• 한국토양비료학회지
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• 제17권1호
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• pp.18-23
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• 1984

화동미사질식양토(華東微砂質埴壤土)의 침투속도(浸透速度)에 대한 공간변이성(空間變異性)이 지질통계학적(地質統計學的) 개념(槪念)인 지역변수이론(地域變數理論)으로 기술(記述)하였고, 구조분석(構造分析)에서 얻어진 정보(情報)를 이용(利用)하여 최양선형불편추정치(最量線型不偏推定値)(BLUE)인 Kriging값을 계산(計算)하였는 바 그 결과(結果)는 다음과 같다. 1. 최소(最少)의 분산(分散)으로 Kriging같이 추정(推定)되었다. 2. 설정(設定)된 선형(線型)의 평균(平均) Variogram은 Kriging값을 추정(推定)하는 데 적합(適合)하다고 판단(判斷)되었다. 3. 표본추출지점(標本抽出地點)에서의 Kriging값과 실관측치(實觀測値)의 상관(相關)은 고도(高度)의 유의성(有意性)을 나타내었다.

• 대한지리학회지
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• 제45권6호
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• pp.806-819
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• 2010

센서스 단위의 인구자료는 기초적인 인문사회 자료로 행정구역 단위로 요약되어 공간분석에 시용된다. 정밀한 인구 분포를 추정하기 위해 기존의 연구에서는 위성영상과 회귀분석 모형을 이용하였다. 하지만 회귀식에 의한 추정치는 공간자료의 공간적자기상관과 잔차 때문에 정확도에 있어 한계가 있었다. 본 연구는 회귀모형과 회귀모형에서 추출된 잔차에 대해 공간적자기상관을 고려하도록 크리깅 보간하는 RK모형(Regression Kriging Model)을 이용하여 인구분포의 추정 정확도를 향상하였다. RK모형을 적용하여 서울시의 4개구를 대상으로 사례분석을 하였으며, 모형의 효율성을 검증하기 위해 회귀분석만을 이용한 예측 결과와 RK모형을 이용한 예측 결과를 서로 비교하였다. 비교한 결과로 상관관계 계수 평균제곱근 오차, G 통계량 수치에서 RK모형의 추정 정확도가 기존의 회귀모형에 비해 높게 나온 것을 확인할수 있었다. 향후 정확한 인구추정을 위해 RK모형이 많이 활용될 수 있을 것이다.

• 한국항공우주학회지
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• 제48권1호
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• pp.13-21
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• 2020

유도무기의 설계 및 제어에서 6자유도 공력계수의 신속한 추정을 위해 공력계수 데이터에 기반한 예측 모형이 주로 이용된다. 고정확도의 공력계수 예측 모형은 다수의 풍동시험 데이터로 생성할 수 있지만, 이는 많은 시간과 자원을 요구한다. 따라서 본 연구에서는 소수의 풍동시험 데이터를 다수의 전산유체역학 데이터와 혼합한 코크리깅 기법을 활용해 고정확도의 공력계수를 신속하고 효율적으로 예측하고자 한다. 풍동시험과 전산유체역학 데이터를 혼용한 예측 모형의 우수성을 보기 위해, 전산유체역학 데이터 보조의 유무에 따라 두 가지 공력계수 예측 모형을 생성한 후 수치적 검증과 예측 경향성 점검으로 두 모형의 예측 정확도를 비교하였다. 그 결과, 전산유체역학 데이터의 도움 덕분에 코크리깅 모형으로 크리깅 모형보다 더 정확한 공력계수 산출이 가능한 것을 확인하였다.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제3권8호
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• pp.251-260
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• 2014

3D 점 데이터는 높은 정확성을 가진 사물의 표면 정보 데이터로 다양한 분야에서 사용되고 있으며, 특히 지리학에서 지형 파악과 분석에 많이 사용되고 있다. 일반적으로 3D 점 데이터의 Gridding 과정을 거치게 되는데 이는 불연속적인 점 데이터를 일정한 좌표 값으로 만드는 과정으로 긴 실행 시간과 높은 비용이 필요하다. 특히 Gridding 과정 중 보간 작업을 위해서 Kriging이 높은 정확성으로 주목받고 있지만 처리과정이 복잡하고 연산이 많아 처리속도가 상대적으로 느리기 때문에 많이 사용되지 않고 있다. 본 논문에서는 Gridding을 고성능으로 처리하기위해 Kriging 연산 과정을 병렬화했으며 격자 자료구조를 MapReduce 패러다임에 맞게 변형하여 Kriging에 적용하였다. 실험은 항공 LiDAR 데이터 약 1.6백만 개와 4.3백만 개의 점 데이터를 이용해서 제안한 MapReduce 구조에 적용하였고, 그 결과 3대의 이기종 클러스터에서 전체 실행시간이 순차적 프로그램에 비해 최대 3.4배 단축하였다.

• Geomechanics and Engineering
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• 제30권2호
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• pp.187-199
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• 2022

Kriging metamodel, as a flexible machine learning method for approximating deterministic analysis models of an engineering system, has been widely used for efficiently estimating slope reliability in recent years. However, the autocorrelation function (ACF), a key input to Kriging that affects the accuracy of reliability estimation, is usually selected based on empiricism. This paper proposes an adaption of the Kriging method, named as Genetic Algorithm optimized Whittle-Matérn Kriging (GAWMK), for addressing this issue. The non-classical two-parameter Whittle-Matérn (WM) function, which can represent different ACFs in the Matérn family by controlling a smoothness parameter, is adopted in GAWMK to avoid subjectively selecting ACFs. The genetic algorithm is used to optimize the WM model to adaptively select the optimal autocorrelation structure of the GAWMK model. Monte Carlo simulation is then performed based on GAWMK for a subsequent slope reliability analysis. Applications to one explicit analytical example and two slope examples are presented to illustrate and validate the proposed method. It is found that reliability results estimated by the Kriging models using randomly chosen ACFs might be biased. The proposed method performs reasonably well in slope reliability estimation.

