• 천문학회보
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• 제41권2호
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• pp.35.1-35.1
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• 2016

To understand the coevolution of Brightest Cluster Galaxies (BCGs) and their host clusters, we conduct a case study on the BCGs in dynamically young and old clusters, Abell 1139 (A1139) and Abell 2589 (A2589). We analyze the pixel color-magnitude diagrams (pCMDs) using deep g- and r-band images, obtained from the CFHT observations. (1) While the overall shapes of the pCMDs are similar to those of typical early-type galaxies, the A2589-BCG tends to have redder mean pixel color and smaller pixel color deviation at given surface brightness than the A1139-BCG. (2) The mean pixel color distribution as a function of pixel surface brightness indicates that the A2589-BCG formed a larger central body by major dry mergers at an early epoch than the A1139-BCG, while they have grown commonly by subsequent minor mergers. (3) The spatial distributions of the pixels with deviated colors reveal that the A1139-BCG experienced considerable tidal events more recently than the A2589-BCG, whereas the A2589-BCG has an asymmetric compact core possibly resulting from major dry merger at an early epoch. (4) The A2589-BCG shows a very large faint-to-bright pixel number ratio compared to early-type non-BCGs, whereas the ratio for the A1139-BCG is not distinctively large. These results imply that the BCG in the dynamically older cluster (A2589) formed earlier and is relaxed better.

• International Journal of Oral Biology
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• 제43권2호
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• pp.101-111
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• 2018

Understanding the classification of malocclusion is a crucial issue in Orthodontics. It can also help us to diagnose, treat, and understand malocclusion to establish a standard for definite class of patients. Principal component analysis (PCA) and k-means algorithms have been emerging as data analytic methods for cephalometric measurements, due to their intuitive concepts and application potentials. This study analyzed the macro- and meso-scale classification structure and feature basis vectors of 1020 (415 male, 605 female; mean age, 25 years) orthodontic patients using statistical preprocessing, PCA, random matrix theory (RMT) and k-means algorithms. RMT results show that 7 principal components (PCs) are significant standard in the extraction of features. Using k-means algorithms, 3 and 6 clusters were identified and the axes of PC1~3 were determined to be significant for patient classification. Macro-scale classification denotes skeletal Class I, II, III and PC1 means anteroposterior discrepancy of the maxilla and mandible and mandibular position. PC2 and PC3 means vertical pattern and maxillary position respectively; they played significant roles in the meso-scale classification. In conclusion, the typical patient profile (TPP) of each class showed that the data-based classification corresponds with the clinical classification of orthodontic patients. This data-based study can provide insight into the development of new diagnostic classifications.

• 가족자원경영과 정책
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• 제13권2호
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• pp.129-151
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• 2009

The first purpose of this study was to reveal the types of shopping value of college students. The second purpose was to examine the change in the perception and management behaviors related to credit cards according to the types of shopping value. The third purpose was to examine the effects of shopping value on perception and management behaviors on credit cards. The data were collected from 392 college students in Seoul by a self-administered questionnaire. Analyses including frequency, mean, factor analysis, Cronbach's alpha, Pearson's correlation analysis, Crosstabulation analysis, analysis of variance, K-means Cluster analysis and Multiple linear regression were conducted using SPSS WIN12.0. The major findings were as follows. First, college students can be categorized into 3 types of shopping values by K-means Cluster analysis of 14 items. The groups were entitled the hedonistic shopping value, the utilitarian shopping value, and the saving shopping value. Second, positive perception and management behaviors related to credit cards were different depending on the types of shopping value. The hedonistic shopping value group had a higher level of positive perception of credit cards and a lower level of credit card management, compared with the other groups. The saving shopping value group had higher levels of both positive perception and management of credit cards. Among the three groups, the utilitarian shopping group had the lowest level of positive perception of credit cards, despite having ahigher level of credit card management. Lastly, the most effective variance on credit card management was the utilitarian shopping value. These results suggest that a healthy shopping value is very important for having a healthy perception and management of credit cards, because shopping value is a critical variance to affect perception and management of credit cards.

