• Journal of applied mathematics & informatics
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• v.26 no.5_6
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• pp.839-850
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• 2008

In this paper, the symmetric accelerated overrelaxation (SAOR) method for solving $n{\times}n$ fuzzy linear system is discussed, then the convergence theorems in the special cases where matrix S in augmented system SX = Y is H-matrices or consistently ordered matrices and or symmetric positive definite matrices are also given out. Numerical examples are presented to illustrate the theory.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2736-2754
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• 2015

Android dominates the mobile operating system market, which stimulates the rapid spread of mobile malware. It is quite challenging to detect mobile malware. System call sequence analysis is widely used to identify malware. However, the malware detection accuracy of existing approaches is not satisfactory since they do not consider correlation of system calls in the sequence. In this paper, we propose a new scheme called Artificial Neural Networks (ANNs) on Co-occurrence Matrices Droid (ANNCMDroid), using co-occurrence matrices to mine correlation of system calls. Our key observation is that correlation of system calls is significantly different between malware and benign software, which can be accurately expressed by co-occurrence matrices, and ANNs can effectively identify anomaly in the co-occurrence matrices. Thus at first we calculate co-occurrence matrices from the system call sequences and then convert them into vectors. Finally, these vectors are fed into ANN to detect malware. We demonstrate the effectiveness of ANNCMDroid by real experiments. Experimental results show that only 4 applications among 594 evaluated benign applications are falsely detected as malware, and only 18 applications among 614 evaluated malicious applications are not detected. As a result, ANNCMDroid achieved an F-Score of 0.981878, which is much higher than other methods.

• Kyungpook Mathematical Journal
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• v.47 no.3
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• pp.311-322
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• 2007

This paper deals with space Beltrami system with three characteristic matrices in even dimensions, which can be regarded as a generalization of space Beltrami system with one and two characteristic matrices. It is transformed into a nonhomogeneous $p$-harmonic equation $d^*A(x,df^I)=d^*B(x,Df)$ by using the technique of out differential forms and exterior algebra of matrices. In the process, we only use the uniformly elliptic condition with respect to the characteristic matrices. The Lipschitz type condition, the monotonicity condition and the homogeneous condition of the operator A and the controlled growth condition of the operator B are derived.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.53 no.4
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• pp.175-182
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• 2004

This paper presents a new method for finding the Block Pulse series coefficients, deriving the Block Pulse integration operational matrices and generalizing the integration operational matrices which are necessary for the control fields using the Block Pulse functions. In order to apply the Block Pulse function technique to the problems of state estimation or parameter identification more efficiently, it is necessary to find the more exact value of the Block Pulse series coefficients and integral operational matrices. This paper presents the method for improving the accuracy of the Block Pulse series coefficients and derives generalized integration operational matrix and applied the matrix to the analysis of linear time-invariant system.

• Journal of applied mathematics & informatics
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• v.29 no.5_6
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• pp.1571-1581
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• 2011

In this paper, we construct inequivalent Hadamard matrices based on several new and old full orthogonal designs, using circulant and symmetric block matrices. Not all orthogonal designs produce inequivalent Hadamard matrices, because the corresponding systems of equations do not possess solutions. The systems of equations arising when we search for inequivalent Hadamard matrices from full orthogonal designs using circulant and symmetric block matrices, can be concisely described using the periodic autocorrelation function of the generators of the block matrices. We use Maple, Magma, C and Unix tools to find many new inequivalent Hadamard matrices.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1875-1877
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• 2002

There exist several forms of transfer function descriptions for multivariable LTI systems. We treat transfer function matrix with characteristic polynomial as its common denominator named Characteristic Transfer-function Matrices (CTM). First, we clarify necessary and sufficient conditions of CTM, then, we show some related lemmas. These interpretations not only offer deeper explanations but they also provide ways for calculations of all possible transfer matrices, system zeros, and inverse polynomial matrices.

• Journal of Korean Society of Transportation
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• v.17 no.1
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• pp.7-17
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• 1999

The expressway network includes a total of about 1,899 km in our country The only 1,016 km of that is being managed by the closed Toll Collection System(TCS) which is composed of 74 tollgates. We obtain inter-tollgate O-D matrices from that system everyday. But, they are not traffic-zonal O-D matrices. So they have not been used for the expressway traffic analysis and the traffic demand estimation despite of their accuracy. If we could estimate the traffic-zonal O-D matrices from TCS O-D ones, we could perform expressway traffic analysis more efficiently. Moreover we could obtain more precise expressway O-D matrices and traffic-zonal O/D ones by this approach than by the conventional ones. In this paper. we proposed the model estimating traffic-zonal O/D matrices from TCS O-D ones. The assigned volumes with the estimated traffic-zonal O-D matrices produced the only 17.9% error all over the TCS expressway section when compared to the real traffic volumes. So, the proposed model enables for us to estimate more accurate O/D matrics than any other existing methods.

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

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

• Journal of applied mathematics & informatics
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• v.22 no.1_2
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• pp.535-539
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• 2006

The ring of invariants of two 2 by 2 matrices C(2, 2) is a polynomial ring with 5 variables [1]. In this paper we find the system of parameters of C(3, 2) by Groebner bases.