• The KIPS Transactions:PartA
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• v.8A no.1
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• pp.42-47
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• 2001

In this paper, we present a curve fitting algorithm using a ball curve. Our algorithm is recursive method for fitting, which is not a traditional ball function but a continuous ball function. This algorithm consists of two steps. The first step, it is classified the composite corner points to joint points until selected from the given data set. The second step is the curve fitting. The basis function for curve fitting is use to ball function. Also, the weighted least square method, to insert knot, is an efficient method for piecewise ball curve and ball curve segments will be smoothly connected at all composit points. The proposed algorithm will be applied to represent image representation, like fonts, digital image and GIS.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.8
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• pp.768-776
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• 2011

This paper considers a data fitting problem for rational function models using the LM (Levenberg-Marquardt) optimization method. Rational function models have various merits on representing a wide range of shapes and modeling complicated structures by polynomials of low degrees in both the numerator and denominator. However, rational functions are nonlinear in the parameter vector, thereby requiring nonlinear optimization methods to solve the fitting problem. In this paper, we propose a data fitting method for rational function models based on the LM algorithm which is renowned as an effective nonlinear optimization technique. Simulations show that the fitting results are robust against the measurement noises and uncertainties. The effectiveness of the proposed method is further demonstrated by the real application to a 3D depth camera calibration problem.

• Journal of the Korean Operations Research and Management Science Society
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• v.20 no.3
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• pp.17-29
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• 1995

Recently, neural network models have been employed as an alternative to regression analysis for point estimation or function fitting in various field. Thus far, however, no theoretical or empirical guides seem to exist for selecting the tool which the most suitable one for a specific function-fitting problem. In this paper, we evaluate performance of three major function-fitting techniques, regression analysis and two neural network models, back-propagation and linear-Hebbian-learning neural networks. The functions to be fitted are simple linear ones of a single independent variable. The factors considered are size of noise both in dependent and independent variables, portion of outliers, and size of the data. Based on comutational results performed in this study, some guidelines are suggested to choose the best technique that can be used for a specific problem concerned.

• 한국정보통신설비학회:학술대회논문집
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• 2006.08a
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• pp.202-204
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• 2006

A new method is presented to design RF filters without the Similarity Transform of their coupling coefficient matrix as circuit parameters which is very tedious due to pivoting and deciding rotation angles needed during the iterations. The transfer function of a filter is directly used for the design and its desired form is derived by the optimized rational-function fitting technique. A 3rd order Coaxial Lowpass filter and an 8th order dual-mode elliptic integral function response filter are taken as an example to validate the proposed method.

• Korean Journal of Optics and Photonics
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• v.33 no.6
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• pp.310-316
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• 2022

In this paper, the difference in modulation transfer function (MTF) results according to the change in the angle of a slant target when measuring a presampled MTF was confirmed, and the difference was reduced by fitting the edge spread function graph obtained to reduce the error by the target's rotation. Due to the feature of the presampled MTF method, the spatial frequency changed due to the sensor's projected intensity being changed by the target's rotation, and it was confirmed that the difference in the MTF value occurred depending on the rotation angle of the target. In this paper, the MTF was calculated after fitting only one column of the acquired image. It was confirmed that the rotation error is smaller compared to the case of the presampled MTF method and this fitting method can be applied to a scene that contains various target angles, such as auto-focusing using the MTF.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.35 no.3
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• pp.223-231
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• 2022

Spectroscopies are the most widely used for understanding the crystallographic, chemical, and physical aspects of materials; therefore, numerous commercial and non-commercial software have been introduced to help researchers better handling their spectroscopic data. However, not many researchers, especially early-stage ones, have a proper background knowledge on the choice of fitting functions and a technique for actual fitting, although the essence of such data analysis is peak fitting. In this regard, we present a practical guide for peak fitting for data analysis. We start with a basic-level theoretical background why and how a certain protocol for peak fitting works, followed by a step-by-step visualized demonstration how an actual fitting is performed. We expect that this contribution is sure to help many active researchers in the discipline of materials science better handle their spectroscopic data.

• Current Optics and Photonics
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• v.2 no.1
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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.

• Nuclear Engineering and Technology
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• v.48 no.6
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• pp.1360-1367
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• 2016

This paper introduces a boron meter with improved accuracy compared with other commercially available boron meters. Its design includes a new fitting function and a multi-detector. In pressurized water reactors (PWRs) in Korea, many boron meters have been used to continuously monitor boron concentration in reactor coolant. However, it is difficult to use the boron meters in practice because the measurement uncertainty is high. For this reason, there has been a strong demand for improvement in their accuracy. In this work, a boron meter evaluation model was developed, and two approaches were considered to improve the boron meter accuracy: the first approach uses a new fitting function and the second approach uses a multi-detector. With the new fitting function, the boron concentration error was decreased from 3.30 ppm to 0.73 ppm. With the multi-detector, the count signals were contaminated with noise such as field measurement data, and analyses were repeated 1,000 times to obtain average and standard deviations of the boron concentration errors. Finally, using the new fitting formulation and multi-detector together, the average error was decreased from 5.95 ppm to 1.83 ppm and its standard deviation was decreased from 0.64 ppm to 0.26 ppm. This result represents a great improvement of the boron meter accuracy.

• Communications of the Korean Mathematical Society
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• v.23 no.2
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• pp.295-305
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• 2008

This paper demonstrates how composite-exponential-fitting interpolation rules can be constructed to fit an oscillatory function using not only pointwise values of that function but also of that functions's derivative on a closed and bounded interval of interest. This is done in the framework of exponential-fitting techniques. These rules extend the classical composite cubic Hermite interpolating polynomials in the sense that they become the classical composite polynomials as a parameter tends to zero. Some examples are provided to compare the newly constructed rules with the classical composite cubic Hermite interpolating polynomials (or recently developed interpolation rules).

• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.841-849
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• 2001

The influence in fitting an equicorrelation model is investigated using the influence function. The influence functions for the model parameters are derived and its sample versions are used for investigating the influence of observations on the estimators of the parameters. Some relationships among the sample versions are found. We will derive a measure for identifying observations that have a large influence on the test of fitting the equicorrelation model using the influence function method. An example is given for illustration.