• Journal of the Chungcheong Mathematical Society
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• v.29 no.2
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• pp.267-281
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• 2016

In this paper, we consider multi-degree reduction of $B{\acute{e}}zier$ curves with continuity of any (r, s) order with respect to $L_2$ norm. With help of matrix theory about generalized inverses we can use Lagrange multipliers to obtain the degree reduction matrix in a very simple form as well as the degree reduced control points. Also error analysis comparing with the least squares degree reduction without constraints is given. The advantage of our method is that the relationship between the optimal multi-degree reductions with and without constraints of continuity can be derived explicitly.

• Journal of electromagnetic engineering and science
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• v.7 no.1
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• pp.17-27
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• 2007

Jacket matrices which are defined to be $n{\times}n$ matrices $A=(a_{jk})$ over a field F with the property $AA^+=nI_n$ where $A^+$ is the transpose matrix of elements inverse of A,i.e., $A^+=(a_{kj}^-)$, was introduced by Lee in 1984 and are used for signal processing and coding theory, which generalized the Hadamard matrices and Center Weighted Hadamard matrices. In this paper, some properties and constructions of Jacket matrices are extensively investigated and small orders of Jacket matrices are characterized, also present the full rate and the 1/2 code rate complex orthogonal space time code with full diversity.

• Journal of the Korea Society of Computer and Information
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• v.14 no.8
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• pp.11-18
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• 2009

This paper proposes the way of improving learning speed in Levenberg-Marquardt algorithm using the principal submatrix of Jacobian matrix. The Levenberg-Marquardt learning uses Jacobian matrix for Hessian matrix to get the second derivative of an error function. To make the Jacobian matrix an invertible matrix. the Levenberg-Marquardt learning must increase or decrease ${\mu}$ and recalculate the inverse matrix of the Jacobian matrix due to these changes of ${\mu}$. Therefore, to have the proper ${\mu}$, we create the principal submatrix of Jacobian matrix and set the ${\mu}$ as the eigenvalues sum of the principal submatrix. which can make learning speed improve without calculating an additional inverse matrix. We also showed that our method was able to improve learning speed in both a generalized XOR problem and a handwritten digit recognition problem.

• Journal of applied mathematics & informatics
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• v.20 no.1_2
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• pp.35-59
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• 2006

In this paper, we generalized the results of [23, 26], and get the results of the condition number of the W-weighted Drazin-inverse solution of linear system W AW\chi=b, where A is an $m{\times}n$ rank-deficient matrix and the index of A W is $k_1$, the index of W A is $k_2$, b is a real vector of size n in the range of $(WA)^{k_2}$, $\chi$ is a real vector of size m in the range of $(AW)^{k_1}$. Let $\alpha$ and $\beta$ be two positive real numbers, when we consider the weighted Frobenius norm $\|[{\alpha}W\;AW,\;{\beta}b]\|$(equation omitted) on the data we get the formula of condition number of the W-weighted Drazin-inverse solution of linear system. For the normwise condition number, the sensitivity of the relative condition number itself is studied, and the componentwise perturbation is also investigated.

• Structural Engineering and Mechanics
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• v.12 no.5
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• pp.527-540
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• 2001

A new sort of learning algorithm named whole learning algorithm is proposed to simulate the nonlinear and dynamic behavior of RC members for the estimation of structural integrity. A mathematical technique to solve the multi-objective optimization problem is applied for the learning of the feedforward neural network, which is formulated so as to minimize the Euclidean norm of the error vector defined as the difference between the outputs and the target values for all the learning data sets. The change of the outputs is approximated in the first-order with respect to the amount of weight modification of the network. The governing equation for weight modification to make the error vector null is constituted with the consideration of the approximated outputs for all the learning data sets. The solution is neatly determined by means of the Moore-Penrose generalized inverse after summarization of the governing equation into the linear simultaneous equations with a rectangular matrix of coefficients. The learning efficiency of the proposed algorithm from the viewpoint of computational cost is verified in three types of problems to learn the truth table for exclusive or, the stress-strain relationship described by the Ramberg-Osgood model and the nonlinear and dynamic behavior of RC members observed under an earthquake.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.9
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• pp.30-40
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• 2014

As the Hadamard transform can be generalized into the Jacket transform, in this paper, we generalize the Haar transform into the Jacket-Haar transform. The entries of the Jacket-Haar transform are 0 and ${\pm}2^k$. Compared with the original Haar transform, the basis of the Jacket-Haar transform is general and more suitable for signal processing. As an application, we present the DCT-II(discrete cosine transform-II) based on $2{\times}2$ Hadamard matrix and HWT(Haar Wavelete transform) based on $2{\times}2$ Haar matrix, analysis the performances of them and estimate them via the Lenna image simulation.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.11
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• pp.32-41
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• 1990

This paper proposes a method of using image features in serving a robot manipulator. Specifically, the con-cept 'feature' is first mathematically defined and then differential relationship between the robot motion and feature vector is derived in terms of Feature Jacobian Matrix and its generalized inverse. Also, by using more features than the number of DOFs of the robot, the visual servoing performance is shown to be improv-ed. Via various examples, the method of feature-based servoing of a robot proposed in this paper is proved to be effective for conducting object-oriented robotic tasks.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.42 no.8 s.338
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• pp.1-10
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• 2005

In this paper, the explicit low density codes construction from the generalized permutation matrices related to algebra theory is investigated, and we design several Jacket inverse block matrices on the recursive formula and permutation matrices. The results show that the proposed scheme is a simple and fast way to obtain the low density codes, and we also Proved that the structured low density parity check (LDPC) codes, such as the $\pi-rotation$ LDPC codes are the low density Jacket inverse block matrices too.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1997.10a
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• pp.262-269
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• 1997

The purpose of this study is to survey the shape finding of the plane truss structures under the prescribed displacement mode by the shape analysis. The shape analysis is peformed by the existence condition of a solution and Moore-Penrose generalized inverse matrix, and the prescribed displacement mode is the homologous deformation of structures. The shape analysis of structures is a kind of inverse problem and become the problem of a nonlinear equation. Newton-Raphson method is used to improve the accuracy of approximated solution. To prove the accuracy and the effectiveness of this method, four different shape examples are analyzed.

• The Journal of the Acoustical Society of Korea
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• v.14 no.3
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• pp.37-48
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• 1995

This paper presents an sstimation of ARMA coefficients of noisy ARMA system using generalized total least square (GTLS) method. GTLS problem for ARMA system is defined as minimizing the errors between the noisy output vectors and estimated noisy-free output. The GTLS problem is solved in closed form by eigen-problem and the perturbation analysis of GTLS is presented. Also its recursive solution (recursive GTLS) is proposed using the power method and the covariance formula of the projected output error vector into the input vector space. The simulation results show that GTLS ARMA coefficients estimator is an unbiased estimator and that recursive GTLS achieves fast convergence.