• Advanced Composite Materials
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• v.17 no.1
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• pp.75-87
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• 2008

Aerospace structural applications, along with high performance marine and automotive applications, require high-strength efficiency, which can be achieved using metal matrix composites (MMCs). Rotating components, such as jet-engine blades and gas turbine parts, require materials that maximize strength efficiency and metallurgical stability at elevated temperatures. Titanium matrix composites (TMCs) are well suited in such applications, since they offer an enhanced resistance to temperature effects as well as corrosion resistance, in addition to optimum strength efficiency. The overall behavior of the composite system largly depends on the properties of the interface between fiber and matrix. Characterization of the fiber.matrix interface at operating temperatures is therefore essential for the developemt of these materials. The fiber fragmentation test shows good reproducibility of results in determining interface properties. This paper deals with the evaluation of fiber fragmentation characteristics in TMCs at elevated temperature and the results are compared with tests at ambient temperature. It was observed that tensile testing at $650^{\circ}C$ of single-fiber TMCs led to limited fiber fragmentation behavior. This indicates that the load transfer from the matrix to the fiber occurs due to interfacial friction, arising predominantly from mechanical clamping of the fiber by radial compressive residual and Poisson stresses. The present work also demonstrates that composite processing conditions can significantly affect the nature of the fiber.matrix interface and the resulting fragmentation of the fiber.

• Tuberculosis and Respiratory Diseases
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• v.64 no.1
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• pp.22-27
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• 2008

Background: Staphylococcus aureus frequently colonizes and infects hospitalized patients. Respiratory infections with Staphylococcus aureus are common in patients with compromised airway defenses. However the mechanisms of S. aureus invasion from colonization to the epithelium are unclear. Cell invasion by S. aureus would require destruction of the extracellular matrix, which is believed to be the result of increased matrix metalloproteinases (MMP) activity. Methods: In this study, respiratory epithelial cells were infected with S. aureus. After removing the extracellular bacteria by washing, the internalized bacteria in the cells were assessed by counting the colonized forming units (CFUs). The cell adhesion proteins, dysadherin and E-cadherin, were evaluated by Western blotting. The MMPs in the bacterial invasion were evaluated by pretreating the cells with GM6001, a MMP inhibitor. Results: The internalization of S. aureus was found to be both time and dose dependent, and the increase in MMP 2 and 9 activity was also dependent on the incubation time and the initial amount of bacterial inoculation. The invasion of S. aureus was attenuated by GM6001 after 12 hours incubation with a multiply of infection (MOI)=50. The expression of dysadherin, a membrane protein, was increased in a time and dose dependent manner, while the expression of E-cadherin was decreased. Conclusion: MMPs may mediate the invasion of S. aureus into epithelial cells.

• East Asian mathematical journal
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• v.28 no.5
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• pp.587-595
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• 2012

In this paper we consider the quadratic matrix equation which can be defined be $$Q(X)=AX^2+BX+C=0$$, where X is a $n{\times}n$ unknown real matrix; A,B and C are $n{\times}n$ given matrices with real elements. Newton's method is considered to find the skew-symmetric solvent of the nonlinear matrix equations Q(X). We also show that the method converges the skew-symmetric solvent even if the Fr$\acute{e}$chet derivative is singular. Finally, we give some numerical examples.

• 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 Korean Mathematical Society
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• v.52 no.2
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• pp.349-372
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• 2015

This paper concerns with exploiting an oblique projection technique to solve a general class of large and sparse least squares problem over symmetric arrowhead matrices. As a matter of fact, we develop the conjugate gradient least squares (CGLS) algorithm to obtain the minimum norm symmetric arrowhead least squares solution of the general coupled matrix equations. Furthermore, an approach is offered for computing the optimal approximate symmetric arrowhead solution of the mentioned least squares problem corresponding to a given arbitrary matrix group. In addition, the minimization property of the proposed algorithm is established by utilizing the feature of approximate solutions derived by the projection method. Finally, some numerical experiments are examined which reveal the applicability and feasibility of the handled algorithm.

