• Communications for Statistical Applications and Methods
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• 제10권2호
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• pp.423-432
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• 2003

The data observed at different positions are called the estimate of interested variable at new observation point on the Kriging utilize the space estimate technique, in which case there is correlation spatially. In this paper we provide the estimate for Variogram and Kriging methods as a field of kriging theory and dealt with actually measured data. And at the same time we forecast the amount of ozone that was not measured at this point by Kriging method and compared Ordinary Kriging method with Inverse Distance Kriging method.

• Spatial Information Research
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• 제11권3호
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• pp.251-260
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• 2003

본 연구에서는 지구통계기법과 GIS를 이용하여 Cl/sup -/ 농도 분포도를 작성하고 이를 통해 해수침투 분포 양상을 파악하였다. 분포도는 탐색적 공간자료 분석을 통해 자료의 분포 패턴을 파악한 후에 정규크리깅과 공동크리깅을 이용하여 작성하였다. 지구통계기법인 크리깅은 시ㆍ공간적인 자료의 분포특성과 상관관계를 이용하여 신뢰할 만한 결과를 제공한다. 공동크리깅의 이차변수는 상관분석을 통해 Cl/sup -/과의 상관성이 큰 TDS, Na/sup ＋/, Br/sup -/을 선정하였다. Cl/sup -/ 농도 분포도를 분석한 결과 공동크리깅에 의한 분포도가 정규크리깅의 분포도보다 더욱 정밀하게 나타났으며, 전반적으로 이민촌 일대와 해안가 지역에서 높은 농도 이상대를 보이고 있음을 확인할 수 있었다.

• 대한기계학회논문집A
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• 제30권8호
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• pp.941-948
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• 2006

In this study, the new method for reliability estimation is proposed using kriging metamodel. Kriging metamodel can be determined by appropriate sampling range and sampling numbers because there are no random errors in the Design and Analysis of Computer Experiments(DACE) model. The first kriging metamodel is made based on widely ranged sampling points. The Advanced First Order Reliability Method(AFORM) is applied to the first kriging metamodel to estimate the reliability approximately. Then, the second kriging metamodel is constructed using additional sampling points with updated sampling range. The Monte-Carlo Simulation(MCS) is applied to the second kriging metamodel to evaluate the reliability. The proposed method is applied to numerical examples and the results are almost equal to the reference reliability.

• Structural Engineering and Mechanics
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• 제41권1호
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• pp.25-41
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• 2012

Kriging surrogate model provides explicit functions to represent the relationships between the inputs and outputs of a linear or nonlinear system, which is a desirable advantage for response estimation and parameter identification in structural design and model updating problem. However, little research has been carried out in applying Kriging model to crack identification. In this work, a scheme for crack identification based on a Kriging surrogate model is proposed. A modified rectangular grid (MRG) is introduced to move some sample points lying on the boundary into the internal design region, which will provide more useful information for the construction of Kriging model. The initial Kriging model is then constructed by samples of varying crack parameters (locations and sizes) and their corresponding modal frequencies. For identifying crack parameters, a robust stochastic particle swarm optimization (SPSO) algorithm is used to find the global optimal solution beyond the constructed Kriging model. To improve the accuracy of surrogate model, the finite element (FE) analysis soft ANSYS is employed to deal with the re-meshing problem during surrogate model updating. Specially, a simple method for crack number identification is proposed by finding the maximum probability factor. Finally, numerical simulations and experimental research are performed to assess the effectiveness and noise immunity of this proposed scheme.

• 대한기계학회논문집A
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• 제33권8호
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• pp.813-818
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• 2009

Design analysis and computer experiments (DACE) model is widely used to express efficiently nonlinear responses in the field of engineering design. As a DACE model, kriging model can approximately replace a simulation model that is very expensive or highly nonlinear. The kriging model is composed of the summation of a global model and a local model representing deviation from the global model. The local model is determined by correlation coefficient with the pre-sampled points, where the accuracy and robustness of the kriging model depends on the selection of proper correlation coefficients. Therefore, to achieve the robust kriging model, the range of the correlation coefficients is explored with respect to the degrees of the global model. Based on this study we propose the proper orders of the global model and range of parameters to make accurate and robust kriging model.