• 한국의류학회지
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• 제17권2호
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• pp.247-264
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• 1993

This study deals with the manufacturing of the foundation garments for the well-balanced somatotype of the Korean middle-aged womenhood. In order to get hold of the different somatotypes, a survey of a total of 134 middle-aged women in Kwangju area, ranging in their age from 45 through 59 was made. The statistical methods used for the analysis of the basic data were the Pearson's correlation coefficient, Anova, Cluster analysis and Stepwise. Emphasis of the try-on test was placed on (1) the comparison of anthropometric data before and after trying on the foundation garments, (2) sensory evaluation, (3) a rating on fit and performance, (4) the comparison by means of photograph. The conclusions obtained are as follows : 1) The 134 women sampled and measured were classified into the five groups of somatotype : the 52 women (34%) belong to Cluster 1 ; the 22 women(14.5%) belong in Cluster 2 ; the 12 women(7.9%) belong in Cluster 3 ; the 15 women(9.9%) belong in Cluster 4 ; the 33 women(27.7%) belong to Cluster 5. 2) As for the characteristics of the foundation garment design, the V-shaped neckline and chest dart was used. The adjust point is right above the perineum point. The foundation garment length is as far as trochanteric point. The materials used are cotton/polyurethane, lace, 100%cotton. The materials used for corrections were the sponge pad for the chest, and non-woven fabric pad for the back, shoulder and the hip. 3) The comparison of the anthropometric data of the subject when dressed in foundation garments showed a significant difference in bust point height, in bust point length and in nipple-ta-nipple breadth, which proves the foundation garments to be effective in correcting such part as the chest, the hip and the abdomen. 4) As considered in terms of the sensory evaluation, the item except for the shoulder and the armhole coincided with each other in the mean value and in the composite reliability coefficient, which also proves the foundation garments to be effective. 5) Subjects were satisfactory on fit, performance, design, of the foundation garment, and their changed appearance. 6) In the case of the comparison through the photographs, the silhouettes of all the five women subjects were found effectively to be balanced.

• 한국균학회지
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• 제42권2호
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• pp.159-164
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• 2014

큰느타리 품종구분을 위한 마커의 개발을 위하여 큰느타리 전체 유전자 염기서열을 바탕으로 제작한 484개의 SSR마커를 사용하여 다형성 분석을 실시하였다. 그 결과 각 275개의 primer에서 다형성이 관찰되었다. 이 중 품종간에 다양한 패턴을 나타내는 5개의 마커를 최종 선발하였다. 이들 마커의 PIC 값은 0.6627에서 0.6848로 나타났고, 평균값은 0.6775였다. 이 결과를 밴드 이미지 인식 방법으로 dendrogram을 작성하였다. UPGMA 집괴분석 결과, 큰느타리 품종은 크게 Cluster 1과 Cluster 2로 구분되었다. SSR primer를 이용한 PCR 결과 나타나는 품종별 고유의 DNA 밴드를 품종특이적 마커로 개발하기 위하여, 선발된 마커중에서 SSR312과 SSR366, SSR178과 SSR 277 마커를 조합하여 초위성체 유래 다중 표지 세트를 개발하였다. Multiplex-SSR 마커의 사용을 통해 두번의 PCR 반응만으로 본 연구에서 사용된 12개의 큰느타리 품종을 구분할 수 있었다.

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1131-1131
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• 2001

To develop a nondestructive quality evaluation technique of fruits, a K-mean algorism is applied to near infrared (NIR) spectroscopy of apples. The K-mean algorism is one of neural network partition methods and the goal is to partition the set of objects O into K disjoint clusters, where K is assumed to be known a priori. The algorism introduced by Macqueen draws an initial partition of the objects at random. It then computes the cluster centroids, assigns objects to the closest of them and iterates until a local minimum is obtained. The advantage of using neural network is that the spectra at the wavelengths having absorptions against chemical bonds including C-H and O-H types can be selected directly as input data. In conventional multiple regression approaches, the first wavelength is selected manually around the absorbance wavelengths as showing a high correlation coefficient between the NIR $2^{nd}$ derivative spectrum and Brix value with a single regression. After that, the second and following wavelengths are selected statistically as the calibration equation shows a high correlation. Therefore, the second and following wavelengths are selected not in a NIR spectroscopic way but in a statistical way. In this research, the spectra at the six wavelengths including 900, 904, 914, 990, 1000 and 1016nm are selected as input data for K-mean analysis. 904nm is selected because the wavelength shows the highest correlation coefficients and is regarded as the absorbance wavelength. The others are selected because they show relatively high correlation coefficients and are revealed as the absorbance wavelengths against the chemical structures by B. G. Osborne. The experiment was performed with two phases. In first phase, a reflectance was acquired using fiber optics. The reflectance was calculated by comparing near infrared energy reflected from a Teflon sphere as a standard reference, and the $2^{nd}$ derivative spectra were used for K-mean analysis. Samples are intact 67 apples which are called Fuji and cultivated in Aomori prefecture in Japan. In second phase, the Brix values were measured with a commercially available refractometer in order to estimate the result of K-mean approach. The result shows a partition of the spectral data sets of 67 samples into eight clusters, and the apples are classified into samples having high Brix value and low Brix value. Consequently, the K-mean analysis realized the classification of apples on the basis of the Brix values.

