• 한국정밀공학회지
• /
• 제2권3호
• /
• pp.59-69
• /
• 1985

The purpose of this paper is to investigate the variations of out-of- roundness due to its assessment methods for the practical workpieces. Experiments were carried out with the test specimens having the typical roundness profile. The roundness measuring system which has the autocentripetal functions and the automatic centering table unit was used. From the experimental results, it was found that the ratio of LSC to MZC values varied with the shape of roundness profiles, and the rate of variation for out-of-roundness was increased logarithmically with the increment of undulations by the effect of filter. Therefore, it is suggested that the least squares center method should be useful with the sufficient accuracy except for some special cases in the roundness profiles, and the condition of filter has considerable influence upon the out-of-roundness to be measured.

• 반도체디스플레이기술학회지
• /
• 제9권1호
• /
• pp.11-16
• /
• 2010

Wafer Pre-Alignment is to find the center and the orientation of a wafer and to move the wafer to the desired position and orientation. In this paper, an area camera based pre-aligning method is presented that captures 8 wafer images regularly during 360 degrees rotation. From the images, wafer edge positions are extracted and used to estimate the wafer's center and orientation using least squares circle fitting. These data are utilized for the proper alignment of the wafer. For accurate alignments, camera calibration methods using high order polynomials are used for converting pixel coordinates into real-world coordinates. A complete pre-alignment system was constructed using mechanical and optical components and tested. Experimental results show that alignment of wafer center and orientation can be done with the standard deviation of 0.002 mm and 0.028 degree, respectively.

• 응용통계연구
• /
• 제21권4호
• /
• pp.621-630
• /
• 2008

본 논문은 개체의 특성으로 다가의 명목형 반응변수가 반복측정 요인인 시간요인에 의해 주기적으로 반복측정 되었을 때, 자료를 분석하기 위한 모형으로 일반화된 주변 로짓모형을 논의하고 있다. 다가의 반응변수에 영향을 미치는 공변량중 일부가 처치로써 상대적으로 큰 크기의 실험단위에 배정되고 반복측정 요인인 시간요인의 수준들이 또한 처치요인으로 비확률화에 의해 상대적으로 작은 크기의 실험단위에 배정될 때 이를 고려한 모형구축과정과 예상되는 공분산 구조의 가정하에서 모수를 추정하기 위한 방법으로 가중최소제곱 방법을 이용할 수 있음을 제시하고 있다.

• IEIE Transactions on Smart Processing and Computing
• /
• 제5권5호
• /
• pp.319-322
• /
• 2016

We propose a parametric blind deconvolution method for bi-level images with unknown intensity levels that estimates unknown parameters for point spread functions and images by minimizing a penalized nonlinear least squares objective function based on normalized correlation coefficients and two regularization functions. Unlike conventional methods, the proposed method does not require knowledge about true intensity values. Moreover, the objective function of the proposed method can be effectively minimized, since it has the special structure of nonlinear least squares. We demonstrate the effectiveness of the proposed method through simulations and experiments.

• Communications for Statistical Applications and Methods
• /
• 제9권1호
• /
• pp.291-304
• /
• 2002

This article is concerned with an effective algorithm for the identification of multiple outliers in linear regression. It proposes a hybrid algorithm which employs the least median of squares estimator, instead of the least squares estimator, to construct an Initial clean subset in the stepwise forward search scheme. The performance of the proposed algorithm is evaluated and compared with the existing competitor via an extensive Monte Carlo simulation. The algorithm appears to be superior to the competitor for the most of scenarios explored in the simulation study. Particularly it copes with the masking problem quite well. In addition, the orthogonal decomposition and Its updating techniques are considered to improve the computational efficiency and numerical stability of the algorithm.

• Journal of the Korean Statistical Society
• /
• 제17권1호
• /
• pp.9-23
• /
• 1988

The classicla theory of statistical calibration assumes that the standard measurement is exact. From a realistic point of view, however, this assumption needs to be relaxed so that more meaningful calibration procedures may be developed. This paper presents a model which explicitly considers errors in both standard and nonstandard measurements. Under the assumption that replicated observations are available in the calibration experiment, three estimation techniques (ordinary least squares, grouping least squares, and maximum likelihood estimation) combined with two prediction methods (direct and inverse prediction) are compared in terms of the asymptotic mean square error of prediction.

• Communications for Statistical Applications and Methods
• /
• 제10권1호
• /
• pp.11-18
• /
• 2003

Quite often normality assumptions are not satisfied in practical applications. In this paper, an estimated generalized least squares(EGLS) analysis are considered in two way mixed linear models with arbitrary types of distributions for random effects. We investigate the power performance of EGLS analysis based on Henderson's method III, ML, REML and MINQUE(1). The power performances depend on the imbalance of design, on the actual values of ratio of variance components, and on the skewness and kurtosis parameters of the underlying distributions slightly. Results of our limited simulation study suggest that the EGLS F-statistics using four estimators and arbitrary distributions produce similar type I error rates and power performance.

• Journal of the Korean Data and Information Science Society
• /
• 제22권6호
• /
• pp.1223-1232
• /
• 2011

Tong and Wang's estimator (2005) is a new approach to estimate the error variance using least squares method such that a simple linear regression is asymptotically derived from Rice's lag- estimator (1984). Their estimator highly depends on the setting of a regressor and weights in small sample sizes. In this article, we propose a new approach via a local quadratic approximation to set regressors in a small sample case. We estimate the error variance as the intercept using a ridge regression because the regressors have the problem of multicollinearity. From the small simulation study, the performance of our approach with some existing methods is better in small sample cases and comparable in large cases. More research is required on unequally spaced points.

• Communications for Statistical Applications and Methods
• /
• 제11권3호
• /
• pp.485-494
• /
• 2004

An algorithm is proposed to identify multiple outliers in linear regression. It is based on the clustering of residuals from the least median of squares estimation. A cut-height criterion for the hierarchical cluster tree is suggested, which yields the optimal clustering of the regression outliers. Comparisons of the effectiveness of the procedures are performed on the basis of the classic data and artificial data sets, and it is shown that the proposed algorithm is superior to the one that is based on the least squares estimation. In particular, the algorithm deals very well with the masking and swamping effects while the other does not.

• Communications for Statistical Applications and Methods
• /
• 제17권2호
• /
• pp.183-191
• /
• 2010

Support vector quantile regression(SVQR) is capable of providing more complete description of the linear and nonlinear relationships among random variables. In this paper we propose an iterative reweighted least squares(IRWLS) procedure to solve the problem of SVQR with a weighted quadratic loss function. Furthermore, we introduce the generalized approximate cross validation function to select the hyperparameters which affect the performance of SVQR. Experimental results are then presented which illustrate the performance of the IRWLS procedure for SVQR.