A Study of the Peak Boundary Method and Effect Evaluation Based on NaI(TI) Detector for γ-Spectrum Analysis

2013 ◽  
Vol 347-350 ◽  
pp. 1006-1011
Author(s):  
Yao Zong Yang ◽  
Fang Fang ◽  
Jian Feng He ◽  
Jun Jun Ran
Keyword(s):  
Spectrum Analysis ◽  
Gaussian Function ◽  
Comparison Method ◽  
Convergence Result ◽  
Weighting Factor ◽  
Gamma Energy ◽  
Common Boundary ◽  
Function Fitting ◽  
Qualitative Detection ◽  
Fitting Method

In order to accurately analyze the result, which comes from quantitative and qualitative detection based on gamma energy spectrum of NaI(T1) detector, peak boundary has become one of the main factors which influence the spectrum analysis. By implementing some common boundary determining algorithms in the Matlab such as simple comparison method, derivative method, symmetry zero-area method, the full width method and gaussian function fitting method, as well as comparing effectiveness among those algorithm, priority of those boundary algorithms is evaluated. At the same time, because the boundary determining algorithm based on traditional gaussian fitting is not ideal, the new boundary determining algorithm based on the least squares fitting of gaussian function with weighting factor is proposed. The practice verifies that this method is stability and can obtain preferable convergence result in boundary determining of unimodal or combination peaks.

scholarly journals Flux control coefficients determined by inhibitor titration: the design and analysis of experiments to minimize errors

1993 ◽  
Vol 296 (2) ◽  
pp. 423-433 ◽  
Author(s):  
J R Small
Keyword(s):  
Random Errors ◽  
Flux Control ◽  
Graph Method ◽  
Function Fitting ◽  
Fitting Method ◽  
Simple Extrapolation ◽  
Experimental Errors ◽  
Flux Control Coefficients ◽  
Fitted Function ◽  
Control Coefficients

This paper is a study into the effects of experimental error on the estimated values of flux control coefficients obtained using specific inhibitors. Two possible techniques for analysing the experimental data are compared: a simple extrapolation method (the so-called graph method) and a non-linear function fitting method. For these techniques, the sources of systematic errors are identified and the effects of systematic and random errors are quantified, using both statistical analysis and numerical computation. It is shown that the graph method is very sensitive to random errors and, under all conditions studied, that the fitting method, even under conditions where the assumptions underlying the fitted function do not hold, outperformed the graph method. Possible ways of designing experiments to minimize the effects of experimental errors are analysed and discussed.

A new peak fitting method for 1D solid-state 29Si NMR spectra based on singular spectrum analysis

2019 ◽  
Vol 18 (01) ◽  
pp. 1941014
Author(s):  
Guiliang Li ◽  
Changjun Li ◽  
Nan Wei
Keyword(s):  
Molecular Structure ◽  
Spectrum Analysis ◽  
Nmr Spectra ◽  
Singular Spectrum Analysis ◽  
Embedding Dimension ◽  
Characteristic Peak ◽  
Gaussian Fitting ◽  
Singular Spectrum ◽  
Peak Fitting ◽  
Fitting Method

[Formula: see text]Si Nuclear Magnetic Resonance (NMR) can measure the molecular structure of silicate in oilfield reinjection water. However, noise in [Formula: see text]Si NMR spectra (NMRS) affects the determination of silicate molecular structure type. To solve this problem, a new peak fitting method (Two-step Greedy-Singular Spectrum Analysis-Gaussian Fitting Method, TSG-SSA-GFM) is proposed in this paper. This method first uses TSG to determine the embedding dimension, then uses SSA to determine the characteristic peak position. Finally, GFM is used to calculate the molar ratio of characteristic peaks. The results show that TSG can quickly determine the embedding dimension and reduce computation by at least 50% vs. the global ergodic method. The mean deviation of characteristic peak positions determined by SSA is 0.07 ppm, while Discrete Wavelet Transform (DWT) and Empirical Mode Decomposition (EMD) cannot determine characteristic peaks of [Formula: see text]Si NMRS containing overlapping peak. The average [Formula: see text]-squared of Gaussian fitting of [Formula: see text]Si NMRS is 98.4% while Lorentzian is 90.6%. Therefore, this study provides an important method for quantitative analysis of [Formula: see text]Si NMRS.

Voltage Sag Source Localization Based on the Voltage Distance Function in Distribution Network

2013 ◽  
Vol 391 ◽  
pp. 607-610 ◽  
Author(s):  
Yu Liu ◽  
Jin Hao Wang ◽  
Chao Ying Yang
Keyword(s):  
Least Squares ◽  
Source Localization ◽  
Distance Function ◽  
Distribution Network ◽  
Voltage Sag ◽  
Sorting Algorithm ◽  
Line Voltage ◽  
Function Fitting ◽  
Fitting Method ◽  
Bus Voltage

To realize voltage sag source localization in distribution network, the paper proposes a function fitting method based on the least squares. Establish a voltage distance function in response to fault distance changes by the line voltage. According to the voltage distance function, combine with the bus voltage after fault to find out likely fault section and distance. Through the sorting algorithm to sort all possible results, weaken the effect of pseudo fault point on the judgment result. Finally the simulation verifies the effectiveness of the method.

