• Communications for Statistical Applications and Methods
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• 제13권1호
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• pp.113-123
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• 2006

Normal mixture model(NMM) frequently used to cluster genes on microarray gene expression data. In this paper some of component means of NMM are modelled by a linear regression model so that its design matrix presents the pattern between sample classes in microarray matrix. This modelling for the component means by given design matrices certainly has an advantage that we can lead the clusters that are previously designed. However, it suffers from 'overfitting' problem because in practice genes often are highly dimensional. This problem also arises when the NMM restricted by the linear model for component-means is fitted. To cope with this problem, in this paper, the use of the factor analyzer NMM restricted by linear model is proposed to cluster genes. Also several design matrices which are useful for clustering genes are provided.

• 한국자동차공학회논문집
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• 제11권2호
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• pp.203-210
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• 2003

A simulation study was carried to analyze the vehicle fuel consumption on component basis. Experiments was also carried out to identify the simulation results, under FTP-75 Hot Phase driving conditions. and arbitrary driving conditions. A good quantitative agreement was obtained. Based on the simulation, fuel energy was used in pumping loss(3.7%), electric power generation(0.7%), engine friction(12.7%), engine inertia(0.7%), torque converter loss(4.6%), drivetrain friction(0.6%), road-load(9.2%), and vehicle inertia(13.4%) under FTP-75 Hot Phase driving conditions. Using simulation program, the effects of capacity factor and idle speed on fuel consumption were estimated. A increment of capacity factor of torque converter resulted in fuel consumption improvement under FTP-75 Hot Phase driving conditions. Effect of a decrement of idle speed on fuel consumption was negligible under the identical driving conditions.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1994년도 추계학술대회 논문집 학회본부
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• pp.135-137
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• 1994

It this paper, a single-phase high power factor rectifier with low-enery requirements for filtering elements is proposed. By switching control, the current containing the 3rd-hamonic component is to flow into the rectifier. Properly selecting the amplitude of the 3rd-hamonic component, it is possible to reduce the stored energy in the rectifier. Boost converter is used for power-factor control while active filter absorbs the 3rd-hamonic component. Simulation results are presented to show low stored energy of the proposed rectifier.

• 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.

• 농촌계획
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• 제12권1호
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• pp.37-48
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• 2006

The objectives of this study were to develop an evaluation method of regional vulnerability to agricultural drought and to classify the vulnerability patterns. In order to test the method, 24 city or county areas of Gyeonggi-do were chose. First, statistic data and digital maps referred for agricultural drought were defined, and the input data of 31 items were set up from 5 categories: land use factor, water resource factor, climate factor, topographic and soil factor, and agricultural production foundation factor. Second, for simplification of the factors, principal component analysis was carried out, and eventually 4 principal components which explain about 80.8% of total variance were extracted. Each of the principal components was explained into the vulnerability components of scale factor, geographical factor, weather factor and agricultural production foundation factor. Next, DVIP (Drought Vulnerability Index for Paddy), was calculated using factor scores from principal components. Last, by means of statistical cluster analysis on the DVIP, the study area was classified as 5 patterns from A to E. The cluster A corresponds to the area where the agricultural industry is insignificant and the agricultural foundation is little equipped, and the cluster B includes typical agricultural areas where the cultivation areas are large but irrigation facilities are still insufficient. As for the cluster C, the corresponding areas are vulnerable to the climate change, and the D cluster applies to the area with extensive forests and high elevation farmlands. The last cluster I indicates the areas where the farmlands are small but most of them are irrigated as much.

• Journal of Korean Society for Atmospheric Environment
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• 제10권E호
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• pp.343-356
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• 1994

In this study, a statistical investigation was carried out for the evaluation of any relationship between polycyclic aromatic hydrocarbons (PAHss) associated with ambient aerosols and other air quality parameters under varying meteorological conditions. Daily measurements for PAHs and air quality/meteorological parameters were selected from a data-base constructed by a comprehensive air monitoring in London during 1985-1987. Correlation coefficients were calculated to examine any significant relationship between the PAHs and other individual variables. Statistical analysis was further Performed for the air quality/meteorological data set using a principal component analysis to derive important factors inherent in the interactions among the variables. A total of six components were identified, representing vehicle emission, photochemical activity/volatilization, space heating, atmospheric humidity, atmospheric stability, and wet deposition. It was found from a stepwise multiple regression analysis that the vehicle emission component is overall the most important factor contributing to the variability of PAHs concentrations at the monitoring site. The photochemical activity/volatilzation component appeared to be also an important factor particularly for the lower molecular weight PAHs. In general, the space heating component was found to be next important factor, while the contributions of other three components to the variance of each PAHs did not appear to be as much important as the first three components in most cases. However, a consistency for these components in their negative correlations with PAHs data was found, indicating their roles in the depletion of PAHs concentrations in the urban atmosphere.

