• Tribology and Lubricants
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• v.26 no.6
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• pp.329-335
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• 2010

Recently, the End-milling processing is needed the high-precise technique to get a good surface roughness and rapid time in manufacturing of precision machine parts and electronic parts. The optimum surface roughness has an effect on end-milling working condition such as, cutting direction, spindle speed, feed rate and depth of cut, and so on. It needs to form the correlation of working conditions and surface roughness. Therefore this study was carried out to presume of surface roughness on end-milling working condition of Al7075 by regression analysis. The results was shown that the coefficient of determination($R^2$) of regression equation had a fine reliability of 87.5% and nonlinear regression equation of surface rough was made by multiple regression analysis.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.209-216
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• 2015

Quantile regression models with covariate measurement errors have received a great deal of attention in both the theoretical and the applied statistical literature. A lot of effort has been devoted to develop effective estimation methods for such quantile regression models. In this paper we propose the partially linear support vector orthogonal quantile regression model in the presence of covariate measurement errors. We also provide a generalized approximate cross-validation method for choosing the hyperparameters and the ratios of the error variances which affect the performance of the proposed model. The proposed model is evaluated through simulations.

• Journal of the Korean Data and Information Science Society
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• v.24 no.2
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• pp.391-399
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• 2013

Partially linear regression is capable of providing more complete description of the linear and nonlinear relationships among random variables. In support vector regression (SVR) the hyper-parameters are known to affect the performance of regression. In this paper we propose an iterative reweighted least squares (IRWLS) procedure to solve the quadratic problem of partially linear support vector regression with a modified loss function, which enables us to use the generalized approximate cross validation function to select the hyper-parameters. Experimental results are then presented which illustrate the performance of the partially linear SVR using IRWLS procedure.

• Communications for Statistical Applications and Methods
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• v.27 no.3
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• pp.365-376
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• 2020

Interval-valued data, a type of symbolic data, is given as an interval in which the observation object is not a single value. It can also occur frequently in the process of aggregating large databases into a form that is easy to manage. Various regression methods for interval-valued data have been proposed relatively recently. In this paper, we introduce a nonparametric regression model using the kernel function and a nonlinear regression model for the interval-valued data. We also propose applying the local linear regression model, one of the nonparametric methods, to the interval-valued data. Simulations based on several distributions of the center point and the range are conducted using each of the methods presented in this paper. Various conditions confirm that the performance of the proposed local linear estimator is better than the others.

• Journal of Mechanical Science and Technology
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• v.19 no.10
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• pp.1939-1949
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• 2005

Many studies have been conducted to increase regression rate of solid fuel in hybrid rocket. One of them resorts to swirl flow since it can extend the residence time of oxidizer in the fuel grain. Also, metal wires may lead to increase the regression rate of solid fuel as shown in solid propellants. In this study, a series of experiments was designed to investigate the enhancement of regression rate of solid fuel by embedded metal wires and by fuel port grain. And fuel port was designed with a helical configuration to attempt to induce swirl flow. PMMA with gaseous oxygen is the solid fuel used for investigation. Test results showed that embedded metal wires turned out to be ineffective method because only $3-4\%$ increases in regression rate were observed. However, fuel port grain configuration yields higher burning performance of up to $50\%$ increase in regression rate. Also pitch number as well as total impulse was found to be a design variable.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.2
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• pp.227-232
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• 2003

Recently, support vector learning attracts an enormous amount of interest in the areas of function approximation, pattern classification, and novelty detection. One of the main reasons for the success of the support vector machines(SVMs) seems to be the availability of global and sparse solutions. Among the approaches sharing the same reasons for success and exhibiting a similarly good performance, we have KFD(kernel Fisher discriminant) approach. In this paper, we consider the problem of function approximation utilizing both predetermined basis functions and the KFD approach for regression. After reviewing support vector regression, semi-parametric approach for including predetermined basis functions, and the KFD regression, this paper presents an extension of the conventional KFD approach for regression toward the direction that can utilize predetermined basis functions. The applicability of the presented method is illustrated via a regression example.

• Restorative Dentistry and Endodontics
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• v.43 no.3
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• pp.34.1-34.5
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• 2018

In the previous section, we established a simple linear regression line by finding the slope and intercept using the least square method as: ${\hat{Y}}=30.79+0.71X$. Finding the regression line was a mathematical procedure. After that we need to evaluate the usefulness or effectiveness of the regression line, whether the regression model helps explain the variability of the dependent variable. Also, statistical inference of the regression line is required to make a conclusion at the population level, because practically, we work with a sample, which is a small part of population. Basic assumption of sampling method is simple random sampling.

• Proceedings of the Safety Management and Science Conference
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• 2011.11a
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• pp.213-217
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• 2011

The research shows misuse cases of quantitative regression analysis used in QC circle activity and six sigma movement which presents guidelines of correct use for quality practitioners. Additionally, the qualitative regression analysis that responses nonconforming ratio of variable y, is reviewed based on misuse cases for proper use by practitioners in the field. In most cases, there are frequent errors that involve the correlation analysis or ANOVA, regardless of using quantitative regression analysis. In addition, qualitative regression analysis for the nonconforming ratio that has dependent variable of discrete and categorical data, is often applied with quantitative regression and result in ineffective quality improvement.

• Journal of the Korean Data and Information Science Society
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• v.23 no.2
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• pp.393-401
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• 2012

This paper deals with the estimations of the least squares support vector regression when the responses are subject to randomly right censoring. The estimation is performed via two steps - the ordinary least squares support vector regression and the least squares support vector regression with censored data. We use the empirical fact that the estimated regression functions subject to randomly right censoring are close to the true regression functions than the observed failure times subject to randomly right censoring. The hyper-parameters of model which affect the performance of the proposed procedure are selected by a generalized cross validation function. Experimental results are then presented which indicate the performance of the proposed procedure.

• Bulletin of the Korean Chemical Society
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• v.22 no.1
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• pp.37-42
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• 2001

Ridge regression is compared with multiple linear regression (MLR) for determination of Research Octane Number (RON) when the baseline and signal-to-noise ratio are varied. MLR analysis of near-infrared (NIR) spectroscopic data usually encounters a collinearity problem, which adversely affects long-term prediction performance. The collinearity problem can be eliminated or greatly improved by using ridge regression, which is a biased estimation method. To evaluate the robustness of each calibration, the calibration models developed by both calibration methods were used to predict RONs of gasoline spectra in which the baseline and signal-to-noise ratio were varied. The prediction results of a ridge calibration model showed more stable prediction performance as compared to that of MLR, especially when the spectral baselines were varied. . In conclusion, ridge regression is shown to be a viable method for calibration of RON with the NIR data when only a few wavelengths are available such as hand-carry device using a few diodes.