Substance identification device and method for extracting statistical feature based on cluster analysis

Patent metadata

Field	Value
Publication number	US-11619599-B2
Application number	US-202016935415-A
Country	US
Kind code	B2
Filing date	Jul 22, 2020
Priority date	Jul 23, 2019
Publication date	Apr 4, 2023
Grant date	Apr 4, 2023

Abstract

The present disclosure provides a substance identification device and a substance identification method. The substance identification device comprises: a classifier establishing unit configured to establish a classifier based on scattering density values reconstructed for a plurality of known sample materials, wherein the classifier comprises a plurality of feature regions corresponding to a plurality of characteristic parameters for the plurality of known sample materials, respectively; and an identification unit for a material to be tested, configured to match the characteristic parameter of the material to be tested with the classifier, and to identify a type of the material to be tested by obtaining a feature region corresponding to the characteristic parameter of the material to be tested.

First claim

We claim:

1. A substance identification device, comprising:
a classifier establishing unit configured to establish a classifier based on scattering density values reconstructed for cosmic ray scattering by a plurality of known sample materials, wherein the classifier comprises a plurality of feature regions corresponding to a plurality of characteristic parameters for the plurality of known sample materials, respectively; and
an identification unit for a material to be tested, configured to match the characteristic parameter of the material to be tested with the plurality of feature regions by using the classifier, to determine a feature region corresponding to the characteristic parameter of the material to be tested from the plurality of feature regions, and to identify a type of the material to be tested based on the feature region corresponding to the characteristic parameter of the material to be tested;
wherein the classifier establishing unit comprises:
a noise reduction processing module configured to perform a noise reduction process on the scattering density values reconstructed for each of the plurality of known sample materials; and
a cluster analysis module configured to perform a cluster analysis on the scattering density values processed by the noise reduction process for each of the plurality of known sample materials, so as to obtain a distribution feature of the scattering density values for each of the plurality of known sample materials.

2. The substance identification device of claim 1 , wherein the classifier establishing unit further comprises:
a feature extraction module configured to extract the characteristic parameters reflecting material features based on the distribution feature of the scattering density values for each of the plurality of known sample materials.

3. The substance identification device of claim 2 , wherein the classifier establishing unit further comprises:
a classifier establishing module configured to establish the classifier comprising the plurality of feature regions based on the characteristic parameters and the types for the plurality of known sample materials, wherein the plurality of feature regions correspond to the types for the plurality of known sample materials, respectively.

4. The substance identification device of claim 3 , wherein the identification unit for the material to be tested comprises:
a feature extraction module for the material to be tested, configured to extract the characteristic parameters of the material to be tested; and
a matching module configured to match the extracted characteristic parameters of the material to be tested with the feature regions in the classifier, so as to determine a matched feature region where the characteristic parameters are located, thereby identifying the type of the material to be tested based on the matched feature region.

5. The substance identification device of claim 4 , wherein
the matching module is further configured to feed the extracted characteristic parameters of the material to be tested and the identified type of the material to be tested into the classifier establishing module; and
the classifier establishing module is further configured to update the classifier based on the characteristic parameters of the material to be tested and the type of the material to be tested.

6. A substance identification method, comprising:
establishing a classifier based on scattering density values reconstructed for cosmic ray scattering by a plurality of known sample materials, wherein the classifier comprises a plurality of feature regions corresponding to a plurality of characteristic parameters for the plurality of known sample materials, respectively; and
matching the characteristic parameter of a material to be tested with the plurality of feature regions by using the classifier, determining a feature region corresponding to the characteristic parameter of the material to be tested from the plurality of feature regions, and identifying a type of the material to be tested based on the feature region corresponding to the characteristic parameter of the material to be tested;
wherein the establishing a classifier based on scattering density values reconstructed for a plurality of known sample materials further comprising:
performing a noise reduction process on the scattering density values reconstructed for each of the plurality of known sample materials; and
performing a cluster analysis on the scattering density values processed by the noise reduction process for each of the plurality of known sample materials, so as to obtain a distribution feature of the scattering density values for each of the plurality of known sample materials.

7. The method of claim 6 , wherein the establishing a classifier based on scattering density values reconstructed for a plurality of known sample materials further comprising:
extracting the characteristic parameters reflecting material features based on the distribution feature of the scattering density values for each of the plurality of known sample materials.

8. The method of claim 7 , wherein the establishing a classifier based on scattering density values reconstructed for a plurality of known sample materials further comprising:
establishing the classifier comprising the plurality of feature regions based on the characteristic parameters and the types for the plurality of known sample materials, wherein the plurality of feature regions correspond to the types for the plurality of known sample materials, respectively.

9. The method of claim 8 , further comprising: extracting the characteristic parameters of the material to be tested; and
the identifying a type of the material based on the feature region corresponding to the characteristic parameter of the material to be tested further comprising: matching the extracted characteristic parameters of the material to be tested with the feature regions in the classifier, so as to determine a matched feature region where the characteristic parameters are located, thereby identifying the type of the material to be tested based on the matched feature region.

10. The method of claim 9 , further comprising:
feeding the extracted characteristic parameters of the material to be tested and the identified type of the material to be tested into the classifier, and the classifier is updated based on the characteristic parameters of the material to be tested and the type of the material to be tested.

11. A non-transitory computer readable medium having recorded thereon a computer program executable by a processor, the computer program comprising program code instructions for implementing the method of claim 6 .

12. An electronic device, comprising:
at least one processor, and
a memory, configured to store at least one computer program which is executable by the processor,
wherein the processor is configured to, when executing the computer program, perform the method of claim 6 .

Assignees

• Univ Tsinghua
• Nuctech Co Ltd

Inventors

• Zeng Zhi
• Pan Xingyu
• Wang Xuewu
• Li Junli
• Zeng Ming
• Li Jianmin
• Zhao Ziran
• Cheng Jianping
• Ma Hao
• Zhang Hui
• Yu Hao
• Liu Bicheng

Classifications

• G06F18/2415

  based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate · CPC title

• G01N23/201 Primary

  by measuring small-angle scattering · CPC title

• G06F2218/12

  Classification; Matching · CPC title

• G01N23/02

  by transmitting the radiation through the material · CPC title

• G06F2218/08

  Feature extraction · CPC title