• 한국지반공학회논문집
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• 제26권5호
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• pp.57-64
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• 2010

크리깅기법은 지반공학분야에서 지층 또는 지반정수의 공간적 분포 추정을 위하여 적용되고 있다. 각각의 크리깅기법에 따라서 추정결과가 상이하기 때문에 추정목적에 부합하는 크리깅 기법 선택은 중요한 문제이다. 본 논문에서는 단순크리깅, 정규크리깅, 일반크리깅을 사용하여 인천 송도국제도시의 압밀층 두께를 추정하였으며, 크리깅 기법에 따른 추정결과의 신뢰성을 분석하였다. 분석결과 단순크리깅은 정규크리깅과 일반크리깅에 비하여 측정자료와 멀리 떨어진 위치에서의 압밀층 두께를 크게 추정하며, 추정결과의 신뢰성도 낮았다. 일반크리킹은 측정자료와 멀리 떨어진 일부 위치에서 압밀층 두께를 음의 값으로 추정하는 것으로 나타났다. 압밀층 두께를 추정하기 위한 가장 적절한 크리깅 기법은 가장 신뢰성이 높고 물리적으로 합당한 추정범위 내의 값을 추정한 정규크리깅 기법인 것으로 분석되었다.

• 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.

• Spatial Information Research
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• 제19권4호
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• pp.81-89
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• 2011

이 논문에서는 공간적으로 소수의 지점에서 획득된 현장 조사 자료의 공간 보간 과정에 범주형 자료를 결합하는 다변량 크리깅 기법을 제안하고자 한다. 범주형 자료를 결합하는 과정에서 기존 범주형 자료의 속성별로 대푯값을 할당하는 단일 지역 평균 기반의 단순 크리깅 방식 대신에, 영역-점 변환 크리깅을 이용하여 원하는 해상도로 상세화시킨 추정값을 가변적 지역 평균으로 이용하였다. 지화학 원소 구리의 공간 보간에 지질도를 이용하는 사례연구를 통해 제안 기법을 예시하였다. 교차 검증 결과, 제안 기법이 단변량 정규 크리깅과 기존 단일 지역 평균 기반의 단순 크리깅 기법에 비해 각각 15%와 25%의 예측 능력의 향상을 나타내었다. 따라서 범주형 자료를 부가 자료로 이용하는 공간 보간에 이 논문에서 제안한 기법이 효율적으로 적용될 수 있을 것으로 기대된다.

• 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.

• 한국습지학회지
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• 제15권1호
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• pp.115-125
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• 2013

본 연구에서는 강수의 공간적 편차가 큰 산악지역에서 축소기법을 적용하기 위한 방법론을 마련하고 이를 이용하여 미래 강수특성의 변화를 추정하고자 하였다. 이를 위하여 한반도내 산악지역이라고 할 수 있는 남한강유역을 대상유역으로 선정하였고 일반적인 축소기법 중의 하나인 신경망과 고도자료를 부가자료로 활용하여 유역의 지형적 특성을 반영할 수 있는 SKlm 기법을 연계하여 신경망-SKlm 모형(ANN-SKlm : Artificial Neural Network - Simple Kriging with varying local means)을 구축하였다. 유역내 6개의 기상관측소 지점의 월강수량을 이용하여 신경망-SKlm 기법과 기존 강수량의 공간분포 방법인 Thiessen 및 Ordinary Kriging 을 적용하여 비교 평가하였다. 유역내에 보다 밀도있게 구성되어 있는 25개 강우관측소 지점을 대상으로 각 기법을 평가한 결과 고도자료를 부가자료로 사용하는 SKlm 기법이 가장 우수한 결과를 나타내었다.