• The Plant Pathology Journal
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• 제19권5호
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• pp.221-226
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• 2003

Twenty universal rice primers (URPs) were used to detect PCR polymorphisms in 25 isolates of six different Alternaria species producing host specific toxins (HST). Eight URPs could be used to reveal PCR polymorphisms of Alternaria isolates at the intra- and inter-species levels. Specific URP-PCR polymorphic bands that are different from those of the other Alternaria spp. were observed on A. gaisen and A. longipes isolates. Unweighted pair-group method with arithmetic mean (UPGMA) cluster analysis using 94 URP polymorphic bands revealed three clustered groups (A. gaisen group, A. mati complex group, and A. logipes group).

• 디지털산업정보학회논문지
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• 제15권4호
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• pp.197-212
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• 2019

The purpose of this study is to identify the maturity stages of venture firms through classification analysis, which is widely used as a big data technique. Venture companies should develop a competitive advantage in the market. And the maturity stage of a company can be classified into five stages. I will analyze a difference in the growth stage of venture firms between the survey response and the statistical classification methods. The firm growth level distinguished five stages and was divided into the period of start-up and declines. A classification method of big data uses popularly k-mean cluster analysis, hierarchical cluster analysis, artificial neural network, and decision tree analysis. I used variables that asset increase, capital increase, sales increase, operating profit increase, R&D investment increase, operation period and retirement number. The research results, each big data analysis technique showed a large difference of samples sized in the group. In particular, the decision tree and neural networks' methods were classified as three groups rather than five groups. The groups size of all classification analysis was all different by the big data analysis methods. Furthermore, according to the variables' selection and the sample size may be dissimilar results. Also, each classed group showed a number of competitive differences. The research implication is that an analysts need to interpret statistics through management theory in order to interpret classification of big data results correctly. In addition, the choice of classification analysis should be determined by considering not only management theory but also practical experience. Finally, the growth of venture firms needs to be examined by time-series analysis and closely monitored by individual firms. And, future research will need to include significant variables of the company's maturity stages.

• Journal of the Korean Data and Information Science Society
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• 제26권1호
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• pp.41-53
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• 2015

기상분야에서는 다양한 미기상해석모듈 (micro scale weather analysis module)을 개발하여 초고분해능의 기상정보서비스를 실시간으로 제공하고자 노력하고 있다. 이와 같은 연구들은 최근 대도시의 양적인 팽창으로 인해 발생되는 도시 미기상 (micro meteorology)의 급격한 변화에 효과적으로 대처할 수 있는 경제적 사회적 활동을 가능케 한다. 따라서 미기상해석모듈의 정확성은 도시 미기상정보서비스의 품질 및 효용성에 직결된다. 본 논문은 미기상해석모듈이 생성하는 시-공간적인 특성을 가진 양적인 결과물의 정확성에 대한 평가체계를 설계하였다. 이와 더불어 평가체계의 구성에 사용될 평가도구로써 시계열평균의 동일성검정 알고리즘을 스펙트럼 분석기법으로 구축하였으며, 동일성 검정통계의 함수를 거리측도로 사용하는 시계열 군집분석법도 함께 개발하였다. 또한, 사례연구를 통해 제안된 군집분석법과 평가알고리즘의 유용성을 보였다.

• 한국농림기상학회지
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• 제17권2호
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• pp.93-101
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• 2015

본 연구는 연륜연대학적 방법을 적용하여 상수리나무의 연륜생장과 기후인자와의 관계분석을 실시하기 위해 수행하였다. 이를 위해 2006년부터 2010년까지 실시된 제5차 국가산림자원조사의 임목조사 자료에 포함된 연륜생장 자료를 활용하였다. 연륜생장과 기후인자와의 관계를 구명하기 위해 월평균 기온과 월강수량 자료를 연도별로 정리한 후 연륜생장 자료를 수집된 시군별로 분류하여 정리하였다. 상수리나무가 분포하는 지역에 대한 기후조건의 유사성에 근거하여 연륜생장 자료의 군집분석을 실시한 결과 4개의 군집으로 분석되었다. 또한 크로스데이팅과 표준화를 실시하여 상수리나무의 군집별 지표연대기를 작성한 후 기초통계량을 산출하여 지표연대기의 적합성을 검정하였으며, 군집별 연륜생장과 기후인자와의 관계 구명을 위해 반응함수 분석을 실시하였다. 본 연구에서 얻은 상수리나무의 연륜생장과 기후인자 간의 통계적인 분석결과를 기반으로 기후변화에 따른 중장기 생장변화의 예측에 필요한 정보를 제공할 수 있을 것으로 기대된다.