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

Min Kyu Baek, Yoon Soo Chung, Seongyeon Lee, Insoo Kang, Jae Joon Ahn and Yong Hyun Chung ()
Additional contact information
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

Sustainability, 2023, vol. 15, issue 7, 1-11

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.

Keywords: multi-sensor network; artificial intelligence; radiation monitoring; nuclear energy; radioactive material (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/7/5915/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/7/5915/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:7:p:5915-:d:1110485

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().