• Journal of Mechanical Science and Technology
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• v.16 no.5
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• pp.619-632
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• 2002

In recent study on design of experiments, the complicate metamodeling has been studied because defining exact model using computer simulation is expensive and time consuming. Thus, some designers often use approximate models, which express the relation between some inputs and outputs. In this paper, we review and compare the complicate metamodels, which are expressed by the interaction of various data through trying many physical experiments and running a computer simulation. The prediction model in this paper employs interpolation schemes known as ordinary kriging developed in the fields of spatial statistics and kriging in Design and Analysis of Computer Experiments (DACE) model. We will focus on describing the definitions, the prediction functions and the algorithms of two kriging methods, and assess the error measures of those by using some validation methods.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.7 no.4
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• pp.149-154
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• 2008

The concept of robust design was introduced by Dr. G. Taguchi in the late 1940s, and his technique has become commonly known as the Taguchi method or the robust design. In this research, a robust design procedure for microgyroscope is suggested based on the kriging and optimization approaches. The kriging interpolation method is introduced to obtain the surrogate approximation model of true function. Robustness is calculated by the kriging model to reduce real function calculations. For this, objective function is represented by the probability of success, thus facilitating robust optimization. The statistics such as mean and variance are obtained based on the reliable kriging model and the second-order statistical approximation method.

• Journal of the Korean Geographical Society
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• v.45 no.6
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• pp.806-819
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• 2010

Population data has been essential and fundamental in spatial analysis and commonly aggregated into political boundaries. A conventional method for population distribution estimation was a regression model with land use data, but the estimation process has limitation because of spatial autocorrelation of the population data. This study aimed to improve the accuracy of population distribution estimation by adopting a Regression-Kriging method, namely RK Model, which combines a regression model with Kriging for the residuals. RK Model was applied to a part of Seoul metropolitan area to estimate population distribution based on the residential zones. Comparative results of regression model and RK model using RMSE, MAE, and G statistics revealed that RK model could substantially improve the accuracy of population distribution. It is expected that RK model could be adopted actively for further population distribution estimation.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.678-683
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• 2008

Moment-based reliability analysis is the method to calculate reliability using Pearson System with first-four raw moments obtained from simulation model. But it is too expensive to calculate first four moments from complicate simulation model. To overcome this drawback the MD(multiplicative decomposition) method which approximates simulation model to kriging metamodel and calculates first four raw moments explicitly with multiplicative decomposition techniques. In general, kriging metamodel is an interpolation model that is decomposed of global model and local model. The global model, in general, can be used as the constant global model, the 1st order global model, or the 2nd order global model. In this paper, the influences of global models on the accuracy and robustness of raw moments are examined and compared. Finally, we suggest the best global model which can provide exact and robust raw moments using MD method.

• KEPCO Journal on Electric Power and Energy
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• v.5 no.3
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• pp.223-228
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• 2019

In order to integrate large amounts of variable generation resources such as wind and solar reliably into power grids, accurate renewable energy forecasting is necessary. Since renewable energy generation output is heavily influenced by environmental variables, accurate forecasting of power generation requires meteorological data at the point where the plant is located. Therefore, a spatial approach is required to predict the meteorological variables at the interesting points. In this paper, we propose the meteorological variable prediction model for enhancing renewable generation output forecasting model. The proposed model is implemented by three geostatistical techniques: Ordinary kriging, Universal kriging and Co-kriging.

• International Journal of Aeronautical and Space Sciences
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• v.17 no.4
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• pp.491-500
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• 2016

This paper presents a highly efficient aeroelastic optimization method based on a surrogate model; the model is verified by considering the case of a high-aspect-ratio composite wing. Optimization frameworks using the Kriging model and genetic algorithm (GA), the Kriging model and improved particle swarm optimization (IPSO), and the back propagation neural network model (BP) and IPSO are presented. The feasibility of the method is verified, as the model can improve the optimization efficiency while also satisfying the engineering requirements. Moreover, the effects of the number of design variables and number of constraints on the optimization efficiency and objective function are analysed in detail. The accuracy of two surrogate models in aeroelastic optimization is also compared. The Kriging model is constructed more conveniently, and its predictive accuracy of the aeroelastic responses also satisfies the engineering requirements. According to the case of a high-aspect-ratio composite wing, the GA is better at global optimization.

• Journal of Information Processing Systems
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• v.11 no.1
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• pp.134-147
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• 2015

This paper presents the applications of Kriging spatial interpolation methods for meteorologic variables, including temperature and relative humidity, in regions of Vietnam. Three types of interpolation methods are used, which are as follows: Ordinary Kriging, Universal Kriging, and Universal Kriging plus Digital Elevation model correction. The input meteorologic data was collected from 98 ground weather stations throughout Vietnam and the outputs were interpolated temperature and relative humidity gridded fields, along with their error maps. The experimental results showed that Universal Kriging plus the digital elevation model correction method outperformed the two other methods when applied to temperature. The interpolation effectiveness of Ordinary Kriging and Universal Kriging were almost the same when applied to both temperature and relative humidity.

• Proceedings of the KSME Conference
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• 2005.11a
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• pp.169.2-169.2
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• 2005

• Journal of Electrical Engineering and Technology
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• v.9 no.5
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• pp.1544-1550
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• 2014

In this paper, an adaptive optimization strategy utilizing Kriging model and genetic algorithm is proposed for the optimal design of electromagnetic devices. The ordinary Kriging assisted by the spherical covariance model is used to construct surrogate models. In order to improve the computational efficiency, the adaptive uniform sampling strategy is applied to generate sampling points in design space. Through several iterations and gradual refinement process, the global optimal point can be found by genetic algorithm. The proposed algorithm is validated by application to the optimal design of a switched reluctance motor, where the stator pole face and shape of pole shoe attached to the lateral face of the rotor pole are optimized to reduce the torque ripple.

• Journal of Korean Society on Water Environment
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• v.26 no.2
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• pp.252-260
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• 2010

The point rainfall measurements need to be converted to the areal rainfall by means of mean areal precipitation (MAP) estimation methods. And it is not appropriate to evaluate the areal rainfall with constant drift because of the geomorphological influences to rainfall field. Non-stationarity should be applied to the estimation of the areal rainfall, therefore, to consider these effects. Kriging methods with special functional would be a suitable tool in this case. Generalized covariance Kriging method is the most developed one among different Kriging methods. From this point of view this study performs the analysis of its applicability to distributed runoff model. For these purpose, distributed rainfall was created by Thiessen and Kriging method. And distributed rainfall of each method was applied into HyGIS-GRM. The result of applying, Runoff was different in the rainfall data form. Therefore, To apply Kriging method with physical meaning is that it is the useful method as distributed rainfall-runoff model.