Nuclear Techniques


Radon daughter subtraction algorithm for artificial radioactive aerosol based on neural network

CHEN Li1, GU Min2, ZENG Guoqiang2, GE Liangquan2, YANG Kun3, XIAO Ming3   

  1. 1. Radiation Environmental Management and Monitoring Center of Sichuan Province, Chengdu 611139, China;
    2. Key Laboratory of Earth Science Nuclear Technology of Sichuan Province, Chengdu University of Technology, Chengdu 610059, China;
    3. CGNPC Jiuyuan. Chengdu;Technology Co. Ltd., Chengdu 610200, China
  • Received:2016-11-23 Revised:2017-04-04 Online:2017-09-10 Published:2017-09-06
  • Supported by:

    Supported by National Natural Science Foundation of China (No.41474159), National 863 Plan Project (No.2012AA061803), Youth Foundation Project of the Science and Technology Department in Sichuan Province (No.2015JQ0035)


Background: The proportion subtraction method used in radon daughters subtraction algorithm for continuous artificial radioactive aerosol monitor has disadvantages such as rough classfication, less accuracy and low adaptability. Purpose: This study aims to improve the accuracy of subtraction to reduce the detection limit. Methods: A novel algorithm is proposed by classifying the spectral lines through clustering analysis and then calculating each clustering using neural network. Experimental verifcation is performed to compare this method with the proportion subtraction method. Results: The results showed that the cluster analysis and neural network subtraction algorithm can reduce more than 20% of the detection limit for the continuous artificial radioactive aerosol monitor. Conclusion: The algorithm proposed in this paper is effective for subtracting radon daughters.

Key words: Aerosol, Radon daughters, Clustering analysis, Neural network

CLC Number: 

  • TL99