Design of LED Light Source for Uniform Illumination in Large Area

2013 ◽  
Vol 401-403 ◽  
pp. 465-468
Author(s):  
Jie Sun ◽  
Jing Wen Zhang
Keyword(s):  
Light Source ◽  
Design Method ◽  
Gaussian Function ◽  
Uniform Illumination ◽  
Large Area ◽  
Led Light ◽  
Function Fitting ◽  
Led Array ◽  
Gauss Function ◽  
Plane Light

Based on the observation of the data measured, the illumination distribution of single LED is set to follow Gaussian function, and with this hypothesis, two forms of light array are analyzed on their illumination distribution. The law of Spyro is used to optimize space between LED in array. Comparison and analysis of the two kinds of array arrangement are given for getting the uniform illumination. The general design method and the calculation example of LED array to produce uniform illumination are also given in this paper. Keywords: Plane light source, Gauss function fitting, LED light source, Uniform illumination

Research on Matlab Simulation Output of Optical Current Transformer

2014 ◽  
Vol 981 ◽  
pp. 133-136
Author(s):  
Tao Shen ◽  
Chao Hu ◽  
Qian Ru Yang ◽  
Ming Xin Song ◽  
Xin Lao Wei
Keyword(s):  
Simulation Analysis ◽  
Noise Analysis ◽  
Current Transformer ◽  
Theory Model ◽  
Matlab Simulation ◽  
Arm Processor ◽  
Quartic Curve ◽  
Function Fitting ◽  
Fitting Method ◽  
The Relationship

The theory model of the reflective optical current transformer (OCT) has been established using Jones matrix method, and the proper devices have been chose. According to the characteristics of the transformer’s output, signal processing circuits including photoelectric translating circuit, preamplifier, correlation detector, lowpass filter and ARM processor have been designed. The simulation mode was established in the Matlab environment. The simulation analysis as well as the noise analysis was carried out. Finally, the analysis of the relationship between transformer's input and output was analyzed by using function fitting method. The fitting coefficients and error were given and quadratic or quartic curve fitting was proposed.

Extract Characteristic Parameters of FID Signal Combining Autocorrelation Function with the Least Absolute Value

2012 ◽  
Vol 239-240 ◽  
pp. 1108-1112
Author(s):  
Hui Cui Hao ◽  
Jun Lin ◽  
Bao Feng Tian ◽  
Qi Wan
Keyword(s):  
Autocorrelation Function ◽  
Least Squares Method ◽  
Measurement Data ◽  
Characteristic Parameters ◽  
Absolute Value ◽  
Nonlinear Fitting ◽  
Least Absolute Value ◽  
Function Fitting ◽  
Parameters Extraction ◽  
Fitting Method

FID signal is the envelope of magnetic resonance signal, and the extraction accuracy of characteristic parameters directly influence the accuracy of the hydrogeologic parameters of the inverse interpretation. In order to improve the accuracy of characteristic parameters extraction, made simulation and study combined the autocorrelation function fitting with the least absolute value nonlinear fitting method in different SNR and different noise in this paper. The simulation results showed that, the characteristic parameters fitting error using this method was smaller than that using linear, nonlinear fitting method or the autocorrelation function with the least squares method, within 7% in lower SNR. The field measurement data and inversion results verified the method validity.

scholarly journals APPLICATION OF MULTI-FACETED FUNCTION MODEL BASED ON VONDRAK FILTER OPTIMIZATION IN UAV AERIAL IMAGE HEIGHT CORRECTION

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 1313-1317
Author(s):  
F. Zhou ◽  
L. Pu ◽  
S. H. Tang ◽  
Y. F. Yang
Keyword(s):  
High Precision ◽  
Surface Fitting ◽  
Aerial Image ◽  
Experimental Comparison ◽  
Cubic Surface ◽  
Aerial Photogrammetry ◽  
Function Fitting ◽  
Fitting Method ◽  
Elevation Correction ◽  
Vondrak Filter

Abstract. With the rapid development of drone technology and digital camera technology, the method of obtaining high-precision coordinates based on UAV aerial photogrammetry technology is popular. The plane coordinate accuracy of the aerial image of the drone has been able to meet the needs of practical applications, but the elevation accuracy is generally low. Aiming at the low elevation accuracy of UAV aerial photogrammetry, a multi-face function fitting method based on Vondrak filter optimization was proposed. The improved fitting model was used to obtain the elevation correction value of the aerial image, thereby obtaining high-precision image elevation data. In this paper, based on the traditional multi-face function fitting method, some known points were used to model and find the difference between the measured elevation value and the measured elevation. The Vondrak filter was used to smooth the fitting result. Finally, a small number of known elevation points were used for checking, so that the obtained elevation was compared with the actual elevation. The experimental comparison showed that the improved multi-face function fitting method used Vondrak filter was improved by 34.76% compared with the quadric surface fitting, and improved by 14.48% compared with the optimized cubic surface fitting method. Research shows that the multi-faceted function method based on Vondrak filtering is superior to the traditional elevation correction method. The experiment verifies the effectiveness and feasibility of the improved method, and provides some reference value for the research of aerial image elevation correction model.

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