@article { author = {Ziyaee Sisakht, ‎Reza and Abbasi Davani‎, ‎Fereydoun and Ghaderi, ‎Rouhollah}, title = {The use of artificial neural networks to distinguish naturally occurring radioactive materials from unauthorized radioactive materials using a plastic scintillation detector}, journal = {Radiation Physics and Engineering}, volume = {1}, number = {2}, pages = {23-26}, year = {2020}, publisher = {K. N. Toosi University of Technology}, issn = {2645-6397}, eissn = {2645-5188}, doi = {10.22034/rpe.2020.63478}, abstract = {‎Distinguishing naturally occurring radioactive (e.g. ceramics‎, ‎fertilizers‎, ‎etc.) from unauthorized materials (e.g. high enriched uranium‎, ‎Pu-239‎, ‎etc.) to reduce false alarms is a prominent characteristic of radiation monitoring port‎. ‎By employing the energy windowing method for the spectrum correspond to the simulation of a plastic scintillator detector using the MCNPX Monte Carlo code together with an artificial neural network‎, ‎the present work proposes a method for distinguishing naturally occurring materials and K-40 from four unauthorized sources including high enriched uranium and Pu-239 (as special nuclear materials)‎, ‎Cs-137 (as an example of dirty bombs)‎, ‎and depleted uranium‎.}, keywords = {‎Energy windowing,‎Naturally occurring radioactive,‎Plastic scintillation detector,‎Radiation monitoring ports,‎Gamma ray detection,‎Radiation monitoring ports‎}, url = {https://rpe.kntu.ac.ir/article_63478.html}, eprint = {https://rpe.kntu.ac.ir/article_63478_dadb4f05ed1687be259e6eb9df44d9ad.pdf} }