Discrimination of internal fault from magnetising inrush current in power transformers based on sine-wave least-squares curve fitting method

In the present work, a program for calculating the coefficients of the Aplanatic Cassegrain Telescope (ACT) system, free from the effects of spherical and coma aberrations, were constructed. In addition, the two-mirrors of the optical system, as aspherical surfaces, were adopted. This means, that the two-equations of the mirrors are assumed to be polynomial function of five even terms only. The numerical method, least-squares curve fitting method to calculate the two-mirror coefficients system, was adopted. For choosing the values and ratios that give the best results, Rayleigh Criterion (Rayleigh Limit), for purpose of comparison and preference, was adopted.

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An investigation of three mathematical models on the COVID-19 pandemic: the case of Turkey

ITM Web of Conferences ◽

10.1051/itmconf/20213601003 ◽

2021 ◽

Vol 36 ◽

pp. 01003

Author(s):

Engin Can

Keyword(s):

Regression Analysis ◽

Mathematical Modelling ◽

Mathematical Models ◽

Least Squares ◽

Curve Fitting ◽

Ministry Of Health ◽

Forecasting Models ◽

Fitting Method ◽

Curve Fitting Method ◽

Reported Data

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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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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