• Journal of Sensor Science and Technology
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• v.21 no.2
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• pp.109-113
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• 2012

We propose an improved interpolating equation to express temperature-resistance characteristics for modern industrial platinum resistance thermometers (PRTs). Callendar-van Dusen equation which has been widely used for platinum resistance thermometer fails to fully describe temperature characteristics of high quality PRTs and leaves systematic residual when the calibration point include temperatures above $300^{\circ}C$. Expanding Callendar-van Dusen to higher-order polynomial drastically improves the uncertainty of the fitting even with reduced degrees of freedom of the fitting. We found that in the fourth-order polynomial fitting, the third-order and fourth-order coefficients have a strong correlation. Using the correlation, we suggest an improved interpolating equation in the form of fourth-order polynomial, but with three fitting parameters. Applying this interpolating equation reduced the uncertainty of the fitting to 32 % of that resulted from the traditional Callendar-van Dusen. This improvement was better than that from a simple third-order polynomial despite that the degrees of the freedom of the fitting was the same.

• Journal of Electrochemical Science and Technology
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• v.8 no.2
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• pp.115-123
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• 2017

Electrochemical noise signals in many cases exhibit a DC drift that should be removed prior to further data analysis. Polynomial fitting and moving average removal method have been used to remove trends of electrochemical noise (EN) in time domain. The corrosion inhibition of synthesized schiff base N,N'-bis(3,5-dihydroxyacetophenone)-2,2-dimethylpropandiimine on API-5L-X70 steel in hydrochloric acid solutions were used to study the effects of drifts removal methods on noise resistance calculation. Also, electrochemical impedance spectroscopy (EIS) was used to study the corrosion inhibition property of the inhibitor. The results showed that for the calculation of $R_n$, both methods were effective in trend removal and the polynomial with m=4 and MAR with p=40 were in agreement.

• Journal of the Korea Institute of Military Science and Technology
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• v.9 no.2 s.25
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• pp.20-25
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• 2006

Low frequency towed line array with high array gain and beam resolution is a long range surveillance sensor for anti-submarine warfare. The beam characteristics is however deteriorated due to the distorted line array sensor caused by low towing speed, wind, current, and towing ship maneuvering. An adaptive beamforming method is utilized in this paper to enhance the distorted line array beam performance by estimating and compensating the nonlinear array shape. A polynomial curve fitting in the least square sense is used to estimate the array shape iteratively with the distributed heading sensors data along the array. Real time array shape estimation and nonlinear array beam calculation is applied to a very long towed line array sensor system and the beam performance is evaluated and compared to the linear beamformer for the simulation and sea trial data.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.5
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• pp.571-575
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• 2003

In this paper, we describe a method for fuzzy polynomial regression analysis for fuzzy input--output data using shape preserving operations for least-squares fitting. Shape preserving operations simplifies the computation of fuzzy arithmetic operations. We derive the solution using mixed nonlinear program.

• Communications of the Korean Mathematical Society
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• v.21 no.2
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• pp.397-407
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• 2006

We construct polynomial-fitting interpolation rules to agree with a function f and its first derivative f' at equally spaced nodes on the interval of interest by introducing a linear functional with which we produce systems of linear equations. We also introduce a matrix whose determinant is not zero. Such a property makes it possible to solve the linear systems and then leads to a conclusion that the rules are uniquely determined for the nodes. An example is investigated to compare the rules with Hermite interpolating polynomials.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.119-122
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• 1986

An algorithm is proposed to find a stable rational function, which is frequently used in the linear system theory, by curve-fitting a given data. This problem is essentially a nonolinear optimization problem. In order to converge faster to the solution, the following method is used. First, the coefficients of the denominator polynomial are fixed and only the coefficients of the numerator polynomial are adjusted by its linear relationships. Then the coefficients of the numerator are fixed and the coefficients of the denominator polynomial are adjusted by nonlinear programming. This whole process is repeated until a convergent solution is found. The solution obtained by this method converges better than by other algorithms and its versatility is demonstrated by applying it to the design of a feedback control system and a low pass filter.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.3
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• pp.163-168
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• 2011

In this paper, we proposed non-linear gamma curve algorithm for gamma correction. The previous non-linear gamma curve algorithm is generated by the least square polynomial using the Gauss-Jordan inverse matrix. However, the previous algorithm has some weak points. When calculating coefficients using inverse matrix of higher degree, occurred truncation errors. Also, only if input sample points are existed regular interval on 10-bit scale, the least square polynomial is accurately works. To compensate weak-points, we calculated accurate coefficients of polynomial using eigenvalue and orthogonal value of mat11x from singular value decomposition (SVD) and QR decomposition of vandemond matrix. Also, we used input data part segmentation, then we performed polynomial curve fitting and merged curve fitting results. When compared the previous method and proposed method using the mean square error (MSE) and the standard deviation (STD), the proposed segmented polynomial curve fitting is highly accuracy that MSE under the least significant bit (LSB) error range is approximately $10^{-9}$ and STD is about $10^{-5}$.

• Communications for Statistical Applications and Methods
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• v.4 no.1
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• pp.177-183
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• 1997

A data-adaptive order selection procedure is proposed for local polynomial nonparametric regression. For each given polynomial order, bias and variance are estimated and the adaptive polynomial order that has the smallest estimated mean squared error is selected locally at each location point. To estimate mean squared error, empirical bias estimate of Ruppert (1995) and local polynomial variance estimate of Ruppert, Wand, Wand, Holst and Hossjer (1995) are used. Since the proposed method does not require fitting polynomial model of order higher than the model order, it is simpler than the order selection method proposed by Fan and Gijbels (1995b).

• Communications for Statistical Applications and Methods
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• v.2 no.1
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• pp.1-12
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• 1995

An estimator of coefficients of polynomial measurement error model with vector predictor and first-order interaction terms is derived using Hermite polynomial. Asymptotic normality of estimator is provided and some simulation study is performed to compare the small sample properties of derived estimator with those of OLS estimator.