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Rapid determination of radon progeny concentration based on artificial neural networks

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Abstract

The rapid measurement of radon progeny concentration is of great significance for improving the efficiency of radon exposure dose evaluation in a specific area. In order to achieve rapid measurement of radon progeny concentration in engineering applications, this study is based on the rapid calculation function of BP (back propagation) neural network and uses other relevant environmental parameters to quickly obtain the radon progeny alpha potential energy. The relative standard uncertainty is 7.6% (k = 2), which met the uncertainty requirements of the standard of JJG (Military) 99-2015.The radon progeny alpha potential energy and its related parameters are substituted into the constructed neural network for training. According to the set training requirements, the range is set to 0–760 μJ/m3, and the simulation results meet the requirements of the "JJG (Military Industry) 99-2015" standard. The effect of rapid measurement can be achieved under the condition of ensuring measurement accuracy.

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References

  1. El-Taher A et al (2013) Radon and its decay products in the main campus of Qassim University, Saudi Arabia, and its radiation hazards. J Am Sci 9(6):257–266

    Google Scholar 

  2. Bruenner S et al (2021) Radon daughter removal from PTFE surfaces and its application in liquid xenon detectors. Eur Phys J C 81(4):1–10

    Article  Google Scholar 

  3. Hao S, Shi F (2019) Research and summary of radon measurement technology. Research on Key Technologies of Indoor Radiation Pollution Control and Improvement in Buildings, Shenzhen

  4. Misdaq MA, Elouardi B, Ouguidi J (2017) 222Rn, 220Rn and their progenies measured in the air of different dwellings and workplaces and resulting alpha radiation doses to the eyes of individuals. Health Phys 113(5):363–374

    Article  CAS  Google Scholar 

  5. Dinu AC et al (2017) Radon levels in Romanian caves: an occupational exposure survey. Environ Geochem Health 39(5):1085–1099

    Article  Google Scholar 

  6. Liu X et al (2021) Deep multi-level fusion network for multi-source image pixel-wise classification. Knowl-Based Syst 221:106921

    Article  Google Scholar 

  7. Chaipimonplin T (2016) Investigation internal parameters of neural network model for Flood Forecasting at Upper river Ping, Chiang Mai. KSCE J Civ Eng 20(1):478–484

    Article  Google Scholar 

  8. Zhou C, Zhang Q, Jiang Y et al (2011) Evaluation of radioactive aerosols in nuclear accident monitoring. Nuclear Tech 34(11):866–871

    CAS  Google Scholar 

  9. Wang X, Shi F, Yu L, Li Y et al (2013) Analysis of 43 cases of MATLAB neural network. Beihang University Press, Beijing, pp 1–2

    Google Scholar 

  10. Ji C, Wang T, Zhang Q. Contrast between “five-stage method” and “three-stage method” for determining the concentration of 222Rn and 220Rn progeny. China Nuclear Control System Engineering, Beijing, 100176

Download references

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Authors and Affiliations

  1. School of Nuclear Science and Technology, University of South China, Hunan, 421001, Hengyang, China

    Xiang-Ming Cai, Jian Shan & Yun-Lin Le

  2. Hunan Provincial Key Laboratory of Radon, Hunan, 421001, Hengyang, China

    Jian Shan & Yun-Lin Le

  3. School of Economics, Management and Law, University of South China, Hunan, 421001, Hengyang, China

    Yuan Luo

  4. Nuclear and Radiation Safety Center, Beijing, 10000, China

    Ya-Dong Li

Authors
  1. Xiang-Ming Cai
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  2. Jian Shan
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  3. Yun-Lin Le
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  4. Yuan Luo
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  5. Ya-Dong Li
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Correspondence to Jian Shan.

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Cai, XM., Shan, J., Le, YL. et al. Rapid determination of radon progeny concentration based on artificial neural networks. J Radioanal Nucl Chem 330, 747–753 (2021). https://doi.org/10.1007/s10967-021-08002-5

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  • DOI: https://doi.org/10.1007/s10967-021-08002-5

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