人工神经网络对137cs γ谱展开的有效性评价

A. Nikitin, E. Mischenko, O. Shurankova
{"title":"人工神经网络对137cs γ谱展开的有效性评价","authors":"A. Nikitin, E. Mischenko, O. Shurankova","doi":"10.46646/2521-683x/2021-2-44-54","DOIUrl":null,"url":null,"abstract":"Development of machine learning methods for spectrum processing is one of the most promising ways for gamma- spectrometry automation and accuracy improvement. Effectiveness of fully connected and convolution neural networks for quantitative γ-spectrometry analysis using scintillation detector NaI(Tl) and lead shielding is presented in the article. Semi-synthetic spectrums were used for the models training; the semi-synthetic spectrums are in channels additions of random spectrums measured at a short duration. The analysis shows advantages of artificial neural networks compare to the common analytical method of spectrum unfolding. The mean square error of activity evaluation is 2–4 times lower than the common method if measuring time is equal to 100 s. In highly standardized conditions of measuring, the advantages of convolution neural networks appear with increasing radiation source activity. Validation with sources not used in training of neural networks has shown fully connected and convolution neural networks can have advantages over the standard method when activity of γ-radiation source is relatively high.","PeriodicalId":269987,"journal":{"name":"JOURNAL OF THE BELARUSIAN STATE UNIVERSITY ECOLOGY","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"EVALUATION OF ARTIFICIAL NEURAL NETWORKS EFFECTIVENESS FOR UNFOLDING GAMMA-SPECTRUM OF 137CS\",\"authors\":\"A. Nikitin, E. Mischenko, O. Shurankova\",\"doi\":\"10.46646/2521-683x/2021-2-44-54\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Development of machine learning methods for spectrum processing is one of the most promising ways for gamma- spectrometry automation and accuracy improvement. Effectiveness of fully connected and convolution neural networks for quantitative γ-spectrometry analysis using scintillation detector NaI(Tl) and lead shielding is presented in the article. Semi-synthetic spectrums were used for the models training; the semi-synthetic spectrums are in channels additions of random spectrums measured at a short duration. The analysis shows advantages of artificial neural networks compare to the common analytical method of spectrum unfolding. The mean square error of activity evaluation is 2–4 times lower than the common method if measuring time is equal to 100 s. In highly standardized conditions of measuring, the advantages of convolution neural networks appear with increasing radiation source activity. Validation with sources not used in training of neural networks has shown fully connected and convolution neural networks can have advantages over the standard method when activity of γ-radiation source is relatively high.\",\"PeriodicalId\":269987,\"journal\":{\"name\":\"JOURNAL OF THE BELARUSIAN STATE UNIVERSITY ECOLOGY\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"JOURNAL OF THE BELARUSIAN STATE UNIVERSITY ECOLOGY\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.46646/2521-683x/2021-2-44-54\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"JOURNAL OF THE BELARUSIAN STATE UNIVERSITY ECOLOGY","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.46646/2521-683x/2021-2-44-54","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

开发用于光谱处理的机器学习方法是提高伽马能谱自动化和准确性的最有前途的方法之一。本文介绍了全连接和卷积神经网络在利用闪烁检测器NaI(Tl)和铅屏蔽进行定量γ光谱分析中的有效性。半合成光谱用于模型训练;半合成光谱是在短时间内测量的随机光谱的信道加法。分析表明,人工神经网络与常用的频谱展开分析方法相比具有优势。当测量时间为100 s时,活度评价的均方误差比常用方法小2-4倍。在高度标准化的测量条件下,随着辐射源活度的增加,卷积神经网络的优势显现出来。对未用于神经网络训练的源的验证表明,当γ辐射源的活度相对较高时,卷积神经网络比标准方法具有完全连接的优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
EVALUATION OF ARTIFICIAL NEURAL NETWORKS EFFECTIVENESS FOR UNFOLDING GAMMA-SPECTRUM OF 137CS
Development of machine learning methods for spectrum processing is one of the most promising ways for gamma- spectrometry automation and accuracy improvement. Effectiveness of fully connected and convolution neural networks for quantitative γ-spectrometry analysis using scintillation detector NaI(Tl) and lead shielding is presented in the article. Semi-synthetic spectrums were used for the models training; the semi-synthetic spectrums are in channels additions of random spectrums measured at a short duration. The analysis shows advantages of artificial neural networks compare to the common analytical method of spectrum unfolding. The mean square error of activity evaluation is 2–4 times lower than the common method if measuring time is equal to 100 s. In highly standardized conditions of measuring, the advantages of convolution neural networks appear with increasing radiation source activity. Validation with sources not used in training of neural networks has shown fully connected and convolution neural networks can have advantages over the standard method when activity of γ-radiation source is relatively high.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信