• Proceedings of the Korea Multimedia Society Conference
• /
• 2002.05c
• /
• pp.176-179
• /
• 2002

Angiogenesis는 존재하는 혈관으로부터 새로운 혈관이 생성되는 과정으로, 암의 성장과 전이에 있어서 필수적 요소이다. 특히, 신생혈관의 밀도는 암의 성장과 밀접한 상관관계를 가지고 있으므로 암의 진단과 예후 추정을 위한 판단근거로 사용되고 있다. 본 연구는 신생혈관의 밀도를 정확한 수치로 정량화 하기 위하여, 칼라 특성값을 이용하여 신생혈관을 분할하였다. 분류기 생성을 위한 학습집단은 신생혈관영역, 배경영역에서 각각 100개씩 픽셀을 추출하였다. 추출된 픽셀에서 9개의 칼라 특성값(R,G,B,H,S,I,I₁,I₂,I₃)을 계산하고, 다변량 판별분석을 이용하여 3개의 분류기를 생성하고 분할된 결과를 비교분석하였다. 분할된 결과를 비교하면 RGB와 I₁ I₂ I₃ 칼라 특성값을 이용하여 생성된 분류기에 의해 분할된 결과가 전문가 의견과 높은 상관관계를 나타내었다.

• Journal of Korean Society of Forest Science
• /
• v.102 no.3
• /
• pp.407-414
• /
• 2013

This study was conducted to classify forest cover types using the multivariate statistical analysis in the natural forest of western Mt. Jiri. On the basis of the vegetation data by point quarter sampling, the adopted analytical methods were species-area curve (SAC), hierarchical cluster analysis (HCA), indicator species analysis (ISA), and multiple discriminant analysis (MDA). SAC selected the outlier tree species which was likely to have no influence on the classification of forest cover types, excluded from all analytical process. Based on forest vegetative information, HCA classified the study area into 2 to 10 clusters and ISA indicated that the optimal number of clusters were seven. MDA was taken to test the clusters that classified with HCA and ISA. The seven clusters were classified appropriately as overall classification success were 91.3%. The classified forest cover types were named by the ratio of the dominant species in the upper layer of each cluster. They were (1) Quercus mongolica Pure forest, (2) Mixed mesophytic forest, (3) Q. mongolica - Q. serrata forest, (4) Abies koreana - Q. mongolica forest, (5) Fraxinus mandshurica forest, (6) Q. serrata forest, and (7) Carpinus laxiflora forest.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.15 no.8
• /
• pp.4881-4894
• /
• 2014

This study selected 83 manufacturing firms that had been delisted from the KOSDAQ market from 2009 to 2012 and the sample firms for the two-paired sampling method were compared with 83 normal firms running businesses with same items or in same industry. The 75 financial ratios for five years immediately before delisting were used for Mean Difference Analysis with those of normal firms. Fifteen variables assumed to be significant variables for five consecutive years out of the analysis were used to in the Dichotomous Classification Technique, Logistic Regression Analysis and Discriminant Analysis. As a result of those three analyses, the Logistic Regression Analysis model was found to show the greatest discrimination. This study is differentiated from previous studies as it assumed that the firm's failure proceeded slowly over long period of time and it tried to predict the firm's failure earlier using the five years' historical data immediately before failure, whereas previous studies predicted it using three years' data only. This study is also differentiated from the proceeding comparative studies by its statistically complex Multi-Variate Analysis and Dichotomous Classification Analysis, which general stakeholders can easily approach.

• Korean journal of applied entomology
• /
• v.32 no.1
• /
• pp.42-50
• /
• 1993

Some multivariate discriminant analyses were done for each population of the native honeybee workers (Apis cerana), which were selected for 11 different localities in spring and 12 in summer from Korea. When the comparison for different localities was conducted, the correct assignment was averaged at 91.67% in spring and 88.44% in summer. And for the comparison between the 2 different seasons, it was averaged at 97.58%. Whereas, that regardless of seasons revealed the lowest correct assignment at 70.16%.

• KSBB Journal
• /
• v.22 no.6
• /
• pp.401-408
• /
• 2007

It's not easy to detect the specific compounds from various compounds that fermented in bioreactor. The electronic nose was an instrument, which comprised of an array of electronic chemical sensors with partial specificity and an appropriate pattern recognition system, capable of recognizing simple or complex volatiles. It can conduct fast analysis and provide simple and straightforward results and is best suited for quality control and process monitoring in field of biotechnology. This review examined the application of electronic nose in biotechnology and brief explanation of its principle. In this minireview numbers of applications of an electronic nose in biotechnology include monitoring fermentation process, to overcome interference with alcohol, and to detect contaminant microorganism were discussed. The electronic nose would be useful for a wide variety of biotechnology when correlating analytical instrumental data with the obtained data from electronic nose.

