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Design of a Nuclear Monitoring System Based on a Multi-Sensor Network and Artificial Intelligence Algorithm

Author

Listed:
  • Min Kyu Baek

    (Department of Radiation Convergence Engineering, Yonsei University, Wonju 26493, Republic of Korea)

  • Yoon Soo Chung

    (Department of Radiation Convergence Engineering, Yonsei University, Wonju 26493, Republic of Korea)

  • Seongyeon Lee

    (Department of Radiation Convergence Engineering, Yonsei University, Wonju 26493, Republic of Korea)

  • Insoo Kang

    (Department of Radiation Convergence Engineering, Yonsei University, Wonju 26493, Republic of Korea)

  • Jae Joon Ahn

    (Division of Data Science, Yonsei University, Wonju 26493, Republic of Korea)

  • Yong Hyun Chung

    (Department of Radiation Convergence Engineering, Yonsei University, Wonju 26493, Republic of Korea)

Abstract

Nuclear power is a sustainable energy source, but radiation management is required for its safe use. Radiation-detection technology has been developed for the safe management of radioactive materials in nuclear facilities but its performance may vary depending on the size and complexity of the structure of nuclear facilities. In this study, a nuclear monitoring system using a multi-sensor network was designed to monitor radioactive materials in a large nuclear facility. Additionally, an artificial-intelligence-based localization algorithm was developed to accurately locate radioactive materials. The system parameters were optimized using the Geant4 Application for Tomographic emission (GATE) toolkit, and the localization algorithm was developed based on the performance evaluation of the Artificial Neural Network (ANN) and Decision Tree (D-Tree) models. In this article, we present the feasibility of the proposed monitoring system by converging the radiation detection system and artificial intelligence technology.

Suggested Citation

  • Min Kyu Baek & Yoon Soo Chung & Seongyeon Lee & Insoo Kang & Jae Joon Ahn & Yong Hyun Chung, 2023. "Design of a Nuclear Monitoring System Based on a Multi-Sensor Network and Artificial Intelligence Algorithm," Sustainability, MDPI, vol. 15(7), pages 1-11, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:7:p:5915-:d:1110485
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