• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.2 s.233
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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.

• 한국지구물리탐사학회:학술대회논문집
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• 2006.06a
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• pp.155-160
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• 2006

In this paper, we propose a spatial filtering scheme using factorial kriging, one of geostatistical filtering methodin order to separate regional and residual gravity anomaly. This scheme is based on the assumption that regional anomalies have longer distance relation and residual anomalies have effected on smaller range. We decomposed gravity anomalies intotwo variogram models with long and short effectiveranges by means of factorial kriging. And decomposed variogram models produced the regional and residual anomalies. This algorithm was examined using by a synthetic gravity data, and applied to a real microgravity data to figure out abandoned mineshaft.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2003.09a
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• pp.249-253
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• 2003

강서구를 제외한 부산 전지역에서 1998년도에 조사된 지하수 수질 중 6개 성분(pH, TS, KMnO$_4$, Cl, SO$_4$, NO$_3$-N)에 대한 일반통계분석 결과 pH 성분을 제외하고는 5개 성분의 중앙값이 평균보다 적은 값을 보이는 양성왜도를 보임으로써, 수질오염정도를 분석하기 위해 지시크리깅이라는 비모수적인 지구통계분석기법을 적용하였다. 6개 수질성분에 대해 음용수 기준치를 적용하여 음용가능은 “1”의 값이, 음용불가능은 “0”의 값이 주어졌다. 이렇게 변환된 자료를 이용하여 각 성분별로 실험적인 베리오그램 분석을 실시한 결과 pH, TS, SO$_4$ 성분은 선형모델이 선정되었으며, KMnO$_4$, Cl, NO$_3$-N 성분은 구상형모델이 선정되었다. 본 연구에서는 지시크리깅을 이용하여 6개 성분의 분포도를 작성하고 부산지역의 오염정도를 분석하였다. 지시크리깅기법은 연구지역 전체의 정량적인 분포를 나타내지는 못하지만, 오염의 유.무와 오염의 크기를 정확하게 파악할 수 있으며 또한, 이상치(outlier)가 크게 영향을 미칠 수 있는 통계학적인 오류를 보완할 수 있다.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.1
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• pp.16-33
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• 2012

The aim of this study is to estimate aboveground biomass carbon stocks using ordinary kriging(OK) which is the most commonly used type of kriging and regression kriging(RK) that combines a regression of the auxiliary variables with simple kriging. The analysis results shows that the forest carbon stock in Danyang is estimated at 3,459,902 tonC with OK and 3,384,581 tonC with RK in which the R-square value of the regression model is 0.1033. The result of RK conducted with sample plots stratified by forest type(deciduous, conifer and mixed) shows the lowest estimated value of 3,336,206 tonC and R-square value(0.35 and 0.18 respectively) is higher than that of when all sample plots used. The result of leave-one-out cross validation of each method indicates that RK with all sample plots reached the smallest root mean square error(RMSE) value(22.32 ton/ha) but the difference between the methods(0.23 ton/ha) is not significant.

• Spatial Information Research
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• v.22 no.1
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• pp.55-63
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• 2014

Near surface air temperature data which are one of the essential factors in hydrology, meteorology and climatology, have drawn a substantial amount of attention from various academic domains and societies. Meteorological observations, however, have high spatio-temporal constraints with the limits in the number and distribution over the earth surface. To overcome such limits, many studies have sought to estimate the near surface air temperature from satellite image data at a regional or continental scale with simple regression methods. Alternatively, we applied various Kriging methods such as ordinary Kriging, universal Kriging, Cokriging, Regression Kriging in search of an optimal estimation method based on near surface air temperature data observed from automatic weather stations (AWS) in South Korea throughout 2010 (365 days) and MODIS land surface temperature (LST) data (MOD11A1, 365 images). Due to high spatial heterogeneity, auxiliary data have been also analyzed such as land cover, DEM (digital elevation model) to consider factors that can affect near surface air temperature. Prior to the main estimation, we calculated root mean square error (RMSE) of temperature differences from the 365-days LST and AWS data by season and landcover. The results show that the coefficient of variation (CV) of RMSE by season is 0.86, but the equivalent value of CV by landcover is 0.00746. Seasonal differences between LST and AWS data were greater than that those by landcover. Seasonal RMSE was the lowest in winter (3.72). The results from a linear regression analysis for examining the relationship among AWS, LST, and auxiliary data show that the coefficient of determination was the highest in winter (0.818) but the lowest in summer (0.078), thereby indicating a significant level of seasonal variation. Based on these results, we utilized a variety of Kriging techniques to estimate the surface temperature. The results of cross-validation in each Kriging model show that the measure of model accuracy was 1.71, 1.71, 1.848, and 1.630 for universal Kriging, ordinary Kriging, cokriging, and regression Kriging, respectively. The estimates from regression Kriging thus proved to be the most accurate among the Kriging methods compared.

