• Journal of Mechanical Science and Technology
• /
• v.18 no.6
• /
• pp.933-945
• /
• 2004

In this paper, S (stochastic)-eigenvalue concept and its S-eigenvector for linear continuous-time systems with probabilistic uncertainties is proposed. The proposed concept is concerned with the perturbation of eigenvalues due to the probabilistic variable parameters in the dynamic model of a plant. S-eigenstructure assignment scheme via the Sylvester equation approach based on the S-eigenvalue concept is also proposed. The proposed design schemes are illustrated by numerical examples, and applied to the longitudinal dynamics of open-loop-unstable aircraft with possible uncertainties in aerodynamic and thrust effects as well as separate dynamic pressure. These results explicitly characterize how S-eigenvalues in the complex plane may impose stability on S-eigenstructure assignment.

• Journal of the Korean Society for Precision Engineering
• /
• v.16 no.10
• /
• pp.152-163
• /
• 1999

The problem of eigenvalue and eigenvector for v-notched cracks in pseudo-isotropic and anisotropic dissimilar materials was obtained to discuss stress singularities from traction free boundary and perfect bonded interface conditions assuming like the form of complex stress function for v-notched cracks in an isotropic material. Eigenvalues were solved by a commercial numerical program, MATHEMATICA. The relation between wedged angle and material property for eigenvalue, ${\lambda}$ indicating stress singularities of v-notched cracks in pseudo-isotropic and anisotropic dissimilar materials was examined.

• Phonetics and Speech Sciences
• /
• v.1 no.1
• /
• pp.69-73
• /
• 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.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.7-12
• /
• 2003

In this paper, S(stochastic)-eigenvalue concept and its S-eigenvector for linear continuous-time systems with probabilistic uncertainties are proposed. The proposed concept is concerned with the perturbation of eigenvalues due to the probabilistic variable parameters in the dynamic model of a plant. S-eigenstructure assignment scheme via the Sylvester equation approach based on the S-eigenvalue concept is also proposed. The proposed design scheme is applied to the longitudinal dynamics of open-loop-unstable aircraft with possible uncertainties in aerodynamic and thrust effects as well as separate dynamic pressure.

• Journal of applied mathematics & informatics
• /
• v.33 no.3_4
• /
• pp.247-260
• /
• 2015

The special form of Schur complement is extended to have a Schur's formula to obtains the explicit formula of determinant, inverse, and eigenvector formula of the doubly Leslie matrix which is the generalized forms of the Leslie matrix. It is also a generalized form of the doubly companion matrix, and the companion matrix, respectively. The doubly Leslie matrix is a nonderogatory matrix.

• The Journal of the Acoustical Society of Korea
• /
• v.32 no.6
• /
• pp.548-555
• /
• 2013

The localization of sources has a numerous number of applications. To estimate the position of sources, the relative delay between two or more received signals for the direct signal must be determined. Although the generalized cross-correlation method is the most popular technique, an approach based on eigenvalue decomposition (EVD) is also popular one, which utilizes an eigenvector of the minimum eigenvalue. The performance of the eigenvalue decomposition (EVD) based method degrades in the low SNR and the correlated environments, because it is difficult to select a single eigenvector for the minimum eigenvalue. In this paper, we propose a new adaptive algorithm based on Canonical Correlation Analysis (CCA) in order to extend the operation range to the lower SNR and the correlation environments. The proposed algorithm uses the eigenvector corresponding to the maximum eigenvalue in the generalized eigenvalue decomposition (GEVD). The estimated eigenvector contains all the information that we need for time delay estimation. We have performed simulations with uncorrelated and correlated noise for several SNRs, showing that the CCA based algorithm can estimate the time delays more accurately than the adaptive EVD algorithm.

• Journal of information and communication convergence engineering
• /
• v.15 no.3
• /
• pp.137-142
• /
• 2017

A general linearly constrained narrowband adaptive array is examined in the eigenvector space. The optimum weight vector in the eigenvector space is shown to have the same performance as in the standard coordinate system, except that the input signal correlation matrix and look direction steering vector are replaced with the eigenvalue matrix and transformed steering vector. It is observed that the variation in gain factor results in the variation in the distance between the constraint plane and the origin in the translated weight vector space such that the increase in gain factor decreased the distance from the constraint plane to the origin, thus affecting the nulling performance. Simulation results showed that the general linearly constrained adaptive array performed better at an optimal gain factor compared with the conventional linearly constrained adaptive array in a coherent signal environment and the former showed similar performance as the latter in a noncoherent signal environment.

• Structural Engineering and Mechanics
• /
• v.10 no.5
• /
• pp.427-440
• /
• 2000

The formulas for the perturbation analysis with modes of close eigenvalues are derived in this paper. Emphasis is made on the consistency of the straightforward perturbation process, given the complete terms of perturbations in the zeroth-order, which is a form of Rayleigh quotient, and in the higher-orders. By dividing the perturbation of eigenvector into two parts, the first-order perturbation with respect to the modes of close eigenvalues is moved into the zeroth-order perturbation. The normality condition is employed to compute the higher-order perturbations of eigenvector. The algorithm can be condensed to a single mode with a distinct eigenvalue, and this can accelerate the convergence of the perturbation analysis. The example confirms that the perturbation approximation obtained from the suggested procedure is in a good accuracy on the eigenvalues, eigenvectors, and normality.

• Proceedings of the KSME Conference
• /
• 2000.04a
• /
• pp.115-120
• /
• 2000

This paper examines that it is possible to apply RWCIM for determining eigenvector coefficients associated with eigenvalues for V-notched cracks in anisotropic dissimilar materials using the complex stress function. To verify the RWCIM algorithm, two tests will be shown. First it is performed to ascertain whether predicted coefficients associated with eigenvectors is obtained exactly. Second, it makes an examination of the state of stress for FEM and RWCIM according to a number of eigenvectors at a location far away from the V-notched crack tip.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.25 no.9
• /
• pp.1368-1375
• /
• 2001

This paper examines that it is possible to apply RWCIM for determining eigenvector coefficients associated with eigenvalues for V-notched cracks in anisotropic dissimilar materials using the complex stress function. To verify the RWCIM algorithm, two tests will be shown. First, it is performed to ascertain whether predicted coefficients associated with eigenvectors are obtained exactly. Second, it makes an examination of the state of stresses for FEM and RWCIM according to a number of eigenvectors at a location far away from the v-notched crack tip.