• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.6
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• pp.1-8
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• 2007

In this paper, research on joint optimization of the image spatial registration and the exposure compensation is conducted. The exposure compensation is performed in a frame work of the intensity compensation based on the polynomial approximation of the relationship between images. This compensation is jointly combined with the registration problem employing the Gauss-Newton nonlinear optimization method. In this paper, to perform for a simple and stable optimization, the block-coordinate method is combined with the Gauss-Newton optimization and extensively compared with the traditional approaches. Furthermore, regression analysis is considered in the compensation part for a better stable performance. By combining the block-coordinate method with the Gauss-Newton optimization, we can obtain a compatible performance reducing the computational complexity and stabilizing the performance. In the numerical result for a particular image, we obtain a satisfactory result for 10 repeats of the iteration, which implies a 50% reduction of the computational complexity. The error is also further reduced by 1.5dB compared to the ordinary method.

• Journal of IKEEE
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• v.19 no.2
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• pp.219-224
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• 2015

Electrical impedance tomography is an imaging technique to reconstruct the internal conductivity distribution based on applied currents and measured voltages in a domain of interest. In this paper, a modified Gauss-Newton method is proposed for conductivity image reconstruction. In the proposed method, the dimension of the inverse term is reduced by replacing the number of elements with the number of measurement data in the conductivity updating equation of the conventional Gauss-Newton method. Therefore, the computation time is greatly reduced as compared to the conventional Gauss-Newton method. Moreover, the regularization parameter is selected by computing the minimum-maximum from the diagonal components of the Jacobian matrix at every iteration. The numerical experiments with several scenarios were carried out to evaluate the reconstruction performance of the proposed method.

• Proceedings of the ESK Conference
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• 1997.10a
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• pp.463-467
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• 1997

인간에게 운동감을 적절히 제시해주기 위하여는 Newton에 의한 운동의 세가지 법칙뿐만 아니라 EInstein의 상대성이론이 첨가되어야 한다. 즉, Newton운동의 제1법칙에 의하여 피실험자가 외력을 받지 않으면 등속운동 또는 정지상태를 계속 유지하게 되어 자신이 등속좌표계에 고정되어있기 때문에 시각적 인 정보가 없으면 어떠한 운동감도 못 느낀다. 이때 피실험자에게 정지해있는 기준좌표계에 대하여 등속 으로 움직이는 것을 인식시켜주기 위하여 피실험자에 대한 기준좌표계의 상대속도를 시각정보로 제공해 주어야 한다. 또한 Newton운동의 제2법칙에 의하여 똑같은 힘이 외력으로 작용하더라도 피실험자의 질량과 가속도는 서로 반비례하므로 화면이동속도변화를 피실험자의 질량에 반비례하도록 제시해 주어야 한다(김 정흠, 1982). 본 연구에서는 이러한 개념에 근거하여, 체중이 다른 여섯 피실험자들로 구성된 시스템에 대해서 각 피실험자에게 서로 다른 변위를 주고자할 때, 여섯가지 외력에 요구되는 작용시간을 Jacobi Iteration 방법과 Gauss-Seidel Iteration 방법으로 구하는 알고리즘을 제시하였다(D.V. Griffiths and I.M. Smith, 1991).

• Journal of IKEEE
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• v.19 no.1
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• pp.33-40
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• 2015

Inverse problem in electrical impedance tomography (EIT) is highly ill-posed therefore prior information is used to mitigate the ill-posedness. Regularization methods are often adopted in solving EIT inverse problem to have satisfactory reconstruction performance. In solving the EIT inverse problem, iterative Gauss-Newton method is generally used due to its accuracy and fast convergence. However, its performance is still suboptimal and mainly depends on the selection of regularization parameter. Although, there are few methods available to determine the regularization parameter such as L-curve method they are sometimes not applicable for all cases. Moreover, regularization parameter is a scalar and it is fixed during iteration process. Therefore, in this paper, a novel method is used to determine the regularization parameter to improve reconstruction performance. Conductivity norm is calculated at each iteration step and it used to obtain the regularization parameter which is a diagonal matrix in this case. The proposed method is applied to human thorax imaging and the reconstruction performance is compared with traditional methods. From numerical results, improved performance of proposed method is seen as compared to conventional methods.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.10a
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• pp.85-86
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• 2006

