• Journal of Welding and Joining
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• v.17 no.4
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• pp.85-92
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• 1999

In GMAW, the spatter is generated because of the variation of the arc state. If the arc state is quantitatively assessed, the control method to make the spatter be reduced is able to develop. This study was attempted to develop the optimal model that could estimate the arc state quantitatively. To do this, the generated spatters was captured under the limited welding conditions, and the waveforms of the arc voltage and of the welding current were collected. From the collected waveforms, the waveform factors and their standard deviations were produced, and the linear and non-linear regression models constituted using the factors and their standard deviations are proposed to estimate the arc state. the performance test to the proposed models was practiced. Obtained results are as follow. From the results of correlation analysis between the factors and the amount of the generated spatters, the standard deviations of the waveform factors have more the multiple regression coefficients than the waveform factors. Because the correlation coefficient between T and {TEX}$T_{a}${/TEX}, and s[T] and s[{TEX}$T_{a}${/TEX}] was nearly one, it was found that these factors have the same effect to the spatter generation. In the regression models to estimate the arc state, it was fond that the linear and the non linear models were also consisted of similar factors. In addition, the linear regression model was assessed the optimal model for estimating the arc state because the variance of data was narrow and multiple regression coefficient was highest among the models. But in the welding conditions which the amount of the generated spatters were small, it was found that the non linear regression model had better the estimation performance for the spatter generation than the linear.

• Environmental Engineering Research
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• v.20 no.3
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• pp.237-245
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• 2015

Particulate matter in indoor environments has caused public concerns in recent years. The objective of this research is to explore the influence of radiators on particle size distributions and concentrations. The particle size distributions as well as concentrations above radiators and in the adjacent indoor air are monitored in forty-two indoor environments in Xi'an, China. The temperatures, relative humidity and air velocities are also measured. The particle size distributions above radiators at ten locations are analyzed. The results show that the functional difference of indoor environments has little impact on the particle size distributions above radiators. Then the effects of the environmental parameters (particle concentrations in the adjacent indoor air, temperatures, relative humidities and air velocities) on particle concentrations above radiators are assessed by applying multiple linear regression analysis. Three multiple linear regression models are established to predict the concentrations of $PM_{10}$, $PM_{2.5}$ and $PM_1$ above radiators.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.12 no.3
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• pp.437-445
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• 1999

This paper presents a simple modification technique of finite element model for dynamic analysis of linear/nonlinear structural system subjected to multiple support excitation. For the sake of verification of the proposed method, dynamic responses obtained by the present technique for a couple of linear and nonlinear structural systems were compared with those by a general-purpose structural analysis software which can deal with the multi-support analysis. The method presented in this paper is expected to be used for multiple support excitation analysis by means of a computer code without the capability of modeling the non-synchronous support motion.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.4
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• pp.23-29
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• 2004

In this study, four kinds of $Si_3N_4$-based ceramic cutting tools with different sintering time were fabricated to investigate the relation among mechanical properties, grain size and tool life. They were used to turn gray cast iron at a cutting speed of 330m/min and depth of cut of 0.5mm and 1mm in dry, continuos cutting conditions. Multiple linear regression model was used to determine the relations among the mechanical property, grain size and the density. It was found that the combination of hardness and fracture toughness showed a good relation with tool life. It was also shown that hardness was the most important single element for the tool life.

• KIEAE Journal
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• v.9 no.1
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• pp.107-113
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• 2009

The energy cost is resulted from the energy use. Its sources are divided into some types and depended on the building use or energy-use type. The energy cost should be affected by the amount of the energy use. The cost could be calculated to consider various factors such as the insulation, heating type, building shape and others. But it can not consider all of the affect factors to the energy cost and need to categorize the factors to the condition for estimating the cost. In this paper, it aimed at providing the estimation model in linear equation and multiple linear regression, utilizing the building exterior condition and management characteristics in apartment housing. Its survey are conducted in two parts of management characteristics and building exterior condition. The correlation analysis is conducted to get rid of the multicolinearity among the inputted factors. The number of linear equation model is 11 and includes the 1st, 2nd and 3rd equation function, power function and others. Among these, it suggested the 2nd and 3rd function and power function in terms of the statistics. In multiple linear regression model, the building volume and management area are inputted to the estimation.

