Application of BP neural network for LRAD-based alpha contamination monitoring inside pipes

WU Xuemei1 TUO Xianguo1; 2 LI Zhe1 LIU Mingzhe1; 2 ZHANG Jinzhao1 LI Pingchuan1 SU Jilong1 1

doi:null

您当前的位置：

首页 >

文章列表页 >

Application of BP neural network for LRAD-based alpha contamination monitoring inside pipes

更新时间：2021-03-05

- Application of BP neural network for LRAD-based alpha contamination monitoring inside pipes
- NUCLEAR TECHNIQUES Vol. 35, Issue 5, (2012)
- 作者机构：
  
  1. 成都理工大学核技术与自动化工程学院
  2. 成都理工大学地质灾害防治与地质环境保护国家重点实验室
- 作者简介：
- 基金信息：
  
  国家自然科学基金(40974065);国家杰出青年科学基金(41025015);四川省青年科技创新研究团队(2011JTD0013)资助
- DOI：
  CLC： TL751
- Published：01 May 2012
- 稿件说明：
移动端阅览
[1]吴雪梅,庹先国,李哲,刘明哲,张金钊,李平川,宿吉龙.LRAD测量管道内α污染活度的BP网络模拟[J].核技术,2012,35(05):369-374.

WU Xuemei1 TUO Xianguo1, 2 LI Zhe1 LIU Mingzhe1, 2 ZHANG Jinzhao1 LI Pingchuan1 SU Jilong1 1. Application of BP neural network for LRAD-based alpha contamination monitoring inside pipes[J]. Nuclear techniques, 2012, 35(5): 369-374.
[1]吴雪梅,庹先国,李哲,刘明哲,张金钊,李平川,宿吉龙.LRAD测量管道内α污染活度的BP网络模拟[J].核技术,2012,35(05):369-374. DOI：

WU Xuemei1 TUO Xianguo1, 2 LI Zhe1 LIU Mingzhe1, 2 ZHANG Jinzhao1 LI Pingchuan1 SU Jilong1 1. Application of BP neural network for LRAD-based alpha contamination monitoring inside pipes[J]. Nuclear techniques, 2012, 35(5): 369-374. DOI：

摘要

针对风速、流量、探源距、测试管管长和管径等因素对核设施退役中管道内α污染测量造成的非线性影响

采用控制变量法开展长距离α测量（Long range alpha detector

LRAD）模拟装置下的多参数影响实验

初步分析了各种因素对系统测量值的影响特征

建立了以影响因素和测量值为输入、放射源活度为输出的BP神经网络模型

分别对948和100组数据进行了模型建立和实例检验

结果说明

预测平均相对误差为3.4218×10–4

实例平均相对误差为2.217×10–2。应用BP网络模型模拟LRAD装置下的α活度是可行且有效的。

Abstract

Factors of airspeed

flux

activity

source position

pipe length and pipe diameter affect nonlinearly source activity readout of the Long Range Alpha Detection(LRAD).In this paper

multiparameter influence experiment is carried out using variable-control method

aiming at studying relationships between the readout and each of the factors.The back propagation(BP) neural network model is established to overcome the nonlinear effects of the factors on the readout

with the readout and the multiparameters being the input

and the source activity being the output.Experiment data of 948 groups are used for BP neural network forecasting

with an average relative error of 3.4218×10–4.And in a 100-group test

an average relative error of 2.217×10–2 is obtained.It shows that with this method source radioactivity in pipes can be simulated.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Design method of high-flux lead-bismuth cooled reactor neutron flux maximization based on BP neural network

Radiation shielding optimization of space reactor based on intelligent decision support system

Application of BP network to nonlinear rectification of non-closed LRAD monitoring pipe interiors

Related Author

XIAO Yingjie

ZHAO Pengcheng

LIU Zijing

WANG Tong

SUN Zheng

ZHAO Pengcheng

CHEN Zhenping

SHAO Jing

Related Institution

College of Nuclear Science and Technology, University of South China

Hunan Digital Reactor Engineering and Technology Research Center, University of South China

School of Nuclear Science and Technology, University of South China

China Institute of Atomic Energy

中核四川环保工程有限公司

AI问答

Address：2019 Jialuo Road, Jiading, Shanghai, China Postal code：201800
Tel：021-39194977 Email：LHB@sinap.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 沪ICP备05005479号-1 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.
Total Visits：881214 Visits Today：741

⁰