• Proceedings of the Korean Geotechical Society Conference
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• 2006.03a
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• pp.720-723
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• 2006

This study performed slope stability analysis by changing analysis methods and shear strength with the slope stability analysis program. The conclusions of the study are as follows. 1) The safe factor of clayey soil applied with Bishop's simple method turned out to be similar to or slightly higher than those of other methods, for both dry and saturated conditions. 2) The safe factor of sandy soil applied with GLE method turned out to be slightly higher than those of other methods. But when applied with Bishop's simple method, it appeared to be slightly higher than those of other methods. 3) The safe factor of ordinary soil applied with GLE method showed the highest result. 4) Janbu method showed the lowest safe factor among all the methods for the above three types of soils.

• Culinary science and hospitality research
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• v.14 no.2
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• pp.99-114
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• 2008

The purpose of this study is to examine the relationships between each type of attitudes toward money and the attributes of choosing family restaurants. A self-administrated questionnaire was completed by 387 students, and data were analysed by frequency, factor, reliability and canonical correlation. Seven factors were obtained from factor analysis of attitudes toward money; Factor 1 "power", Factor 2 "obsession", Factor 3 "retention", Factor 4 "achievement", Factor 5 "anxiety", Factor 6 "distrust", and Factor7 "evaluation". The attributes of family restaurant choice were extracted into six factors: Factor 1 "quality of food", Factor 2 "restaurant event", Factor 3 "interior environment", Factor 4 "value of food", Factor 5 "convenience for approach", and Factor 6 "employees' service". Canonical correlation analysis showed three significant functions. Canonical function 1 showed that the attitudes of considering the power of money, its retention and achievement were indicated to have significantly positive relationships with the quality of food in the attributes of choosing family restaurants. Canonical function 2 showed that significantly negative relationships between distrust and restaurant events and convenience for approach. Canonical function 3 also showed that significantly positive relationships between obsession and anxiety and the interior of restaurants and employees' service, and significantly negative relationships between evaluation and the interior of restaurants and employees' service.

• International Commerce and Information Review
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• v.8 no.3
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• pp.71-95
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• 2006

In recent, T-commerce is widely dispersed as alternative type of commerce. It is forecasted that t-commerce system is used more than e-commerce system. Therefore more and more t-commerce-related industries are also recognizing that t-commerce is a critical business model. It is needed to understand the concept of t-commerce and develop the t-commerce marketing strategy. CEO analyses consumer's behaviors according to the data about buyers and applies the advantage of t-commerce to the communication with customers. This t-commerce system plays an important role in maximizing customer satisfaction and affecting their intention to reuse it. Therefore this paper attempts to identify T-commerce critical success factors and divide between use-intention group and unuse-intention group by taking out a discriminant function by the discriminant analysis. This lays a foundation in developing T-commerce strategy. According to the discriminant function extracted, convenience factor, amusement factor, system quality factor, product perception factor are significant in the sequence of influential degree. However, usefulness factor and speedy connection factor are not significant. In result, the target hitting rate is 77.9% in the first unuse-intention group and it is 95.2% in the second use-intention group. The total discriminant target hitting rate is computed to higher value, 86.55%. The statistic package, SPSS 12.0, is used to survey and analyse data and test the hypothesis. The validity and reliability of variables are verified by both reliability analysis and factor analysis. The discriminant analysis is used to tell the difference between use-intention group and unuse-intention group.

• Journal of Korean Physical Therapy Science
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• v.9 no.1
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• pp.123-128
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• 2002

Objectives: The objective of this study was to investigate factor analysis of low back pain patients. Methods: The data were collected from hospital located in Daegu. Observation was made on 40 low back pain patients and factor analysis study was also performed in terms of their abnormal physical findings. Results: The low back pain patients occupied 20% of all clinical patients in male group and 20% in female group. The occurrence of acute low back pain in the first factor tended to be higher among in male group than among in female group. While it was significantly higher muscle weakness of the back in female group. The most prevailing abnormal finding among low back pain patients in the first factor were tender point on the back, While it was significantly lower Decreased tendon reflex in both sexes Conclusions: The factor pattern of abnormal physical findings in low back pain patients was much different between male and female patients, suggesting the different pattern of etiology. Although low back pain is one of the most common symptoms causing limitation activity, as yet there is no known specific effective treatment.

• Journal of applied mathematics & informatics
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• v.14 no.1_2
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• pp.377-386
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• 2004

A technique that provides prediction intervals based on a model called an empirical linear model is discussed. The technique, high-dimensional empirical linear prediction (HELP), involves principal component analysis, factor analysis and model selection. HELP can be viewed as a technique that provides prediction (and confidence) intervals based on a factor analysis models do not typically have justifiable theory due to nonidentifiability, we show that the intervals are justifiable asymptotically.

