• Proceedings of the Safety Management and Science Conference
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• 2006.04a
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• pp.123-131
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• 2006

Multi-criteria decision making is deducing the relative importance in the criterion of decision making and each alternative which is able to making a variety of choices measures the preferred degree in the series of low-raking criterions. Moreover, this is possible by synthesizing them systematically. In general, a fundamental problem decision maker solve for multi-criteria decision making is evaluating a set of activities which are considered as the target logically, and this kind of work is evaluated and synthesized by various criterions of the value which a chain of activities usually hold in common. In this paper, we are the eigenvector methods in weights calculating. For the purpose of making optimal decision, the data of five different car models are used. For computing, we used Visual Numerica Version 1.0 software package.

• The Journal of the Acoustical Society of Korea
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• v.23 no.4
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• pp.282-287
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• 2004

This paper proposes the nearfield eigenvector method for array shape estimation using reference signals basted on the nearfield signal modeling. Generally. direction finding methods assume the reference signals to be plainwave. However, in case of the reference signals in nearfield, this assumption is inadequate for array shape estimation. In this paper. the nearfield reference signals are modeled. and we propose the nearfield eigenvector method. The numerical experiments indicated that the proposed method shows good performance for array shape estimation regardless of the ranges of the reference signals.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.3
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• pp.515-523
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• 1997

A high resolution algorithm is presented for resolving multiple coherent spread spectrum signals that are incident on an equispaced linear array. Unlike the conventional noise-eigenvector based methods, this algorithm makes use of the signal eigenvectors of the array spectral density matrix that are associates with eigenvalues that are larger than the sensor noise level. Simulation results are shown to demonstate the high performance of the proposed approach in comparison with MUSIC in which coherent signal subspace method (CSM) is employed.

• Journal of the Korea Society of Computer and Information
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• v.21 no.1
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• pp.25-31
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• 2016

There are many methods to describe the importance of a node, centrality, in a graph. In this paper, we focus on the eigenvector centrality. In this paper, an analytical method to estimate the difference of centrality with an additional edge in a graph is proposed. In order to validate the analytical method to estimate the centrality, two problems, to decide an additional edge that maximizes the difference of all centralities of all nodes in the graph and to decide an additional edge that maximizes the centrality of a specific node, are solved using three kinds of random graphs and the results of the estimated edge and observed edge are compared. Though the estimated centrality difference is slightly different from the observed real centrality in some cases, it is shown that the proposed method is effective to estimate the centrality difference with a short running time.

• Journal of Korea Multimedia Society
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• v.9 no.1
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• pp.33-40
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• 2006

This paper presents an efficient face recognition method using both first moment of image and eigenvector. First moment is a method for finding centroid of image, which is applied to exclude the needless backgrounds in the face recognitions by shitting to the centroid of face image. Eigenvector which are the basis images as face features, is extracted by principal component analysis(PCA). This is to improve the recognition performance by excluding the redundancy considering to second-order statistics of face image. The proposed methods has been applied to the problem for recognizing the 60 face images(15 persons *4 scenes) of 320*243 pixels. The 3 distances such as city-block, Euclidean, negative angle are used as measures when match the probe images to the nearest gallery images. In case of the 45 face images, the experimental results show that the recognition rate of the proposed methods is about 1.6 times and its the classification is about 5.6 times higher than conventional PCA without preprocessing. The city-block has been relatively achieved more an accurate classification than Euclidean or negative angle.

• Phonetics and Speech Sciences
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• v.1 no.1
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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.

• The Journal of the Acoustical Society of Korea
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• v.27 no.5
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• pp.221-228
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• 2008

To estimate array shape with reference sources in SONAR systems, nearfield signal modeling is required for the reference sources near a towed array. Array shape estimation method based on the nearfield signal modeling generally exploits the spatial covariance matrix of the received reference sources. Among those method, nearfield eigenvector method uses the eigenvector corresponding to the maximum eigenvalue as a steering vector of the reference source. In this paper, we propose a simplified subspace fitting method based on the nearfield signal modeling with spherical wave modeling. Furthermore, we analyze performance of the array shape estimation methods based on the nearfield signal modeling for various environments. The results of the numerical experiments indicate that the simplified subspace fitting method and the nearfield eigenvector method with single reference source shows almost similar performance. Furthermore, the simplified subspace fitting method with 2 reference sources consistently estimates the shape of the array regardless of the incident angle of the reference sources, whereas the nearfield eigenvector method cannot apply for the case of 2 reference sources.

• Proceedings of the Safety Management and Science Conference
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• 2006.11a
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• pp.427-435
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• 2006

We study on the consistency of AHP. It is research that extend of SAW methods by [1]. For tools that measure judgment of inconsistency eigenvector methods, we research consistency that introduced consistency ratio by Saaty. in general, the higher consistence of compare matrix the bigger error within matrix. In this paper, we use the AHP for the optimal decision making. By this method, we have optimal decision making numenical example which three models of any domestic motors companies.

• Computational Structural Engineering
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• v.9 no.4
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• pp.219-228
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• 1996

A method used for measuring the stiffness of hinging reinforced concrete frame structures is developed. The so called Stiffness Measure Method is used to evaluate the tangent stiffness of hinge regions while the structure is responding in nonlinear ranges. Eigenvector methods for nonlinear response have not been especially popular because of the need for regenerating eigenvectors as the time history proceeds. In the present work the eigenvectors sets and corresponding nonlinear state variables, i. e., the tangent stiffnesses of the hinge regions, are stored. There is an expectation that previously generated eigenvectors can be reused as the analysis proceeds. The stiffness measure is used to compare the current tangent stiffnesses of hinge regions with those of previously stored eigenvectors sets. Since eigenvector calculations are diminished the method is effective in reducing computational effort for reinforced concrete frame structures subjected to strong ground motions.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2001.10a
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• pp.515-522
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• 2001

A simplified method is presented for the computation of eigenvalue and eigenvector derivatives associated with repeated eigenvalues. In the proposed method, adjacent eigenvectors and orthonormal conditions are used to compose an algebraic equation whose order is (n+m)x(n+m), where n is the number of coordinates and m is the number of multiplicity of the repeated eigenvalue. One algebraic equation developed can be computed eigenvalue and eigenvector derivatives simultaneously. Since the coefficient matrix of the proposed equation is symmetric and based on N-space, this method is very efficient compared to previous methods. Moreover the numerical stability of the method is guaranteed because the coefficient matrix of the proposed equation is non-singular, This method can be consistently applied to both structural systems with structural design parameters and mechanical systems with lumped design parameters. To verify the effectiveness of the proposed method, the finite element model of the cantilever beam and a 5-DOF mechanical system in the case of a non-proportionally damped system are considered as numerical examples. The design parameter of the cantilever beam is its width, and that of the 5-DOF mechanical system is a spring.