• The korean journal of orthodontics
• /
• v.39 no.2
• /
• pp.112-119
• /
• 2009

Objective: To develop a mixed dentition analysis method in consideration of the normal variation of tooth sizes. Methods: According to the tooth-size of the maxillary central incisor, maxillary 1st molar, mandibular central incisor, mandibular lateral incisor, and mandibular 1st molar, 307 normal occlusion subjects were clustered into the smaller and larger tooth-size groups. Multiple regression analyses were then performed to predict the sizes of the canine and premolars for the 2 groups and both genders separately. For a cross validation dataset, 504 malocclusion patients were assigned into the 2 groups. Then multiple regression equations were applied. Results: Our results show that the maximum errors of the predicted space for the canine, 1st and 2nd premolars were 0.71 and 0.82 mm residual standard deviation for the normal occlusion and malocclusion groups, respectively. For malocclusion patients, the prediction errors did not imply a statistically significant difference depending on the types of malocclusion nor the types of tooth-size groups. The frequency of prediction error more than 1 mm and 2 mm were 17.3% and 1.8%, respectively. The overall prediction accuracy was dramatically improved in this study compared to that of previous studies. Conclusions: The computer aided calculation method used in this study appeared to be more efficient.

• Korean Journal of Food Science and Technology
• /
• v.28 no.6
• /
• pp.1171-1176
• /
• 1996

The changes in volatiles of the model system were analyzed by GC and GC-MS before and after retorting. The GC data were analyzed statistically by applying the analysis of variance, and 42 peaks were selected at 5% significance level. Multivariate statistical analysis was performed with these 42 peaks as independent variables. Through the stepwise discriminant analysis, 8 peaks, which corresponded to the compounds such as 2-heptanone, cis-3-hexenal, 2-pentyl-furan, 1-methyl-trans-1,2-cyclohexanediol, 2-hexanone, 3-octanone, trans, trans-nona-2,4-dienal and 1-octen-3-ol, were obtained in sequence to distinguish the samples with and without retorting. The principal component analysis of a set of 8 independent variables resulted in 3 principal components which accounted for 96.1% of the variance, while the first principal component (PC 1) explained 76.5% of the total variance. In addition, through the factor analysis of the principal components, the peaks 11, 20 and 21 could be grouped togather in accordance with the direction and the size while the peaks 9, 33 and 39 constituted the second group in the direction.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.6 no.3
• /
• pp.237-245
• /
• 2004

In designing a tunnel, RMR has been widely used to classify rock mass and to decide the support pattern according to the class of rock mass. However, this RMS system can't help relying on the empirical judgment of engineers who use variables which can be obtained only through consideration of the site conditions. In actuality, it is impossible to consider all the rating factors of RMS when using RMR system at the stage of designing. Therefore, in order to confirm possibility of RMR by use of only the quantitative factors for designing, this paper has done discriminant analysis. Rock strength or RQD has high coefficient of correlation with RMR value, and in consideration of the existing standards for rock mass classification, rock intensity and RQD are important factors for classification of rock mass. Through rock mass classification by the existing RMR system and rock mass classification by the discriminant analysis which has considered two variables only, the discriminant analysis using the rock intensity as an independent variable has shown 74.8% accuracy while the discriminant analysis using RQD as an independent variable has shown 74.3% accuracy. In case of the discriminant analysis which has considered both rock intensity and RQD, it has shown 82.5% accuracy. The existing cases have shown 40.3% accuracy at the stage of designing in which all the RMR factors are considered. It means that at the stage of designing, RMR system can work only with the rock intensity and RQD.

• Animal Systematics, Evolution and Diversity
• /
• no.nspc2
• /
• pp.243-250
• /
• 1988

The wing morphometric characters of P.elaeagni complex feeding on the genus Elaeagnus plants was analysed by the multivariate methods using clustering of generalized distance and discriminant analysis. On the clustering of the species, the effect of sexual differences, seasonal variation and geographic population sensitively appeared . However, four species of this group was precicely divided by the discriminant analysis.

• Korean Chemical Engineering Research
• /
• v.48 no.4
• /
• pp.483-489
• /
• 2010

An image analysis-based soft sensor is designed and applied to automatic quality classification of product appearance with color-textural characteristics. In this work, multiresolutional multivariate image analysis (MR-MIA) is used in order to analyze product images with color as well as texture. Fisher's discriminant analysis (FDA) is also used as a supervised learning method for automatic classification. The use of FDA, one of latent variable methods, enables us not only to classify products appearance into distinct classes, but also to numerically and consistently estimate product appearance with continuous variations and to analyze characteristics of appearance. This approach is successfully applied to automatic quality classification of intermediate and final products in industrial manufacturing of engineered stone countertops.