N-gamma discrimination method based on semi-supervised support vector machine

A support vector machine and semi-supervised technology, applied in computer parts, character and pattern recognition, measurement with scintillation detectors, etc., can solve problems such as huge network, inability to obtain standard samples, complex structure, etc., and reduce training samples Requirements for scale, reducing time and computing power overhead, and ensuring consistency

Active Publication Date: 2020-02-11
INST OF ELECTRONICS ENG CHINA ACAD OF ENG PHYSICS
View PDF13 Cites 1 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0012] 5) For each ray particle measured by the detector, use S / S t As feature information, n-γ screening can be performed in combination with the statistical results in the above figure: for S / S t Less than γ accepts the boundary BoundaryG(BoundaryG=u n -3σ n , u n is the neutron distribution expectation, σ n neutron distribution standard deviation) is considered to be gamma rays, S / S t Greater than n accept boundary BoundaryN(BoundaryN=u γ +3σ γ ) are considered to be n-rays, and for S / S t Between BoundaryG and BoundaryN (such as image 3 middle shaded area) cannot be accurately identified
However, due to the amount of calculation and the calculation of complexity, this method only uses the sample points between 20 and 40 nS after the pulse peak as the feature information
At the same time, the neural network method has the following two disadvantages: one is that the method has a complex structure and a huge network; the other is that the method requires a large number of pulse signal data sets generated by known types of neutrons and gamma rays in the detector as standard samples. For training, it is almost impossible to obtain enough such standard samples in the actual environment

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • N-gamma discrimination method based on semi-supervised support vector machine
  • N-gamma discrimination method based on semi-supervised support vector machine
  • N-gamma discrimination method based on semi-supervised support vector machine

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0051] A kind of n-γ discrimination method based on semi-supervised support vector machine, it is characterized in that comprising the following steps:

[0052] a) Use an analog-to-digital converter to perform analog-to-digital conversion on the output pulse signal of the detector, collect the digitized detector pulse signal, and form a training data set; use the training data set combined with a semi-supervised learning method to train a support vector machine, and obtain a Optimal classification hyperplane;

[0053] b) Digitize the newly detected pulse and perform feature extraction preprocessing: extract important feature information of the pulse signal;

[0054] c) Input the extracted feature information into the support vector machine, and use the above-mentioned classification hyperplane combined with the above-mentioned extracted features to classify and predict the newly detected pulse samples.

[0055] The detailed step of said step a) is to use an analog-to-digital ...

Embodiment 2

[0062] A kind of n-γ discrimination method based on semi-supervised support vector machine, it is characterized in that comprising the following steps:

[0063] a) Use an analog-to-digital converter to perform analog-to-digital conversion on the output pulse signal of the detector, collect the digitized detector pulse signal, and form a training data set; use the training data set combined with a semi-supervised learning method to train a support vector machine, and obtain a Optimal classification hyperplane;

[0064] b) Digitize the newly detected pulse and perform feature extraction preprocessing: extract important feature information of the pulse signal;

[0065] c) Input the extracted feature information into the support vector machine, and use the above-mentioned classification hyperplane combined with the above-mentioned extracted features to classify and predict the newly detected pulse samples.

[0066] The detailed step of said step a) is to use an analog-to-digital ...

Embodiment 3

[0069] A kind of n-γ discrimination method based on semi-supervised support vector machine, it is characterized in that comprising the following steps:

[0070] a) Use an analog-to-digital converter to perform analog-to-digital conversion on the output pulse signal of the detector, collect the digitized detector pulse signal, and form a training data set; use the training data set combined with a semi-supervised learning method to train a support vector machine, and obtain a Optimal classification hyperplane;

[0071] b) Digitize the newly detected pulse and perform feature extraction preprocessing: extract important feature information of the pulse signal;

[0072] c) Input the extracted feature information into the support vector machine, and use the above-mentioned classification hyperplane combined with the above-mentioned extracted features to classify and predict the newly detected pulse samples.

[0073] The detailed step of said step a) is to use an analog-to-digital ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention belongs to the technical field of neutron detection, and particularly relates to an n-gamma discrimination method based on a semi-supervised support vector machine, being characterized by comprising the following steps: a) performing analog-to-digital conversion on a detector output pulse signal by using an analog-to-digital converter, and collecting a digitized detector pulse signalto form a training data set; training a support vector machine by using the training data set in combination with a semi-supervised learning method to obtain an optimal classification hyperplane; b)digitalizing the newly detected pulse, and carrying out feature extraction preprocessing: extracting important feature information of the pulse signal, and c) inputting the extracted feature information into a support vector machine, and carrying out classification prediction on the newly detected pulse sample by using the classification hyperplane in combination with the extracted features.

Description

technical field [0001] The invention relates to the technical field of neutron detection, in particular to an n-γ discrimination method based on a semi-supervised support vector machine. Background technique [0002] Due to the inelastic scattering of neutrons and the surrounding environment, the radiation capture of slowed neutrons, etc., the occasions where neutrons exist are almost always accompanied by a large amount of γ-ray background; , Environmental radiation detection, medical and military fields are widely used, and n-γ discrimination (neutron and gamma ray discrimination) technology has gradually become a key technology in neutron detection. At present, the commonly used n-γ screening techniques include rise time method, charge comparison method, neural network method, pulse gradient method, and wavelet transform method; and these methods only use one or two kinds of characteristic information of the pulse signal for n-γ screening. . [0003] A typical detector ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G01T3/06
CPCG01T3/06G06F18/2155G06F18/2411Y02E30/30
Inventor 刘寅宇刘利芳代刚邢占强李顺
Owner INST OF ELECTRONICS ENG CHINA ACAD OF ENG PHYSICS
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products