• 한국농공학회논문집
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• 제47권6호
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• pp.3-14
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• 2005

Accurate estimation of the spatial distribution of rainfall is critical to the successful modeling of hydrologic processes. The objective of this study is to evaluate the applicability of spatially distributed rainfall data. Spatially distributed rainfall was calculated using Kriging method and Thiessen method. The application of spatially distributed rainfall was appreciated to the runoff response from the watershed. The results showed that for each method the coefficient of determination for observed hydrograph was $0.92\~0.95$ and root mean square error was $9.78\~10.89$ CMS. Ordinary Kriging method showed more exact results than Simple Kriging, Universal Kriging and Thiessen method, based on comparison of observed and simulated hydrograph. The coefncient of determination for the observed peak flow was 0.9991 and runoff volume was 0.9982. The accuracy of rainfall-runoff prediction depends on the extent of spatial rainfall variability.

• 환경영향평가
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• 제20권6호
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• pp.879-890
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• 2011

The GOSAT (Greenhouse gases Observing SATellite) data provide new opportunities the most regionally complete and up-to-date assessment of $CO_2$. However, in practice, GOSAT records often suffer from missing data values mainly due to unfavorable meteorological condition in specific time periods of data acquisition. The aim of this research was to identify optimal spatial interpolation techniques to ensure the continuity of $CO_2$ from samples taken in the North East Asia. The accuracy among ordinary kriging (OK), universal kriging (UK) and simple kriging (SK) was compared based on the combined consideration of $R^2$ values, Root Mean Square Error (RMSE), Mean Error (ME) for variogram models. Cross validation for 1312 random sampling points indicate that the (UK) kriging is the best geostatistical method for spatial predictions of $CO_2$ in the East Asia region. The results from this study can be useful for selecting optimal kriging algorithm to produce $CO_2$ map of various landscapes. Also, data users may benefit from a statistical approach that would allow them to better understand the uncertainty and limitations of the GOSAT sample data.

• 대한원격탐사학회지
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• 제7권1호
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• pp.45-67
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• 1991

An overview is given on stochastic techniques with which remotely sensed data may be used together with ground measurements for purposes of state estimation and prediction. They can explicitly account for spatiotemporal differences in measurement characteristics between ground measurements and remotely sensed data, and are suitable for highly variant space or space-time processes, such as atmosperic processes, which may be viewed as (containing) a random process. For state estimation of static ststems, optimal linear estimation is described. As alternatives, various co-kriging estimation techniques are also described, including simple, ordinary, universal, lognormal, disjunctive, indicator, and Bayesian extersion to simple and lognormal. For illustrative purposes, very simple examples of optimal linear estimation and simple co-kriging are given. For state estimation and prediction of dynamic system, distributed-parameter kalman filter is described. Issues concerning actual implemention are given, and with application potential are described.

• Composites Research
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• 제16권4호
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• pp.36-43
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• 2003

본 논문에서는 메타모델을 이용한 복합재 구조물의 지적화가 수행되었다. 일반적으로 복합재 구조물에서 시간이 오래 걸리는 해석의 결과를 최적화하고자 할 때 전체 최적화에 많은 시간이 든다. 따라서 이와 같은 구조물 해석을 메타 모델로 치환해 보았다. 본 연구에서는 RSM, kriging과 같은 메타모델을 사용하였다. 복합재 평판의 최종 파손 강도 해석을 메타모델을 이용해 근사화하였다 최종 파손 강도를 최대화하는 최적화를 유전자 알고리즘과 메타로델을 이용해 수행하였다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume I
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• pp.406-408
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• 2006

Various geological thematic maps such as grain size or ground water level maps have been generated by interpolating sparsely sampled ground survey data. When there are sampled data at a limited number of locations, to use secondary information which is correlated to primary variable can help us to estimate the attribute values of the primary variable at unsampled locations. This paper applies two multivariate geostatistical algorithms to integrate remote sensing imagery with sparsely sampled ground survey data for spatial estimation of grain size: simple kriging with local means and kriging with an external drift. High-resolution IKONOS imagery which is well correlated with the grain size is used as secondary information. The algorithms are evaluated from a case study with grain size observations measured at 53 locations in the Baramarae beach of Anmyeondo, Korea. Cross validation based on a one-leave-out approach is used to compare the estimation performance of the two multivariate geostatistical algorithms with that of traditional ordinary kriging.