• Communications of the Korean Mathematical Society
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• v.11 no.4
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• pp.1147-1157
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• 1996

Nonlinear ill-posed problems arise in many application including parameter estimation and inverse scattering. We introduce a least squares regularization method to solve nonlinear ill-posed problems with constraints robustly and efficiently. The regularization method uses Trust-Region approach to handle the constraints on variables. The Generalized Cross Validation is used to choose the regularization parameter in computational tests. Numerical results are given to exhibit faster convergence of the method over other methods.

• Communications for Statistical Applications and Methods
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• v.21 no.5
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• pp.395-409
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• 2014

Data limited, partial, or incomplete are known as an ill-posed problem. If the data with ill-posed problems are analyzed by traditional statistical methods, the results obviously are not reliable and lead to erroneous interpretations. To overcome these problems, we propose a dual generalized maximum entropy (dual GME) estimator for panel data regression models based on an unconstrained dual Lagrange multiplier method. Monte Carlo simulations for panel data regression models with exogeneity, endogeneity, or/and collinearity show that the dual GME estimator outperforms several other estimators such as using least squares and instruments even in small samples. We believe that our dual GME procedure developed for the panel data regression framework will be useful to analyze ill-posed and endogenous data sets.

• Journal of applied mathematics & informatics
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• v.29 no.3_4
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• pp.909-920
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• 2011

A regularized Newton method for nonlinear ill-posed problems is considered. In each Newton step an implicit iterative method with an appropriate stopping rule is proposed and analyzed. Under certain assumptions on the nonlinear operator, the convergence of the algorithm is proved and the algorithm is stable if the discrepancy principle is used to terminate the outer iteration. Numerical experiment shows the effectiveness of the method.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.9
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• pp.37-49
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• 1992

The standard definition of computational vision is a set of inverse problems of recovering surfaces from images. Thus the common characteristics of the most early vision problems are ill-posed. The main idea for solving ill-posed problems is to restrict the class of admissible solutions by introducing suitable a priori knowledge. Standard regurarization methods lead to satisfactory solutions of early vision problems but cannot deal effectively and directly with a few general problems, such as discontinuity and fusion of information from multiple modules. In this paper, we discuss limitations of standard regularization theory and present new stochastic method. We will outline a rigorous approach to overcome part of ill-posedness of image restoration, edge detection, and stereo vision problems, based on Bayes estimation and MRF(Markov random field) model, that effectively deals with the problems. This result makes one hope that this framework could be useful in the solution of other vision problems.

• Communications for Statistical Applications and Methods
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• v.16 no.4
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• pp.659-667
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• 2009

Recently Song and Cheon (2006) and Cheon and Lim (2009) developed the generalized maximum entropy(GME) estimator to solve ill-posed problems for the regression coefficients in the simple panel model. The models discussed consider the individual and a spatial autoregressive disturbance effects. However, in many application in economics the data may contain nested groupings. This paper considers a two-way error component model with nested groupings for the ill-posed data and proposes the GME estimator of the unknown parameters. The performance of this estimator is compared with the existing methods on the simulated dataset. The results indicate that the GME method performs the best in estimating the unknown parameters in terms of its quality when the data are ill-posed.

• International Journal of Air-Conditioning and Refrigeration
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• v.8 no.2
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• pp.11-22
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• 2000

Holographic interferometric tomography can provide reconstruction of instantaneous three-dimensional gross flow fields. The technique however confronts ill-posed reconstruction problems in practical applications. Experimental data are usually limited in projection and angular scanning when a field is captured instantaneously or under the obstruction of test models and test section enclosures. An algorithm, based on a series expansion method, has been developed to improve the reconstruction under the ill-posed conditions. A three-dimensional natural convection flow around two interacting isothermal cubes is experimentally investigated. The flow can provide a challenging reconstruction problem and lend itself to accurate numerical solution for comparison. The refractive index fields at two horizontal sections of the thermal plume with and without an opaque object are reconstructed at a limited view angle of 80$\circ$. The experimental reconstructions are then compared with those from numerical calculation and thermocouple thermometry. It confirms that the technique is applicable to reconstruction of reasonably complex, three-dimensional flow fields.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.11 no.6
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• pp.749-757
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• 1999

Holographic interferornetric tomography can provide reconstruction of instantaneous three dimensional gross flow fields. The technique however confronts ill-posed reconstruction problems in practical applications. Experimental data are usually limited in projection and angular scanning when a field is captured instantaneously or under the obstruction of test models and test section enclosures. An algorithm, based on a series expansion method, has been developed to improve the reconstruction under the ill-posed conditions. A three-dimensional natural convection flow around two interacting isothermal cubes is experimentally investigated. The flow can provide a challenging reconstruction problem and lend itself to accurate numerical solution for comparison. The refractive index fields at two horizontal sections of the thermal plume with and without an opaque object are reconstructed at a limited view angle of 80" The experimental reconstructions are then compared with those from numerical calculation and thermocouple thermometry. It confirms that the technique is applicable to reconstruction of reasonably complex, three-dimensional flow fields.elds.

• Communications for Statistical Applications and Methods
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• v.23 no.1
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• pp.71-83
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• 2016

In this paper we discuss a deconvolution density estimator obtained using the support vector machines (SVM) and Tikhonov's regularization method solving ill-posed problems in reproducing kernel Hilbert space (RKHS). A remarkable property of SVM is that the SVM leads to sparse solutions, but the support vector deconvolution density estimator does not preserve sparsity as well as we expected. Thus, in section 3, we propose another support vector deconvolution estimator (method II) which leads to a very sparse solution. The performance of the deconvolution density estimators based on the support vector method is compared with the classical kernel deconvolution density estimator for important cases of Gaussian and Laplacian measurement error by means of a simulation study. In the case of Gaussian error, the proposed support vector deconvolution estimator shows the same performance as the classical kernel deconvolution density estimator.

• Journal of applied mathematics & informatics
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• v.27 no.3_4
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• pp.621-638
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• 2009

The linear system Ax = b will have (i) no solution, (ii) only one non-trivial (trivial) solution, or (iii) infinity of solutions. Our focus will be on cases (ii) and (iii). The mathematical models of many real-world problems give rise to (a) ill-conditioned linear systems, (b) singular linear systems (A is singular with all its linearly independent rows are sufficiently linearly independent), or (c) ill-conditioned singular linear systems (A is singular with some or all of its strictly linearly independent rows are near-linearly dependent). This article highlights the scope and need of a randomized algorithm for ill-conditioned/singular systems when a reasonably narrow domain of a solution vector is specified. Further, it stresses that with the increasing computing power, the importance of randomized algorithms is also increasing. It also points out that, for many optimization linear/nonlinear problems, randomized algorithms are increasingly dominating the deterministic approaches and, for some problems such as the traveling salesman problem, randomized algorithms are the only alternatives.

• Journal of Korea Society of Industrial Information Systems
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• v.9 no.2
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• pp.59-64
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• 2004

Most features of nature and phenomena we encounter in many branches of science are inherently very irregular and have fractal aspects. Thus the analysis of them with the traditional methods such as a differential operator may result in their ill-posed problems. To settle these problems, one may use several type of mean filters which smooth the input signal. However when a given function or data are complex in their nature, there may be loss of some original information during these process. In this paper, we utilized the tangent plane method instead of mean filters for the purpose of less loss of information and more smoothness. After then we attempt to take more accurate edges for the irregular image on the basis of the Otzu threshold. Finally we introduce the effective edge extracting method which use the fractal dimension representing the complexity of the given image.