• Journal of the Korean Data and Information Science Society
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• v.18 no.4
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• pp.1135-1143
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• 2007

The singular value decomposition and the spectral decomposition are the useful methods in the area of matrix computation for multivariate techniques such as principal component analysis and multidimensional scaling. These techniques aim to find a simpler geometric structure for the data points. The singular value decomposition and the spectral decomposition are the methods being used in these techniques for this purpose. In this paper, the singular value decomposition and the spectral decomposition are compared.

• East Asian mathematical journal
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• v.37 no.3
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• pp.295-305
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• 2021

In this paper, we analyze the efficiency of a domain decomposition method for the two-dimensional telegraph equations. We formulate the theoretical spectral radius of the iteration matrix generated by the domain decomposition method, because the rate of convergence of an iterative algorithm depends on the spectral radius of the iteration matrix. The theoretical spectral radius is confirmed by the experimental one using MATLAB. Speedup and operation ratio of the domain decomposition method are also compared as the two measurements of the efficiency of the method. Numerical results support the high efficiency of the domain decomposition method.

• Journal of the Korean Mathematical Society
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• v.59 no.5
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• pp.987-996
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• 2022

We introduce the notions of symbolic expansivity and symbolic shadowing for homeomorphisms on non-metrizable compact spaces which are generalizations of expansivity and shadowing, respectively, for metric spaces. The main result is to generalize the Smale's spectral decomposition theorem to symbolically expansive homeomorphisms with symbolic shadowing on non-metrizable compact Hausdorff totally disconnected spaces.

• Korean Journal of Mathematics
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• v.16 no.2
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• pp.157-163
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• 2008

Using informations of subsets of divergence points and the relation between members of spectral classes, we give another complete decomposition of spectral classes generated by lower(upper) local dimensions of a self-similar measure on a self-similar Cantor set with full information of their dimensions. We note that it is a complete refinement of the earlier complete decomposition of the spectral classes. Further we study the packing dimension of some uncountable union of distribution sets.

• Journal of the Chungcheong Mathematical Society
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• v.35 no.1
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• pp.91-101
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• 2022

We study some properties of nonwandering set Ω(𝜙) and chain recurrent set CR(𝜙) for an expansive flow which has the POTP on a compact TVS-cone metric spaces. Moreover we shall prove a spectral decomposition theorem for an expansive flow which has the POTP on TVS-cone metric spaces.

• Wind and Structures
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• v.16 no.5
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• pp.517-540
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• 2013

This paper presents applications of proper orthogonal decomposition in both the time and frequency domains based on both cross spectral matrix and covariance matrix branches to analyze multi-variate unsteady pressure fields on prisms and to study spanwise and chordwise pressure distribution. Furthermore, modification of proper orthogonal decomposition is applied to a rectangular spanwise coherence matrix in order to investigate the spanwise correlation and coherence of the unsteady pressure fields. The unsteady pressure fields have been directly measured in wind tunnel tests on some typical prisms with slenderness ratios B/D=1, B/D=1 with a splitter plate in the wake, and B/D=5. Significance and contribution of the first covariance mode associated with the first principal coordinates as well as those of the first spectral eigenvalue and associated spectral mode are clarified by synthesis of the unsteady pressure fields and identification of intrinsic events inside the unsteady pressure fields. Spanwise coherence of the unsteady pressure fields has been mapped the first time ever for better understanding of their intrinsic characteristics.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.22 no.7
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• pp.1030-1040
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• 1998

One of the main unresolved issues in large-eddy simulation(LES) of wall-bounded turbulent flows is the requirement of high spatial resolution in the near-wall region, especially in the spanwise direction. Such high resolution required in the near-wall region is generally used throughout the computational domain, making simulations of high Reynolds number, complex-geometry flows prohibitive. A grid-embedding strategy using a nonconforming spectral domain-decomposition method is proposed to address this limitation. This method provides an efficient way of clustering grid points in the near-wall region with spectral accuracy. LES of transitional and turbulent channel flow has been performed to evaluate the proposed grid-embedding technique. The computational domain is divided into three subdomains to resolve the near-wall regions in the spanwise direction. Spectral patching collocation methods are used for the grid-embedding and appropriate conditions are suggested for the interface matching. Results of LES using the grid-embedding strategy are promising compared to LES of global spectral method and direct numerical simulation. Overall, the results show that the spectral domain-decomposition grid-embedding technique provides an efficient method for resolving the near-wall region in LES of complex flows of engineering interest, allowing significant savings in the computational CPU and memory.

• East Asian mathematical journal
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• v.39 no.1
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• pp.75-85
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• 2023

In this paper, a non-overlapping rectangular domain decomposition method is presented in order to numerically solve two-dimensional telegraph equations. The method is unconditionally stable and efficient. Spectral radius of the iteration matrix and convergence rate of the method are provided theoretically and confirmed numerically by MATLAB. Numerical experiments of examples are compared with several methods.

• The Journal of the Acoustical Society of Korea
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• v.21 no.3
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• pp.315-322
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• 2002

In this paper, a new temporal decomposition method is proposed. which takes into consideration not only spectral distortion but also bit rates. The interpolation functions, which are one of necessary parameters for temporal decomposition, are obtained from the training speech corpus. Since the interval between the two targets uniquely defines the interpolation function, the interpolation can be represented without additional information. The locations of the targets are determined by minimizing the bit rates while the maximum spectral distortion maintains below a given threshold. The proposed method has been applied to compressing the LSP coefficients which are widely used as a spectral parameter. The results of the simulation show that an average spectral distortion of about 1.4 dB can be achieved at an average bit rate of about 8 bits/Frame.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.2
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• pp.162-170
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• 2015

Commute time embedding involves computing the spectral decomposition of the graph Laplacian. It requires the computational burden proportional to $o(n^3)$, not suitable for large scale dataset. Many methods have been proposed to accelerate the computational time, which usually employ the Nystr${\ddot{o}}$m methods to approximate the spectral decomposition of the reduced graph Laplacian. They suffer from the lost of information by dint of sampling process. This paper proposes to reduce the errors by approximating the spectral decomposition of the graph Laplacian using that of the affinity matrix. However, this can not be applied as the data size increases, because it also requires spectral decomposition. Another method called approximate commute time embedding is implemented, which does not require spectral decomposition. The performance of the proposed algorithms is analyzed by computing the commute time on the patch graph.