• Transactions of the Korean Society of Mechanical Engineers A
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• v.22 no.1
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• pp.23-32
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• 1998

A shape estimation is needed to control actively a smart structure. A method is, hence, proposed to predict the deformed shape of the structure subjected to unknown external load using the signal from sensors attached to the structure. The shape estimation is based on the relationship between the deformation of the structure and the signal from the sensors. The matrix containing the relationship between the deformation and signal is obtained using fictitious force or eigenvector of global stiffness matrix. Then the deformed shape can be predicted using the linear matrix and signal from sensors attached to the structure. To verify this method, experiment and FEM were performed and it was shown that the shape estimation method based on the fictitious force predicts deflections well and more accurately than that based on eigenvector.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.5
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• pp.503-510
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• 2004

In this paper, the well-conditioned observer for a stochastic system is designed so that the observer is less sensitive to the ill-conditioning factors in transient and steady-state observer performance. These factors include not only deterministic uncertainties such as unknown initial estimation error, round-off error, modeling error and sensing bias, but also stochastic uncertainties such as disturbance and sensor noise. In deterministic perspectives, a small value in the L$_{2}$ norm condition number of the observer eigenvector matrix guarantees robust estimation performance to the deterministic uncertainties. In stochastic viewpoints, the estimation variance represents the robustness to the stochastic uncertainties and its upper bound can be minimized by reducing the observer gain and increasing the decay rate. Both deterministic and stochastic issues are considered as a weighted sum with a LMI (Linear Matrix Inequality) formulation. The gain in the well-conditioned observer is optimally chosen by the optimization technique. Simulation examples are given to evaluate the estimation performance of the proposed observer.

• Journal of the Korean Data and Information Science Society
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• v.3 no.1
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• pp.33-46
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• 1992

The set of eigenvectors of the second moment matrix and the mean vector are the measures of orientation for a distribution supported on the unit sphere. Bootstrap confidence cone for the eigenvector is constructed and the consistency of this method is discussed. The performance of our bootstrap cone for the eigenvector is compared with that of the asymptotic confidence cones for two measures under the parametric assumptions for the underlying distributions and that of the bootstrap cone for the mean vector by Monte Carlo simulation.

• 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.

• Communications for Statistical Applications and Methods
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• v.12 no.3
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• pp.653-657
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• 2005

Eigenstructure of a spatial design matrix of Matheron's variogram estimator in $R^1$ is derived. It is shown that the spatial design matrix in $R^1$ with n/2$\le$h < n has a nice spectral decomposition. The mean, variance, and covariance of this estimator are obtained using the eigenvalues of a spatial design matrix. We also found that the lower bound and the upper bound of the normalized Matheron's variogram estimator.

• Journal for History of Mathematics
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• v.20 no.3
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• pp.91-104
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• 2007

In this paper we study the method to estimate weights of the elementary mathematics achievement factors to get reference index which improves elementary mathematics teaching. For it, we discuss not only a synopsis of AHP but also write out pairwise comparison matrix through statistical survey and estimate weights by eigenvector method.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.11
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• pp.1621-1628
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• 2004

This paper proposes a design methodology for the robust observer using the eigenstructure assignment in multi-output systems so that the observer is less sensitive to the ill-conditioning factors such as unknown initial estimation error, modeling error and measurement bias in transient and steady-state observer performance. The robustness of the observer can be achieved by selecting the desired eigenvector matrix to have a small condition number that guarantees the small upper bound of the estimation error. So the left singular vectors of the unitary matrix spanned by space of the achievable eigenvectors are selected as a desired eigenvectors. Also, this paper proposes how to select the desired eigenvector based on the measure of observability and designs the observer with small gain. An example of a spindle drive system is simulated to validate the robustness to the ill-conditioning factors in the observer performance.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.6
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• pp.517-523
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• 2010

In this paper, we proposed an algorithm to detect aggressive driving status by analysing six kinds of driving patterns, which was achieved by comparing for the feature vectors using mahalanobis distance. The first step is to construct feature matrix of $6{\times}2$ size using frequency response of the time-series accelerometer data. Singular value decomposition makes it possible to find the dominant eigenvalue and its corresponding eigenvector. We use the eigenvector as the feature vector of the driving pattern. We conducted real experiments using three drivers to see the effects of recognition. Although there exists differences from individual drivers, we showed that driving patterns can be recognized with about 80% accuracy. Further research topics will include the development of aggressive driving warning system by improving the proposed technique and combining with post-processing of accelerometer signals.

• International Journal of Control, Automation, and Systems
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• v.5 no.1
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• pp.93-98
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• 2007

A class of new observers in descriptor linear systems, proportional-derivative(PD) observers, are proposed. A parametric design approach for such observers is proposed based on a complete parametric solution to the generalized Sylvester matrix equation. The approach provides complete parameterizations for all the observer gains, gives the parametric expression for the corresponding left eigenvector matrix of the observer system matrix, realizes elimination of impulsive behaviors, and guarantees the regularity of the observer system.

• Journal of applied mathematics & informatics
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• v.7 no.3
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• pp.1045-1058
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• 2000

In usual synchronous parallel computing, workload balance is a crucial factor to reduce idle times of some processors that have finished their jobs earlier than others. However, it is difficult to achieve on a heterogeneous workstation clusters where the available computing power of each processor is unpredictable. As a way to overcome such a problem, the idea of asynchronous methods has grown out and is being increasingly used and studied, but there is none for eigenvalue problems yet. In this paper, we suggest a new asynchronous method to solve some singular matrix problems, that can also be used for finding a certain eigenvector of some matrices.