• 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.

• 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.

• The Korean Journal of Ecology
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• v.26 no.4
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• pp.203-208
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• 2003

Losses in the dry weight of leaf litter from six tree species were studied during 16 months on the forest floor in temperate deciduous forest of Mt. Cheonma in the vicinity of Seoul in Korea by using litter bag method. The decomposition rate of each leaf litter varies with each species. After 16 months elapsed, the leaf litter of Acer pseudo-sieboidianum showed the highest decomposition constant (0.82) as Olson´s decomposition constant, while that of Pinus densiflora showed the lowest decomposition constant (0.33). The decomposition constant of Quercus mongolica, Q. serrata, Betula ermani and Carpinus laxiflora showed 0.43, 0.37, 0.66 and 0.75, respectively. The decomposition constant of leaf litter was considered with temperature and precipitation which accumulated daily during each term of litter bag collection. The decomposition constant of leaf litter showed closely positive correlation with daily accumulative temperature and precipitation. The relationships between decomposition constant and the daily accumulative temperature and precipitation at each period of litter bag collection were analyzed through multi-regression analysis. The correlation coefficients as a result of multi-regression analysis in Q. mongolica, Q. serrata, P densiflora, B. ermani, C. laxiflorais and A. pseudo-sieboldianum were 0.83, 0.81, 0.69, 0.77, 0.77 and 0.62, respectively. The precipitation showed higher effect, about 10 times, on the leaf litter decomposition than the daily accumulative temperature.

• New & Renewable Energy
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• v.2 no.4 s.8
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• pp.70-77
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• 2006

The butane decomposition over the catalyst is an attractive method for the hydrogen production. The objective of the work was investigated the catalysis of carbon black in butane decomposition reaction. The Butane decomposition was performed over carbon black catalyst in a range of $500-1100^{\circ}C$. The butane conversion of thermal decomposition and catalytic decomposition were increased with increasing the reaction temperature The butane conversion of the thermal decomposition was higher than the butane conversion of the catalytic decomposition. Hydrogen and methane were mostly observed in the butane decomposition over $1000^{\circ}C$. Especially, the hydrogen yield was steadily increased with raising the reaction temperature, It could be known that the hydrogen yield of the catalytic decomposition was higher than one of the thermal cracking because the hydrogen productivity was improved by the catalyst. The deactivation of the catalyst was not observed in the reactivity test. The surface and crystalline of the fresh and used catalysts were characterized by TEM, BET surface area and XRD analysis, respectively. The fresh carbon black particles had mostly smoothly round-shaped surfaces. In the surface of the carbon black after the reaction, the deposited carbon was formed as the protrusion-shaped carbon and the cone-shaped. The proper peaks of carbon black appeared in XRD analysis.

• Journal of the Korean Statistical Society
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• v.25 no.1
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• pp.49-66
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• 1996

The singular value decomposition is one of the most useful methods in the area of matrix computation. It gives dimension reduction which is the centeral idea in many multivariate analyses. But this method is not resistant, i.e., it is very sensitive to small changes in the input data. In this article, we derive the resistant version of singular value decomposition for principal component analysis. And we give its statistical applications to biplot which is similar to principal component analysis in aspects of the dimension reduction of an n x p data matrix. Therefore, we derive the resistant principal component analysis and biplot based on the resistant singular value decomposition. They provide graphical multivariate data analyses relatively little influenced by outlying observations.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.8
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• pp.3384-3390
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• 2011

This paper describes a domain decomposition method of parallel finite element analysis for elasto-plastic structural problems. As a parallel numeral algorithm for the finite element analysis, the authors have utilized the domain decomposition method combined with an iterative solver such as the conjugate gradient method. Here the domain decomposition method algorithm was applied directly to elasto-plastic problem. The present system was successfully applied to three-dimensional elasto-plastic structural problems.

