• Structural Engineering and Mechanics
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• v.65 no.1
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• pp.69-79
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• 2018

This paper is devoted to two new techniques for free vibration analysis of concrete arch dam-reservoir systems. The proposed schemes are quadratic ideal-coupled eigen-problems, which can solve the originally non-symmetric eigen-problem of the system. To find the natural frequencies and mode shapes, a new special-purpose eigen-value solution routine is developed. Moreover, the accuracy of the proposed approach is thoroughly assessed, and it is confirmed that the new scheme is very accurate under all practical conditions. It is also concluded that both decoupled and ideal-coupled strategy proposed in the previous works can be considered as special cases of the current more general procedure.

• Journal of the Korean Graphic Arts Communication Society
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• v.19 no.1
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• pp.4-16
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• 2001

Most important step for the color matching in scanner is the color coordinate transformation from the scanner RGB space to device independent uniform color space. A variety of color calibration technologies have been developed for input device. Linear or nonlinear matrices have been conveniently applied to correct the color filter's mismatch with color matching function in scanners. The color matching accuracy is expected to be further improved when the nonlinear matrices are optimized into subdivided smaller color spaces than in single matrix of the entire color space. This article proposed the scanner calibration method using subspace division regression analysis and it were compared with conventional method.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1998.10a
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• pp.431-438
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• 1998

An efficient solution method is presented to solve the eigenvalue problem arising in tile dynamic analysis of non-proportionally damped structural systems with multiple or close eigenvalues. The proposed method is obtained by applying the modified Newton-Raphson technique and the orthonormal condition of the eigenvectors to the quadratic eigenvalue problem. Even if the shift value is an eigenvalue of the system, the proposed method guarantees nonsingularity, which is analytically proved. The initial values of the proposed method can be taken as the intermediate results of iteration methods or results of approximate methods. Two numerical examples are also presented to demonstrate the effectiveness of the proposed method and the results are compared with those of the well-known subspace iteration method and the Lanczos method.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1990.10a
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• pp.16-21
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• 1990

Recently, the finite element method has been sucessfully extended to treat the rather couplet phenomena such as nonlinear buckling problems which are of considerable practical interest. In this study, a finite element program to evaluate the elasto-plastic buckling stress is developed. The Stowell's deformation theory for the plastic buckling of flat plates, which is in good agreement with experimental results, is used to evaluate bending stiffness matrix. A bifurcation analysis is performed to compute the elasto-plastic buckling stress. The subspace iteration method is employed to find the eigenvalues. The results are compared with corresponding enact solutions to the governing equations presented by Stowell and also with experimental data due to Pride. The developed program Is applied to obtain elastic and elasto-plastic buckling stresses for various loafing cases. The effect of different plate aspect ratio is also investigated.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.3
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• pp.162-170
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• 2014

Appearance-based subspace models such as eigenfaces have been widely recognized as one of the most successful approaches to face recognition and tracking. The success of eigenfaces mainly has its origins in the benefits offered by principal component analysis (PCA), the representational power of the underlying generative process for high-dimensional noisy facial image data. The sparse extension of PCA (SPCA) has recently received significant attention in the research community. SPCA functions by imposing sparseness constraints on the eigenvectors, a technique that has been shown to yield more robust solutions in many applications. However, when SPCA is applied to facial images, the time and space complexity of PCA learning becomes a critical issue (e.g., real-time tracking). In this paper, we propose a very fast and scalable greedy forward selection algorithm for SPCA. Unlike a recent semidefinite program-relaxation method that suffers from complex optimization, our approach can process several thousands of data dimensions in reasonable time with little accuracy loss. The effectiveness of our proposed method was demonstrated on real-world face recognition and tracking datasets.

• Proceedings of the KIPE Conference
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• 2005.07a
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• pp.192-194
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• 2005

Development of nonlinear loads and compensation instruments make PQ(Power Quality) problem into important issue. Few studies by signal processing and pattern classification as NN(Neural Network), Wavelet Transform, and Fuzzy present feature extraction. A lot of Input features make not always good result and they are difficult to make realtime system. Thus, The dimentionality reduction is indispensable process. PCA(Principal Component Analysis) reduces high-dimensional input features onto a lower-dimensional subspace effectively. It will be useful to apply to realtime system and NN.

• Journal of the Korean Institute of Telematics and Electronics
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• v.10 no.5
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• pp.39-44
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• 1973

In general, solution of electric networks requires both node and loop analysis, in which node pair voltages and loop currents are treated as network variables, but the conjugate quantities of these variables (the branch currents and node pair voltages respectively) are to be obtained through additional solving operation. In case of networks with magnetic coupling, however, the coupling keeps the conjugate variables mutually dependent and its final solution requires further calculation. In this paper is analyzed the method of obtaining the conjugate quantities through treatment of the problrm in a subspace with dimensions of number of magnetically coupled branches.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2733-2746
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• 2018

The two inverse regression estimation methods, SIR and SAVE to estimate the central space are computationally easy and are widely used. However, SIR and SAVE may have poor performance in finite samples and need strong assumptions (linearity and/or constant covariance conditions) on predictors. The two non-parametric estimation methods, MAVE and dMAVE have much better performance for finite samples than SIR and SAVE. MAVE and dMAVE need no strong requirements on predictors or on the response variable. MAVE is focused on estimating the central mean subspace, but dMAVE is to estimate the central space. This paper explores and compares four methods to explain the dimension reduction. Each algorithm of these four methods is reviewed. Empirical study for simulated data shows that MAVE and dMAVE has relatively better performance than SIR and SAVE, regardless of not only different models but also different distributional assumptions of predictors. However, real data example with the binary response demonstrates that SAVE is better than other methods.

• Communications for Statistical Applications and Methods
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• v.28 no.3
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• pp.267-279
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• 2021

A regression with multi-dimensional responses is quite common nowadays in the so-called big data era. In such regression, to relieve the curse of dimension due to high-dimension of responses, the dimension reduction of predictors is essential in analysis. Sufficient dimension reduction provides effective tools for the reduction, but there are few sufficient dimension reduction methodologies for multivariate regression. To fill this gap, we newly propose two fused slice-based inverse regression methods. The proposed approaches are robust to the numbers of clusters or slices and improve the estimation results over existing methods by fusing many kernel matrices. Numerical studies are presented and are compared with existing methods. Real data analysis confirms practical usefulness of the proposed methods.

• Communications for Statistical Applications and Methods
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• v.28 no.2
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• pp.205-215
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• 2021

Sufficient dimension reduction is useful dimension reduction tool in regression, and sliced inverse regression (Li, 1991) is one of the most popular sufficient dimension reduction methodologies. In spite of its popularity, it is known to be sensitive to the number of slices. To overcome this shortcoming, the so-called fused sliced inverse regression is proposed by Cook and Zhang (2014). Unfortunately, the two existing methods do not have the direction application to large p-small n regression, in which the dimension reduction is desperately needed. In this paper, we newly propose seeded sliced inverse regression and seeded fused sliced inverse regression to overcome this deficit by adopting iterative projection approach (Cook et al., 2007). Numerical studies are presented to study their asymptotic estimation behaviors, and real data analysis confirms their practical usefulness in high-dimensional data analysis.