• Communications for Statistical Applications and Methods
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• v.23 no.3
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• pp.231-239
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• 2016

A graphical diagnostic method based on multiple case deletions in a regression context is introduced by using the sampling distribution of the difference between two least squares estimators with and without multiple cases. Principal components analysis plays a key role in deriving this diagnostic method. Multiple case deletions of test statistic are also considered when a new observation is fitted to a given regression model. The result is useful for detecting influential observations in econometric data analysis, for example in checking whether the consumption pattern at a later time is the same as the one found before or not, as well as for investigating the influence of cases in the usual regression model. An illustrative example is given.

• Bulletin of the Korean Chemical Society
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• v.7 no.1
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• pp.59-62
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• 1986

Folded Box coherent anti-Stokes Raman spectroscopy (FBCARS) spectra of the fundamental symmetric stretch(213 $cm^{-1}$) and the first overtone(421 $cm^{-1}$) of $I_2$ have been recorded over the pump wavelength range from 495 nm to 580 nm. The lineshapes were fitted using the least squares method and the A and C term of CARS were calculated. From the fitting of A and C terms, the real(R) and imaginary(I) term intensity of CARS were calculated using harmonic oscillator program. The calculated CARS excitation profile of $I_2$ with R and I term data was about 270 $cm^{-1}$ red shifted from the absorption spectrum.

• Bulletin of the Korean Chemical Society
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• v.19 no.5
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• pp.501-503
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• 1998

In this work, we applied the 1D $11^B$ nutation NMR method for the analysis of the local structural environments in powdered borosilicates $(SiO_2-B_2O_3)$. Spin dynamics during a rf irradiation for spin I=3/2 was analytically calculated with a density ma trix formalism. Spectral simulation programs were written in MATLAB on a PC. Two borosilicates prepared by the sol-gel process at different stabilization temperature were used for the 1D $11^B$ nutation NMR experiment. The $11^B$ NMR parameters, quadrupole coupling constants $(e^2qQ/h)$ and asymmetry parameters (η), for each borosilicate were extracted from the nonlinear least-squares fitting. The effects of heat treatments on the local structures of boron sites in borosilicates were discussed.

• Bulletin of the Korean Chemical Society
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• v.19 no.5
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• pp.519-524
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• 1998

In this work, we applied the 1D 11B nutation NMR method for the analysis of the local structural environments in powdered borosilicates (SiO2-B2O3). Spin dynamics during a rf irradiation for spin I=3/2 was analytically calculated with a density matrix formalism. Spectral simulation programs were written in MATLAB on a PC. Two borosilicates prepared by the sol-gel process at different stabilization temperature were used for the 1D 11B nutation NMR experiment. The 11B NMR parameters, quadrupole coupling constants (e2qQ/h) and asymmetry parameters (η), for each borosilicate were extracted from the nonlinear least-squares fitting. The effects of heat treatments on the local structures of boron sites in borosilicates were discussed.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.4112-4112
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• 2001

The feasibility of NIR spectroscopy as a quick and nondestructive method for quality control of uniformity of coating thickness of pharmaceutical tablets was investigated. Near infrared spectra of a set of pharmaceutical tablets with varying coating thickness were measured with a diffuse reflectance fiber optic probe connected to a Broker IFS 28/N FT-NIR spectrometer. The challenging issues encountered in this study included: 1. The similarity of the formulation of the core and coating materials, 2. The lack of sufficient calibration samples and 3. The non-linear relationship between the NIR spectral intensity and coating: thickness. A peak at 7184 $cm^{-1}$ was identified that differed for the coating material and the core material when M spectra were collected at 2 $cm^{-1}$ resolution (0.4 nm at 7184 $cm^{-1}$). The study showed that the coating thickness can be analyzed by polynomial fitting of the peak area of the selected peak, while least squares calibration of the same data failed due to the lack of availability of sufficient calibration samples. Samples of coal powder and solid pieces of coal were analyzed by FT-NIR diffuse reflectance spectroscopy with the goal of predicting their ash content, percentage of volatile components, and energy content. The measurements were performed on a Broker Vector 22N spectrometer with a fiber optic probe. A partial least squares model was constructed for each of the parameters of interest for solid and powdered sample forms separately. Calibration models varied in size from 4 to 10 PLS ranks. Correlation coefficients for these models ranged from 86.6 to 95.0%, with root-mean-square errors of cross validation comparable to the corresponding reference measurement methods. The use of FT-NIR diffuse reflectance measurement techniques was found to be a significant improvement over existing measurement methodologies in terms of speed and ease of use, while maintaining the desired accuracy for all parameters and sample forms.(Figure Omitted).

