• Wind and Structures
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• v.39 no.2
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• pp.125-140
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• 2024

The simulation of non-stationary wind velocity is particularly crucial for the wind resistant design of slender structures. Recently, some data-driven simulation methods have received much attention due to their straightforwardness. However, as the number of simulation points increases, it will face efficiency issues. Under such a background, in this paper, a time-varying coherence matrix decomposition method based on Diagonal Proper Orthogonal Decomposition (DPOD) interpolation is proposed for the data-driven simulation of non-stationary wind velocity based on S-transform (ST). Its core idea is to use coherence matrix decomposition instead of the decomposition of the measured time-frequency power spectrum matrix based on ST. The decomposition result of the time-varying coherence matrix is relatively smooth, so DPOD interpolation can be introduced to accelerate its decomposition, and the DPOD interpolation technology is extended to the simulation based on measured wind velocity. The numerical experiment has shown that the reconstruction results of coherence matrix interpolation are consistent with the target values, and the interpolation calculation efficiency is higher than that of the coherence matrix time-frequency interpolation method and the coherence matrix POD interpolation method. Compared to existing data-driven simulation methods, it addresses the efficiency issue in simulations where the number of Cholesky decompositions increases with the increase of simulation points, significantly enhancing the efficiency of simulating multivariate non-stationary wind velocities. Meanwhile, the simulation data preserved the time-frequency characteristics of the measured wind velocity well.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.2
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• pp.89-96
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• 2015

In this paper, time-frequency analysis algorithms, empirical mode decomposition and local mean decomposition, are reviewed and their applications to nonlinear and nonstationary real-world data are discussed. In addition, their generic extensions to complex domain are addressed for the analysis of multichannel data. Simulations of these algorithms on synthetic data illustrate the fundamental structure of the algorithms and how they are designed for the analysis of nonlinear and nonstationary data. Applications of the complex version of the algorithms to the synthetic data also demonstrate the benefit of the algorithms for the accurate frequency decomposition of multichannel data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.2
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• pp.279-286
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• 2015

EMD is a fully data-driven signal processing method without using any predetermined basis function and requiring any user parameters setting. However EMD experiences a problem of mode mixing which interferes with decomposing the signal into similar oscillations within a mode. To overcome the problem, EEMD method was introduced. The algorithm performs the EMD method over an ensemble of the signal added independent identically distributed white noise of the same standard deviation. Even so EEMD created problems when the decomposition is complete. The ensemble of different signal with added noise may produce different number of modes and the reconstructed signal includes residual noise. This paper propose an modified EEMD method to overcome mode mixing of EMD, to provide an exact reconstruction of the original signal, and to separate modes with lower cost than EEMD's. The experimental results show that the proposed method provides a better separation of the modes with less number of sifting iterations, costs 20.87% for a complete decomposition of the signal and demonstrates superior performance in the signal reconstruction, compared with EEMD.

• Journal of Korean Society for Atmospheric Environment
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• v.22 no.1
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• pp.43-51
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• 2006

Nonthermal plasma-driven catalysis (PDC) was investigated for the decomposition of benzene and toluene as model compounds of volatile organic compounds (VOCs) at atmospheric pressure and low temperature. Two types of catalysts Ag/$TiO_{2}$ and Pt/$\gamma-Al_{2}O_{3}$ were tested in this study. The amount of catalysts packed in the PDC reactor did not influence on the decomposition efficiency of benzene. The type of catalysts also had no influence on the decomposition efficiency of toluene and carbon balance. The Ag/$TiO_{2}$ catalyst showed constant $CO_{2}$ selectivity of about $73\%$ regardless of the specific input energy. However, the selectivity of $CO_{2}$ was greatly enhanced with the Pt/$\gamma-Al_{2}O_{3}$ catalysts, and reached $97\%$ at 205 J/L. Two test runs with 20 fold difference in the gas flow clearly indicated that lab-scale data can be successfully applied for the scaling-up of PDC system.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.389-390
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• 2019

Tensor decomposition, proven to be an efficient data processing method, can be used to provide data-driven services. we propose a novel datadriven mutation strategy for parent individuals selection, namely tensor-based DE with parapatric and cross-generation(TPCDE).

