• Communications for Statistical Applications and Methods
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• v.27 no.2
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• pp.255-268
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• 2020

A mixture of multivariate canonical fundamental skew t-distribution (CFUST) has been of interest in various fields. In particular, interest in the unsupervised learning society is noteworthy. However, fitting the model via EM algorithm suffers from significant processing time. The main cause is due to the calculation of many multivariate t-cdfs (cumulative distribution functions) in E-step. In this article, we provide an approximate, but fast calculation method for the in univariate fashion, which is the product of successively conditional univariate t-cdfs with Taylor's first order approximation. By replacing all multivariate t-cdfs in E-step with the proposed approximate versions, we obtain the admissible results of fitting the model, where it gives 85% reduction time for the 5 dimensional skewness case of the Australian Institution Sport data set. For this approach, discussions about rough properties, advantages and limits are also presented.

• The Journal of the Korea Contents Association
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• v.13 no.5
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• pp.451-458
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• 2013

In this paper, we have developed an intellectual sizing and fitting system for bike user. In order to prevent the disease, we should adjust the bike's frame and select several components in the body size. Then it propose an extractions algorithm of avatar imagination with the cromakey method and realize the sizing and fitting.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.4
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• pp.1063-1075
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• 2012

To locate an object accurately in the wireless sensor networks, the distance measure based on time-delay plays an important role. In this paper, we propose a maximum likelihood (ML) time-delay estimation algorithm in multi-path wireless propagation channel. We get the joint probability density function after sampling the frequency domain response of the multi-path channel, which could be obtained by the vector network analyzer. Based on the ML criterion, the time-delay values of different paths are estimated. Considering the ML function is non-linear with respect to the multi-path time-delays, we first obtain the coarse values of different paths using the subspace fitting algorithm, then take them as an initial point, and finally get the ML time-delay estimation values with the pattern searching optimization method. The simulation results show that although the ML estimation variance could not reach the Cramer-Rao lower bounds (CRLB), its performance is superior to that of subspace fitting algorithm, and could be seen as a fine algorithm.

• Nuclear Engineering and Technology
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• v.52 no.1
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• pp.101-108
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• 2020

In order to solve the contradiction between requirements of high sampling rate for acquiring accurate energy information of pulses and large amount of data to be processed timely, the method with an algorithm to correct errors caused by reducing the sampling rate is normally used in front-end read-out system, which is conductive to extract accurate energy information from digitized waveform of pulse. The functions and effects of algorithms, which mainly include polynomial fitting with different fitting times, double exponential function fitting under different sampling modes, and integral area algorithm, are analyzed and evaluated, and some meaningful results is presented in this paper. The algorithm described in the paper has been used preliminarily in a prototype system of Positron Emission Tomography (PET) for heavy-ion cancer therapy facility.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.16 no.7 s.112
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• pp.769-776
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• 2006

A measured frequency response function can be represented as a ratio of two polynomials. A curve-fitting of frequency responses with Legendre polynomialis suggested in the paper. And the suggested curve-fitting algorithm is based on the least-square error method. Since the Legendre polynomials satisfy the orthogonality condition, the curve-fitting with the polynomials results to more reliable curve-fitting than ordinary polynomial method. Though the proposed curve-fitting with Legendre polynomials cannot cover all frequency range of interest, example shows that the suggested method is quite applicable in a limited frequency band.

• Korean Journal of Optics and Photonics
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• v.17 no.3
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• pp.216-222
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• 2006

We propose the use of the Gaussian-curve fitting algorithm for the improvement of measurement accuracy in wavelengthscanned Fabry-Perot filter based demodulation systems. The peak locations of FBG sensors were calculated from the fitted curves rather than from distorted PD profiles, resulting in much better measurement accuracy than that of the highest-peak search algorithm. Also, the algorithm was proved to minimize measurement uncertainty of spectrally-distorted grating sensors. From our experimental results, a temperature resolution as small as ${\sim}0.3^{\circ}C$ was readily achieved by use of the Gaussian-curve fitting algorithm whereas the highest-peak search algorithm gave a temperature resolution larger than ${\sim}4^{\circ}C$.

• Journal of Urban Science
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• v.6 no.2
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• pp.49-57
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• 2017

A new Fiber Bragg Grating (FBG) wavelength demodulation scheme is studied in the paper, which consists of an improved de-noising method and Gaussian fitting peak searching algorithm. The improved translational invariant wavelet without threshold adjust factor is proposed to get a better de-noising performance for FBG sensor signal and overcome the drawbacks of soft or hard threshold wavelets. In order to get a high wavelength demodulation precision of FBG sensor signal, this de-noising method is designed to combine with Gaussian fitting peak searching algorithm. The simulation results show that the wavelength maximum measurement error is lower than 1pm, and can get a much higher accuracy.

• Journal of the military operations research society of Korea
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• v.13 no.1
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• pp.65-90
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• 1987

This thesis describes the curve fitting algorithm that the Portable Wear Metal Analyzer used for calculating concentrations in ppm and compares this with some alternative algorithms. Each algorithm considered fits a curve to the three standards used, which were $20\%,\;50\%,\;and\;100\%$ of the full scale for all nine wear metals. APL was used for all programs.

• Journal of Korean Society for Quality Management
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• v.33 no.1
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• pp.83-87
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• 2005

For the count response we normally consider Poisson regression model. However, the conventional fitting algorithm for Poisson regression model is not reliable at all when the response variable is measured with sizable contamination. In this study, we propose an alternative fitting algorithm that is resistant to outlying values in response and report a case study in semiconductor industry.

• Journal of Computational Design and Engineering
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• v.1 no.2
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• pp.88-95
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• 2014

Because the cost of performance testing using actual products is expensive, manufacturers use lower-cost computer-aided design simulations for this function. In this paper, we propose using hexahedral meshes, which are more accurate than tetrahedral meshes, for finite element analysis. We propose automatic hexahedral mesh generation with sharp features to precisely represent the corresponding features of a target shape. Our hexahedral mesh is generated using a voxel-based algorithm. In our previous works, we fit the surface of the voxels to the target surface using Laplacian energy minimization. We used normal vectors in the fitting to preserve sharp features. However, this method could not represent concave sharp features precisely. In this proposal, we improve our previous Laplacian energy minimization by adding a term that depends on multi-normal vectors instead of using normal vectors. Furthermore, we accentuate a convex/concave surface subset to represent concave sharp features.