• The Pure and Applied Mathematics
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• v.11 no.2
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• pp.133-138
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• 2004

In this paper we derive a decomposition of the solution of Fredholm equations of the second kind in terms of reproducing kernels in the space ${W_2}^2$($\Omega$)

• Communications of the Korean Mathematical Society
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• v.3 no.2
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• pp.179-187
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• 1988

• The Korean Journal of Applied Statistics
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• v.33 no.5
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• pp.569-578
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• 2020

By estimating conditional quantile functions of the response, quantile regression (QR) can provide comprehensive information of the relationship between the response and the predictors. In addition, kernel quantile regression (KQR) estimates a nonlinear conditional quantile function in reproducing kernel Hilbert spaces generated by a positive definite kernel function. However, it is infeasible to use the KQR in analysing a massive data due to the limitations of computer primary memory. We propose a divide and conquer based KQR (DC-KQR) method to overcome such a limitation. The proposed DC-KQR divides the entire data into a few subsets, then applies the KQR onto each subsets and derives a final estimator by aggregating all results from subsets. Simulation studies are presented to demonstrate the satisfactory performance of the proposed method.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.732-735
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• 1995

A meshless method which is the new computational method being developed recently, is applied to the simulation of large deformation problems. Among the many types of meshless methods, the Reproducing Kernel particle method (RKPM) is used and the nearly incompressible hyperelastic materials are employed in simulations. The meshless methods can avoid metsh distortions and mesh entanglements that may frequently happen when the mesh-based methods like finite element method are used for the simulations of largely deformed materials. A general features of meshless methods are reviewed and the formulation of RKPM is presented. Next, the performance of explicit RKPM is demonstrated by examples.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1997.10a
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• pp.16-23
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• 1997

The Reproducing Kernel Particle Method (RKPM), one of the popular meshless methods, is developed and applied to stress concentration problems. Since the meshless methods require only a set of particles (or nodes) and the description of boundaries in their formulation, the adaptivity can be implemented with much more ease than finite element method. In addition, due to its intrinsic property of multiresolution, the shape function of RKPM provides us a new criterion for adaptivity. Recently, this multiple scale Reproducing Kernel Particle Method and its adaptive procedure have been formulated for large deformation problems by the authors. They are also under development for damage materials and localization problems. In this paper the multiple scale RKPM for linear elasticity is presented and the adaptive procedure is applied to stress concentration problems. Therefore, this work may be regarded as the edition of linear elasticity in the complete framework of multiple scale RKPM and the associated adaptivity.

• Structural Engineering and Mechanics
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• v.17 no.5
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• pp.671-690
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• 2004

A meshless method with novel variation of point collocation by finite mixture approximation is developed in this paper, termed the meshless finite mixture (MFM) method. It is based on the finite mixture theorem and consists of two or more existing meshless techniques for exploitation of their respective merits for the numerical solution of partial differential boundary value (PDBV) problems. In this representation, the classical reproducing kernel particle and differential quadrature techniques are mixed in a point collocation framework. The least-square method is used to optimize the value of the weight coefficient to construct the final finite mixture approximation with higher accuracy and numerical stability. In order to validate the developed MFM method, several one- and two-dimensional PDBV problems are studied with different mixed boundary conditions. From the numerical results, it is observed that the optimized MFM weight coefficient can improve significantly the numerical stability and accuracy of the newly developed MFM method for the various PDBV problems.

• Honam Mathematical Journal
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• v.26 no.4
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• pp.455-462
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• 2004

We prove that if ${\mathbb{K}}^*$ is adjoint operator on $W_2{^2}({\Omega})$, then ${\mathbb{K}}^*v(t,\;{\tau})=,\;v(x,\;y){\in}W_2{^2}({\Omega})$ ; it is also related to the decomposition of solution of Fredholm equations.

• Computers and Concrete
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• v.7 no.2
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• pp.119-143
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• 2010

This paper presents results from a study concerning the capability afforded by the RKPM (reproducing kernel particle method) meshfree analysis formulation to predict responses of concrete and UHPC components resulting from projectile impacts and blasts from nearby charges. In this paper, the basic features offered by the RKPM method are described, especially as they are implemented in the analysis code KC-FEMFRE, which was developed by Karagozian & Case (K&C).

• 한국전산유체공학회:학술대회논문집
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• 2003.10a
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• pp.130-130
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• 2003

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.3B
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• pp.277-284
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• 2011

The limitations of existing Markov chain model for reproducing extreme rainfalls are a known problem, and the problems have increased the uncertainties in establishing water resources plans. Especially, it is very difficult to secure reliability of water resources structures because the design rainfall through the existing Markov chain model are significantly underestimated. In this regard, aims of this study were to develop a new daily rainfall simulation model which is able to reproduce both mean and high order moments such as variance and skewness using a piecewise Kernel-Pareto distribution. The proposed methods were applied to summer and fall season rainfall at three stations in Han river watershed in Korea. The proposed Kernel-Pareto distribution based Markov chain model has been shown to perform well at reproducing most of statistics such as mean, standard deviation and skewness while the existing Gamma distribution based Markov chain model generally fails to reproduce high order moments. It was also confirmed that the proposed model can more effectively reproduce low order moments such as mean and median as well as underlying distribution of daily rainfall series by modeling extreme rainfall separately.