• Smart Structures and Systems
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• v.31 no.3
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• pp.229-245
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• 2023

The absence of excitation measurements may pose a big challenge in the application of structural damage identification owing to the fact that substantial effort is needed to reconstruct or identify unknown input force. To address this issue, in this paper, an iterative strategy, a synergy of Tikhonov regularization method for force identification and modified Jaya algorithm (M-Jaya) for stiffness parameter identification, is developed for damage identification with partial output-only responses. On the one hand, the probabilistic clustering learning technique and nonlinear updating equation are introduced to improve the performance of standard Jaya algorithm. On the other hand, to deal with the difficulty of selection the appropriate regularization parameters in traditional Tikhonov regularization, an improved L-curve method based on B-spline interpolation function is presented. The applicability and effectiveness of the iterative strategy for simultaneous identification of structural damages and unknown input excitation is validated by numerical simulation on a 21-bar truss structure subjected to ambient excitation under noise free and contaminated measurements cases, as well as a series of experimental tests on a five-floor steel frame structure excited by sinusoidal force. The results from these numerical and experimental studies demonstrate that the proposed identification strategy can accurately and effectively identify damage locations and extents without the requirement of force measurements. The proposed M-Jaya algorithm provides more satisfactory performance than genetic algorithm, Gaussian bare-bones artificial bee colony and Jaya algorithm.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.5
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• pp.322-331
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• 2003

Advances of high-definition television(HDTV) and personal computers call for the mutual conversion between interlaced signal and progressive signal. Especially, deinterlacing which is known as an interlaced to progressive conversion has been recently required and investigated. In this paper, we propose new deinterlacing algorithm considering sub-pixel motion information. In order to reduce the error of motion estimation, we analyze the effect of inaccurate sub-pixel motion information and model it as zero-mean Gaussian noises added respectively to each low resolution image(field). The error caused by inaccurate motion information is reduced by determining regularization parameter according to the error of motion estimation in each channel. The validity of the proposed algorithm is demonstrated both theoretically and experimentally in this paper.

• Proceedings of the KSEEG Conference
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• 2002.04a
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• pp.167-169
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• 2002

The extended Born, or localized nonlinear approximation of integral equation (IE) solution has been applied to inverting single-hole electromagnetic (EM) data using a cylindrically symmetric model. The extended Born approximation is less accurate than a full solution but much superior to the simple Born approximation. When applied to the cylindrically symmetric model with a vertical magnetic dipole source, however, the accuracy of the extended Born approximation is greatly improved because the electric field is scalar and continuous everywhere. One of the most important steps in the inversion is the selection of a proper regularization parameter for stability. Occam's inversion (Constable et al., 1987) is an excellent method for obtaining a stable inverse solution. It is extremely slow when combined with a differential equation method because many forward simulations are needed but suitable for the extended Born solution because the Green's functions, the most time consuming part in IE methods, are repeatedly re-usable throughout the inversion. In addition, the If formulation also readily contains a sensitivity matrix, which can be revised at each iteration at little expense. The inversion algorithm developed in this study is quite stable and fast even if the optimum regularization parameter Is sought at each iteration step. Tn this paper we show inversion results using synthetic data obtained from a finite-element method and field data as well.

• Journal of KIISE:Software and Applications
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• v.32 no.11
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• pp.1021-1028
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• 2005

The general solution for classification and regression problems can be found by matching and modifying matrices with the information in real world and then these matrices are teaming in neural networks. This paper treats primary space as a real world, and dual space that Primary space matches matrices using kernel. In practical study, there are two kinds of problems, complete system which can get an answer using inverse matrix and ill-posed system or singular system which cannot get an answer directly from inverse of the given matrix. Further more the problems are often given by the latter condition; therefore, it is necessary to find regularization parameter to change ill-posed or singular problems into complete system. This paper compares each performance under both classification and regression problems among GCV, L-Curve, which are well known for getting regularization parameter, and kernel methods. Both GCV and L-Curve have excellent performance to get regularization parameters, and the performances are similar although they show little bit different results from the different condition of problems. However, these methods are two-step solution because both have to calculate the regularization parameters to solve given problems, and then those problems can be applied to other solving methods. Compared with UV and L-Curve, kernel methods are one-step solution which is simultaneously teaming a regularization parameter within the teaming process of pattern weights. This paper also suggests dynamic momentum which is leaning under the limited proportional condition between learning epoch and the performance of given problems to increase performance and precision for regularization. Finally, this paper shows the results that suggested solution can get better or equivalent results compared with GCV and L-Curve through the experiments using Iris data which are used to consider standard data in classification, Gaussian data which are typical data for singular system, and Shaw data which is an one-dimension image restoration problems.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.28 no.10
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• pp.1271-1278
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• 2004

