• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 춘계학술대회 논문집 전력기술부문
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• pp.17-19
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• 2001

In this paper, eigenvalue perturbation theory and its applications for the augmented system matrix are described. This theory is quite useful in the cases where any change in a system parameter results in signifiant changes to most of the elements of the augmented matrix or where the forming of sensitivity matrix so complicate. And AMEP(augmented matrix eigenvalue perturbation) for the excitation system parameters are computed for analysis of small signal stability of KEPCO 215-machine 791-bus system.

• 대한기계학회논문집A
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• 제21권2호
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• pp.288-296
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• 1997

Curved beam elements with two nodes based on shallow beam geometry and strain interpolations are employed in eigenvalue analysis. In these elements, the displacement interpolation functions and mass matrices are consistent with strain fields. To assess the quality of the element mass matrix in free vibration problems, several numerical experiments are performed. In these analysis, both the inconsistent mass matrices using linear displacement interpolation function and the consistent mass matrices are used to show the difference. The numerical results demonstrate that the accuracy is closely related to the property of the mass matrix as well as that of the stiffness matrix and that the mass matrix consistent with strain fields is very beneficial to eigenvalue analysis. Also, it is proved that the strain based elements are very efficient in a wide range of element aspect ratios and curvature properties.

• 대한전자공학회논문지
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• 제23권4호
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• pp.455-459
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• 1986

A large set of linear equations which arise in many applications, such as in digital signal processing, image filtering, estimation theory, numerical analysis, etc. involve the problem of a tridiagonal matrix. In this paper, the determinant, eigenvalue and inverse matrix of a tridiagoanl matrix are analytically evaluated.

• Kyungpook Mathematical Journal
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• 제57권2호
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• pp.211-222
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• 2017

In this paper, we consider three inverse eigenvalue problems for a special type of acyclic matrices. The acyclic matrices considered in this paper are described by a graph called a broom on n + m vertices, which is obtained by joining m pendant edges to one of the terminal vertices of a path on n vertices. The problems require the reconstruction of such a matrix from given partial eigen data. The eigen data for the first problem consists of the largest eigenvalue of each of the leading principal submatrices of the required matrix, while for the second problem it consists of an eigenvalue of each of its trailing principal submatrices. The third problem has an eigenvalue and a corresponding eigenvector of the required matrix as the eigen data. The method of solution involves the use of recurrence relations among the leading/trailing principal minors of ${\lambda}I-A$, where A is the required matrix. We derive the necessary and sufficient conditions for the solutions of these problems. The constructive nature of the proofs also provides the algorithms for computing the required entries of the matrix. We also provide some numerical examples to show the applicability of our results.

• 말소리와 음성과학
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• 제1권1호
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• pp.69-73
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• 2009

This paper proposes an efficient global covariance-based principal component analysis (GCPCA) for speaker identification. Principal component analysis (PCA) is a feature extraction method which reduces the dimension of the feature vectors and the correlation among the feature vectors by projecting the original feature space into a small subspace through a transformation. However, it requires a larger amount of training data when performing PCA to find the eigenvalue and eigenvector matrix using the full covariance matrix by each speaker. The proposed method first calculates the global covariance matrix using training data of all speakers. It then finds the eigenvalue matrix and the corresponding eigenvector matrix from the global covariance matrix. Compared to conventional PCA and Gaussian mixture model (GMM) methods, the proposed method shows better performance while requiring less storage space and complexity in speaker identification.

• Journal of applied mathematics & informatics
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• 제33권5_6호
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• pp.627-633
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• 2015

In this paper, we generalize the concept of the energy of Seidel matrix S(G) which denoted by S^α(G) and obtain some results related to this matrix. Also, we obtain an upper and lower bound for S^α(G) related to all of graphs with |detS(G)| ≥ (n - 1); n ≥ 3.

• Journal of applied mathematics & informatics
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• 제39권1_2호
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• pp.125-131
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• 2021

In the paper, we investigate the representation of the numerical range W(An) for the 2 × 2 complex matrix A, in terms of the numerical range W(A) of the matrix A, and the elements of A or the eigenvalue of A.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.44.5-44
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• 2002

We propose a concept of the S-eigenvalue(stochastic-eigenvalue) along with corresponding eigenvector, and then we define the PDF corresponding to the S-eigenvalue on a complex plane. Based on the S-eigenvalue concept, we will establish the S-stability concept for linear continuous-time systems with probabilistic uncertainties in the system matrix. These results explicitly characterize how the S-eigenvalue in the complex plane may impose S-stability on S-eigenstructure assignment. Finally, we present numerical examples to illustrate the proposed concept.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 A
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• pp.345-347
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• 2004

In small signal stability analysis of power systems, eigenvalue analysis is the most useful method and the detailed modeling of generator has an important effect to the eigenvalues. Generator full model is used for precise dynamic analysis of generators and controllers while two-axis model is used for multi-machine systems because of the reduced order of the state matrix. Also, the eigenvalue sensitivity coefficients are used for optimizing controller parameters to improve system stability. This paper compare the first and second order eigenvalue sensitivity coefficients of controllers using generator full model with those of two-axis model. As a result of an example, the estimated eigenvalues using the first and the second eigenvalue sensitivity coefficients using generator full model is very close to those of state matrix. Also the error ratios throughout a wide range of controller parameters is less than $1\%$.

• 대한전기학회논문지:전력기술부문A
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• 제51권11호
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• pp.577-584
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• 2002

This paper presents a novel approach based on eigenvalue perturbation of augmented matrix(AMEP) to estimate the eigenvalue for variation of controller parameter. AMEP is a useful tool in the analysis and design of large scale power systems containing many different types of exciters, governors and stabilizers. Also, it can be used to find possible sources of instability and to determine the most sensitivity parameters for low frequency oscillation modes. This paper describes the application results of AMEP algorithm with respect to all controller parameter of KEPCO systems. Simulation results for interarea and local mode show that the proposed AMEP algorithm can be used for turning controller parameter, and verifying system data and linear model.