• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2003.05e
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• pp.49-52
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• 2003

The performance of insulators under contaminated conditions is the underlying and the most factor that determines insulation design for outdoor applications, Among the contamination factors, The sea salt is the most dangerous factor, and the salt factor have closed relation with climatic conditions, such as wind, temperature, humidity and so on, Effect of these factors to insulation system is different of each other, and need to show the correlation by multiple regression analysis techniques. In this paper, predicted and analyzed equivalent salt deposit density (ESDD) by change climatic condition through multiple regression analysis.

• Journal of the Korean Society of Safety
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• v.15 no.3
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• pp.1-6
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• 2000

In fatigue crack growth test, it is important not only to analyze characteristics of fatigue crack growth but also to determine the threshold stress intensity factor, ${\Delta}K_{th}$. which is the threshold value of fatigue crack growth. Linear regression analysis using fatigue test data near the threshold is suggested to determine the ${\Delta}K_{th}$ in the standard test method but the ${\Delta}K_{th}$ can be affected by a fitting method. And there are some limitations on the linear regression analysis in the case of small number of test data near the threshold. The objective of this study is to investigate differences of the ${\Delta}K_{th}$ due to regression analysis method and to evaluate the relative error range of the ${\Delta}K_{th}$ in same fatigue crack growth test data.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.11 no.4
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• pp.118-124
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• 2012

In this study, we turning mold steel (SKD61) using whisker reinforced ceramic tool (WA1) to get affected factor to surface roughness and regression equation. For this study, we adapt system of experiments. Results are follows; From the analysis of variance, it was found that affected factor to surface roughness was feed rate, cutting speed, depth of cut in order. From multi-regression analysis, we calculated regression equation and the coefficient of determination($R^2$). $R^2$ was 0.978 and It means regression equation is significant. Regression equation means if feed rate increase 0.039mm/rev, surface roughness will increase $0.8391{\mu}m$, if cutting speed increase 50m/min, surface roughness will decrease $0.034{\mu}m$, if depth of cut increase 0.1mm, surface roughness will increase $0.0203{\mu}m$. From the experimental verification, it was confirmed that surface roughness was predictable by system of experiments.

• Journal of Environmental Health Sciences
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• v.48 no.4
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• pp.236-243
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• 2022

Background: In the case of multiple exposures to different types of heavy metals, such as the conditions faced by residents living near a smelter, it would be preferable to group hazardous substances with similar characteristics rather than individually related substances and evaluate the effects of each group on the human body. Objectives: The purpose of this study is to evaluate the utility of factor analysis in the assessment of health effects caused by exposure to two or more hazardous substances with similar characteristics, such as in the case of residents living near a smelter. Methods: Heavy metal concentration data for 572 people living in the vicinity of the Janghang smelter area were grouped based on several subfactors according to their characteristics using factor analysis. Using these factor scores as an independent variable, multiple regression analysis was performed on health effect markers. Results: Through factor analysis, three subfactors were extracted. Factor 1 contained copper and zinc in serum and revealed a common characteristic of the enzyme co-factor in the human body. Factor 2 involved urinary cadmium and arsenic, which are harmful metals related to kidney damage. Factor 3 encompassed blood mercury and lead, which are classified as related to cardiovascular disease. As a result of multiple linear regression analysis, it was found that using the factor index derived through factor analysis as an independent variable is more advantageous in assessing the relevance to health effects than when analyzing the two heavy metals by including them in a single regression model. Conclusions: The results of this study suggest that regression analysis linked with factor analysis is a good alternative in that it can simultaneously identify the effects of heavy metals with similar properties while overcoming multicollinearity that may occur in environmental epidemiologic studies on exposure to various types of heavy metals.

• Journal of the Korean Society of Costume
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• v.54 no.3
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• pp.73-85
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• 2004

The purpose of this study was to investigate the characteristic and sensibility of Japanism fashion designs which represented by Japanese designers and Western designers. The stimulus were 29 pictures of contemporary fashion designs which represented the Japanism style fashion designs from fashion collections. The data were analyzed by Cluster analysis, Factor analysis, Multidimensional Scaling Method and Regression Analysis. The specific objectives were as follows ; 1) As result of design analysis, Japanism fashion sensibility is unique and good-looking. 2) As result of the factor analysis. 4 factors which are Attractiveness, Attention, Maturity and Hardness and softness. 3) According to sensibility positioning, The Japanism fashion design was classified by Decorative-Simple, Hard-Soft. 4) As result of the Regression Analysis, The preference of Japanism fashion design was related to attractive factor. 5) As result of the Regression Analysis. The buying desirable of Japanism fashion design was related to attractive, attentive and mature factor.