In order to measure non-intrusively velocity profile in liquid metal flow, a modified electromagnetic flowmeter was designed, which was based on electromagnetic tomography technique. Under the assumption that flow is fully-developed, axisymmetric and rectilinear, the velocity profile was reconstructed after the flowmeter equation, the first kind of Fredholm integration equation, was linearized. In reconstruction process Tikhonov regularization method with regularization parameter was used. The reconstructed velocity profile had the nearly same as turbulent flow profile which was approximately represented as log law. In addition, flowmeter output fur a fixed magnet rotation angle was linearly proportional to flow rate. When magnet rotation angle was 54$^{\circ}$, axisymmetric weight function was nearly uniform so that the flowmeter gives a constant signal for any fully-developed, axisymmetric and rectilinear profile with a constant flow rate.

• Proceedings of the KSME Conference
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• 2004.11a
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• pp.1288-1293
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• 2004

Inverse radiation problems are solved for estimating the boundary conditions such as temperature distribution and wall emissivity in axisymmetric absorbing, emitting and scattering medium, given the measured incident radiative heat fluxes. Various regularization methods, such as hybrid genetic algorithm, conjugate-gradient method and Newton method, were adopted to solve the inverse problem, while discussing their features in terms of estimation accuracy and computational efficiency. Additionally, we propose a new combined approach of adopting the genetic algorithm as an initial value selector, whereas using the conjugate-gradient method and Newton method to reduce their dependence on the initial value.

• Journal of the Korean Mathematical Society
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• v.54 no.2
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• pp.613-626
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• 2017

For the overdetermined linear system, when both the data matrix and the observed data are contaminated by noise, Total Least Squares method is an appropriate approach. Since an ill-conditioned data matrix with noise causes a large perturbation in the solution, some kind of regularization technique is required to filter out such noise. In this paper, we consider a Dual regularized Total Least Squares problem. Unlike the Tikhonov regularization which constrains the size of the solution, a Dual regularized Total Least Squares problem considers two constraints; one constrains the size of the error in the data matrix, the other constrains the size of the error in the observed data. Our method derives two nonlinear equations to construct the iterative method. However, since the Jacobian matrix of two nonlinear equations is not guaranteed to be nonsingular, we adopt a trust-region based iteration method to obtain the solution.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.29 no.8 s.239
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• pp.903-910
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• 2005

Inverse radiation problems are solved for estimating the boundary conditions such as temperature distribution and wall emissivity in axisymmetric absorbing, emitting and scattering medium, given the measured incident radiative heat fluxes. Various regularization methods, such as hybrid genetic algorithm, conjugate-gradient method and finite-difference Newton method, were adopted to solve the inverse problem, while discussing their features in terms of estimation accuracy and computational efficiency. Additionally, we propose a new combined approach that adopts the hybrid genetic algorithm as an initial value selector and uses the finite-difference Newton method as an optimization procedure.

• Journal of the Korean Mathematical Society
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• v.54 no.5
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• pp.1557-1571
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• 2017

The L-curve method is a parametric plot of interrelation between the residual norm of the least squares problem and the solution norm. However, the L-curve method may be hard to apply to the total least squares problem due to its no closed form solution of the regularized total least squares problems. Thus the sequence of the solution norm under the fixed regularization parameter and its corresponding residual need to be found with an efficient manner. In this paper, we suggest an efficient algorithm to find the sequence of the solutions and its residual in order to plot the L-curve for the total least squares problems. In the numerical experiments, we present that the proposed algorithm successfully and efficiently plots fairly 'L' like shape for some practical regularized total least squares problems.

• Communications for Statistical Applications and Methods
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• v.9 no.1
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• pp.87-100
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• 2002

Poorly-posed problems in the balanced discriminant analysis was considered. We restrict consideration to the case of observations and the number of variables are the same and small. When these problems exist, we do not use a maximum likelihood estimates(MLE) to estimate covariance matrices. Instead of MLE, an alternative estimate for the covariance matrices are proposed. This alternative method make good use of two regularization parameters, $\lambda$} and $\gamma$. A new test rule for the discriminant function is suggested and examined via limited hut informative simulation study. From the simulation study, it is shown that the suggested test rule gives better test result than other previously suggested method in terms of error rate criterion.