• Bulletin of the Korean Mathematical Society
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• v.44 no.1
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• pp.125-130
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• 2007

We propose a simple and intuitive method to derive the exact convergence rate of global $L_{2}-norm$ error for strong numerical approximation of stochastic differential equations the result of which has been reported by Hofmann and $M{\"u}ller-Gronbach\;(2004)$. We conclude that any strong numerical scheme of order ${\gamma}\;>\;1/2$ has the same optimal convergence rate for this error. The method clearly reveals the structure of global $L_{2}-norm$ error and is similarly applicable for evaluating the convergence rate of global uniform approximations.

• The Journal of the Acoustical Society of Korea
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• v.39 no.1
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• pp.32-37
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• 2020

This paper proposes a new l₁-norm-Recursive Least Squares (RLS) algorithm which is numerically more robust than the conventional l₁-norm-RLS. The l₁-norm-RLS was proposed by Eksioglu and Tanc in order to estimate the sparse acoustic channel. However the algorithm has numerical instability in the inverse matrix calculation. In this paper, we propose a new algorithm which is robust against the numerical instability. We show that the proposed method improves stability under several numerically erroneous situations.

• The Journal of the Acoustical Society of Korea
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• v.27 no.1
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• pp.26-32
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• 2008

UBSS (unde-determined blind source separation) is composed of the stages of BMMR (blind mixing matrix recovery) and BSR (blind source recovery). Generally, these two stages are executed using the sparseness of the observed data, and their performance is influenced by the accuracy of the measure of the sparseness. In this paper, as introducing the measure of the sparseness using the Gini coefficient to BSR stage, we obtained more accurate measure of the sparseness and better performance of BSR than methods using the $l_1$-norm, $l_q$-norm, and hyperbolic tangent, which was confirmed via computer simulations.

• Proceedings of the KSRS Conference
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• 2000.04a
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• pp.96-101
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• 2000

SAR(Synthetic Aperture Radar) interferometry 기술은 co-registration, 정밀궤도 계산, phase unwrapping, 지형보정과 같은 기술로 구성되어있다. 구속화된 위상값을 절대 위상값으로 변환하는 과정인 phase unwrapping 기술은 정밀지형고도를 얻는데 있어서 핵심기술이다. 본 연구에서는 JERS-1 SAR 영상으로부터 interferogram을 구하고, 이로부터 추출된 위상정보를 이용하여 branch cut(Goldstein et. al, 1988), minimum discontinuity(Flynn, 1997) 그리고 minimum $L^p$-norm(Ghiglia and Romero, 1996)방법 적용결과에 대한 비교 분석을 실시하였다. Goldstein 알고리즘은 수행속동가 매우 빠르지만 residue를 연결한 branch cut에 의해 분활된 영역 내에서, 서로 다른 적분 경로로 인해 위상이 단절되었다. 영상내의 모든 화소에서 절대 위상을 구한 minimum discontinuity와 minimum $L^p$-norm 알고리즘 수행 결과는 상관관계가 0.995로 매우 유사하였는데, 가중된 불연속선의 합을 최소화하는 minimum discontinuity 알고리즘이 minimum $L^p$-norm에 비해 영상 일부 지역에서 발생하는 위상 오차를 전파시키지 않는다는 장점이 있다. Minimum $L^p$-norm 방법은 다른 두 방법과 달리 위상정보 내에 많은 잡음이 있더라도 적절한 해를 구할 수 있다는 장점이 있다. 각 방법은 대상 자료의 특성에 따라 효율성이 있으나 Flynn의 알고리즘이 지역적 특성과 무관하게 가장 효과적임을 알 수 있었다.

• Journal of applied mathematics & informatics
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• v.5 no.3
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• pp.735-748
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• 1998

We analyze the error in the p version of the of the finite element method when the effect of the quadrature error is taken in the load vector. We briefly study some results on the $H^{1}$ norm error and present some new results for the error in the $L^{2}$ norm. We inves-tigate the quadrature error due to the numerical integration of the right hand side We present theoretical and computational examples showing the sharpness of our results.

• Proceedings of the KSEEG Conference
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• 2001.04a
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• pp.134-137
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• 2001

• Kyungpook Mathematical Journal
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• v.48 no.3
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• pp.387-393
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• 2008

In this note, we present some inequalities for the norm and numerical radius of 2-homogeneous polynomials from the 2-dimensional real space $l_p^2$, (1 < p < $\infty$) to itself in terms of their coefficients. We also give an upper bound for n^{(k)}(l_p^2), (k = 2, 3, $\cdots$).

• Journal of the Korean Statistical Society
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• v.16 no.2
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• pp.80-91
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• 1987

Minimum $L_i$ norm estimation is a robust procedure ins the sense that it leads to an estimator which has greater statistical eficiency than the least squares estimator in the presence of outliers. And the $L_1$ norm estimator has some desirable statistical properties. In this paper a new computational procedure for $L_1$ norm estimation is proposed which combines the idea of reweighted least squares method and the linear programming approach. A modification of the projective transformation method is employed to solve the linear programming problem instead of the simplex method. It is proved that the proposed algorithm terminates in a finite number of iterations.

• Journal of the Korean Mathematical Society
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• v.41 no.1
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• pp.21-38
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• 2004

We prove that a Banach space that is convex-transitive and such that for some element u in the unit sphere, and for every subspace Μ containing u, it happens that the subset of norm attaining functionals on Μ is second Baire category in $M^{*}$ is, in fact, almost-transitive and superreflexive. We also obtain a characterization of finite-dimensional spaces in terms of functions that attain their supremum: a Banach space is finite-dimensional if, for every equivalent norm, every rank-one operator attains its numerical radius. Finally, we describe the subset of norm attaining functionals on a space isomorphic to $\ell$$_1$, where the norm is the restriction of a Luxembourg norm on $L_1$. In fact, the subset of norm attaining functionals for this norm coincides with the subset of norm attaining functionals for the usual norm.m.

• The Korean Journal of Applied Statistics
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• v.28 no.1
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• pp.9-21
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• 2015

The support vector machine has been successfully applied to various classification areas due to its flexibility and a high level of classification accuracy. However, when analyzing imbalanced data with uneven class sizes, the classification accuracy of SVM may drop significantly in predicting minority class because the SVM classifiers are undesirably biased toward the majority class. The weighted $L_2$-norm SVM was developed for the analysis of imbalanced data; however, it cannot identify irrelevant input variables due to the characteristics of the ridge penalty. Therefore, we propose the weighted $L_1$-norm SVM, which uses lasso penalty to select important input variables and weights to differentiate the misclassification of data points between classes. We demonstrate the satisfactory performance of the proposed method through simulation studies and a real data analysis.