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

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

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

NUCLEAR TECHNIQUES, Volume. 46, Issue 7, 070502(2023)

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

Lin TANG^1,2,3,4, Yong LI¹, Yufeng TANG⁵, Ze LIU^1、*, and Bingqi LIU⁵

Author Affiliations

¹School of Electronic Information and Electrical Engineering, Chengdu University, Chengdu 610106, China

²National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230039, China

³(Geomathematics Key Laboratory of Sichuan Province (Chengdu University of Technology), Chengdu 610059, China)

⁴School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore

⁵School of Mechanical Engineering, Chengdu University, Chengdu 610106, China

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Figures & Tables(6)

Fig. 1. Principle diagram of peak correction (color online)

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Fig. 2. Principle diagram of dataset generation

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Fig. 3. Structure diagram of network model

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Fig. 4. Iterative graph of loss and accuracy on training and validation sets during model training

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Fig. 5. Comparison of shadow peak correction effects with the primitive peaks (color online)

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Fig. 6. Repair ratio of peak areas (color online)

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Lin TANG, Yong LI, Yufeng TANG, Ze LIU, Bingqi LIU. Application of an LSTM model based on deep learning through X-ray fluorescence spectroscopy[J]. NUCLEAR TECHNIQUES, 2023, 46(7): 070502

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Paper Information

Category: Research Articles

Received: Feb. 15, 2023

Accepted: --

Published Online: Aug. 3, 2023

The Author Email:

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

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