• Communications for Statistical Applications and Methods
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• v.10 no.1
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• pp.167-175
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• 2003

In this paper we develop a robust procedure to estimate regression coefficients for a linear model with censored and truncated data based on simplicial regression depth. Simplicial depth of a point is defined as the proportion of data simplices containing it. This simplicial depth can be extended to regression problem with censored and truncated data. Any line can be given a depth and the deepest regression line is the line with the maximum simplicial regression depth. We show how the proposed regression performs through analyzing AIDS incubation data.

• The Korean Journal of Applied Statistics
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• v.20 no.2
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• pp.313-322
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• 2007

Several robust censored depth regression methods are compared under contamination. Park and Hwang(2003) suggested a way to circumvent the censoring issue by incorporating Kaplan-Meier type weight in halfspace regression depth and Park(2003) used a similar technique to simplicial regression depth. Hubert et al. (2001) suggested a high breakdown point regression depth based on projection called rcent. A new method to implement censoring in rcent is suggested and compared with two precedents under various contamination and censoring schemes.

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.307-316
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• 2004

Principal Component Regression(PCR) and Partial Least Squares Regression(PLSR) are the two most popular regression techniques in chemometrics. In the field of chemometrics usually the number of regressor variables greatly exceeds the number of observation. So we have to reduce the number of regressors to avoid the identifiability problem. In this paper we compare PCR and PLSR techniques combined with various robust regression methods including regression depth estimation. We compare the efficiency, goodness-of-fit and robustness of each estimators under several contamination schemes.

• The Korean Journal of Applied Statistics
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• v.17 no.3
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• pp.475-488
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• 2004

Several robust regression estimators are compared under contamination. Symmetric and asymmetric contamination schemes are used to measure the variance and MSE of regression estimators. Under asymmetric contamination depth-based regression estimator, especially projection based regression estimator(rcent) outperforms the rest and under symmetric contamination HBR performs relatively well.

• 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.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.11 no.6
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• pp.36-41
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• 2012

In this study, we turning plastic mold steel (STAVAX) against cutting speed, depth of cut, feed rate using whisker reinforced ceramic tool (WA1). To predict cutting force, analyze principal, radial, feed force with multi-regression analysis. Results are follows: From the analysis of variance, affected factor to cutting force feed rate, depth of cut, cutting speed in order and cutting speed was very small affect to cutting force. From multi-regression analysis, we extracted regression equation and the coefficient of determination$(R^2)$ was 0.9, 0.88, 0.856 at principal, radial and feed force. It means regression equation is significant. From the experimental verification, it was confirmed that principal, radial and feed force was predictable by regression equation.

• Journal of Biosystems Engineering
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• v.9 no.2
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• pp.27-36
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• 1984

This study was carried out to investigate the effects of the tilling depth, tilling travel speed and soil shear stress on the tilling load characteristics and power requirement for rotary tillers. The results obtained from the study are summarized as follows. 1. The average and maximum PTO torque increased as the tilling depth, tilling pitch and soil shear stress increased. A multiple linear regression equation to estimate the average PTO torque in terms of the above parameters was developed. 2. The ratios of maximum PTO torque to the average torque were in the range of 1.17 to 1.65 for the various tilling conditions tested. The variation in PTO torque increased greatly as the tilling pitch and soil shear stress increased, but decreased as the tilling depth increased. 3. Power requirement for the PTO shaft increased with the tilling depth, travel speed and soil shear stress, but decreased slightly as the tilling pitch increased. A multiple linear regression equation to estimate power requirement for the PTO shaft in terms of the above parameters was developed. 4. The specific power requirement for the rotary tiller was in the range of $0.008-0.015ps/cm^2$ for the various tilling conditons tested. The specific tilling capacity decreased as the tilling depth and soil shear stress increased, but increased with the tilling pitch. A multiple linear regression equation to estimate the specific tilling capacity in terms of the above parameters was developed.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.25 no.1
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• pp.83-88
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• 2016

Because of the increasing tool cost for cutting hard-to-cut materials, abrasive water jet (AWJ) milling recently has been regarded as a potential alternative machining method. However, it is difficult to control the depth and width of cut in AWJ milling because they vary depending on many AWJ cutting parameters. On 27 conditions within a limited range of pressure, feed rate, and abrasive flow rate, AWJ cutting was conducted on titanium, and depth profiles were measured with a laser sensor. From the depth profile data, depth and width of cut were acquired at each condition. The relationships between depth and parameters and between width and parameters were derived through regression analysis. The former can provide proper cutting conditions and the latter the proper pick feed necessary to generate a milled surface. It is verified that pressure mostly affects depth, whereas abrasive flow rate mostly affects width.

• Journal of The Korean Society of Agricultural Engineers
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• v.47 no.1
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• pp.35-42
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• 2005

The research has been done to obtain a empirical equation for the depth of replacement by the analysis of data collected from 8 sea-dike construction sites of south coast of Korean peninsula. The correlation analysis results show that the depth of replacement was mainly dependent upon the height of embankment and the undrained shear strength of soft soil. The suggested regression equation was quite well predicted the depth of replacement and recommended to use under certain restrictions where the embankment height was less than 10m and under 0.2 kgf/cm^{2} of the undrained shear strength of soil.

• Journal of Korean Tunnelling and Underground Space Association
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• v.12 no.1
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• pp.1-14
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• 2010

Until now, the evaluation method of spalling depth using Martin et al. (1999)'s linear regression relations has long been known applicable. However, it is not likely that the proposed equation is applicable to the openings other than circular type and mostly overpredict the spalling depth in comparison with actual spalling cases. Moreover, the evaluation method to estimate the spalling width has not been presented yet; it is essential to evaluate the spalling width in addition to the spalling depth, because the shape of the spalled region influences the choice of suitable rock reinforcement. In this study, linear regression equations, in which normalized spalling depth ($d_f/W_D$) and normalized spalling width ($w_f/W_D$) are functions of three spalling evaluation indices, ${\sigma}_1/{\sigma}_c,\;D_{is}(={\sigma}_{max}/{\sigma}_c)$ and ${\sigma}_{dev}/{\sigma}_{cm}$, are established based on in-situ spalling observations and CWFS simulation results. Confidence intervals of 95% using the statistical inference theory are used in verifying the reliability of linear regression equations. Spalling depth ($d_f$) and spalling width ($w_f$) predicted from the proposed linear regression relations, which take three spalling evaluation indices into account, showed reasonable match with in-situ observations by adopting weighting factors considering the degree of variance of linear regression relations.