基于神经网络的人工放射性气溶胶中氡子体扣除算法

陈立; 顾民; 曾国强; 葛良全; 杨坤; 肖明

doi:10.11889/j.0253-3219.2017.hjs.40.090402

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基于神经网络的人工放射性气溶胶中氡子体扣除算法

核电子学与仪器 | 更新时间：2021-05-19

- 基于神经网络的人工放射性气溶胶中氡子体扣除算法
- Radon daughter subtraction algorithm for artificial radioactive aerosol based on neural network
- 核技术 2017年40卷第9期文章编号：090402
- 作者机构：
  
  1.四川省辐射环境管理监测中心站成都 611139
  2.成都理工大学地学核技术四川省重点实验室成都 610059
  3.中广核久源(成都)科技有限公司成都 610200
- 作者简介：
  
  [ "陈立, 男, 1974年出生, 1997年毕业于四川大学, 研究领域为辐射环境监测", "CHEN Li, male, born in 1974, graduated from Sichuan University in 1997, focusing on radiation environment monitoring" ]
  顾民, E-mail:gumin@cdut.cnGU Min, E-mail: gumin@cdut.cn
- 基金信息：
  
  国家自然科学基金(41474159);国家863计划项目(2012AA061803)
- DOI：10.11889/j.0253-3219.2017.hjs.40.090402
  中图分类号： TL99
- 收稿日期：2016-11-23，
  
  修回日期：2017-04-04，
  
  纸质出版日期：2017-09
- 稿件说明：
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陈立, 顾民, 曾国强, 等. 基于神经网络的人工放射性气溶胶中氡子体扣除算法[J]. 核技术, 2017,40(9):090402

Li CHEN, Min GU, Guoqiang ZENG, et al. Radon daughter subtraction algorithm for artificial radioactive aerosol based on neural network[J]. Nuclear techniques, 2017, 40(9): 090402
陈立, 顾民, 曾国强, 等. 基于神经网络的人工放射性气溶胶中氡子体扣除算法[J]. 核技术, 2017,40(9):090402 DOI： 10.11889/j.0253-3219.2017.hjs.40.090402.

Li CHEN, Min GU, Guoqiang ZENG, et al. Radon daughter subtraction algorithm for artificial radioactive aerosol based on neural network[J]. Nuclear techniques, 2017, 40(9): 090402 DOI： 10.11889/j.0253-3219.2017.hjs.40.090402.

摘要

介绍和分析了人工放射性气溶胶在线监测仪氡子体扣除算法中比例系数扣除法，现有算法存在分类粗糙、扣除准确度不高以及适应性不强等不足。为进一步提高扣除的准确度，降低检测限，提出了利用聚类分析先对谱线进行分类，然后在每个类中利用神经网络进行计算，最后进行扣除的方法。测试结果证明了聚类分析和神经网络扣除方法均能明显降低人工放射性气溶胶在线监测仪的检测限。

Abstract

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.

关键词

Keywords

references

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