• 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.

• Bulletin of the Korean Chemical Society
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• v.14 no.1
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• pp.38-42
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• 1993

Factor analysis was applied to study the thermal decomposition of barium titanyl oxalate (BTO) which is used as the precursor of barium titanate. BTO was synthesized in $H_2O$ solvent and calcined at various temperatures. The X-ray diffraction patterns were obtained to make the data matrix of peak intensity vs. 2${\theta}$. Abstract factor analysis and target transformation factor analysis were applied to this data matrix. It has been found that the synthesized BTO consists of the crystals of $BaC_2O_4{\cdot}0.5H_2O\;and\;BaC_2O_4{\cdot}2H_2O$ as well as the amorphous solid of TiO-oxalate. The results also indicate that the BTO was transformed via $BaCO_3\;to\;BaTiO_3\;and\;Ba_2TiO_4$ during the thermal decomposition.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.810-831
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• 2019

Recently, continuous dimensional emotion recognition from audiovisual clues has attracted increasing attention in both theory and in practice. The large amount of data involved in the recognition processing decreases the efficiency of most bimodal information fusion algorithms. A novel algorithm, namely the incomplete Cholesky decomposition based kernel cross factor analysis (ICDKCFA), is presented and employed for continuous dimensional audiovisual emotion recognition, in this paper. After the ICDKCFA feature transformation, two basic fusion strategies, namely feature-level fusion and decision-level fusion, are explored to combine the transformed visual and audio features for emotion recognition. Finally, extensive experiments are conducted to evaluate the ICDKCFA approach on the AVEC 2016 Multimodal Affect Recognition Sub-Challenge dataset. The experimental results show that the ICDKCFA method has a higher speed than the original kernel cross factor analysis with the comparable performance. Moreover, the ICDKCFA method achieves a better performance than other common information fusion methods, such as the Canonical correlation analysis, kernel canonical correlation analysis and cross-modal factor analysis based fusion methods.

• KIEE International Transactions on Electrophysics and Applications
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• v.3C no.2
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• pp.48-54
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• 2003

To evaluate the radiation degradation of ethylene propylene rubber (EPR), radiation effects on EPR were investigated by using dielectric analysis and thermal-gravimetric analysis. Permittivity, loss factor, tan$\delta$, and thermal decomposition temperature were observed for ${\gamma}$-ray irradiated EPR. As the radiation dose was increased, the peak temperature of the loss factor and tans of EPR were increased and loss factor and tan$\delta$ at peak temperature were decreased. Activation energies were calculated using loss factor and thermal decomposition for ${\gamma}$-ray irradiated EPR as well. The trends of both calculated activation energies showed the same tendencies as radiation dose was increased.

• Journal of Climate Change Research
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• v.9 no.4
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• pp.407-421
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• 2018

In this paper, I analyze the GHG (greenhouse gas) emission factor of the domestic railway transportation sector using the LMDI (Log Mean Divisia Index) methodology. These GHG factors are the emission factor effect, energy intensity effect, transportation intensity effect, and economic activity effect. The analysis period was from 2011 to 2016, and the analysis objects were an intercity railway, wide area railway, and urban railway. The results show that the GHG emission of railway transportation sector decreased during these 6 years. The factors decreasing the GHG emission are the emission factor effect, energy intensity effect, and transportation intensity effect, while the factor increasing the GHG emission is the economic activity effect.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.34 no.11
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• pp.421-429
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• 1985

With the rapid increase of contingency cases due to complication of power system, the reduction of computation time in contingency analysis has become more significant than ever before. This paper deals with the development of a fast contingency analysis algorithm by using a matrix decomposition method. The proposed matrix decomposition method of contingency analysis yields an accurate solution by using the original triangular factor table. An outstanding feature of this method is of no need of factor table modification for network changes due to contingency outages. The proposed method is also applicable to multiple contingency analysis withremarkable reduction of computation time. The algorithm has been tested for a number of single and multiple contigencies in 17-bus and 50-bus systems. The numerical results show its applicability to practical power systems.

• Journal of the Korean Society of Propulsion Engineers
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• v.3 no.4
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• pp.23-30
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• 1999

The thermal decomposition of phase stabilized ammonium nitrate(PSAN) was studied by means of thermogravimetric analysis(TGA). In this study, potassium nitrate and zinc oxide were used as the phase stabilizers in the range of contents from 0 wt.% to 8 wt.%. The kinetics and mechanism for the decomposition were evaluated using integral methods. It was found that the thermal kinetic parameters such as activation energy(I) and frequency factor(A) increase with the increase of the stabilizer contents, and the mechanism of decomposition also changes.

• Environmental and Resource Economics Review
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• v.31 no.4
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• pp.825-848
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• 2022

This paper analyzed the factors for increasing energy consumption in the domestic manufacturing sector using the LMDI (Log mean division index) decomposition method for the period from 1999 to 2019. Among the LMDI decomposition analysis methods, both additive and multiplicative factor decomposition methods were used. in this analysis. According to the result of the analysis, the factor that increased energy consumption in the domestic manufacturing industry was the production effect, and the structure effect and intensity effect were found to be the factors that decreased energy consumption. In particular, the reduction of energy consumption due to the structure effect was greater than that of energy consumption effect due to the intensity effect. By period, it can be seen that energy consumption increased rapidly due to the production effect until 2011, but after that, the increase in energy consumption due to the production effect slowed down. On the other hand, after that, the energy reduction effect due to the structure effect and the intensity effect became prominent. In order to save energy in the manufacturing sector in the future, energy diagnosis and management through EMS (Energy management system) and FEMS (Factory energy management system) are more necessary. In addition, restructuring into a low-energy consumption industry seems more necessary.

• Structural Engineering and Mechanics
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• v.32 no.1
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• pp.37-53
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• 2009

In this paper the problem of calculating the probability that the responses of a wind-excited structure exceed specified thresholds within a given time interval is considered. The failure domain of the problem can be expressed as a union of elementary failure domains whose boundaries are of quadratic form. The Domain Decomposition Method (DDM) is employed, after being appropriately extended, to solve this problem. The probability estimate of the overall failure domain is given by the sum of the probabilities of the elementary failure domains multiplied by a reduction factor accounting for the overlapping degree of the different elementary failure domains. The DDM is extended with the help of Line Sampling (LS), from its original presentation where the boundary of the elementary failure domains are of linear form, to the current case involving quadratic elementary failure domains. An example involving an along-wind excited steel building shows the accuracy and efficiency of the proposed methodology as compared with that obtained using standard Monte Carlo simulations (MCS).

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.2 s.308
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• pp.82-86
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• 2006

The design of a pattern recognition system generally involves the three aspects: preprocessing, feature extraction, and decision making. Among them, a feature extraction method determines an appropriate subspace of dimensionality in the original feature space of dimensionality so that it can reduce the complexity of the system and help to improve successful recognition rates. Linear transforms, such as principal component analysis, factor analysis, and linear discriminant analysis have been widely used in pattern recognition for feature extraction. This paper shows that singular value decomposition (SVD) can be applied usefully in feature extraction stage of pattern recognition. As an application, a remote sensing problem is applied to verify the usefulness of SVD. The experimental result indicates that the feature extraction using SVD can improve the recognition rate about 25% compared with that of PCA.