• Journal of applied mathematics & informatics
• /
• v.27 no.3_4
• /
• pp.673-683
• /
• 2009

We point out: to make Hermtian matrices A and B satisfy Styan matrix inequality, the condition "positive definite property" demanded in the present literatures is not necessary. Furthermore, on the premise of abandoning positive definite property, we derive Styan matrix inequality of Hadamard product for inverse Hermitian matrices and the sufficient and necessary conditions that the equation holds in our paper.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.15 no.5
• /
• pp.39-49
• /
• 2015

Some characters and construction theorems of Pseudo Jacket Matrix which is generalized from Jacket Matrix introduced by Jacket Matrices: Construction and Its Application for Fast Cooperative Wireless signal Processing[27] was announced. In this paper, we proposed some examples of Pseudo inverse Jacket matrix, such as $2{\times}4$, $3{\times}6$ non-square matrix for the MIMO channel. Furthermore we derived MIMO singular value decomposition (SVD) pseudo inverse channel and developed application to utilize SVD based on channel estimation of partitioned antenna arrays. This can be also used in MIMO channel and eigen value decomposition (EVD).

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.15 no.3
• /
• pp.25-33
• /
• 2015

As a reversible Jacket is having the compatibility of two sided wearing, the matrix that both the inside and the outside are compatible is called Jacket matrix, and the matrix is having both inside and outside by the processes of element-wise inverse and block-wise inverse. This concept had been completed by one of the authors Moon Ho Lee in 1989, and finally that resultant matrix has been christened as Jacket matrix, in 2000. This is the most generalized extension of the well known Hadamard matrices, which includes both orthogonal and non-orthogonal matrices. This matrix addresses many problems in information and communication theories. we investigate the properties of the Jacket matrix, i.e. determinants, eigenvalues, and kronecker product. These computations are very useful for signal processing and orthogonal codes design. In our proposal, we provide some results to calculate these values by using a very simple mathematical model with less complexity.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.20 no.10
• /
• pp.923-930
• /
• 2010

Finite element models of dynamic systems can be updated in two stages. In the first stage, mass and stiffness matrices are updated neglecting damping, and in the second stage, damping matrices are estimated with the mass and stiffness matrices fixed. Three methods to estimate damping matrices for this purpose are proposed in this paper. The methods include one for proportional damping systems and two for non-proportional damping systems. Method 1 utilizes orthogonality of normal modes and estimates damping matrices using the modal parameters extracted from the measured responses. Method 2 estimates damping matrices from impedance matrices which are the inverse of FRF matrices. Method 3 estimates damping using the equation which relates a damping matrix to the difference between the analytical and measured FRFs. The characteristics of the three methods are investigated by applying them to simulated discrete system data and experimental cantilever beam data.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.16 no.6
• /
• pp.319-327
• /
• 2016

In this paper we investigate that most of plants and animals have the symmetric property, such as a tree or a sheep's horn. In addition, the human body is also symmetric and contains the DNA. We can see the logarithm helices in Fibonacci series and animals, and helices of plants. The sunflower has a shape of circle. A circle is circular symmetric because the shapes are same when it is shifted on the center. Einstein's spatial relativity is the relation of time and space conversion by the symmetrically generalization of time and space conversion over the spacial. The left and right helices of plants and animals are the symmetric and have element-wise inverse relationships each other. The weight of center weight Hadamard matrix is 2 and is same as the base 2 of natural logarithm. The helix matrices are symmetric and have element-wise inverses.

• Proceedings of the Earthquake Engineering Society of Korea Conference
• /
• 2002.03a
• /
• pp.125-132
• /
• 2002

In this study, a numerical method is developed for nonlinear analysis for soil-pile-structure interaction system in time domain. Finite elements considering material nonlinearity are used for the near field and boundary elements for the far field. In the near field, frame elements are used for modeling a pile and plane-strain elements for surrounding soil and superstructure. In. the far field, boundary element formulation using the dynamic fundamental solution is adopted and coupled with the near field. Transformation of stiffness matrices of boundary elements into time domain is performed by inverse FFT. Stiffness matrices in the near field and far field are coupled. Newmark direct time integration method is applied. Developed soil-pile-structure interaction analysis method is verified with available literature and commercial code. Also, parametric studies by developed numerical method are performed. And seismic response analysis is performed using actual earthquake records.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2007.11a
• /
• pp.863-869
• /
• 2007

An improved method based on a normal frequency response function (FRF) is proposed to identify structural parameters such as mass, stiffness and damping matrices directly from the FRFs of a linear mechanical system. The method for estimating structural parameters directly from the measured FRFs of a structure is presented. This paper demonstrates that the characteristic matrices are extracted more accurately by using a weighted equation and eliminating the matrix inverse operation. The method is verified for a four degree-of-freedom lumped parameter system and an eight degree-of-freedom finite element beam. Experimental verification is also performed for a free-free steel beam whose size and physical properties are the same as those of the finite element beam. The results show that the structural parameters, especially the damping matrix, can be estimated more accurately by the proposed method.

• Proceedings of the KIEE Conference
• /
• 1995.07b
• /
• pp.729-732
• /
• 1995

This paper presents a Generalized Iteration (GI) which includes power method, inverse power method, shifted inverse power method, and Rayleigh quotient iteration (RQI), and modified RQI (MRQI). Furthermore, we propose a GI-based algorithm to find arbitrary eigenpairs for Hermitian matrices. The proposed algorithm appears to be much faster and more accurate than the valuable generalized MRQI of Hu (GMRQI-Hu). The idea of GI is also employed to speed up the GMRQI-Hu and we propose a modified version of Hu's GMRQI (GMRQI-Hu-mod) which is improved in the convergence rate. Some numerical simulation results are presented to confirm our contributions

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.46 no.5
• /
• pp.15-24
• /
• 2009

Compressed sensing addresses the recovery of a sparse vector from its few linear measurements. Recently, the success for the vector case has been extended to the matrix case. Compressed sensing of low-rank matrices solves the ill-posed inverse problem with fie low-rank prior. The problem can be formulated as either the rank minimization or the low-rank approximation. In this paper, we survey recently proposed efficient algorithms to solve these two formulations.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.19 no.10
• /
• pp.1021-1027
• /
• 2009

Finite element models of dynamic systems can be updated in two stages. In the first stage, mass and stiffness matrices are updated neglecting damping. In the second stage, a damping matrix is estimated with the mass and stiffness matrices fixed. Methods to estimate a damping matrix for this purpose are proposed in this paper. For a system with proportional damping, a damping matrix is estimated using the modal parameters extracted from the measured responses and the modal matrix calculated from the mass and stiffness matrices from the first stage. For a system with non-proportional damping, a damping matrix is estimated from the impedance matrix which is the inverse of the FRF matrix. Only one low or one column of the FRF matrix is measured, and the remaining FRFs are synthesized to obtain a full FRF matrix. This procedure to obtain a full FRF matrix saves time and effort to measure FRFs.