• 한국정밀공학회지
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• 제23권8호
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• pp.119-126
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• 2006

Kriging model is widely used as design DACE(analysis and computer experiments) model in the field of engineering design to accomplish computationally feasible design optimization. In this paper, the optimization of gate valve was performed using Kriging based approximation model. The DACE modeling, known as the one of Kriging interpolation, is introduced to obtain the surrogate approximation model of the function. In addition, we describe the definition, the prediction function and the algorithm of Kriging method and examine the accuracy of Kriging by using validation method.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2008년도 추계학술대회A
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• pp.701-705
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• 2008

Design analysis and computer experiments (DACE) model is widely used to express efficiently the nonlinear responses in the field of engineering design. Kriging model, a DACE model, can approximately replace a simulation model that is very expensive or highly nonlinear. The kriging model is composed of the summation of a global model and a local model representing deviation from global model. The local model is determined by correlation coefficient of the pre-sampled points, where determination of the correct correlation coefficient has an effect on accuracy and robustness of the kriging model. Therefore, robustness of the correlation coefficient is explored with respect to degrees of the global model. Then we propose the range of correlation coefficient to make correct and robust kriging model and the influence of the correlation coefficients on the degrees of global model with respect to the nonlinearity of the pre-sampled responses.

• 한국전산유체공학회지
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• 제13권1호
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• pp.41-48
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• 2008

The fluid-structure interaction analysis such as a static aeroelastic analysis requires the result of each analysis as an input to the other analysis. Usually the grids for the fluid analysis and the structural analysis are different, so the results should be transformed properly for each other. The Infinite Plate Spline(IPS) and the Thin Plate Spline(TPS) are used in interpolating the displacement and the pressure. In this study, such interpolation methods are compared with kriging which provides a precise response surface. The static aeroelastic analysis is performed for the supersonic flow field with shock waves and the pressure field is interpolated by the TPS and kriging. The TPS shows tendency to weaken the shock strength, whereas kriging preserves the shock strength.

• 한국전산유체공학회:학술대회논문집
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• 한국전산유체공학회 2005년도 추계 학술대회논문집
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• pp.141-144
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• 2005

The fluid-structure interaction analysis such as a static aeroelastic analysis requires the result of each analysis as an input to other analysis. Usually the grids for the fluid analysis and the structural analysis are different, so the results should be transformed properly for each other. The Infinite Plate Spline(IPS) and the Thin Plate Spline(TPS) are used in interpolating the displacement and the pressure. In this study, such interpolation methods are compared with kriging which provides a precise response surface. The static aeroelastic analysis is performed for the supersonic flow field with shock waves and the pressure field is interpolated by the TPS and kriging. The TPS shows tendency to weaken the shock stength, whereas kriging preserves the shock strength.

• 대한기계학회논문집A
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• 제29권2호
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• pp.277-283
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• 2005

Kriging model is widely used as design analysis and computer experiment (DACE) model in the field of engineering design to accomplish computationally feasible design optimization. In general, kriging model has been applied to many engineering applications as an interpolation model because it is usually constructed from deterministic simulation responses. However, when the responses include not only global nonlinearity but also numerical error, it is not suitable to use Kriging model that can distort global behavior. In this research, generalized kriging model that can represent both interpolation and regression is proposed. The performances of generalized kriging model are compared with those of interpolating kriging model for numerical function with error of normal distribution type and trigonometric function type. As an application of the proposed approach, the response of a simple dynamic model with numerical integration error is predicted based on sampling data. It is verified that the generalized kriging model can predict a noisy response without distortion of its global behavior. In addition, the influences of maximum likelihood estimation to prediction performance are discussed for the dynamic model.