Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
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Reproduction strategy of radiation data with compensation of data loss using a deep learning technique

  • Received : 2020.09.20
  • Accepted : 2021.01.11
  • Published : 2021.07.25
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Abstract

In nuclear-related facilities, such as nuclear power plants, research reactors, accelerators, and nuclear waste storage sites, radiation detection, and mapping are required to prevent radiation overexposure. Sensor network systems consisting of radiation sensor interfaces and wxireless communication units have become promising tools that can be used for data collection of radiation detection that can in turn be used to draw a radiation map. During data collection, malfunctions in some of the sensors can occasionally occur due to radiation effects, physical damage, network defects, sensor loss, or other reasons. This paper proposes a reproduction strategy for radiation maps using a U-net model to compensate for the loss of radiation detection data. To perform machine learning and verification, 1,561 simulations and 417 measured data of a sensor network were performed. The reproduction results show an accuracy of over 90%. The proposed strategy can offer an effective method that can be used to resolve the data loss problem for conventional sensor network systems and will specifically contribute to making initial responses with preserved data and without the high cost of radiation leak accidents at nuclear facilities.

Keywords

Acknowledgement

This work was supported by the Energy Technology Program of the Korea Institute of Energy Technology Evaluation and Planning granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea (No. 20191510301290) and by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT under 2017M2A8A4056388, 2018M2C7A1A02071506, and 2020M2A8A1000830.

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