• 한국기계가공학회지
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• 제7권3호
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• pp.17-24
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• 2008

Rail clamps are mechanical components installed to fix the container crane to its lower members against wind blast or slip. According to rail clamps should be designed to survive harsh wind loading conditions. In this study, a jaw structure, which is a part of a wedge-typed rail clamp, is optimized with respect to its strength under a severe wind loading condition. According to the classification of structural optimization, the structural optimization of a jaw is included in the category of shape optimization. Conventional structural optimization methods have difficulties in defining complex shape design variables and preventing mesh distortions. To overcome the difficulties, the metamodel using Kriging interpolation method is introduced to replace the true response by an approximate one. This research presents the shape optimization of a jaw using iterative Kriging interpolation models and a simulated annealing algorithm. The new Kriging models are iteratively constructed by refining the former Kriging models. This process is continued until the convergence criteria are satisfied. The optimum results obtained by the suggested method are compared with those obtained by the DOE (design of experiments) and VT (variation technology) methods built in ANSYS WORKBENCH.

• 대한기계학회논문집A
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• 제30권4호
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• pp.465-472
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• 2006

Recently, the regulations of the government and the concerns of people have rise to the interest in noise pollution levels as compared to other vehicles. In this area, many researchers have studied to reduce this noise in the field of automotive engineering. This paper proposes an optimal design scheme to reduce the noise of the intake system by adapting Kriging with two meta-heuristic techniques. For this, as a measuring tool for the performance of the intake system, the performance prediction software, was used. Then, the length and radius of each component of the current intake system are selected as input variables and the orthogonal arrays is adapted as a space-filling design. With these simulated data, we can estimate a correlation parameter in Kriging by solving the nonlinear problem with a genetic algorithm and find an optimal level for the intake system by optimizing Kriging estimated with simulated annealing. We notice that this optimal design scheme gives noticeable results and is a preferable way to analyze the intake system. Therefore, an optimal design for the intake system is proposed by reducing the noise of its system.

• 한국소음진동공학회논문집
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• 제19권5호
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• pp.509-515
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• 2009

This paper proposes an optimal design scheme to reduce the noise of the engine cooling fan by adapting Kriging with two meta-heuristic techniques. An engineering model has been developed for the prediction of the noise spectrum of the engine cooling fan. The noise of the fan is expressed as the discrete frequency noise peaks at the BPF and its harmonics and line spectrum at the broad band by noise generation mechanisms. The object of this paper is to find the optimal design for noise reduction of the engine cooling fan. We firstly show a comparison of the measured and calculated noise spectra of the fan for the validation of the noise prediction program. Orthogonal array is applied as design of experiments because it is suitable for Kriging. With these simulated data, we can estimate a correlation parameter of Kriging by solving the nonlinear problem with genetic algorithm and find an optimal level for the noise reduction of the cooling fan by optimizing Kriging estimates with simulated annealing. We notice that this optimal design scheme gives noticeable results. Therefore, an optimal design for the cooling fan is proposed by reducing the noise of its system.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2007년도 추계학술대회논문집
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• pp.1307-1312
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• 2007

This paper proposes an optimal design scheme to reduce the noise of the engine cooling fan by adapting Kriging with two meta-heuristic techniques. An engineering model has been developed for the prediction of the noise spectrum of the engine cooling fan. The noise of the fan is expressed as the discrete frequency noise peaks at the BPF and its harmonics and line spectrum at the broad band by noise generation mechanisms. The object of this paper is to find the Optimal Design for Noise Reduction of the Engine Cooling Fan. We firstly show a comparison of the measured and calculated noise spectra of the fan for the validation of the noise prediction program. Orthogonal array is applied as design of experiments because it is suitable for Kriging. With these simulated data, we can estimate a correlation parameter of Kriging by solving the nonlinear problem with genetic algorithm and find an optimal level for the noise reduction of the cooling fan by optimizing Kriging estimates with simulated annealing. We notice that this optimal design scheme gives noticeable results. Therefore, an optimal design for the cooling fan is proposed by reducing the noise of its system.

• 대한원격탐사학회지
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• 제27권6호
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• pp.625-635
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• 2011

This paper investigates the effects of spatial autocorrelation structures in low resolution data on downscaling without ground measurements or secondary data, as well as the potential of geostatistical downscaling. An advanced geostatistical downscaling scheme applied in this paper consists of two analytical steps: the estimation of the point-support spatial autocorrelation structure by variogram deconvolution and the application of area-to-point kriging. Point kriging of block data without variogram deconvolution is also applied for a comparison purpose. Experiments using two low resolution thematic maps derived from remote sensing data showing very different spatial patterns are carried out to discuss the objectives. From the experiments, it is demonstrated that the advanced geostatistical downscaling scheme can generate the downscaling results that well preserve overall patterns of original low resolution data and also satisfy the coherence property, regardless of spatial patterns in input low resolution data. Point kriging of block data can produce the downscaling result compatible to that by area-to-point kriging when the spatial continuity in block data is strong. If heterogeneous local variations are dominant in input block data, the treatment of the low resolution data as point data cannot generate the reliable downscaling result, and this simplification should not be applied to donwscaling.