• Journal of applied mathematics & informatics
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• v.7 no.1
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• pp.61-82
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• 2000

In this paper two so-called regularized Green's functions are introduced to derive the optimal maximum norm error estimates for the unknown function and the adjoint vector-valued function for mixed finite element methods of Laplacian operator. One contribution of the paper is a demonstration of how the boundedness of $L^1$-norm estimate for the second Green's function ${\lambda}_2$ and the optimal maximum norm error estimate for the adjoint vector-valued function are proved. These results are seemed to be to be new in the literature of the mixed finite element methods.

• Steel and Composite Structures
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• v.28 no.3
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• pp.279-288
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• 2018

In this paper, a new method has been proposed to detect crack in beam structures under moving mass using regularized extreme learning machine. For this purpose, frequencies of beam under moving mass used as input to train machine. This data is acquired by the analysis of cracked structure applying the finite element method (FEM). Also, a validation study used for verification of the FEM. To evaluate performance of the presented method, a fixed simply supported beam and two span continuous beam are considered containing single or multi cracks. The obtained results indicated that this method can provide a reliable tool to accurately identify cracks in beam structures under moving mass.

• Management Science and Financial Engineering
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• v.12 no.2
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• pp.71-85
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• 2006

Collaborative filtering, among other recommender systems, has been known as the most successful recommendation technique. However, it requires the user-item rating data, which may not be easily available. As an alternative, some collaborative filtering algorithms have been developed recently by utilizing the market basket data in the form of the binary user-item matrix. Viewing the recommendation scheme as a two-class classification problem, we proposed a new collaborative filtering scheme using a regularized discriminant analysis applied to the binary user-item data. The proposed discriminant model was built in terms of the major principal components and was used for predicting the probability of purchasing a particular item by an active user. The proposed scheme was illustrated with two modified real data sets and its performance was compared with the existing user-based approach in terms of the recommendation precision.

• Journal of the Korean Mathematical Society
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• v.54 no.5
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• pp.1557-1571
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• 2017

The L-curve method is a parametric plot of interrelation between the residual norm of the least squares problem and the solution norm. However, the L-curve method may be hard to apply to the total least squares problem due to its no closed form solution of the regularized total least squares problems. Thus the sequence of the solution norm under the fixed regularization parameter and its corresponding residual need to be found with an efficient manner. In this paper, we suggest an efficient algorithm to find the sequence of the solutions and its residual in order to plot the L-curve for the total least squares problems. In the numerical experiments, we present that the proposed algorithm successfully and efficiently plots fairly 'L' like shape for some practical regularized total least squares problems.

• Annual Conference of KIPS
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• 2016.10a
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• pp.569-570
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• 2016

본 논문은 뇌파 기반 감정 분류 기술의 상용화를 위한 낮은 성능을 보완하기 위하여 Regularized Common Spatial Pattern 필터링을 통한 전처리 방식을 제안하고 있다. RCSP 필터는 뇌파 기반 행동 인식 시스템에서 높은 성능 향상을 보이는 것으로 알려져 있다. 본 연구에서는 장기적이고 복합적인 뇌파의 감성 인지 연구에도 RCSP 필터의 적용 방법을 설명하고, 제안하는 알고리즘이 뇌파를 통한 감정 인식에 성능 향상을 보여준다는 것을 설명하고 있다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.9C
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• pp.743-747
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• 2010

This paper presents an iterative adaptive hybrid image restoration algorithm for fast convergence. The local variance, mean, and maximum value are used to constrain the solution space. These parameters are computed at each iteration step using partially restored image at each iteration, and they are used to impose the degree of local smoothness on the solution. The resulting iterative algorithm exhibits increased convergence speed and better performance than typical regularized constrained least squares (RCLS) approach.

• Journal of Information Display
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• v.5 no.4
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• pp.31-37
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• 2004

In this paper, an effective method for reconstruction of stereoscopic image pair through the regularized adaptive disparity estimation is proposed. Although the conventional adaptive disparity window matching can sharply improve the PSNR of a reconstructed stereo image, but there still exist some problems of overlapping between the matching windows and disallocation of the matching windows, because the size of the matching window tend to changes adaptively in accordance with the magnitude of the feature values. In the proposed method, the problems relating to the conventional adaptive disparity estimation scheme can be solved and the predicted stereo image can be more effectively reconstructed by regularizing the extimated disparity vector with the neighboring disparity vectors. From the experimental results, it is found that the proposed algorithm show improvements the PSNR of the reconstructed right image by about 2.36${\sim}$2.76 dB, on average, compared with that of conventional algorithms.

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.1059-1066
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• 2016

In this paper we propose a robust version of varying coefficient models, which is based on the regularized regression with L1 regularization. We use the iteratively reweighted least squares procedure to solve L1 regularized objective function of varying coefficient model in locally weighted regression form. It provides the efficient computation of coefficient function estimates and the variable selection for given value of smoothing variable. We present the generalized cross validation function and Akaike information type criterion for the model selection. Applications of the proposed model are illustrated through the artificial examples and the real example of predicting the effect of the input variables and the smoothing variable on the output.

• East Asian mathematical journal
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• v.32 no.1
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• pp.129-138
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• 2016

In this paper, we propose an efficient regularization technique (The Method of Regularized Ratios) for the reconstruction of the shape of a rigid elastic scatterer from far field measurements. The approach used is based on the factorization method and creates via Picard's condition ratios, baptized Regularized Ratios, that serve to effectively remove unwanted singular values that may lead to poor reconstructions. This is achieved through the use of a sophisticated algorithm that progressively adjusts an initially set moderate tolerance. In comparison with the well established Tikhonov-Morozov regularization techniques our new algorithm appears to be more computationally efficient as it doesn't require computation of the regularization parameter for each point in the grid.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.11A
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• pp.1889-1896
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• 2001

This paper proposes the hierachically regularized motion estimation technique for the efficient and accurate motion estimation. To use hierachical technique increases the reliability of motion vectors. And the regularization of neighbor vectors decreases bit rate of motion vectors. Also, using fast motion estimation algorithm with a few candidate vectors, the processing time added by regularization can be decreased. In the result of the experiment, the fast motion estimation with hierachical regularization technique achieves less computations and decreases estimation and distribution of false vectors.