• Journal of the Computational Structural Engineering Institute of Korea
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• v.20 no.3
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• pp.321-327
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• 2007

This study presents a new gridless finite difference method for solving elastic crack problems. The method constructs the Taylor expansion based on the MLS(Moving Least Squares) method and effectively calculates the approximation and its derivatives without differentiation process. Since no connectivity between nodes is required, the modeling of discontinuity embedded in the domain is very convenient and discontinuity effect due to crack is naturally implemented in the construction of difference equations. Direct discretization of the governing partial differential equations makes solution process faster than other numerical schemes using numerical integration. Numerical results for mode I and II crack problems demonstrates that the proposed method accurately and efficiently evaluates the stress intensity factors.

• The Korean Journal of Applied Statistics
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• v.12 no.1
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• pp.109-124
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• 1999

일반적으로 오차항이 자기상관되어 있는 선형회귀 모형에서는 회귀계수에 대한 보통최소제곱추정량이 효율적이지 못 하다고 알려져 있다. 그러나 이러한 일반화선형회귀모형에서 독립변수의 형태에 따라서는 OLSE의 사용 가능성을 제시하는 모형이 있다. 본 연구에서는 오차항이 일차 이동평균 과정을 따르는 선형회귀모형에서 여러 추정량들 (GLSE, APX, MAPX)에 대한 OLSE의 상대효율함수를 유도하고 비교 분석하고자 한다. 특히 소표본에서 정확한 상대효율값을 구하여 OLSE의 효율성이 크게 떨어지지 않거나 효율성이 나은 회귀모형들을 제시한다.

• The Korean Journal of Applied Statistics
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• v.19 no.1
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• pp.97-110
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• 2006

Since it is well-established that even high quality data tend to contain outliers, one would expect fat? greater reliance on robust regression techniques than is actually observed. But most of all robust regression estimators suffers from the computational difficulties and the lower efficiency than the least squares under the normal error model. The weighted self-tuning estimator (WSTE) recently suggested by Lee (2004) has no more computational difficulty and it has the asymptotic normality and the high break-down point simultaneously. Although it has better properties than the other robust estimators, WSTE does not have full efficiency under the normal error model through the weighted least squares which is widely used. This paper introduces a new approach as called the reweighted WSTE (RWSTE), whose scale estimator is adaptively estimated by the self-tuning constant. A Monte Carlo study shows that new approach has better behavior than the general weighted least squares method under the normal model and the large data.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4A
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• pp.677-687
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• 2006

Kriging interpolation is one of the generally used interpolation techniques in Geostatistics field. This technique includes the experimental and theoretical variograms and the formulation of kriging interpolation. In contrast to the conventional least square method for stress recovery, kriging interpolation is based on the weighted least square method to obtain the estimated exact solution from the stress data at the Gauss points. The weight factor is determined by variogram modeling for interpolation of stress data apart from the conventional interpolation methods that use an equal weight factor. In addition to this, the p-level is increased non-uniformly or selectively through a posteriori error estimation based on SPR (superconvergent patch recovery) technique, proposed by Zienkiewicz and Zhu, by auto mesh p-refinement. The cut-out plate problem under tension has been tested to validate this approach. It also provides validity of kriging interpolation through comparing to existing least square method.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.32 no.4
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• pp.215-222
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• 2019

The heat conduction problem with discontinuous material coefficients generally consists of the conservative equation, boundary condition, and interface condition, which should be additionally satisfied in the solution procedure. This feature often makes the development of new numerical schemes difficult as it induces a layered singularity in the solution fields; thus, a special approximation is required to capture the singular behavior. In addition to the approximation, the construction of a total system of equations is challenging. In this study, a wedge function is devised for enriching the approximation, and the interface condition itself is embedded in the moving least squares(MLS) derivative approximation to consistently satisfy the interface condition. The heat conduction problem is then discretized in a strong form using the developed derivative approximation, which is named as the interface immersed MLS difference method. This method is able to efficiently provide a numerical solution for such interface problems avoiding both numerical quadrature as well as extra difference equations related to the interface condition enforcement. Numerical experiments proved that the developed numerical method was highly accurate and computationally efficient at solving the heat conduction problem with interfacial jump as well as the problem with a geometrically induced interfacial singularity.

• Journal of the Korean Data and Information Science Society
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• v.20 no.2
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• pp.251-260
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• 2009

In this study, we propose a new estimation method based on autocovariance for selecting optimal estimators of the regression coefficients in the simple linear regression model. Although this method does not seem to be intuitively attractive, these estimators are unbiased for the corresponding regression coefficients. When the exploratory variable takes the equally spaced values between 0 and 1, under mild conditions which are satisfied when errors follow an autoregressive moving average model, we show that these estimators have asymptotically the same distributions as the least squares estimators. Additionally, under the same conditions as before, we provide a self-contained proof that these estimators converge in probability to the corresponding regression coefficients.

• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.839-847
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• 2011

In this study, we derive an estimator based on autocovariance for the regression coefficients vector in the multiple linear regression model. This method is suggested by Park (2009), and although this method does not seem to be intuitively attractive, this estimator is unbiased for the regression coefficients vector. When the vectors of exploratory variables satisfy some regularity conditions, under mild conditions which are satisfied when errors are from autoregressive and moving average models, this estimator has asymptotically the same distribution as the least squares estimator and also converges in probability to the regression coefficients vector. Finally we provide a simulation study that the forementioned theoretical results hold for small sample cases.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2002.10a
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• pp.591-598
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• 2002

The interface element method for non-matching FEM meshes is extended using stabilized nodal integration. Two non-matching meshes are shown to be joined together compatibly, with the aid of the moving least square approximation. Using stabilized nodal integration, the interface element method is able to satisfy the patch test, which guarantees the convergence of the method.

• Korean Journal of Optics and Photonics
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• v.17 no.4
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• pp.359-365
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• 2006

Wave-front error(WFE) is the main parameter that determines the optical performance of the opto-mechanical system. In the development of opto-mechanics, WFE due to the main loading conditions are set to the important specifications. The deformation of the optical surface can be exactly calculated thanks to the evolution of numerical methods such as the finite element method(FEM). To calculate WFE from the deformation results of FEM, another approximation of the optical surface deformation is required. It needs to construct additional grid or element mesh. To construct additional mesh is troublesomeand leads to transformation error. In this work, the moving least-squares approximation is used to reconstruct wave front error It has the advantage of accurate approximation with only nodal data. There is no need to construct additional mesh for approximation. The proposed method is applied to the examples of GOCI scan mirror in various loading conditions. The validity is demonstrated through examples.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.25 no.2
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• pp.139-148
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• 2012

The MLS(Moving Least Squares) Difference Method is a numerical scheme that combines the MLS method of Meshfree method and Taylor expansion involving not numerical quadrature or mesh structure but only nodes. This paper presents an dynamic algorithm of MLS difference method for solving transient solid mechanics problems. The developed algorithm performs time integration by using Newmark method and directly discretizes strong forms. It is very convenient to increase the order of Taylor polynomial because derivative approximations are obtained by the Taylor series expanded by MLS method without real differentiation. The accuracy and efficiency of the dynamic algorithm are verified through numerical experiments. Numerical results converge very well to the closed-form solutions and show less oscillation and periodic error than FEM(Finite Element Method).