• The Korean Journal of Applied Statistics
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• v.33 no.4
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• pp.365-377
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• 2020

We discuss latent factor regression when constructing a common structure inherent among explanatory variables to solve multicollinearity and use them as regressors to construct a linear model of a response variable. Bayesian estimation with LASSO prior of a large penalty parameter to construct a significant factor loading matrix of intrinsic interests among infinite latent structures. The estimated factor loading matrix with estimated other parameters can be inversely transformed into linear parameters of each explanatory variable and used as prediction models for new observations. We apply the proposed method to Product Service Management data of HBAT and observe that the proposed method constructs the same factors of general common factor analysis for the fixed number of factors. The calculated MSE of predicted values of Bayesian latent factor regression model is also smaller than the common factor regression model.

• Journal of Acupuncture Research
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• v.27 no.1
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• pp.11-20
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• 2010

Objectives : The study was performed to evaluate a traditional Korean Medicine content in drama "Daejanggeum". Methods : One hundred sixty-nine participants in Taiwan responded to the survey with 10 items, regarding components of success of drama "Daejanggeum". Principal component factor analysis and multiple regression analysis were performed to identify the possible factors to satisfaction with watching drama "Daejanggeum". Results : Factor analysis revealed that dramatic factor(44.8%), content factor(12.3%), and cultural factor(11.3%) were the most important factors to success of drama "Daejanggeum". Multiple regression analysis showed that dramatic factor(beta = .342), content factor(beta = .278), and cultural factor(beta = .131) were associated with the satisfaction with watching drama "Daejanggeum"($R^2$ = .394, with F = 32.280, p<.001). Conclusions : This study demonstrated that dramatic factor, content factor, and cultural factor are the most important factors associated with satisfaction with drama "Daejanggeum" in Taiwan. These findings suggest that a traditional Korean Medicine as a content factor would be very influential in enhancing the possibility of success of drama.

• Journal of Industrial Technology
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• v.22 no.A
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• pp.201-210
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• 2002

The object of this study is to evaluate the factors affecting the water quality and to propose the influence of dominant factor quantitatively. The correlation analysis was performed to know the correlationship among the water quality items As a result of partial correlation analysis, it was shown that the water quality items are affected by the rainfall item directly. The factor analysis was performed to grasp some number of factors on each point for deducing the items of similar variable characteristics. The four points were divided into different factor groups. It was grasped that $NH_3-N$ and $NO_3-N$ Items have different variable characteristics after comparing the items. The Multiple regression analysis can decrease the number of observation. In the deduced multiple regression formula, it was shown that the rate of T-N, $NH_3-N$ and $NO_3-N$ in the independent variable took about 60% among all the regression formulas.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.2
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• pp.87-96
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• 2004

This paper summarizes the research results of the load management for pole transformers done in 1997-1998 and 2000-2002. The purpose of the research is to enhance the accuracy of peak load estimation in pole transformers. We concentrated our effort on the acquisition of massive actual load data for modifying the load regression coefficients, which related to the peak load estimation of lamp-use customers, and adjusting the demand-factor coefficients, which used for the peak load prediction of motor-use customers. To enhance the load regression equations, the 264 load data acquisition devices are equipped to the sample pole transformers. For the modification of demand factor coefficients, the peak load currents are measured in each customer and pole transformer for 13 KEPCO (Korea Electric Power Corporation) distribution branch offices. Case studies for 50 sample pole transformers show that the proposed coefficients could reduce estimating error of the peak load for pole transformers, compared with the conventional one.

• Journal of Biomedical Engineering Research
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• v.22 no.4
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• pp.369-375
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• 2001

The statistical regression model is one of the most frequently used clinical analysis methods. It has basic assumption of linearity, additivity and normal distribution of data. However, most of biological data in medical field are nonlinear and unevenly distributed. To overcome the discrepancy between the basic assumption of statistical model and actual biological data, we propose a new analytical method based on artificial neural network. The newly developed multilayer perceptron(MLP) is trained with 120 data set (60 normal, 60 patient). On applying test data, it shows the discrimination power of 0.76. The diabetic risk factors were also identified from the MLP neural network model and the logistic regression model. The signigicant risk factors identified by MLP model were post prandial glucose level(PP2), sex(male), fasting blood sugar(FBS) level, age, SBP, AC and WHR. Those from the regression model are sex(male), PP2, age and FBS. The combined risk factors can be identified using the MLP model. Those are total cholesterol and body weight, which is consistent with the result of other clinical studies. From this experiment we have learned that MLP can be applied to the combined risk factor analysis of biological data which can not be provided by the conventional statistical method.