• 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).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.5C
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• pp.673-682
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• 2004

The adaptive beam-forming is promising approach for noise reduction in hearing aids. This approach has come in the focus of interest only recently, because of the availability of new and powerful digital signal processors. The adaptation U using usually a Least Mean Squares algorithm, updates the weight vector. In this Paper, we propose a fast wavelet based adaptive algorithm using variable step size algorithm which varies adaptive constant by the change of signal environment. We compared the performance of the proposed algorithm with the known adaptive algorithm using computer simulation of multi channel adaptive bemformer in hearing aids. As the result the proposed algorithm is suitable for adaptive signal processing area using hearing aids and has advantages reducing computational complexity. And we show the beam-forming system using proposed algorithm converges stably in a sudden change of system environment.

• Journal of the Institute of Convergence Signal Processing
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• v.14 no.1
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• pp.27-32
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• 2013

It plays an important role to detect the shape of an ellipse in many application areas of image processing. But it is very difficult to detect the ellipse in the real image because the noise was involved in the image, other objects obscured the ellipse or the ellipses were overlap with each other. In this paper, we extract the boundary (edge) to detect ellipse in the image and perform the grouping process in order to reduce amount of information. As a result, the speed of the ellipse detection was improved. Also in order to the ellipse detection, we selected the five ellipse parameters at random And then to select the optimal parameters of the ellipse, the linear least-squares approximation is applied. To verify the ellipse detection, RANSAC algorithm is applied. After the algorithm proposed in this study was implemented, the results applied to the real images showed an aocuracy of 75% and speed was very fast to compared with other researches. It mean that the proposed algorithm was valuable to detect the ellipses in the image.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.274-279
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• 1998

For the accurate generation of DEMs, the determination of match quality in adaptive least square matching algorithm is significantly important. Traditionally, only the degree of convergence of a solution matrix in least squares estimation has been considered for the determination of match quality. It is, however, not enough to determine the true match quality. This paper reports two approaches of match quality determination based on adaptive least square correlation : the conventional if-then logic approaches with scene geometry and correlation as additional quality measures; and, the fuzzy logic approaches. Through these, accurate decision of match quality will minimize the number of blunder and maximize the number of exact match. The proposed methods have been tested on JERS and SPOT images and the results show good performance.

• Journal of the Korean Data and Information Science Society
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• v.21 no.1
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• pp.185-194
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• 2010

In recommender systems which are used widely at e-commerce, collaborative filtering needs the information of user-ratings and neighbor user-ratings. These are an important value for recommendation in recommender systems. We investigate the in-formation of rating in NBCFA (neighbor Based Collaborative Filtering Algorithm), we suggest new algorithm that improve prediction accuracy of recommender system. After we analyze relations between two variable and Error Value (EV), we suggest new algorithm and apply it to fitted line. This fitted line uses Least Squares Method (LSM) in Exploratory Data Analysis (EDA). To compute the prediction value of new algorithm, the fitted line is applied to experimental data with fitted function. In order to confirm prediction accuracy of new algorithm, we applied new algorithm to increased sparsity data and total data. As a result of study, the prediction accuracy of recommender system in the new algorithm was more improved than current algorithm.

• 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.

• Geophysics and Geophysical Exploration
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• v.10 no.2
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• pp.147-153
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• 2007

The conjugate gradient (CG) method is one of the most efficient algorithms for solving a linear system of equations. In addition to being used as a linear equation solver, it can be applied to a least-squares problem. When the CG method is applied to large-scale three-dimensional inversion of magnetotelluric data, two approaches have been pursued; one is the linear CG inversion in which each step of the Gauss-Newton iteration is incompletely solved using a truncated CG technique, and the other is referred to as the nonlinear CG inversion in which CG is directly applied to the minimization of objective functional for a nonlinear inverse problem. In each procedure we only need to compute the effect of the sensitivity matrix or its transpose multiplying an arbitrary vector, significantly reducing the computational requirements needed to do large-scale inversion.

• Geophysics and Geophysical Exploration
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• v.10 no.2
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• pp.111-123
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• 2007

Unlike the conventional reverse time migration method which is implemented by simply extrapolating wavefield in reverse time, this paper presents a derivation of another reverse time migration operator as the exact adjoint of the presumed forward wavefield extrapolation operator. The adjoint operator is obtained by formulating the forward time extrapolation operator in an explicit matrix equation form and then taking the adjoint to this matrix equation followed by determining the corresponding operator. The reverse time migration operator as the exact adjoint to the implied forward operator can be used not only as a migration algorithm but also as an adjoint operator which is required in the imaging through an inversion such as least-squares migration.

• Geophysics and Geophysical Exploration
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• v.10 no.2
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• pp.124-130
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• 2007

Least squares ($l^2$ norm) solutions of seismic inversion tend to be very sensitive to data points with large errors. The $l^1$ norm minimization gives more robust solutions, but usually with higher computational cost. Iteratively reweighted least squares (IRLS) method gives efficient approximate solutions of these $l^1$ norm problems. I propose an efficient implementation of the IRLS method for a hybrid $l^1/l^2$ minimization problem that behaves as $l^2$ norm fit for small residual and $l^1$ norm fit for large residuals. The proposed algorithm shows more robust characteristics to the decision of the threshold value than the l1 norm IRLS inversion does with respect to the threshold value to avoid singularity.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.689-692
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• 2003

In this paper, we propose an adaptive beamforming algorithm for array antenna. The proposed beamforming algorithm, based on Block LMS (Block - Least Mean Squares) algorithm, has a variable step size from coefficient update. This method shows some advantages that the convergence speed is fast and the calculation time can reduced using a block LMS algorithm from frequency domain. As the adaptive parameter approaches a stationary state, it could reduce the number of filter coefficient update with the help of various step size. In this paper we compared the efficiency of the proposed algorithm with a standard LMS algorithm which is a representative method of adaptive beamforming.