• Journal of applied mathematics & informatics
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• v.26 no.3_4
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• pp.471-479
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• 2008

Let $R\;{\in}\;C^{m{\times}m}$ and $S\;{\in}\;C^{n{\times}n}$ be nontrivial unitary involutions, i.e., $R^*\;=\;R\;=\;R^{-1}\;{\neq}\;I_m$ and $S^*\;=\;S\;=\;S^{-1}\;{\neq}\;I_m$. We say that $G\;{\in}\;C^{m{\times}n}$ is a generalized reflexive matrix if RGS = G. The set of all m ${\times}$ n generalized reflexive matrices is denoted by $GRC^{m{\times}n}$. In this paper, an efficient method for the least squares solution $X\;{\in}\;GRC^{m{\times}n}$ of the matrix equation AXB = D with arbitrary coefficient matrices $A\;{\in}\;C^{p{\times}m}$, $B\;{\in}\;C^{n{\times}q}$and the right-hand side $D\;{\in}\;C^{p{\times}q}$ is developed based on the canonical correlation decomposition(CCD) and, an explicit formula for the general solution is presented.

• Proceedings of the KSR Conference
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• 2010.06a
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• pp.1231-1235
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• 2010

The tensile failure behavior of polymer matrix composite materials was investigated with the aid of a nondestructive evaluation (NDE) technique. The materials, E-glass fiber reinforced epoxy matrix composites, which are applicable to carbody materials in railway vehicles to reduce weight, were used for this investigation. In order to explain stress-strain behavior of polymer matrix composite sample, the infrared thermography technique was employed. A high-speed infrared (IR) camera was used for in-situ monitoring of progressive damages of polymer matrix composite samples during tensile testing. In this investigation, the IR thermography technique was used to facilitate a better understanding of damage evolution, fracture mechanism, and failure mode of polymer matrix composite materials during monotonic loadings.

• Journal of applied mathematics & informatics
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• v.27 no.5_6
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• pp.1061-1071
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• 2009

For real generalized reflexive matrices R, S, i.e., $R^T$ = R, $R^2$ = I, $S^T$ = S, $S^2$ = I, we say that real matrix X is (R,S)-symmetric, if RXS = X. In this paper, an iterative algorithm is proposed to solve the least-squares problem of matrix equation AXB = C with (R,S)-symmetric X. Furthermore, the optimal approximation solution to given matrix $X_0$ is also derived by this iterative algorithm. Finally, given numerical example and its convergent curve show that this method is feasible and efficient.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.11 no.4
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• pp.39-46
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• 2007

One of well known and much studied nonlinear matrix equations is the matrix polynomial which has the form G(X)=$A_0X^m+A_1X^{m-1}+{\cdots}+A_m$ where $A_0$, $A_1$, ${\cdots}$, $A_m$ and X are $n{\times}n$ real matrices. We show how the minimization methods can be used to solve the matrix polynomial G(X) and give some numerical experiments. We also compare Polak and Ribi$\acute{e}$re version and Fletcher and Reeves version of conjugate gradient method.

• Journal of Digital Contents Society
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• v.16 no.4
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• pp.605-613
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• 2015

This paper addresses the problem to make sparse the projection matrix in pattern recognition method. Recently, the size of computer program is often restricted in embedded systems. It is very often that developed programs include some constant data. For example, many pattern recognition programs use the projection matrix for dimension reduction. To improve the recognition performance, very high dimensional feature vectors are often extracted. In this case, the projection matrix can be very big. Recently, RSR(roated sparse regression) method[1] was proposed. This method has been proved one of the best algorithm that obtains the sparse matrix. We propose three methods to improve the RSR; outlier removal, sampling and elastic net RSR(E-RSR) in which the penalty term in RSR optimization function is replaced by that of the elastic net regression. The experimental results show that the proposed methods are very effective and improve the sparsity rate dramatically without sacrificing the recognition rate compared to the original RSR method.