristics of various surrogate models depending on the Latin hypercube sampling (LHS) procedure (sample size and spatial distribution) for a diverse set of optimization problems. The analysis was carried out for two types of problems: (1) thermal-fluid design problems (optimizations of convergent-divergent micromixer coupled with pulsatile flow and boot-shaped ribs), and (2) analytical test functions (six-hump camel back, Branin-Hoo, Hartman 3, and Hartman 6 functions). The three surrogate models, namely, response surface approximation, Kriging, and radial basis neural networks were tested. The important findings are illustrated using Box-plots. The surrogate models were analyzed in terms of global exploration (accuracy over the domain space) and local exploitation (ease of finding the global optimum point). Radial basis neural networks showed the best overall performance in global exploration characteristics as well as tendency to find the approximate optimal solution for the majority of tested problems. To build a surrogate model, it is recommended to use an initial sample size equal to 15 times the number of design variables. The study will provide useful guidelines on the effect of initial sample size and distribution on surrogate construction and subsequent optimization using LHS sampling plan.

• 한국구조물진단유지관리공학회 논문집
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• 제19권6호
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• pp.72-79
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• 2015

이 논문에서는 해안구조물에 대한 실질적이고 효율적인 구조신뢰성평가 기법을 제시하였다. 제안기법은 파랑, 조류 등의 하중관련 변수 그리고 콘크리트의 탄성계수와 압축강도, 지반정수 및 경계조건 등과 같은 저항관련 설계변수의 불확실성을 명확히 고려한 복잡한 해안구조물의 신뢰성을 평가할 수 있다. 라틴 하이퍼큐브 샘플링(LHS), 몬테카를로 시뮬레이션(MCS) 및 유한요소법을 합리적으로 결합한 제안기법에서 LHS기반 MCS는 신뢰성평가에 필요한 샘플링 수를 대폭 줄여주므로 계산노력이 획기적으로 감소된다. 검증예제를 통하여 제안기법이 상대적인 정확도를 보장하며 계산상의 효율성이 우수한 것으로 확인되었다. 또한 실제의 케이슨형식 방파제 구조물을 대상으로 한 수치예제를 통하여 그 적용성과 효율성을 입증하였다. 특히 유한요소법 또는 유한차분법과 같은 알고리즘 형태의 암시적 한계상태함수를 갖는 경우와 비선형해석, 복합재료, 다양한 기하형상 등을 복잡한 구조거동을 고려해야 하는 실제적인 구조물의 신뢰성평가에 적합한 것으로 판단된다.

• Nuclear Engineering and Technology
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• 제52권7호
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• pp.1587-1596
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• 2020

The location selection for nuclear power plants (NPP) is a strategic decision, which has significant impact operation of the plant and sustainable development of the region. Further, the ranking of the alternative locations and selection of the most suitable and efficient locations for NPPs is an important multi-criteria decision-making problem. In this paper, the non-sequential Monte Carlo probabilistic method and the Latin hypercube sampling probabilistic method are used to evaluate and select the optimal locations for NPP. These locations are identified by the power plant's onsite loads and the average of the lowest number of relay protection after the NPP's trip, based on electricity considerations. The results obtained from the proposed method indicate that in selecting the optimal location for an NPP after a power plant trip with the purpose of internal onsite loads of the power plant and the average of the lowest number of relay protection power system, on the IEEE RTS 24-bus system network given. This paper provides an effective and systematic study of the decision-making process for evaluating and selecting optimal locations for an NPP.

• 한국CDE학회논문집
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• 제16권6호
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• pp.397-406
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• 2011

Since tolerance allocation in a mobile phone camera manufacturing process greatly affects production cost and reliability of optical performance, a systematic design methodology for allocating optimal tolerances is required. In this study, we proposed the tolerance optimization procedure for determining tolerances that minimize production cost while satisfying the reliability constraints on important optical performance indices. We employed Latin hypercube sampling for evaluating the reliabilities of optical performance and a function-based sequential approximate optimization technique that can reduce computational burden and well handle numerical noise in the tolerance optimization process. Using the suggested tolerance optimization approach, the optimal production cost was decreased by 30.3 % compared to the initial cost while satisfying the two constraints on the reliabilities of optical performance.