A piecewise curve fitting method of processing meteorological detecting data based on the least squares of orthogonal polynomial

Mathematical modelling plays a major role in assessing, controlling, and forecasting potential outbreaks. This article consists of implementing mathematical forecasting models by Liang [14], Ma [16], and the curve fitting method with the least squares as a standard approach in regression analysis and compares those calculate results obtained from the Covid-19 outbreak data, which is announced by the Turkish Ministry of Health, between March 10, 2020, and May 15, 2020, in Turkey. For this purpose, we presented an overview of three mathematical models and finally, demonstrated their applications using the Ministry of Health of Turkey’s publicly reported data.

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Numerical distance relaying algorithm based on Mathematical Morphology and Least-Squares Curve Fitting method

Electric Power Systems Research ◽

10.1016/j.epsr.2011.01.003 ◽

2011 ◽

Vol 81 (5) ◽

pp. 1144-1150 ◽

Cited By ~ 19

Author(s):

A.P. Morais ◽

G. Cardoso ◽

L. Mariotto ◽

G.D. Ferreira

Keyword(s):

Least Squares ◽

Mathematical Morphology ◽

Curve Fitting ◽

Numerical Distance ◽

Fitting Method ◽

Least Squares Curve Fitting ◽

Distance Relaying ◽

Curve Fitting Method

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Least squares curve-fitting method for endpoint detection of chelatometric titration with metal indicator

Analytical Chemistry ◽

10.1021/ac60295a030 ◽

1970 ◽

Vol 42 (13) ◽

pp. 1477-1482 ◽

Cited By ~ 10

Author(s):

Hisakuni. Sato ◽

Kozo. Momoki

Keyword(s):

Least Squares ◽

Curve Fitting ◽

Endpoint Detection ◽

Fitting Method ◽

Least Squares Curve Fitting ◽

Curve Fitting Method

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Application of the Least Squares Support Vector Machine for Life Prediction of Vital Parts

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.584-586.2129 ◽

2014 ◽

Vol 584-586 ◽

pp. 2129-2132

Author(s):

Hong Kai Wang ◽

Ji Sheng Ma ◽

Li Qing Fang ◽

Da Lin Wu ◽

Yan Feng Yang

Keyword(s):

Support Vector Machine ◽

Least Squares ◽

Curve Fitting ◽

Life Prediction ◽

Large Scale ◽

Development Trend ◽

Small Sample ◽

Support Vector ◽

Fitting Method ◽

Curve Fitting Method

In order to better study the wear state of vital parts of the large scale equipment, and overcoming the disadvantage of small sample of vital parts, we use the least squares support vector machine (LS_SVM) algorithm to predict the wear state of vital parts. Using of quantum particle swarm optimization (QPSO) to optimize parameters least squares support vector machine, and achieved good results. Compared those with the method that use of curve fitting to predict the data development trend, show that this method is superior to the curve fitting method, and has good application value.

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Using A Segmented Voltage Sweep Mode and A Gaussian Curve Fitting Method to Improve Heavy Metal Measurement System Performance

Metrology and Measurement Systems ◽

10.2478/v10178-012-0033-8 ◽

2012 ◽

Vol 19 (2) ◽

pp. 381-394

Author(s):

José Pereira ◽

Octavian Postolache ◽

Pedro Girão

Keyword(s):

Heavy Metals ◽

Heavy Metal ◽

Measurement System ◽

Curve Fitting ◽

System Performance ◽

Redox Potentials ◽

Voltage Sweep ◽

Gaussian Curve ◽

Fitting Method ◽

Curve Fitting Method

Using A Segmented Voltage Sweep Mode and A Gaussian Curve Fitting Method to Improve Heavy Metal Measurement System PerformanceThis paper presents a voltammetric segmented voltage sweep mode that can be used to identify and measure heavy metals' concentrations. The proposed sweep mode covers a set of voltage ranges that are centered around the redox potentials of the metals that are under analysis. The heavy metal measurement system can take advantage of the historical database of measurements to identify the metals with higher concentrations in a given geographical area, and perform a segmented sweep around predefined voltage ranges or, alternatively, the system can perform a fast linear voltage sweep to identify the voltammetric current peaks and then perform a segmented voltage sweep around the set of voltages that are associated with the voltammetric current peaks. The paper also includes the presentation of two auto-calibration modes that can be used to improve system's reliability and proposes the usage of a Gaussian curve fitting of voltammetric data to identify heavy metals and to evaluate their concentrations. Several simulation and experimental results, that validate the theoretical expectations, are also presented in the paper.

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A Useful Curve Fitting Method for Concrete Uniaxial Compressive Experiment Data

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.291-294.1015 ◽

2011 ◽

Vol 291-294 ◽

pp. 1015-1020 ◽

Cited By ~ 1

Author(s):

Chong Jin ◽

Hong Wang ◽

Xiao Zhou Xia

Keyword(s):

Experimental Data ◽

Orthogonal Polynomial ◽

Least Square ◽

Base Function ◽

Concrete Material ◽

Fitting Method ◽

Orthogonal Base ◽

The Matrix ◽

Uniform Function ◽

Curve Fitting Method

Based on the superiority avoiding the matrix equation to be morbid for those fitting functions constructed by orthogonal base, the Legendre orthogonal polynomial is adopted to fit the experimental data of concrete uniaxial compression stress-strain curves under the frame of least-square. With the help of FORTRAN programming, 3 series of experimental data is fitted. And the fitting effect is very satisfactory when the item number of orthogonal base is not less than 5. What’s more, compared with those piecewise fitting functions, the Legendre orthogonal polynomial fitting function obtained can be introduced into the nonlinear harden-soften character of concrete constitute law more convenient because of its uniform function form and continuous derived feature. And the fitting idea by orthogonal base function will provide a widely road for studying the constitute law of concrete material.

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