• Journal of the Korean Statistical Society
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• v.30 no.1
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• pp.139-151
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• 2001

Although lots of works are done in influence diagnostics, results in the multivariate analysis are quite rare. One of recent works done by Fung(1995) is about the single case influence diagnostics in the linear discriminant analysis. In this paper we extend Fung's results to the multiple cases diagnostics which are necessary in the linear discriminant analysis for two reasons among others; First, the masking effect cannot be detected by single case diagnostics and secondly two populations are concerned in the discriminant analysis, i.e., influential cases can occur in one or both populations.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.5
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• pp.537-543
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• 2006

There have been many recent studies on the nonlinear rainfall-runoff modeling, where the use of neural networks is shown to be quite successful. Due to fundamental limitation of linear structures, employing linear models has often been considered inferior to the neural network approaches in this area. However, we believe that with an appropriate extension, the concept of linear impulse responses can be a viable tool since it enables us to understand underlying dynamics principles better. In this paper, we propose the use of multiple impulse responses for the problem of rainfall-runoff analysis. The proposed method is based on a simple and fixed strategy for switching among multiple linear impulse-response models, each of which satisfies the constraints of non-negativity and uni-modality. The computational analysis performed for a certain Korean hydrometeorologic data set showed that the proposed method can yield very meaningful results.

• Ocean and Polar Research
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• v.37 no.2
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• pp.161-177
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• 2015

This study analyzes the stock assessment of yellow croaker caught mainly by the Korean stow net and gill net fisheries focusing on single species and multiple fisheries. This study standardizes fishing efforts for the two fisheries using the general linear model and uses a surplus production model based on the exponential growth model. The Clarke Yoshimoto Pooley model estimates a maximum sustainable yield(MSY), an allowable biological catch(ABC), fishing efforts for MSY($E_{MSY}$) and for ABC($E_{ABC}$). The bio-economic model is used to estimate the maximum economic yield(MEY) and fishing efforts for MEY($E_{MSY}$). Also, the study employs an economic analysis to estimate the economic interaction between stow net and gill net fisheries. The economic analysis shows the profit accruing to the two fisheries from estimated ABC. Finally, the study compares TACs based on single species and single fishery to TAC based on single species and multiple fisheries. The study proposes that the TAC assessment is necessary for single species and multiple fisheries in order to preserve resources.

• Clinics in Shoulder and Elbow
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• v.16 no.1
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• pp.63-72
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• 2013

In medical research, multivariate analysis, especially multiple regression analysis, is used to analyze the influence of multiple variables on the result. Multiple regression analysis should include variables in the model and the problem of multi-collinearity as there are many variables as well as the basic assumption of regression analysis. The multiple regression model is expressed as the coefficient of determination, $R^2$ and the influence of independent variables on result as a regression coefficient, ${\beta}$. Multiple regression analysis can be divided into multiple linear regression analysis, multiple logistic regression analysis, and Cox regression analysis according to the type of dependent variables (continuous variable, categorical variable (binary logit), and state variable, respectively), and the influence of variables on the result is evaluated by regression coefficient${\beta}$, odds ratio, and hazard ratio, respectively. The knowledge of multivariate analysis enables clinicians to analyze the result accurately and to design the further research efficiently.

• Journal of the Society of Naval Architects of Korea
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• v.51 no.6
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• pp.530-538
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• 2014

The weight estimation of floating offshore structures such as FPSO, TLP, semi-Submersibles, Floating Offshore Wind Turbines etc. in the preliminary design, is one of important measures of both construction cost and basic performance. Through both literature investigation and internet search, the weight data of floating offshore structures such as FPSO and TLP was collected. In this study, the weight estimation model was suggested for FPSO. The weight estimation model using non-linear regression analysis was established by fixing independent variables based on this data and the multiple regression analysis was introduced into the weight estimation model. Its reliability was within 4% of error rate.