• KEPCO Journal on Electric Power and Energy
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• v.6 no.4
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• pp.447-454
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• 2020

Since SF₆ gas was discovered in the early 1900s, it has been widely used as an insulation material for electrical equipment. While various indicators have been developed to diagnose oil-immersed transformers, there are still insufficient indicators for the diagnosis of gas-insulated transformers. When necessary, chemical diagnostic methods can be used for gas-insulated transformers. However, the field suitability and accuracy of those methods for transformer diagnosis have not been verified. In addition, since various types of decomposition gases are generated therein, it is also necessary to establish appropriate analysis methods to cover the variety of gases. In this study, a gas-insulated transformer was diagnosed through the analysis of decomposition gases. Reliability assessments of both simple analysis methods suitable for on-site tests and precise analysis methods for laboratory level tests were performed. Using these methods, a gas analysis was performed for the internal decomposition gases of a 154 kV transformer in operation. In addition, simulated discharge and thermal fault experiments were demonstrated. Each major decomposition gas generation characteristics was identified. The results showed that an approximate diagnosis of the inside of a gas-insulated transformer is possible by analyzing SO₂, SOF₂, and CO using simple analysis methods on-site. In addition, since there are differences in the types of decomposition gas generation patterns with various solid materials of the internal transformer, a detailed examination should be performed by using precise analysis methods in the laboratory.

• Journal of the Korean Statistical Society
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• v.25 no.1
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• pp.67-80
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• 1996

Factor analysis is a multivariate technique for describing the in-terrelationship among many variables in terms of a few underlying but unobservable random variables called factors. There are various approaches for this factor analysis. In particular, principal factor analysis is one of the most popular methods. This follows the mathematical algorithm of the principal component analysis based on the singular value decomposition. But it is known that the singular value decomposition is not resistant, i.e., it is very sensitive to small changes in the input data. In this article, using the resistant singular value decomposition of Choi and Huh (1994), we derive a resistant principal factor analysis relatively little influenced by notable observations.

• Journal of Biosystems Engineering
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• v.34 no.1
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• pp.44-49
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• 2009

This study was conducted to investigate the pyrolysis characteristics of switchgrass using TGA-FTIR instrument. Switchgrass is a high yielding perennial grass that has been designated as a potential energy crop, because of its high energy value. Ground switchgrass were pyrolysed at different heating rates of 10, 20, 30, and $40^{\circ}C/min$ in a TGA-FTIR instrument. The thermal decomposition characteristics of switchgrass were analyzed, and the gases volatilized during the experiment were identified. The thermal decomposition of switchgrass started at approximately $220^{\circ}C$, followed by a major loss of weight, where the main volatilization occurred, and the thermal decomposition was essentially completed by $430^{\circ}C$. The pyrolysis process was found to compose of four stages; moisture evaporation, hemicellulose decomposition, cellulose decomposition, and lignin degradation. The peak temperatures for hemicellulose decomposition ($306^{\circ}C$ to $327^{\circ}C$) and cellulose decomposition ($351^{\circ}C$ to $369^{\circ}C$) were increased with greater heating rates. FTIR analysis showed that the following gases were released during the pyrolysis of switchgrass; $CO_2$, CO, $CH_4$, $NH_3$, COS, $C_{2}H_{4}$, and some acetic acid. The most gas species were released at low temperature from 310 to $380^{\circ}C$, which was corresponding well with the observation of thermal decomposition.

• Journal of the Korean Wood Science and Technology
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• v.29 no.3
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• pp.59-67
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• 2001

Rice husk flours were analyzed by chemical composition and thermogravimetric methods in nitrogen atmosphere to discuss its feasibility as a raw material for manufacturing agricultural lignocellulosic fiber-thermoplastic polymer composite. It was revealed in the chemical composition analysis that rice husk flour was composed of moisture, 5.0%; lignin, 21.6%; holocellulose, 60.8%; ash, 12.6%. In the thermogravimetric analysis (TGA), thermal decomposition behavior of rice husk flour from room temperature to $350^{\circ}C$ was similar to that of wood flour, but rice husk flour was more thermally stable from 350 to $800^{\circ}C$ than wood flour because of higher silica content in the rice husk flour and smaller particle size of rice husk flour. The activation energy of thermal decomposition was evaluated using Flynn & Wall expression. As the thermal decomposition proceeded in rice husk flour, the activation energy of thermal decomposition appeared almost constant up to ${\alpha}=0.25$, but thereafter increased. Activation energy of thermal decomposition in wood flour, however, decreased steeply up to ${\alpha}=0.3$, but thereafter remained almost constant. From the results, rice husk flour was thought be a substitute for wood flour in manufacturing agricultural lignocellulosic fiber-thermoplastic polymer composite in the aspect of thermal decomposition.