• Journal of Distribution Science
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• v.12 no.7
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• pp.5-12
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• 2014

Purpose - This study identified internationalization factors forKorean SMEs and explored factor differences between AEO and non-AEO authorized companies. Research design, data, and methodology - The study was designed to assess internationalization factors for AEO authorization in Korea through a questionnaire survey and an empirical analysis. The questionnaires were conducted for AEO and Non-AEO authorized companies that were undergoing AEO authorization. The study was conducted through e-mail and AEO manager education classes. Ninety-five questionnaires were collected. We employed the exploratory factor analysis methodology to derive internationalization factors for KoreanSMEs, and explored the factor differences between AEO and Non-AEO authorized companies. Results - AEO authorized companies outperformed Non-AEO authorized companies in R&D and technology. This indicated that AEO authorized companies were recognized as reliable and safe companies by the Korea Customs Service and other Customs services in trade facilitation and customs clearance processes. Conclusions - This study has some implications for AEO authorization and internationalization processes, and involved the empirical analysis of SMEs and the exploratory factor analysis in the internationalization process.

• Journal of Environmental Science International
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• v.25 no.5
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• pp.715-726
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• 2016

This study investigates the characteristics of metallic and ionic elements concentration, concentration according to transport path, and factor analysis in $PM_{10}$ at Guducsan in Busan in the springtime of 2015. $PM_{10}$ concentration in Guducsan and Gwaebeopdong were $59.5{\pm}9.04{\mu}g/m^3$ and $87.5{\pm}20.2{\mu}g/m^3$, respectively. Contribution rate of water-soluble ions and secondary ion in $PM_{10}$ concentration in Guducsan were 37.0% and 27.8% respectively. [$NO_3{^-}/SO{_4}^{2-}$] ratio and contribution rate of sea salt of $PM_{10}$ in Guducsan and Gwaebeopdong were 0.91 and 1.12, 7.0% and 5.3%, respectively. The results of the backward trajectory analysis indicates that $PM_{10}$ concentration, total inorganic water-soluble ions and total secondary ions were high when the air parcels moved from Sandong region in China than non-Sandong and northen China to Busan area. The results of the factor analysis at Guducsan indicates that factor 1 was anthropogenic source effects such as automobile emissions and industrial combustion processes, factor 2 was marine sources such as sea salts from sea, and factor 3 was soil component sources.

• 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.

• The Journal of the Korea Contents Association
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• v.7 no.10
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• pp.202-210
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• 2007

Factor analysis, an important research method, has been conducted as a pre-analysis of empirical researches. Sometimes, the researchers expect more information than factor analysis' limitation as well. Therefore, I suggest a method to overcome this problem. This study presents a combined research method which allow to solve the limitation of the factor analysis in social science. The combined research method consist of factor analysis, AHP(analytic hierarchy process) analysis, diffusion theory and system dynamics, which is able to explain causalities.

• Journal of the Korean Home Economics Association
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• v.34 no.2
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• pp.85-97
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• 1996

The purpose of this study was to analyze the differences of breast shapes by age, to classify breast types, and to investigate the characteristics of breast shapes by type and the distributions of types by age group, using comparative analysis for women in 20's and 40's. The subjects of anthropometry were 323 women in 20's and 40's. The anthropometric measurements were analyzed by mean, standard deviation, frequency, T-test, F-test, Duncan test, factor and cluster analysis. The results are as follws : 1) According to the increase of age, the items of height were decreased and the items of width, depth, circumference and length were increased, being obeser and breast points were dropped. So the volume and bottom area of 40's women's breast were larger than 20's women's and the breast point to breast point width of 40's women was being wider by the increase of the intereior of breast. 2) 6 factor were extracted from factor analysis by age group. There was no significant difference in consist of factor between age group. There was no significant difference in consist of facotr between age group, but were differences in the connection of factor extracted items and factor loading. Through factor analysis of all age froup, 5 factors were extracted as important factor of breast shapes (obesity of breast and location of breast point ; breast height and volume ; upper dimensions of breast/lower dimensions of breast ; interior dimensions of breast/exterior dimensions of breast ; volume of the lower part and drop of breast). 3) The breast shapes were classified into 4 types by cluster analysis. The frequency of 20's women's breast types appeared in order of type1(53.0%), type3(32.0%), type2(11.0%), type4(4.0%), but that of breast types of 40's women appeared in order of type2(37.9%), type3(31.1%), type4(26.5%), type1(4.5%). Namely, there was few type4 in 20's women and few type1 in 40's women. 20's women's breast types were characterized as type1·3 and type2·3·4 in 40's women. So 40's women had more various breast types than 20's women's.