• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2006.05a
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• pp.233-238
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• 2006

Hybrid rocket had many advantage with compared to solid and liquid rockets. In this study, swirl flow hybrid motor was designed and manufactured. And the methods of regression rate improvement were considered. Thrust was calculated with pressure of the combustion chamber and the regression rate was measured by using ultrasonic sensor technique in entire firing conditions. In this study, PMMA fuel and HTPB solid fuel were used in firing test.

• Proceedings of the KSME Conference
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• 2004.04a
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• pp.1148-1153
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• 2004

Recently the high speed end-milling processing is demanded the high-precise technique with good surface rougj1ness and rapid time in aircraft, automobile part and molding industry. The working factors of high speed end-milling has an effect on surface roughness of cutting surface. Therefore this study was carried out to analyze the working factors to get the optimum surface roughness by design of experiment. From this study, surface roughness have an much effect according to priority on Spindle speed, feed rale, hardness and axial depth of cut By design of experiment, it is effectively represented shape characteristics of surface roughness in high speed end-milling And determination($R^2$) coefficient of regression equation had a satisfactory reliability of 89.7% and regression equation of surface roughness is made by regression analysis.

• Journal of the Semiconductor & Display Technology
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• v.11 no.3
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• pp.19-25
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• 2012

The accuracy of Shape From Focus (SFF) technique depends on the quality of the focus measurements which are computed through a focus measure operator. In this paper, we introduce a new approach to estimate 3D shape of an object based on Gaussian process regression. First, initial depth is estimated by applying a conventional focus measure on image sequence and maximizing it in the optical direction. In second step, input feature vectors consisting of eginvalues are computed from 3D neighborhood around the initial depth. Finally, by utilizing these features, a latent function is developed through Gaussian process regression to estimate accurate depth. The proposed approach takes advantages of the multivariate statistical features and covariance function. The proposed method is tested by using image sequences of various objects. Experimental results demonstrate the efficacy of the proposed scheme.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2011.05a
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• pp.191-192
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• 2011

The construction projects contain a lot of variables and risk affecting productivity. The duration of the project must be recognized important as for quality, unit cost and safety. There is need for improving work efficiency by investigating relationship of works to prevent delay. This study focuses on analysing the delay factors of steel staircase system to suggest regression model that enables construction productivity estimation. The position of the observers and construction delay factors were expressed by the independent variable of the regression model and productivity was expressed by a dependent variable. This paper suggests quantitative productivity and it is expected that will be helpful estimating application in construction new technologies.

• The KIPS Transactions:PartD
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• v.16D no.5
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• pp.729-736
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• 2009

The accurate estimation of software development cost is important to a successful development in software engineering. Until recent days, the model using regression analysis based on statistical algorithm and machine learning method have been used. However, this paper estimates the software cost using support vector regression, a sort of machine learning technique. Also, it finds the best set of optimized parameters applying genetic algorithm. The proposed GA-SVR model outperform some recent results reported in the literature.

• The Korean Journal of Applied Statistics
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• v.11 no.2
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• pp.389-401
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• 1998

This paper introduces CybeRClass(Cyber Regression Class). CybeRClass uses the technique of animation arid voice to teach regression analysis. The structure of this system make it possible to extend to multivariate analysis methods such as discriminant analysis and cluster analysis. Tools for multimedia is Multimedia ToolBook, and Xlisp-Stat is used for statistical computation and statistical graphics.

• Journal of Korean Society for Quality Management
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• v.21 no.1
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• pp.161-170
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• 1993

This study attempts to present the linear regression analysis that involves more than one regressor variable, because regression analysis is the most widely used statistical technique for describing, predicting and estimating the relationships between given data. The model of multiple linear regression may be solved directly by the two linear programming methods, i.e., to minimize the sum of the absolute deviation (MSD) and to minimize the maximum deviation(MMD). In addition, some results was compared to each techniques for accuracy and tested to the validity of statistical meaning.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.6
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• pp.52-59
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• 2004

Recently the high speed end-milling processing is demanded the high-precise technique with good surface roughness and rapid time in aircraft, automobile part and molding industry. The working factors of high speed end-milling has an effect on surface roughness of cutting surface. Therefore this study was carried out to analyze the working factors to get the optimum surface roughness by design of experiment. From this study, surface roughness have an much effect according to priority on distance of cut, feed rate, revolution of spindle and depth of cut. By design of experiment, it is effectively represented shape characteristics of surface roughness in high speed end-milling. And determination($R^2$) coefficient of regression equation had a satisfactory reliability of 76.3% and regression equation of surface roughness is made by regression analysis.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.3
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• pp.145-153
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• 2015

This study aims to accurately estimate population distribution more specifically than administrative unites using a RK (Regression-Kriging) model. The RK model is the areal interpolation technique that involves linear regression and the Kriging model. In order to estimate a population’s distribution using a sample region, four different models were used, namely; a regression model, RK model, OK (Ordinary Kriging) model and CK (Co-Kriging) model. The results were then compared with each other. Evaluation of the accuracy and validity of evaluation analysis results were the basis RMSE (Root Mean Square Error), MAE (Mean Absolute Error), G statistic and correlation coefficient (ρ). In the sample regions, every statistic value of the RK model showed better results than other models. The results of this comparative study will be useful to estimate a population distribution of the metropolitan areas with high population density

• Food Science and Biotechnology
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• v.17 no.6
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• pp.1352-1356
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• 2008

Bootstrap method, a computer-intensive statistical technique to estimate the distribution of a statistic was applied to deal with uncertainty and variability of the experimental data in stochastic prediction modeling of microbial growth on a chill-stored food. Three different bootstrapping methods for the curve-fitting to the microbial count data were compared in determining the parameters of Baranyi and Roberts growth model: nonlinear regression to static version function with resampling residuals onto all the experimental microbial count data; static version regression onto mean counts at sampling times; dynamic version fitting of differential equations onto the bootstrapped mean counts. All the methods outputted almost same mean values of the parameters with difference in their distribution. Parameter search according to the dynamic form of differential equations resulted in the largest distribution of the model parameters but produced the confidence interval of the predicted microbial count close to those of nonlinear regression of static equation.