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#EXPONENTIAL CURVE FITTING IGOR PRO FULL#

The Predicting Model of E-commerce Site Based on the Ideas of Curve Fitting Purpose of this paper is to rule out the polynomial curve fitting using least square numerical techniques via MATLAB compatible language. Control rod worth depends largely upon the value of the neutron flux at the location of the rod and reflected by a polynomial curve. To compensate this excess reactivity, it is necessary to introduce an amount of negative reactivity by adjusting or controlling the control rods at will.

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RTP must have sufficient excess reactivity to compensate the negative reactivity feedback effects such as those caused by the fuel temperature and power defects of reactivity, fuel burn-up and to allow full power operation for predetermined period of time. Muhammad Husamuddin Abdul Khalil Mark Dennis Usang Julia Abdul Karim Mohd Amin Sharifuldin Salleh Polynomial curve fitting for control rod worth using least square numerical analysis â†’ Usage of figure of merit demonstrated for six different materials. â†’ Written in MATLAB so readily implemented on variety of computers. Highlights: â†’ Robust algorithm for performing thermoluminescent dosimeter glow curve analysis. Output is a list of fit parameters, peak areas, and graphs for each fit, evaluating each glow curve in 1.5 s or less. Tested materials are LiF:Mg,Ti, CaF 2 :Dy, CaF 2 :Tm, CaF 2 :Mn, LiF:Mg,Cu,P, and CaSO 4 :Dy, with most having an average figure of merit (FOM) of 1.3% or less, with CaSO 4 :Dy 2.2% or less. The MATLAB program fits the glow peaks using the first-order kinetics model. Kearfott, Kimberlee J.Ī computerized glow curve analysis (GCA) program for handling of thermoluminescence data originating from WinREMS is presented. International Nuclear Information System (INIS) Conic curve was the optimal choice based on the specific geometrical properties.Ī computerized glow curve analysis (GCA) method for WinREMS thermoluminescent dosimeter data using MATLAB Polynomial and conic section are both the alternative methods for corneal curve fitting.

#EXPONENTIAL CURVE FITTING IGOR PRO MANUAL#

The manual identifying approach is an indispensable complement for detection. Edge detection has better repeatability and higher efficiency. It is feasible to simulate the corneal surface with mathematical curve with Matlab software. There were no significant differences between 'e' values by corneal topography and conic section ( t =0.9143, P =0.3760 >0.05). Conic section could calculate the tilted symmetry axis, eccentricity, circle center, etc. Polynomial curve was lack of geometrical properties and unstable. Binomial curve was greatly affected by tilt angle. The ordinates of manual identifying were close to the inside of the actual edges. Five edge detection algorithms all had continuous coordinates which indicated the edge of the corneal surface. Finally, the eccentricity (e) obtained by corneal topography and conic section were compared with paired t -test. The relative merits among three fitting curves were analyzed. Binomial curve (y=Ax 2 +Bx+C), Polynomial curve and Conic section (Ax 2 +Bxy+Cy 2 +Dx+Ey+F=0) were used for curve fitting the corneal surface respectively. The differences among these methods were compared. Then two manual identifying methods (ginput and getpts) were applied to identify the edge coordinates respectively. Five edge detection methods (Canny, Log, Prewitt, Roberts, Sobel) were used to identify the corneal surface. The corneal images from optical coherence tomography (OCT) were imported into Matlab software. To select the optimal edge detection methods to identify the corneal surface, and compare three fitting curve equations with Matlab software. Edge detection and mathematic fitting for corneal surface with Matlab software.

Exponential curve fitting igor pro