• 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.

• Journal of applied mathematics & informatics
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• v.18 no.1_2
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• pp.621-637
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• 2005

A hybrid method solving regularized structured linear total least norm (RSTLN) problems, which have highly ill-conditioned coefficient matrix with special structures, is suggested and analyzed. This scheme combining RSTLN algorithm and separation by parts guarantees the convergence of parameters and has an advantages in reducing the residual norm and relative error of solutions. Computational tests for problems arisen in signal processing and image formation process confirm that the presenting method is effective for more accurate solutions to (R)STLN problem than the (R)STLN algorithm.

• 한국지구물리탐사학회:학술대회논문집
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• 2005.05a
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• pp.227-232
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• 2005

Least squares (${\ell}^2-norm$) solutions of seismic inversion tend to be very sensitive to data points with large errors. The ${\ell}^p-norm$ minimization for $1{\le}p<2$ gives more robust solutions, but usually with higher computational cost. Iteratively reweighted least squares (IRLS) gives efficient approximate solutions of these ${\ell}^p-norm$ problems. I propose a simple way to implement the IRLS method for a hybrid ${\ell}^1/{\ell}^2$ minimization problem that behaves as ${\ell}^2$ fit for small residual and ${\ell}^1$ fit for large residuals. Synthetic and a field-data examples demonstrates the improvement of the hybrid method over least squares when there are outliers in the data.

• 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.

• Journal of applied mathematics & informatics
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• v.22 no.1_2
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• pp.373-385
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• 2006

To solve a class of nonlinear parameter estimation problems, a method combining the regularized structured nonlinear total least norm (RSNTLN) method and parameter separation scheme is suggested. The method guarantees the convergence of parameters and has an advantages in reducing the residual norm over the use of RSNTLN only. Numerical experiments for two models appeared in signal processing show that the suggested method is more effective in obtaining solution and parameter with minimum residual norm.

• Journal of applied mathematics & informatics
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• v.19 no.1_2
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• pp.151-164
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• 2005

The regularized structured total least norm (RSTLN) method finds an approximate solution x and error matrix E to the overdetermined linear system (H + E)x $\approx$ b, preserving structure of H. A new separation scheme by parts of variables for the regularized structured total least norm on blind deconvolution problem is suggested. A method combining the regularized structured total least norm method with a separation by parts of variables can be obtain a better approximated solution and a smaller residual. Computational results for the practical problem with Block Toeplitz with Toeplitz Block structure show the new method ensures more efficiency on image restoration.

• Journal of the Korean Institute of Landscape Architecture
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• v.30 no.6
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• pp.38-46
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• 2003

Perceived crowding is known as a necessary method to evaluate social carrying capacity in recreational settings. But according to the results of previous research, perceived crowding, use density, and satisfaction have shown weak and indirect correlations. The theory of visitors’ adjustment is one of several possible explanations for this poor relation. But the validity of the visitors’ adjustment theory has not been not inspected clearly. Therefore, the purposes of this study are to understand visitors’ adjustment theory and to examine visitors’ adjustment to the overuse of recreational settings. Study hypotheses were formulated through literature review and related to visitors’ adjustment in recreation density. Pour hypotheses were established and inspected with the case study, i.e., Rationalization : Visitors’ satisfaction isn't related to use density in recreation setting, 2) Product-shift : Preference norm is related to current use density, 3) Self-selection : Visitors’ satisfaction for the use level is generally high, and 4) Displacement : Norm interference is related to willingness to revisit. The case study was conducted during May and June,2001. According to the results of this survey, visitors adjust to overuse of recreation setting through rationalization and product shift (hypotheses l/2 acceptance). Current use density isn't related to visitors’ satisfaction and willingness to revisit (see table 3). And visitors’ preference norm is modified by situation (see table 4). Visitors’ satisfaction and willingness to revisit don't show a high correlation but moderately high (see table 5, hypothesis 3 acceptance). Differences between visitors’ preference norm and current use density is norm interference. Norm interference isn't related to willingness to revisit (see table 7). Therefore, the norm interference concept is not a useful method to explain visitors’ adjustment to the degree of overuse in a recreational setting (hypothesis 4 rejection). As for future directions, the following are proposed: 1) correctly understanding and reestablishing the visitor norm and norm interference concept, 2) introducing a composite research method to monitor visitors’ behavior and survey visitors’ attitudes and coping responses. These efforts would be helpful in the Planning and management of recreational settings to improve the quality of visitors’ experiences.

• Journal of the Chungcheong Mathematical Society
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• v.6 no.1
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• pp.25-43
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• 1993

This paper introduces a numerical method approximating the minimum norm solution to the first kind integral equation Kf = g with its kernel satisfying a certain property, where g belongs to the range space of K. Most of the existing expansion methods suffer from choosing a set of basis functions, whereas this method automatically provides an optimal set of basis functions approximating the minimum norm solution of Kf = g. Perturbation results and numerical experiments are also provided to analyze this method.

• Journal of IKEEE
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• v.20 no.3
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• pp.226-234
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• 2016

Electrical resistance tomography (ERT) is an imaging technique where the internal resistivity distribution inside an object is reconstructed. The ERT image reconstruction is a highly nonlinear ill-posed problem, so regularization methods are used to achieve desired image. The reconstruction outcome is dependent on the type of regularization method employed such as l2-norm, l1-norm, and total variation regularization method. That is, use of an appropriate regularization method considering the flow characteristics is necessary to attain good reconstruction performance. Therefore, in this paper, regularization methods are tested through numerical simulations with different flow conditions and the performance is compared.

• 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.