• The Journal of the Korea Contents Association
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• v.19 no.4
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• pp.111-119
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• 2019

The purpose of this study was to explore the determinants of minor and double major satisfaction of university students. To achieve this, F.G.I. was performed with the university teaching assistants to create a questionnaire. The survey was performed to the subject of 239 university students completing minor and double major. A statistical program (SPSS) was used for data analysis and factor analysis, reliability analysis, and multiple regression analysis were conducted. First, it drew three principal factors including need-achievement, class discrimination, and course registration discrimination by conducting factor analysis regarding survey questions on minor and double major. Next, the multiple regression analysis identifying the effects of the three principal factors was conducted. The results indicated three principal factors to be statistically significant. The degree of influence over satisfaction marked the highest in the order of need-achievement, class discrimination, and course registration discrimination. In the conclusion, the positive function and possibilities of minor and double major are proposed.

• Journal of Aerospace System Engineering
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• v.9 no.4
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• pp.31-36
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• 2015

The fatigue analysis for cutout panel used for the weight reduction of aircraft has been conventionally performed through the open hole concept using the reference stress and stress concentration factor (Kt). However, in the actual structure cases, the goal of weight reduction might be less meaningful due to the conservative approach induced by the difficulties of extracting the confident reference stress from FE-Analysis in the complicated loading behavior. Therefore a new approach is proposed in order to secure the effectiveness of weight reduction and validate the confidence of the analysis results using the interaction of max-min principal stress at the critical location of open hole edge line.

• Journal of Environmental Science International
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• v.7 no.5
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• pp.591-598
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• 1998

This study was conducted to evaluate water quality in the Keum River estuary using principal component analysis. The results was summarized as follow; Water quality in the Keum River estuary could be explained up to 70.40% by three factors which were included in the inffluent loading by the Keum River and Kyungpo cheon(38.99%), seasonal variation and organic matter pollution(19.05%), sediment resuspension and internal metabolism(12.35%). For spatial variation of factor score, artificial pollutant loading is highest at st.1, below Keum River barrage, and decreases toward the outer sea. For annual variation of factor score, factor 1 was highly related to artificial pollutant leading, and it was gently increased in 1994. Also, organic matter pollution, sediment resuspension and internal metabolism were increased to every year. It is necessary to control the nutrient leading by Keum river and Kyongpo cheon for Water quality management of estuary.

• Journal of the Korean Society of Costume
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• v.20
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• pp.73-82
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• 1993

The Purpose of this study was to identify the constructing factors of female suit image. The questionnaire consisted of 69 words expressing suit image were developed, and six suit slides were selected for stimulus. Sixty-eight female university students majored in clothing and textiles were responded to each sledes, and then factor analysis was conducted. Six factors, such as attractiveness, peculiarity, grace, femininity, youthfullness, and comfort were found out as constructing factors of suit image(total vari-ance 60.5%) by the principal component analysis. The attractiveness factor which explained the largest variance included words such as countrified, refinement, and beautiful. Peculiarity factor included words such as peculiar, bold, complex, and decorative. Grace factor included words such as classic, grace, and elegant. Femininity factor included such as masculine, feminine, soft, and dressy. Youthfullness factor included words such as youth, bright, and charming. And comfort factor included words such as casual, comfort-able, active, and natural. The Cronbach's $\alpha$of the each factors were. 78~92.

• Environmental Engineering Research
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• v.22 no.1
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• pp.31-39
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• 2017

The primary purpose of this research is to understand those elements that define heavy metals contamination and to propose a novel assessment method based on principal component analysis (PCA) in the Yangping River region of Lingbao City, China. This paper makes detailed calculations regarding such factors the single-factor assessment ($P_i$) and Nemerow's multi-factor index ($P_N$) of heavy metals found in the surface water of the Yangping River. The maximum values of $P_i$ (Cd) and $P_i$ (Pb) were determined to be 892.000 and 113.800 respectively. The maximum value of $P_N$ was calculated to be 639.836. The results of Pearson's correlation analysis, hierarchical cluster analysis, and PCA indicated heavy metal groupings as follows: Cu, Pb, Zn and As, Hg, Cd. The PCA-based pollution index ($P_{an}$) of samplings was subsequently calculated. The relative coefficient square was valued at 0.996 between $P_{an}$ and $P_N$, which indicated that $P_{an}$ is able to serve as a new heavy metal pollution index; not only this index able to eliminate the influence of the maximum value of $P_i$, but further, this index contains the principal component elements needed to evaluate heavy metal pollution levels.

