• Communications for Statistical Applications and Methods
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• 제24권4호
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• pp.383-396
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• 2017

The model in our approach assumes that computer responses are a realization of a Gaussian processes superimposed on a regression model called a Gaussian process regression model (GPRM). Selecting a subset of variables or building a good reduced model in classical regression is an important process to identify variables influential to responses and for further analysis such as prediction or classification. One reason to select some variables in the prediction aspect is to prevent the over-fitting or under-fitting to data. The same reasoning and approach can be applicable to GPRM. However, only a few works on the variable selection in GPRM were done. In this paper, we propose a new algorithm to build a good prediction model among some GPRMs. It is a post-work of the algorithm that includes the Welch method suggested by previous researchers. The proposed algorithms select some non-zero regression coefficients (${\beta}^{\prime}s$) using forward and backward methods along with the Lasso guided approach. During this process, the fixed were covariance parameters (${\theta}^{\prime}s$) that were pre-selected by the Welch algorithm. We illustrated the superiority of our proposed models over the Welch method and non-selection models using four test functions and one real data example. Future extensions are also discussed.

• Journal of Astronomy and Space Sciences
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• 제9권2호
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• pp.143-153
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• 1992

Spectra of the $\zeta$ Aurigae type eclipsing binary system 32 Cygni were taken at the Asiago Observatory. Using the Gaussian fitting method we can estimate the radial velocity and equivalent widths of some metalic lines.

• Current Optics and Photonics
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• 제2권1호
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• pp.69-78
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• 2018

This paper presents a precise edge detection algorithm for the critical dimension (CD) measurement of a Thin-Film Transistor Liquid-Crystal Display (TFT-LCD) pattern. The sigmoid surface function is proposed to model the blurred step edge. This model can simultaneously find the position and geometry of the edge precisely. The nonlinear least squares fitting method (Levenberg-Marquardt method) is used to model the image intensity distribution into the proposed sigmoid blurred edge model. The suggested algorithm is verified by comparing the CD measurement repeatability from high-magnified blurry and noisy TFT-LCD images with those from the previous Laplacian of Gaussian (LoG) based sub-pixel edge detection algorithm and error function fitting method. The proposed fitting-based edge detection algorithm produces more precise results than the previous method. The suggested algorithm can be applied to in-line precision CD measurement for high-resolution display devices.

• 한국멀티미디어학회논문지
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• 제22권12호
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• pp.1396-1403
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• 2019

The radar wind profiler data contaminates with various non-atmospheric components that produce errors in moments and wind velocity estimations. This study implemented an adaptive Gaussian model to detect and remove the clutter from the radar return. This model includes DC filtering, ground clutter recognition, Gaussian fitting, and cost function to mitigate the clutter component. The adaptive model tested for the various types of clutter components and found that it is effective in clutter removal process. It is also applied for the both time series and spectrum datasets. The moments estimated using this method are compared with those derived using conventional DC-filtering clutter removal method. The comparisons show that the proposed method effectively removes the clutter and produce reliable moments.

• 응용통계연구
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• 제26권3호
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• pp.389-399
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• 2013

본 연구에서는 가우시안 과정회귀방법을 소개하고 시계열 마이크로어레이 유전자 발현데이터에 대해 가우시안 과정회귀를 적용한 사례를 보이고자한다. 가우시안 과정회귀를 적합하여 로그 주변우도함수 비를 이용한 유전자를 선별방법에 대한 모의실험을 통해 민감도, 특이도, 위발견율 등을 계산하여 선별방법으로의 활용성을 보였다. 실제 효모세포주기 데이터에 대해 제곱지수공분산함수를 고려한 가우시안 과정회귀를 적합하여 로그 주변우도함수 비를 이용하여 차변화된 유전자를 선별한 후, 선별된 유전자들에 대해 가우시안 모형기반 군집화를 하고 실루엣 값으로 군집유효성을 보였다.

• 천문학회보
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• 제46권2호
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• pp.68.3-69
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• 2021

We present a novel profile stacking technique based on optimal profile decomposition of a 3D spectral line data cube, and its performance test using the HI data cubes of sample galaxies from HI galaxy surveys, THINGS and LITTLE THINGS. Compared to the previous approach which aligns all the spectra of a cube using their central velocities derived from either moment analysis, single Gaussian or hermite h3 polynomial fitting, the new method makes a profile decomposition of the profiles from which an optimal number of single Gaussian components is derived for each profile. The so-called superprofile which is derived by co-adding all the aligned profiles from which the other Gaussian models are subtracted is found to have weaker wings compared to the ones constructed in a typical manner. This could be due to the reduced number of asymmetric profiles in the new method. A practical test made on the HI data cubes of the THINGS and LITTLE THINGS galaxies shows that our new method can extract more mass of kinematically cold HI components in the galaxies than the previous results. Additionally, we fit a double Gaussian model to the superprofiles whose S/N is boosted, and quantify not only their profile shapes but derive the ratio of the Gaussian model parameters, such as the intensity ratio and velocity dispersion ratio of the narrower and broader Gaussian components. We discuss how the superprofile properties of the sample galaxies are correlated with their other physical properties, including star formation rate, stellar mass, metallicity, and gas mass.

