Application of an LSTM model based on deep learning through X-ray fluorescence spectroscopy

TANG Lin; LI Yong; TANG Yufeng; LIU Ze; LIU Bingqi

doi:10.11889/j.0253-3219.2023.hjs.46.070502

您当前的位置：

首页 >

文章列表页 >

Application of an LSTM model based on deep learning through X-ray fluorescence spectroscopy

NUCLEAR PHYSICS, INTERDISCIPLINARY RESEARCH | 更新时间：2024-10-25

- Application of an LSTM model based on deep learning through X-ray fluorescence spectroscopy
- “In the field of X-ray fluorescence spectroscopy analysis, experts use the CNN-LSTM model to effectively correct shadow peaks caused by distorted pulse amplitude loss and improve the accuracy of characteristic peak counting rate.”
- NUCLEAR TECHNIQUES Vol. 46, Issue 7, Article number: 070502(2023)
- 作者机构：
  
  1.成都大学电子信息与电气工程学院成都 610106
  2.安徽大学农业生态大数据分析与应用国家工程研究中心合肥 230039
  3.（数学地质四川省重点实验室（成都理工大学）成都 610059）
  4.南洋理工大学电气与电子工程学院新加坡 639798
  5.成都大学机械工程学院成都 610106
- 作者简介：
  
  TANG Lin, female, born in 1988, graduated from Chengdu University of Technology with a doctoral degree in 2019, associate professor, visiting scholar of Nanyang Technological University, Singapore, focusing on nuclear radiation detection and electronics
  LIU Ze, E-mail: liuze@cdu.edu.cn
- 基金信息：
  
  National Natural Science Foundation of China(42104174);the Sichuan Natural Science Youth Fund Project(2023NSFSC1366);the Open Research Fund of National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University(AE202209);the Research Fund of Guangxi Key Lab of Multi-source Information Mining & Security(MIMS22-04)
- DOI：10.11889/j.0253-3219.2023.hjs.46.070502
  CLC： O657.34
- Received：15 February 2023，
  
  Revised：13 March 2023，
  
  Published：15 July 2023
- 稿件说明：
移动端阅览
唐琳,李勇,唐羽锋等.基于深度学习的LSTM模型在X荧光光谱中的应用[J].核技术,2023,46(07):070502.

TANG Lin,LI Yong,TANG Yufeng,et al.Application of an LSTM model based on deep learning through X-ray fluorescence spectroscopy[J].NUCLEAR TECHNIQUES,2023,46(07):070502.
唐琳,李勇,唐羽锋等.基于深度学习的LSTM模型在X荧光光谱中的应用[J].核技术,2023,46(07):070502. DOI： 10.11889/j.0253-3219.2023.hjs.46.070502.

TANG Lin,LI Yong,TANG Yufeng,et al.Application of an LSTM model based on deep learning through X-ray fluorescence spectroscopy[J].NUCLEAR TECHNIQUES,2023,46(07):070502. DOI： 10.11889/j.0253-3219.2023.hjs.46.070502.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Application of lightweight neural network models for nuclear pulse parameter prediction

Noise-robust fusion power supply fault diagnosis based on wavelet integrated one-dimension convolutional neural network

Application of multi-head attention mechanism with embedded positional encoding in amplitude estimation of stacked pulses

X-ray crystallography experimental data screening based on convolutional neural network algorithms

Related Author

LI Bo

TANG Lin

ZHOU Shuang

LIAO Xianli

ZHANG Heng

LI Hua

ZHONG Lingpeng

HANG Qin

Related Institution

School of Nuclear Technology and Automation Engineering, Chengdu University of Technology

College of Electronic Information and Electrical Engineering, Chengdu University

Institute of Plasma Physics, Hefei Institutes of Physical Science, Chinese Academy of Sciences

College of Computer Science and Technology, Chongqing University of Posts and Telecommunications

Shanghai Institute of Applied Physics, Chinese Academy of Sciences

AI导读

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：881299 Visits Today：17

⁰

Application of an LSTM model based on deep learning through X-ray fluorescence spectroscopy

DOI：10.11889/j.0253-3219.2023.hjs.46.070502