• Journal of the Korean Society for Precision Engineering
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• v.17 no.5
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• pp.180-185
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• 2000

An interface V-notched crack problem can be formulated as a eigenvalue problem. there are the eigenvalues which give stress singularities at the V-notched crack tip. The RWCIM is a method of calculating the eigenvector coefficients associated with eigenvalues for a V-notched crack problem. Obtaining the stress intensity factors for an interface crack in dissimilar materials is examined by the RWCIM. The results of stress intensity factors for an interface crack are compared with those of the displacement extrapolation method by the BEM

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.235-237
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• 1997

The distance in load parameter space to the closest voltage collapse point (CSNB) provides the worst case power margin and the left eigenvector identifies the most effective direction to steer the system to maximize voltage stability under contingency. This paper proposes a new generation redispatch algorithm, which uses left eigenvector at CSNB to enhance the voltage stability. A Newton method is used to detect CSNB point. Proposed method is applicable to the selection of appropriate reactive power compensation and load shedding point detection. But this paper make a point of voltage stability enhancement only with generation redispatch. The proposed method has been tested for Klos Kerner 11-bus system.

• Journal of Korea Multimedia Society
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• v.23 no.4
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• pp.517-524
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• 2020

Recently, many researches have been conducted to automatically recognize license plates of vehicles and use the analyzed information to manage stolen vehicles and track the vehicle. However, such a system must eventually be investigated by people through direct monitoring. Therefore, in this paper, the system of tracking a vehicle is implemented by sharing the information analyzed by the vehicle image among cameras registered in the IoT environment to minimize the human intervention. The distance between cameras is indicated by the node and the weight value of the weighted-graph, and the eigenvector centrality is used to select the camera to search. It demonstrates efficiency by comparing the time between analyzing data using weighted graph searching algorithm and analyzing all data stored in databse. Finally, the path of the vehicle is indicated on the map using parsed json data.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.10-10
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• 2000

Eigenstructure assignment is a typical method with the capability of the consideration of the specifications in time-domain in designing a 1]near control system. In general eigenstructure assignment such that all the desired eigenvalues are exactly assigned to the closed-loop system, the assignment of the eigenvectors is very restrictive. However if the arbitrary point in a certain area as an eigenvalue is allowed to be assigned t the closed-loop system, the assignment of the eigenvector corresponding to this eigenvalue can be much less restrictive. In this paper, the flexible eigenstructure assignment that can assign more closely the desired eigenvector to the closed-loop system by using an optimization technique is proposed.

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

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.2
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• pp.340-346
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• 1996

In this paper, we propose a ne weighted backward covariance matrix method to enhance the resolution for direction-of-arrival(DOA) estimation. The proposed method (MEVM:modified eigenvector method) is an enhanced covariance matrix method which is an extended form of the conventional covariance matrix. We analyze the effect of using the weighted forward-baskward covariance matrix on the performance of the eigenvector method(EVM). By comparing the perturbation angle of the noise-subspace, we show that the spectral estimate obtained using the proposed method is less distorted than the spectral estimate obtained using the conventional EVM. The simulation results show that the new method is more accurate and has better resolution than the conventional EVM under the same noise conditions.

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

• Wind and Structures
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• v.7 no.6
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• pp.359-372
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• 2004

Wind tunnel model studies were carried out to determine the wind load distribution on tributary areas near the gable-end of large, low-rise buildings with high pitch planar and curved roof shapes. Background pressure fluctuations on each tributary area are described by a series of uncorrelated modes given by the eigenvectors of the force covariance matrix. Analysis of eigenvalues shows that the dominant first mode contributes around 40% to the fluctuating pressures, and the eigenvector mode-shape generally follows the mean pressure distribution. The first mode contributes significantly to the fluctuating load effect, when its influence line is similar to the mode-shape. For such cases, the effective static pressure distribution closely follows the mean pressure distribution on the tributary area, and the quasi-static method would provide a good estimate of peak load effects.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.18 no.34
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• pp.1-8
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• 1995

The practical problem of multi-attribute decision making are formed by the uncertain attribute that the attribute by the alternatives cannot be defined or judged crisphy but only as vague. In this case the final judgements are also represented by vague which have to be ordered to determine the optimal alternative. The problem is more complex if the evaluations of alternatives according to each attribute are expresed vague. This paper described the results of a study done to determine how well multi-attribute decision marking perform in helping a decision maker arrive at a preferred solution to a multi-attribute problem with vague attribute. Particular area of research has concentrated on the issue of combining quantitative and qualitative data supplied by estimation. Futher study considers some method for suitable evaluation of qualitative data.

• 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.