• 대한전기학회논문지:전기기기및에너지변환시스템부문B
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• 제55권5호
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• pp.258-264
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• 2006

This paper presents an integrative control scheme for series-type active power filters combined with shunt passive filters not only to compensate for the source voltage unbalance and current harmonics but also to correct the power factor. To reduce the power capacity of the active filters, passive filters are connected in parallel. Diode rectifiers are replaced by the PWM converters in order to feed the real power back to the source. Power factor control is performed by changing the phase of the load voltage so that the phase of the source current coincides with that of the source voltage. The resultant voltage reference is the addition of the voltage component compensating for the source voltage unbalance and harmonic currents and the voltage component correcting the power factor. The validity of the proposed algorithm has been verified by experimental results.

• 한국지진공학회:학술대회논문집
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• 한국지진공학회 1997년도 춘계 학술발표회 논문집 Proceedings of EESK Conference-Fall 1997
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• pp.88-93
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• 1997

Amplication factor spectrum has been obtained and compared with standard Response Spectrum using the observed strong ground motions database. The observed ground motions from the Miramichi, Nahanni, Saguenay and New Madrid Earthquake (vertical component 19. horizontal component 36). which are estimated to represent domestic seismotectonic characteristics such as seismic source, attenuation, and site effect, are used for the analysis of amplication factor spectrum. Amplication factor has been calculated using both observed peak values and results from responses to the observed horizontal and vertical ground motions. The comparison shows that the amplication factors resultant from this study exceeds those of Standard Response Spectrum at relatively higher frequencies. The results implie that the characteristics of the seismic strong ground motion which may represent the domestic seismotectonic characteristics differs from those of Standard Response Spectrum, which are resultant from the strong ground motions observed mainly at the westem United States.

• PNF and Movement
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• 제1권1호
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• pp.1-9
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• 2003

Objectives: The main purpose of this study is to analyze the importance of the rotatory factor in PNF pattern. In this article has to analyze and clarify the importance of the rotatory factor in PNF pattern by comparing the general concepts, facilitation technics, and neuroscience of muscle spindle, etc. Methods: This is a literature study with books. Results : There is close relationships between the pattern in PNF and rotatory factor. PNF pattern is priorily made up the diagonal and spiral pattern. the rotatory factor influences and guides from the start to end in the pattern. Conclusions : Rotation component is most important of the three component that one flexion or extension, abduction or adduction and rotation. It is that provide longer muscle length and stimulate the muscle spindles.

• 한국빅데이터학회지
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• 제5권2호
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• pp.135-143
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• 2020

본 논문은 농식품 소비자패널 데이터에서 소비자의 유형을 나눌 때에 변수간 연관성이 많은 장바구니 분석에서 전처리 방법과 차원축소의 방법을 제안한다. 군집분석은 다변량 자료에서 관측 개체를 몇 개의 군집으로 나눌 때 널리 사용되는 분석기법이다. 하지만 여러 개의 변수가 연관성을 가진 경우에는 차원축소를 통한 군집분석이 더 효과적일 수 있다. 본 논문은 1,987 가구를 대상으로 조사한 식품소비 데이터를 K-means 방법을 사용하여 군집화하였으며, 군집을 나누기 위해 17개의 변수를 선정하였고, 17개의 다중공선성 문제와 군집을 나누기 위한 차원축소의 방법 중 주성분 분석과 요인분석을 비교하였다. 본 연구에서는 주성분분석과 요인분석 모두 2개의 차원으로 축소하였으며 주성분분석에서는 3개의 군집으로 나뉘었지만 분석하고자 하였던 소비 패턴에 대한 군집의 특성이 잘 나타나지 않았으며 요인분석에서는 분석가가 보고자 하는 소비 패턴의 특징이 잘 나타났다.