• Computational Structural Engineering
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• v.10 no.2
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• pp.139-148
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• 1997

A finite element method is programmed to analyse the nonlinear behavior of axisymmetric structures. The lst order Mindlin shell theory which takes into account the transversal shear deformation is used to formulate a conical two node element with six degrees of freedom. To evade the shear locking phenomenon which arises in Mindlin type element when the effect of shear deformation tends to zero, the reduced integration of one point Gauss Quadrature at the center of element is employed. This method is the Updated Lagrangian formulation which refers the variables to the state of the most recent iteration. The solution is searched by Newton-Raphson iteration method. The tangent matrix of this method is obtained by a finite difference method by perturbating the degrees of freedom with small values. For the moment this program is limited to the analyses of non-linear elastic problems. For structures which could have elastic stability problem, the calculation is controled by displacement.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.12
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• pp.3007-3019
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• 1993

For the same configuration, the stiffness of 6-node two dimensional isoparametric is stiffer than that of 8-node two dimensional isoparametric element. This phenomenon may be called 'Relative Stiffness Stiffening Phenomenon.' In this paper, the relative stiffness stiffening phenomenon was studied, and could be corrected by modifying the position of Gauss integral points used in the numerical integration of the stiffness matrix. For the same deformation (bending) energy of 6-node and 8-node two dimensional isoparametric elements, Gauss integral points of 6-node element have to move closer, in comparison with those of 8-node element, in the case of numerical integration along the thickness direction.

• Transactions of the Korean Society of Mechanical Engineers
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• v.12 no.4
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• pp.642-651
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• 1988

The present paper develops finite element procedures to calculate displacements, strains and stresses in initially stressed elastic solids subjected to static or time-dependent loading conditions. As a point of departure, we employ Hamilton's principle to obtain nonlinear equations of motion characterizing the displacement in a solid. The equations of motion reduce to linear equations of motion if incremental stresses are assumed to be infinitesimal. In the case of linear problem, finite element solutions are obtained by Newmark's direct integration method and by modal analysis. An analytic solution is referred to compare with the linear finite element solution. In the case of nonlinear problem, finite element solutions are obtained by Newton-Raphson iteration method and compared with the linear solution. Finally, the effect of the order of Gauss-Legendre numerical integration on the nonlinear finite element solution, has been investigated.

• Geophysics and Geophysical Exploration
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• v.7 no.3
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• pp.207-212
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• 2004

This article reviews recent developments in three-dimensional (3-D) magntotelluric (MT) imaging. The inversion of MT data is fundamentally ill-posed, and therefore the resultant solution is non-unique. A regularizing scheme must be involved to reduce the non-uniqueness while retaining certain a priori information in the solution. The standard approach to nonlinear inversion in geophysis has been the Gauss-Newton method, which solves a sequence of linearized inverse problems. When running to convergence, the algorithm minimizes an objective function over the space of models and in the sense produces an optimal solution of the inverse problem. The general usefulness of iterative, linearized inversion algorithms, however is greatly limited in 3-D MT applications by the requirement of computing the Jacobian(partial derivative, sensitivity) matrix of the forward problem. The difficulty may be relaxed using conjugate gradients(CG) methods. A linear CG technique is used to solve each step of Gauss-Newton iterations incompletely, while the method of nonlinear CG is applied directly to the minimization of the objective function. These CG techniques replace computation of jacobian matrix and solution of a large linear system with computations equivalent to only three forward problems per inversion iteration. Consequently, the algorithms are efficient in computational speed and memory requirement, making 3-D inversion feasible.

• The Pure and Applied Mathematics
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• v.2 no.2
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• pp.133-140
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• 1995

The D-optimal design criterion for precise parameter estimation in nonlinear regression analysis is called the determinant criterion because the determinant of a matrix is to be maximized. In this thesis, we derive the gradient and the Hessian of the determinant criterion, and apply a QR decomposition for their efficient computations. We also propose an approximate form of the Hessian matrix which can be calculated from the first derivative of a model function with respect to the design variables. These equations can be used in a Gauss-Newton type iteration procedure.