Truncated pulse height estimator based on an improved UNet model

TANG Lin; ZHOU Shuang; LIAO Xianli; LIU Ze; LI Bo

doi:10.11889/j.0253-3219.2023.hjs.46.110505

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Truncated pulse height estimator based on an improved UNet model

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

- Truncated pulse height estimator based on an improved UNet model
- “In the field of pulse height analysis, researchers have proposed a composite neural network model to accurately predict the truncated pulse height and provide a solution for measurement systems.”
- NUCLEAR TECHNIQUES Vol. 46, Issue 11, Article number: 110505(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.成都理工大学核技术与自动化工程学院成都 610059
  3.南洋理工大学电气与电子工程学院新加坡 639798
- 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, E-mail: tanglin@cdu.edu.cn
  LI Bo, E-mail: libo@cdu.edu.cn
- 基金信息：
  
  National Natural Science Youth Foundation of China(12305214);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)
- DOI：10.11889/j.0253-3219.2023.hjs.46.110505
  CLC： O657.34
- Received：31 May 2023，
  
  Revised：06 July 2023，
  
  Published：15 November 2023
- 稿件说明：
移动端阅览
唐琳,周爽,廖先莉等.基于改进UNet模型的截断脉冲高度估计器[J].核技术,2023,46(11):110505.

TANG Lin,ZHOU Shuang,LIAO Xianli,et al.Truncated pulse height estimator based on an improved UNet model[J].NUCLEAR TECHNIQUES,2023,46(11):110505.
唐琳,周爽,廖先莉等.基于改进UNet模型的截断脉冲高度估计器[J].核技术,2023,46(11):110505. DOI： 10.11889/j.0253-3219.2023.hjs.46.110505.

TANG Lin,ZHOU Shuang,LIAO Xianli,et al.Truncated pulse height estimator based on an improved UNet model[J].NUCLEAR TECHNIQUES,2023,46(11):110505. DOI： 10.11889/j.0253-3219.2023.hjs.46.110505.

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Truncated pulse height estimator based on an improved UNet model

DOI：10.11889/j.0253-3219.2023.hjs.46.110505