• Journal of the Korean Society of Visualization
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• v.15 no.2
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• pp.59-67
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• 2017

To verify floatability of ABB (Air bubble barrier), we compared bubble swarm behavior with and without the air-driven ejector. Experiment was conducted using the fabricated air-driven ejector with 5 mm nozzle on the bottom of 1 m3 water tank. Reynolds number of air in the nozzle was ranged 1766-13248. We analyzed data with statistical method using image processing, particle mage velocimetry (PIV) and proper orthogonal decomposition (POD) analysis. As a result of POD analysis, there was no significant eigenmode in bubbly flow generated from the ejector. It means that more complex turbulent flows were formed by the ejector, thereby (1) making bubbles finer, (2) promoting three-dimensional energy transfer between bubble and water, and (3) making evenly distributed velocity profile of water. It is concluded that the air-driven ejector could enhance the performance of ABB.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.475-478
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• 2015

The objective of this study is to apply a hybrid model for estimating solar radiation and investigate their accuracy. A hybrid model is wavelet-based support vector machines (WSVMs). Wavelet decomposition is employed to decompose the solar radiation time series into approximation and detail components. These decomposed time series are then used as inputs of support vector machines (SVMs) modules in the WSVMs model. Results obtained indicate that WSVMs can successfully be used for the estimation of daily global solar radiation at Champaign and Springfield stations in Illinois.

• 한국전산유체공학회:학술대회논문집
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• 1998.11a
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• pp.147-152
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• 1998

2-D flow fields are studied by using a shared memory parallel computer with a parallel flow analysis program which uses domain decomposition method and MPI library for data exchange at overlapped interface. Especially, effects of directional domain decomposition on parallel efficiency are studied for 2-D Lid-Driven cavity flow and flow through square cavity. It is known from the present study that domain decomposition along the main flow direction gives better parallel efficiency in 1-D partitioning than along the other direction. 2-D partitioning, however, is less sensitive to flow directions and gives good parallel efficiency for most of the cases considered.

• Journal of Powder Materials
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• v.9 no.6
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• pp.422-432
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• 2002

Mechanically driven decomposition of intermetallics during mechanical milling(MM 1 was investigated. This process for Fe-Ce and Fe-Sn system was studied using conventional XRD, DSC, magnetization and alternative current susceptibility measurements. Mechanical alloying and milling form products of the following composition (in sequence of increasing Gecontent): $\alpha$(${\alpha}_1$) bcc solid solution, $\alpha$+$\beta$-phase ($Fe_{2-x}Ge$), $\beta$-phase, $\beta$+FeGe(B20), FeGE(B20), FeGe(B20)+$FeGe_2$,$FeGe_2$,$FeGe_2$+Ge, Ge. Incongruently melting intermetallics $Fe_6Ge_5$ and $Fe_2Ge_3$ decompose under milling. $Fe_6Ge_5$ produces mixture of $\hat{a}$-phase and FeGe(B20), $Fe_2Ge_3$ produces mixture of FeGe(B20) and $FeGe_2$ phases. These facts are in good agreement with the model that implies local melting as a mechanism of new phase for-mation during medchanical alloying. Stability of FeGe(B20) phase, which is also incongruently melting compound, is explained as a result of highest density of this phase in Fe-Ge system. Under mechanical milling (MM) in planetary ball mill, FeSn intermetallic decomposes with formation $Fe_5Sn_3$ and $FeSn_2$ phases, which have the biggest density among the phases of Fe-Sn system. If decomposition degree of FeSn is relatively small(<60%), milled powder shows superparamagnetic behavior at room temperature. For this case, magnetization curves can be fitted by superposition of two Langevin functions. particle sizes for ferromagnetic $Fe_5Sn_3$ phase determined from fitting parameters are in good agreement with crystalline sizes determined from XRD data and remiain approximately chageless during MM. The decomposition of FeSn is attributed to the effects of local temperature and local pressure produced by ball collisions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1586-1591
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• 2022

Kirchhoff-Love plate (KLP) equation is a well established theory for a description of a deformation of a thin plate under certain outer source. Meanwhile, analysis of a vibrating plate in a frequency domain is important in terms of obtaining the main frequency/eigenfunctions and predicting the vibration of plate. Among various modal analysis methods, dynamic mode decomposition (DMD) is one of the efficient data-driven methods. In this work, we carry out DMD based modal analysis for KLP where thin plate is under effects of sine-type outer force. We first construct discrete time series of KLP solutions based on a finite difference method (FDM). Over 720,000 number of FDM-generated solutions, we select only 500 number of solutions for the DMD implementation. We report the resulting DMD-modes for KLP. Also, we show how DMD can be used to predict KLP solutions in an efficient way.