• 제어로봇시스템학회논문지
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• 제14권5호
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• pp.415-419
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• 2008

Detecting and correcting defects on LCD glasses early in the manufacturing process becomes important for panel makers to reduce the manufacturing costs and to improve productivity. Many attempts have been made and were successfully applied to detect and identify simple defects such as scratches, dents, and foreign objects on glasses. However, it is still difficult to robustly detect low-contrast defect region, called Mura or blemish area on glasses. Typically, these defect areas are roughly defined as relatively large, several millimeters of diameter, and relatively dark and/or bright region of low Signal-to-Noise Ratio (SNR) against background of low-frequency signal. The aim of this article is to present a robust algorithm to segment these blemish defects. Early 90's, a highly robust estimator, known as the Model-Fitting (MF) estimator was developed by X. Zhuang et. al. and have been successfully used in many computer vision application. Compared to the conventional Least-Square (LS) estimator the MF estimator can successfully estimate model parameters from a dataset of contaminated Gaussian mixture. Such a noise model is defined as a regular white Gaussian noise model with probability $1-\varepsilon$ plus an outlier process with probability $varepsilon$. In the sense of robust estimation, the blemish defect in images can be considered as being a group of outliers in the process of estimating image background model parameters. The algorithm developed in this paper uses a modified MF estimator to robustly estimate the background model and as a by-product to segment the blemish defects, the outliers.

• 한국전기전자재료학회논문지
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• 제35권3호
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• pp.223-231
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• 2022

분광학은 재료의 결정학적, 화학적 구조를 분석하기 위해 가장 보편적으로 활용되는 분석 기법이다. 이러한 기조에 따라 다양한 분석 소프트웨어와 peak fitting과 관련된 기술적 가이드라인이 보급되었지만, 정작 '왜' 중간 계산 과정을 거치고 해당 함수를 쓰는지에 대한 논의는 부족한 실정이다. 따라서 본 tutorial에서는 X선 기반 분광광도계를 통해 얻은 데이터 분석의 기초를 논하고자 한다. 이를 위해 관련된 peak fitting을 위해 필요한 실용적 배경지식을 제시하였다. 나아가, 하나의 예시로 임의로 선정한 X선 광전자 분광법 데이터에 대한 curve fitting 과정을 순서에 따라 알기 쉽게 소개하였다. 제시한 기초 이론은 특정 소프트웨어에 국한된 내용이 아니라 fitting tool이 있는 모든 소프트웨어에서 그대로 활용 가능할뿐더러 다른 분광법 데이터를 분석하는 데 적용 가능하기 때문에, 본 내용을 숙지한다면 보다 수월한 연구 진행을 위한 바탕이 될 수 있을 것이라 기대한다.

• Journal of Korean Vacuum Science & Technology
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• 제3권2호
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• pp.107-111
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• 1999

We developed the experimental function (ERF) which can be used for the numerical curve fitting analysis in photoelectron spectroscopy (PES). We selected the core-levels of Ag 3d5/2 and Au 4f7/2 to obtain the ERF from the measured core-level spectra. For the numerical fourier transformation we applied the fast transform (FFT) algorithm. we considered optical (Wiener) filtering with the FFT due to noise and used Hann window function to remedy the information leakage in frequency domain due to discrete and finite sampling of measurement. The comparison of the curve fitting results using the ERF obtained in this work and the mathematical response function with a gaussian in the conventional approach shows clearly the improvement of the curve fitting analysis.

• 자원환경지질
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• 제28권4호
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• pp.433-438
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• 1995

The most popular minimization method is based on the least-squares criterion, which uses the $L_2$ norm to quantify the misfit between observed and synthetic data. The solution of the least-squares problem is the maximum likelihood point of a probability density containing data with Gaussian uncertainties. The distribution of errors in the geophysical data is, however, seldom Gaussian. Using the $L_2$ norm, large and sparsely distributed errors adversely affect the solution, and the estimated model parameters may even be completely unphysical. On the other hand, the least-absolute-deviation optimization, which is based on the $L_1$ norm, has much more robust statistical properties in the presence of noise. The solution of the $L_1$ problem is the maximum likelihood point of a probability density containing data with longer-tailed errors than the Gaussian distribution. Thus, the $L_1$ norm gives more reliable estimates when a small number of large errors contaminate the data. The effect of outliers is further reduced by M-fitting method with Cauchy error criterion, which can be performed by iteratively reweighted least-squares method.