Cubic Spline Interpolation for Data Infections of COVID-19 Pandemic in Iraq

In this paper, we use an empirical equation and cubic spline interpolation to fit Covid-19 data available for accumulated infections and deaths in Iraq. For Scientific visualization of data interpretation, it is useful to use interpolation methods for purposes fitting by data interpolation. The data used is from 3 January 2020 to 21 January 2021 in order to obtain graphs to analysing the rate of increasing the pandemic and then obtain predicted values for the data infections and deaths in that period of time. Stochastic fit to the data of daily infections and deaths of Covid-19 is also discussed and showed in figures. The results of the cubic splines and the empirical equation used will be numerically compared. The principle of least square errors will be used for both these interpolations. The numerical results will be indicated that the cubic spline gives an accurate fitting to data.

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The Application of Cubic Spline Interpolation Function in Determining Rock Rheological Long-Term Strength

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.580-583.205 ◽

2014 ◽

Vol 580-583 ◽

pp. 205-208

Author(s):

Mo Li Zhao ◽

Qiang Yong Zhang

Keyword(s):

Spline Interpolation ◽

Least Square Method ◽

Cubic Spline ◽

Least Square ◽

Maximum Deviation ◽

Interpolation Function ◽

Term Strength ◽

Cubic Spline Interpolation ◽

Dagangshan Hydropower Station

The rheological long-term strength is determined according to the triaxial rheological test data of diabase at the dam area of Dagangshan Hydropower Station. Firstly, based on the stress-strain isochronous curve method and connected the test points with cubic spline interpolation function, the maximum deviation point in the long-term interval is determined as the turning point and established the long-term strength by nonlinear least square method. The results show that this method is consistent with the other methods. Finally, the advantage and disadvantage of this method is analyzed. This method can overcome the randomness of artificial selecting the turning points. Therefore, maximum deviation point method is relatively a reasonable and effective method to determine the rheological long-term strength of rock.

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Wind Velocity Data Interpolation Using Rational Cubic Spline

MATEC Web of Conferences ◽

10.1051/matecconf/201822504006 ◽

2018 ◽

Vol 225 ◽

pp. 04006

Author(s):

Samsul Ariffin Bin Abdul Karim ◽

S. Suresh Kumar Raju

Keyword(s):

Wind Velocity ◽

Hermite Polynomial ◽

Spline Interpolation ◽

Cubic Spline ◽

Numerical Results ◽

Data Interpolation ◽

Velocity Data ◽

Cubic Spline Interpolation ◽

Rational Spline ◽

The Given

Wind velocity data is always having positive value and the minimum value approximately close to zero. The standard cubic spline interpolation (not-a-knot and natural) as well as cubic Hermite polynomial may be produces interpolating curve with negative values on some subintervals. To cater this problem, a new rational cubic spline with three parameters is constructed. This rational spline will be used to preserve the positivity of the wind velocity data. Numerical results shows that the proposed scheme work very well and give visually pleasing interpolating curve on the given domain.

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An Improving Method for Background Correction in Laser Induced Breakdown Spectroscopy

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.751.86 ◽

2015 ◽

Vol 751 ◽

pp. 86-91 ◽

Cited By ~ 3

Author(s):

Bo Zhang ◽

Lan Xiang Sun ◽

Hai Bin Yu

Keyword(s):

Signal Analysis ◽

Interpolation Method ◽

Spline Interpolation ◽

Cubic Spline ◽

Background Correction ◽

Laser Induced Breakdown Spectroscopy ◽

Cubic Spline Interpolation ◽

Interpolation Methods ◽

Breakdown Spectroscopy ◽

Laser Induced Breakdown

Background correction is an essential part in LIBS signal analysis. The interpolation method of background correction has major drawbacks. This paper introduces an interpolation method to overcome the shortcomings of linear and cubic spline interpolation methods. Finally, we compare different interpolation methods to verify the proposed interpolation method.

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