• 대한전자공학회논문지
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• 제25권12호
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• pp.1532-1540
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• 1988

Shape from shading problem, such as other most early visions, is ill-posed problems, which can be solved by the use of regularization methods. This paper proposes the three kinds of stabilizer for the regularization. These are integrability constraints and spline functionals. Parallel iterative schemes are derived in the form of the finite difference approximation. Experimental results, show that the average error in surface orientation is less than 5%.

• 한국통신학회논문지
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• 제26권11A호
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• pp.1889-1896
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• 2001

본 논문에서는 보다 효율적이고 정확한 움직임 벡터를 추정하기 위하여 계층적 평활화 방법(hierachical regularization technique)을 이용한 움직임 추정 알고리듬을 제안한다. 계층적 평활화 기법을 이용하여 움직임 벡터들의 신뢰도를 증가시켰고, 주위 벡터와의 평활화를 통해 움직임 벡터들의 비트량을 감소시켰다. 또한 적은 후보 벡터를 이용하여 움직임 벡터를 예측하는 고속 움직임 추정 알고리듬을 적용하여 평활화 과정의 추가로 인해 생기는 많은 연산량을 감소시켰다. 실험 결과 제안한 계층적 평활화 방법을 이용한 고속 움직인 추정 알고리듬은 전방향 탐색(full search) 알고리듬과 비교하여 비슷한 영상 화질에서 많은 연산량 감소를 얻을 수 있었으며 잘못된 벡터의 추정 및 확산을 줄일 수 있음을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권11호
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• pp.5481-5495
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• 2018

Single pixel imaging technology has developed for years, however the video acquisition on the single pixel camera is not a well-studied problem in computer vision. This work proposes a new scheme for single pixel camera to acquire video data and a new regularization for robust signal recovery algorithm. The method establishes a single pixel video compressive sensing scheme to reconstruct the video clips in spatial domain by recovering the difference of the consecutive frames. Different from traditional data acquisition method works in transform domain, the proposed scheme reconstructs the video frames directly in spatial domain. At the same time, a new regularization called spatial cluster is introduced to improve the performance of signal reconstruction. The regularization derives from the observation that the nonzero coefficients often tend to be clustered in the difference of the consecutive video frames. We implement an experiment platform to illustrate the effectiveness of the proposed algorithm. Numerous experiments show the well performance of video acquisition and frame reconstruction on single pixel camera.

• 대한수학회논문집
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• 제11권4호
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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.

• Journal of applied mathematics & informatics
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• 제35권3_4호
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• pp.399-411
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• 2017

We first provide the linear operator equations corresponding to the Tikhonov regularization image denoising problems with different regularization terms, and then we propose how to choose Kronecker product preconditioners which are required for accelerating the $G{\ell}$-PCG method. Next, we provide how to apply the $G{\ell}$-PCG method with Kronecker product preconditioner to the linear operator equations. Lastly, we provide numerical experiments for image denoisng problems to evaluate the effectiveness of the $G{\ell}$-PCG with Kronecker product preconditioner.

• 에너지공학
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• 제10권3호
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• pp.183-187
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• 2001

Impedance imaging for binary mixture is a kind of nonlinear inverse problem, which is usually solved iteratively by the Newton-Raphson method. Then, the ill-posedness of Hessian matrix often requires the use of a regularization method to stabilize the solution. In this study, the Levenberg-Marquredt regularization method is introduced for the binary-mixture system with various resistivity contrasts (1:2∼1:1000). Several mixture distribution are tested and the results show that the Newton-Raphson iteration combined with the Levenberg-Marquardt regularization can reconstruct reasonably good images.

• 한국해양공학회지
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• 제22권4호
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• pp.1-7
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• 2008

The primary aim of the paper is to solve an unstable axisymmetric initial value problem of wave propagation when given initial data that is deteriorated by noise such as measurement error. To overcome the instability of the problem, Tikhonov's regularization, known as a non-iterative numerical regularization method, is introduced to solve the problem. The L-curvecriterion is introduced to find the optimal regularization parameter for the solution. It is confirmed that fairly stable solutions are realized and that they are accurate when compared to the exact solution.

• 대한기계학회논문집B
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• 제28권9호
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• pp.1124-1132
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• 2004

The inverse problem of determining unknown inlet temperature in thermally developing, hydrodynamically developed two phase laminar flow in a parallel plate duct is considered. The inlet temperature profile is determined by measuring temperature in the flow field. No prior information is needed for the functional form of the inlet temperature profile. The inverse convection problem is solved by minimizing the objective function with regularization method. The conjugate gradient method as iterative method and the Tikhonov regularization method are employed. The effects of the functional form of inlet temperature, the number of measurement points and the measurement errors are investigated. The accuracy and efficiency of these two methods are compared and discussed.

• 산업공학
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• 제24권2호
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• pp.151-155
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• 2011

In this study, we propose a signomial classification method with ₀-regularization (₀-)which seeks a sparse signomial function by solving a mixed-integer program to minimize the weighted sum of the ₀-norm of the coefficient vector of the resulting function and the $L_1$-norm of loss caused by the function. $SC_0$ gives an explicit description of the resulting function with a small number of terms in the original input space, which can be used for prediction purposes as well as interpretation purposes. We present a practical implementation of $SC_0$ based on the mixed-integer programming and the column generation procedure previously proposed for the signomial classification method with $SL_1$-regularization. Computational study shows that $SC_0$ gives competitive performance compared to other widely used learning methods for classification.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2003년도 가을 학술발표회 논문집
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• pp.340-347
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• 2003

The aim of regularization of a structural configuration is to obtain a structure that consists of elements with identical or nearly identical length. And it is also possible to modify the configuration in a manner that the size of the elements vary in accordance with a specified pattern. For practical purpose, geodesic dome is cut off at a suitable place in order to make it fit on horizontal. Inevitably this pattern effects a change of element lengths. The purpose of this study is to verify a method for regularization of structural configuration by genetic algorithms and modify the element lengths of the dome. As a result of regularization of domes with various rise-span ratio, modified configurations have more regular element lengths and are more economical than initial configurations.