• Journal of the Korean Statistical Society
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• 제25권1호
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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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• 제14권1호
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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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• 제13권2호
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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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• 제3C권2호
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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.

• 한국기후변화학회지
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• 제9권4호
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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.

• 대한전기학회논문지
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• 제34권11호
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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.

• 한국추진공학회지
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• 제3권4호
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• pp.23-30
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• 1999

상안정화 질산암모늄(PSAN)의 열분해 특성에 대한 연구를 thermogravimetric analysis(TGA)를 사용하여 수행하였다. 본 연구에서는 질산칼륨과 산화아연이 상안정화제로 0%에서 8%사이에서 사용되었다. 열분해에 대한 속도론적 특성과 메카니즘들을 적분법을 사용하여 평가하였다. 활성화에너지(E)와 잦음율인자(A) 같은 열적 속도계수들은 상안정화제의 함량이 증가함에 따라 증가하였고, 분해 메카니즘 또한 변화되었다.

• 자원ㆍ환경경제연구
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• 제31권4호
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• pp.825-848
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• 2022

이 논문은 국내 제조업부문의 에너지소비 증가 요인을 LMDI(Log mean divisia index) 분해 분석방법을 이용하여 분석하였다. 1999년부터 2019년까지 20년간의 에너지소비 변화를 분석하였다. LMDI 분해 분석방법 중 에너지소비 증가량을 분석한 가법적 요인분해 분석과 에너지소비 증가율을 분석한 승법적 요인분해 분석 모두를 사용하였다. 분석결과, 국내 제조업의 에너지소비를 증가시킨 요인은 생산효과이며, 구조효과와 집약도 효과는 에너지소비를 감소시키는 요인으로 나타났다. 특히 구조효과에 의한 에너지소비 감소가 집약도 효과에 의한 에너지소비 효과보다 더 크게 나타났다. 시기별로 보면, 2011년까지는 에너지소비가 생산효과에 의해 급속히 증가한 반면 그 이후에는 생산효과에 의한 에너지소비 증가가 둔화된 것을 알 수 있다. 이에 반해 그 이후에는 구조효과와 집약도효과에 의한 에너지 감소효과가 두드러지고 있는데 이는 2011년부터 실시된 온실가스·에너지목표관리제와 2015년 이후 실시된 배출권거래제의 효과가 나타난 결과로 보인다. 향후 제조업부문의 에너지절약을 위해서는 EMS(Energy management system), FEMS(Factory energy management system) 등을 통한 에너지진단과 관리가 더욱 필요해 보인다. 아울러 에너지저소비형 산업으로의 구조조정도 더 필요해 보인다.

• Structural Engineering and Mechanics
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• 제32권1호
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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).

• 대한전자공학회논문지SP
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• 제43권2호
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• pp.82-86
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• 2006

패턴인식 시스템은 일반적으로 데이터의 전처리, 특징 추출, 학습단계의 과정을 거쳐서 개발되어 진다. 그중에서도 특징 추출 과정은 다차원 공간을 가진 입력 데이터의 복잡도를 줄여서 다음 단계인 학습단계에서 계산 복잡도와 인식률을 향상시키는 역할을 한다. 패턴인식에서 특징 추출 기법으로써 principal component analysis, factor analysis, linear discriminant analysis 같은 방법들이 널리 사용되어져 왔다. 이 논문에서는 singular value decomposition (SVD) 방법이 패턴인식 시스템의 특징 추출과정에 유용하게 사용될 수 있음을 보인다. 특징 추출단계에서 SVD 기법의 유용성을 검증하기 위하여 원격탐사 응용에 적용하였는데, 실험결과는 널리 쓰이는 PCA에 비해 약 25%의 인식률의 향상을 가져온다는 것을 알 수 있다.