• Geomechanics and Engineering
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• v.27 no.6
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• pp.551-560
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• 2021

Due to the low strength and high compressibility characteristics, the loess deposits tunnels are prone to large deformations and collapse. An accurate stability evaluation for loess deposits is of considerable significance in deformation control and safety work during tunnel construction. 37 groups of representative data based on real loess deposits cases were adopted to establish the stability evaluation model for the tunnel project in Yan'an, China. Physical and mechanical indices, including water content, cohesion, internal friction angle, elastic modulus, and poisson ratio are selected as index system on the stability level of loess. The data set is randomly divided into 80% as the training set and 20% as the test set. Firstly, principal component analysis (PCA) is used to convert the five index system to three linearly independent principal components X1, X2 and X3. Then, the principal components were used as input vectors for probabilistic neural network (PNN) to map the nonlinear relationship between the index system and stability level of loess. Furthermore, Leave-One-Out cross validation was applied for the training set to find the suitable smoothing factor. At last, the established model with the target smoothing factor 0.04 was applied for the test set, and a 100% prediction accuracy rate was obtained. This intelligent classification method for loess deposits can be easily conducted, which has wide potential applications in evaluating loess deposits.

• The Journal of Industrial Distribution & Business
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• v.10 no.5
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• pp.7-20
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• 2019

Purpose - This study provides appropriate procedures for EFA to help researchers conduct empirical studies by using PLS-SEM. Research design, data, and methodology - This study addresses the absolute and relative sample size criteria, sampling adequacy, factor extraction models, factor rotation methods, the criterion for the number of factors to retain, interpretation of results, and reporting information. Results - The factor analysis procedure for PLS-SEM consists of the following five stages. First, it is important to look at whether both the Bartlett test of sphericity and the KMO MSA meet the qualitative criteria. Second, PAF is a better choice of methodology. Third, an oblique technique is a suitable method for PLS-SEM. Fourth, a combined approach is strongly recommended to factor retention. PA should be used at the onset. Next, it is recommended using the K1 criterion. In addition, it is necessary to extract factors that increase the total variance explanatory power through the PVA-FS. Finally, it is appropriate to select an item with a factor loading into 0.5 or higher and a communality of 0.5. Conclusions - It is expected that the accurate factor analysis processed for PLS-SEM as previously presented will help us extract more precise factors of the structural model.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.1
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• pp.1-5
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• 2012

Data reduction has been used widely in data mining for convenient analysis. Principal component analysis (PCA) and factor analysis (FA) methods are popular techniques. The PCA and FA reduce the number of variables to avoid the curse of dimensionality. The curse of dimensionality is to increase the computing time exponentially in proportion to the number of variables. So, many methods have been published for dimension reduction. Also, data augmentation is another approach to analyze data efficiently. Support vector machine (SVM) algorithm is a representative technique for dimension augmentation. The SVM maps original data to a feature space with high dimension to get the optimal decision plane. Both data reduction and augmentation have been used to solve diverse problems in data analysis. In this paper, we compare the strengths and weaknesses of dimension reduction and augmentation for classification and propose a classification method using data reduction for classification. We will carry out experiments for comparative studies to verify the performance of this research.

• Journal of Korean Society on Water Environment
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• v.27 no.6
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• pp.905-913
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• 2011

This study was conducted to characterize the discharge feature of pollutant load from the upper and lower watershed influencing on the water quality of South Han River using the empirical equation and Factor Analysis. The results of regression analysis between flow rate and pollutant load were as follows. In the streams of the upper watershed of South Han river, $BOD_5$ and $COD_{Mn}$ were increased as the flow rate was increased. Also, steep increases in SS and TP were observed with positive correlation with the flow rate while change in TN was slightly shown. On the other hand, in the streams of the lower watershed of South Han river, $BOD_5$ was negatively correlated with the flow rate, being decreased with the increase in the flow rate. However, changes in $COD_{Mn}$, TN, SS, and TP showed a similar trend with those observed in the upper watershed. With Factor Analysis of the water quality and various components, it was appeared that the flow rate, SS, and TP were significantly correlated each other and they were indicated as the principal component influencing on water quality in the streams of the upper watershed. In contrast, $BOD_5$, $COD_{Mn}$ and TOC were significantly correlated each other and they were included as the principal pollution component of the streams in the lower watershed. From these results, it was conclusive that the upper watershed of South Han River was mainly affected by non point source pollutants while the lower watershed was influenced by point source pollutants from the developed areas.

• Bulletin of the Society of Naval Architects of Korea
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• v.25 no.4
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• pp.28-38
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• 1988

This paper describes the statistical analysis method of predicting the ship resistance. The equation for the wavemaking resistance coefficient is derived as the principal dimensions and sectional area coefficients by using the wavemaking resistance theory and its regression coefficients are determined from the regression analysis of the resistance test results. The equation for the form factor is derived by purely regression analysis of the principal dimensions, sectional area coefficients and resistance test results. Also, it is shown that the wavemaking resistance can be minimize by varying the sectional area curve without changing the principal dimensions of the ship. This methods were applied to the resistance prediction of a bulk carrier. And the, the modified hull form with minimum wavemaking resistance was obtained and the reduction of effective power was confirmed by the resistance test.