• Journal of Aerospace System Engineering
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• v.18 no.2
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• pp.104-116
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• 2024

A satellite is ultimately verified by performing a coupled load analysis with the launch vehicle. To increase the accuracy of the coupled load analysis results, it is important to have good accuracy of the finite element model. Therefore, finite element model correlation is essential. In general, model correlation is performed by changing the material properties and thickness one by one, but this process takes a lot of time and cost. The current paper proposes an efficient model correlation method using optimization. Significant variables were selected through analysis of variance, and the time and cost required for analysis and optimization were reduced by using the Kriging surrogate model. The method proposed in this paper can be applied only with the vibration test results, and it has a great advantage in terms of efficiency in that it can significantly reduce the numerical calculation cost and time required.

• Spatial Information Research
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• v.22 no.1
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• pp.9-17
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• 2014

This study was intended to reliably predict the traffic green house gas emission in Daegu with the use of spatial statistical technique and calculate the traffic green house gas emission of each administrative district on the basis of the accurately predicted emission. First, with the use of the traffic actually surveyed at a traffic observation point, and traffic green house gas emission was calculated. Secondly, on the basis of the calculation, and with the use of Universal Kriging technique, this researcher set a suitable variogram modeling to accurately and reliably predict the green house gas emission at non-observation point suitable through spatial correlation, and then performed cross validation to prove the validity of the proper variogram modeling and Kriging technique. Thirdly, with the use of the validated kriging technique, traffic green gas emission was visualized, and its distribution features were analyzed to predict and calculate the traffic green house gas emission of each administrative district. As a result, regarding the traffic green house gas emission of each administration, it was found that Bukgu had the highest green house gas emission of $291,878,020kgCO_2eq/yr$.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.2
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• pp.125-130
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• 2011

Since conventional optimization that is classified as a deterministic method does not consider the uncertainty involved in a modeling or manufacturing process, an optimum design is often determined to be on the boundaries of the feasible region of constraints. Reliability-based design optimization is a method for obtaining a solution by minimizing the objective function while satisfying the reliability constraints. This method includes an optimization process and a reliability analysis that facilitates the quantization of the uncertainties related to design variables. Moment-based reliability analysis is a method for calculating the reliability of a system on the basis of statistical moments. In general, on the basis of these statistical moments, the Pearson system estimates seven types of distributions and determines the reliability of the system. However, it is technically difficult to practically consider the Pearson Type IV distribution. In this study, we propose an enhanced Pearson Type IV distribution based on a kriging model and validate the accuracy of the enhanced Pearson Type IV distribution by comparing it with a Monte Carlo simulation. Finally, reliability-based design optimization is performed for a system with type IV distribution by using the proposed method.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2009.04a
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• pp.101-104
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• 2009

최적설계는 설계자가 요구하는 제한조건을 만족시키는 범위에서 목적함수가 최소가 되는 설계점을 찾는 방법이다. 그러나 기존의 최적설계는 불확실성의 영향을 고려하지 않아 최적해가 제한조건의 경계에 위치하고 이것은 모델링과정이나 가공 등으로 인한 오차에 대한 영향을 고려하지 않는 문제점이 있다. 신뢰성 기반 최적설계는 불확실성을 정량화하면서 신뢰도를 계산하는 신뢰도 해석과정과 최적설계과정을 포함한다. 일반적으로 신뢰성 해석은 크게 추출법, 급속 확률 적분법, 모멘트 기반 신뢰성해석이 있다. 가장 널리 사용되는 급속 확률 적분법 중 최대 손상 가능점(MPP) 방법은 많은 MPP점이 존재하는 경우 수치적 비용이 증가하는 문제점과 표준 정규분포 공간으로 변환하는 과정에서 제한조건의 비선형성을 증가시켜 큰 오차를 발생시키는 문제점이 있다. 본 논문에서는 RBDO를 수행하기에 앞서 선행되어야 할 신뢰성해석 방법으로 곱분해기법을 사용하였고 이로부터 민감도 정보를 유도하여 기울기 기반 최적화 알고리즘을 적용하였다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.16 no.1
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• pp.119-132
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• 1998

For the purpose of producing of grid D.E.M based on National Digital Map accurately and efficiently, We must carefully consider arrangement and numbers of it's elevation information, supplement interpolation method of control point information for maintaining accuracy. According to each combination, each of them has an effect on estimate elevations. This study, after finishing experimental analysis of several grid distance and interpolation methods, aims at presenting the optimal grid distance and interpolation method in the production of grid D.E.M by using of National Digital Map. The results are as follows: First, The result of experimental analysis shows that the method of Kriging is a very excellent interpolation method in the production of grid D.E. M by using National Digital Map. Second, For the purpose of determining grid distance, this study present that twice of the amount of contour interval to make producing grid D.E.M is optimal distance.