• Journal of Institute of Control, Robotics and Systems
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• v.5 no.6
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• pp.647-655
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• 1999

For linear time-varying systems described by the triple (A(t),B(t),C(t)) where A(t),B(t),C(t) are the system, the input, and the output matrices, respectively, we propose concepts for measures of modal and gross controllability /observability. We introduce a differential algebraic eigenbvalue theory for linear time-varying systems to calculate the PD-eigenvalues and left and right PD-eigenvectors of the system matrix A(t) which will be used to derive the concepts for the measures. The time-dependent angle between the left PD-eigenvectors of the system matrix A(t) and the columns of the input matrix B(t), and the magnitude of the each element of the input matrix B(t) are used to propose the modal controllability measure. Similarly, the time-dependent angle between the right PD-eigenvectors of the system matrix A(t) and the rows of the output matrix C(t) are used to propose the madal observability measure. Gross measure of controllability of a mode from all inputs and its gross measure of observability in all outputs for the linear time-varying systems are also proposed. Numerical examples are presented to illustrate the proposed concepts.

• Journal of Korean Society of Steel Construction
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• v.8 no.3 s.28
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• pp.21-34
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• 1996

A numerical method is presented for computation of eigenvector derivatives used an iterative procedure with guaranteed convergence. An approach for treating the singularity in calculating the eigenvector derivatives is presented, in which a shift in each eigenvalue is introduced to avoid the singularity. If the shift is selected properly, the proposed method can give very satisfactory results after only one iteration. A criterion for choosing an adequate shift, dependent on computer hardware is suggested ; it is directly dependent on the eigenvalue magnitudes and the number of bits per numeral of the computer. Another merit of this method is that eigenvector derivatives with repeated eigenvalues can be easily obtained if the new eigenvectors are calculated. These new eigenvectors lie "adjacent" to the m (number of repeated eigenvalues) distinct eigenvectors, which appear when the design parameter varies. As an example to demonstrate the efficiency of the proposed method in the case of distinct eigenvalues, a cantilever plate is considered. The results are compared with those of Nelson's method which can find the exact eigenvector derivatives. For the case of repeated eigenvalues, a cantilever beam is considered. The results are compared with those of Dailey's method which also can find the exact eigenvector derivatives. The design parameter of the cantilever plate is its thickness, and that of the cantilever beam its height.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.6
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• pp.568-572
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• 2006

This paper presents the device of recognizing image of the studded paving blocks, transmitting, the information by vibration to a visually handicapped person. Usually the blind uses the walking stick to recognize the studded paving block. This research uses a PCA (Principal Component Analysis) based image processing approach for recognizing the paving blocks. We classify the studded paving blocks into 5 classes, that is, vertical line block, right-declined line block, left-declined line block, dotted block and flat block. The 8 images for each of 5 classes are captured for each block by 112*120 pixels, then the eigenvectors are obtained in magnitude order of eigenvectors by using principal component analysis. The principal components for images can be calculated using projection of transformation matrix composed of eigenvectors. The classification has been executed using Euclidean's distance, so the block having minimum distance with a image is chosen as matched one. The result of classification is transmitted to the blind by electric vibration signals with different magnitudes and frequencies.

• Kyungpook Mathematical Journal
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• v.9 no.1_2
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• pp.67-69
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• 1969

• International Journal of Precision Engineering and Manufacturing
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• v.3 no.2
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• pp.33-44
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• 2002

The problem of eigenvalues and eigenvectors is obtained from a v-notched crack in pseudo-isotropic dissimilar materials by the traction free boundary and the perfect bonded conditions at interface. The complex stress function of the two-term William's type is used. The eigenvalues are solved by a commercial numerical program, MATHEMATICA. Stress singularities for v-notched cracks in pseudo-isotropic dissimilar materials are discussed. The RWCIM(Reciprocal Work Contour Integral Method) is applied to the determination of eigenvector coefficients associated with eigenvalues with egenvalues. The RWCIM algorithm is also coded by the MATHEMATICA.

• ETRI Journal
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• v.34 no.6
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• pp.869-878
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• 2012

A beam design method based on signal-to-leakage-plus-noise ratio (SLNR) has been recently proposed as an effective scheme for multiuser multiple-input multiple-output downlink channels. It is shown that its solution, which maximizes the SLNR at a transmitter, can be simply obtained by the generalized eigenvectors corresponding to the dominant generalized eigenvalues of a pair of covariance matrices of a desired signal and interference leakage plus noise. Under time-varying channels, however, generalized eigendecomposition is required at each time step to design the optimal beam, and its level of complexity is too high to implement in practical systems. To overcome this problem, a predictive beam design method updating the beams according to channel variation is proposed. To this end, the perturbed generalized eigenvectors, which can be obtained by a perturbation theory without any iteration, are used. The performance of the method in terms of SLNR is analyzed and verified using numerical results.

• Journal of the Korean Data and Information Science Society
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• v.14 no.2
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• pp.345-354
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• 2003

An incremental kernel principal component analysis(IKPCA) is proposed for the nonlinear feature extraction from the data. The problem of batch kernel principal component analysis(KPCA) is that the computation becomes prohibitive when the data set is large. Another problem is that, in order to update the eigenvectors with another data, the whole eigenvectors should be recomputed. IKPCA overcomes this problem by incrementally updating the eigenspace model. IKPCA is more efficient in memory requirement than a batch KPCA and can be easily improved by re-learning the data. In our experiments we show that IKPCA is comparable in performance to a batch KPCA for the classification problem on nonlinear data set.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.618-621
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• 1995

In this paper, in order to obtain a disturbance decouplable as well as effective and disturbance suppressible controller, a simultaneous assignment methodology of the left and right eigenstructure is proposed. The biorthogonality property between the left and right model matrices of a system as well as the relations between the achievable right model matrix and states selsction matrices are used to develop the methodology. the proposed concurrent eigenstructure assignment methodologh guarantees that the desired eigenvalues are achieved exactly and the desired left and right eigenvectors are assigned to the best possible(achievable) sets of eigenvectors in the least square sense, respectively.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.25 no.11
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• pp.731-738
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• 2015

This study proposed the method of the multi-crack detection using the sensitivity coefficient matrix which is calculated from the change of eigenvalues and eigenvectors before and after the crack. Each crack is modeled by a rotational springs. The method is applied to the cantilever beam with miulti-crack. The eigenvalues and eigenvectors are determined for different crack locations and depths. The prediction of multi-crack detection are in good agreement with the results of structural reanalysis.

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