LSNet:高效核素谱识别网络

IF 1.5 3区 化学 Q3 CHEMISTRY, ANALYTICAL
Pengzhang Yu, Yingrui Hu, Xu Wang, Ying Cai, Daji Ergu, Yong Xu, Shengbo Tan
{"title":"LSNet:高效核素谱识别网络","authors":"Pengzhang Yu,&nbsp;Yingrui Hu,&nbsp;Xu Wang,&nbsp;Ying Cai,&nbsp;Daji Ergu,&nbsp;Yong Xu,&nbsp;Shengbo Tan","doi":"10.1007/s10967-025-10032-2","DOIUrl":null,"url":null,"abstract":"<div><p>To address the interference from non-radioactive components in the environment and achieve efficient identification of mixed nuclide spectra, we propose a novel isotope identification method, LSNet. Experimental results demonstrate that, with comparable parameter counts and computational complexity, LSNet outperforms models such as the Gate-Recurrent-Unit (GRU) across various evaluation metrics. Additionally, LSNet shows significantly higher accuracy in identifying anomalous nuclide spectra compared to other models. Finally, we demonstrate that LSNet effectively identifies mixed nuclides.</p></div>","PeriodicalId":661,"journal":{"name":"Journal of Radioanalytical and Nuclear Chemistry","volume":"334 4","pages":"2703 - 2714"},"PeriodicalIF":1.5000,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"LSNet: efficient nuclide spectrum recognition network\",\"authors\":\"Pengzhang Yu,&nbsp;Yingrui Hu,&nbsp;Xu Wang,&nbsp;Ying Cai,&nbsp;Daji Ergu,&nbsp;Yong Xu,&nbsp;Shengbo Tan\",\"doi\":\"10.1007/s10967-025-10032-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>To address the interference from non-radioactive components in the environment and achieve efficient identification of mixed nuclide spectra, we propose a novel isotope identification method, LSNet. Experimental results demonstrate that, with comparable parameter counts and computational complexity, LSNet outperforms models such as the Gate-Recurrent-Unit (GRU) across various evaluation metrics. Additionally, LSNet shows significantly higher accuracy in identifying anomalous nuclide spectra compared to other models. Finally, we demonstrate that LSNet effectively identifies mixed nuclides.</p></div>\",\"PeriodicalId\":661,\"journal\":{\"name\":\"Journal of Radioanalytical and Nuclear Chemistry\",\"volume\":\"334 4\",\"pages\":\"2703 - 2714\"},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2025-03-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Radioanalytical and Nuclear Chemistry\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s10967-025-10032-2\",\"RegionNum\":3,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"CHEMISTRY, ANALYTICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Radioanalytical and Nuclear Chemistry","FirstCategoryId":"92","ListUrlMain":"https://link.springer.com/article/10.1007/s10967-025-10032-2","RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
引用次数: 0

摘要

为了解决环境中非放射性成分的干扰,实现对混合核素谱的高效识别,我们提出了一种新的同位素识别方法LSNet。实验结果表明,在参数数量和计算复杂度相当的情况下,LSNet在各种评估指标上都优于Gate-Recurrent-Unit (GRU)等模型。此外,LSNet对异常核素谱的识别精度明显高于其他模型。最后,我们证明了LSNet可以有效地识别混合核素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
LSNet: efficient nuclide spectrum recognition network

To address the interference from non-radioactive components in the environment and achieve efficient identification of mixed nuclide spectra, we propose a novel isotope identification method, LSNet. Experimental results demonstrate that, with comparable parameter counts and computational complexity, LSNet outperforms models such as the Gate-Recurrent-Unit (GRU) across various evaluation metrics. Additionally, LSNet shows significantly higher accuracy in identifying anomalous nuclide spectra compared to other models. Finally, we demonstrate that LSNet effectively identifies mixed nuclides.

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
CiteScore
2.80
自引率
18.80%
发文量
504
审稿时长
2.2 months
期刊介绍: An international periodical publishing original papers, letters, review papers and short communications on nuclear chemistry. The subjects covered include: Nuclear chemistry, Radiochemistry, Radiation chemistry, Radiobiological chemistry, Environmental radiochemistry, Production and control of radioisotopes and labelled compounds, Nuclear power plant chemistry, Nuclear fuel chemistry, Radioanalytical chemistry, Radiation detection and measurement, Nuclear instrumentation and automation, etc.
×
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