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.8
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• pp.921-931
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• 2011

This paper presents a data-fitting technique in which a B-spline hypervolume is used to approximate a given data set of scattered data samples. We describe the implementation of the data structure of a B-spline hypervolume, and we measure its memory size to show that the representation is compact. The proposed technique includes two algorithms. One is for the determination of the knot vectors of a B-spline hypervolume. The other is for the control points, which are determined by solving a linear least-squares minimization problem where the solution is independent of the data-set complexity. The proposed approach is demonstrated with various data-set configurations to reveal its performance in terms of approximation accuracy, memory use, and running time. In addition, we compare our approach with existing methods and present unconstrained optimization examples to show the potential for various applications.

• Journal of the Korean Electrochemical Society
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• v.11 no.1
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• pp.33-36
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• 2008

In the present study, impedance spectrometry has been used for predicting State-of-Charge (SoC) of gel type, Valve Regulated Lead Acid (VRLA), battery. The impedance measurements of VRLA battery (2V/1.2 Ah) at various SoC were made over the frequency range from 100kHz to 10mHz with an amplitude 10 mV. The impedance parameters have been evaluated by the analysis of the data using an equivalent circuit and a complex non-linear least squares (CNLS) fitting method. The charge transfer resistance values and double layer capacitance values of the positive electrode were higher than those of the negative electrode. The gel resistance values increased with decreasing in SoC. This indicates that the gel resistance is an important parameter for predicting SoC of VRLA battery.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.200-209
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• 2019

When forecasting future values, a model estimated after minimizing training errors can yield test errors higher than the training errors. This result is the over-fitting problem caused by an increase in model complexity when the model is focused only on a given dataset. Some regularization and resampling methods have been introduced to reduce test errors by alleviating this problem but have been designed for use with only a given dataset. In this paper, we propose a new optimization approach to reduce test errors by transforming a test error minimization problem into a training error minimization problem. To carry out this transformation, we needed additional data for the given dataset, termed pseudo data. To make proper use of pseudo data, we used three types of missing data imputation techniques. As an optimization tool, we chose the least squares method and combined it with an extra pseudo data instance. Furthermore, we present the numerical results supporting our proposed approach, which resulted in less test errors than the ordinary least squares method.

• The KIPS Transactions:PartB
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• v.16B no.1
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• pp.47-54
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• 2009

Automatic object recognition in 3D measuring data is of great interest in many application fields e.g. computer vision, reverse engineering and digital factory. In this paper we present a software tool for a fully automatic object detection and parameter estimation in unordered and noisy point clouds with a large number of data points. The software consists of three interactive modules each for model selection, point segmentation and model fitting, in which the orthogonal distance fitting (ODF) plays an important role. The ODF algorithms estimate model parameters by minimizing the square sum of the shortest distances between model feature and measurement points. The local quadric surface fitted through ODF to a randomly touched small initial patch of the point cloud provides the necessary initial information for the overall procedures of model selection, point segmentation and model fitting. The performance of the presented software tool will be demonstrated by applying to point clouds.

• Applied Chemistry for Engineering
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• v.9 no.6
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• pp.811-816
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• 1998

Rebar corrosion in cement mortar containing chloride ions was investigated by electrochemical AC impedance spectroscopy. Corrosion of mild steel bar was accelerated by an acceleration test equipment in short period. Impedance values obtained from AC-impedance method could be adapted to the proposed electrochemical equivalent circuit model and they were consistent with calculated values obtained by CNLS fitting method. The weight loss of rebar could be calculated by charge transfer resistance($R_2$) with time and it was close to real value.