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Information Journal Paper

Title

The use of artificial neural networks to distinguish naturally occurring radioactive materials from unauthorized radioactive materials using a plastic scintillation detector

Pages

  23-26

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.

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    APA: Copy

    Ziyaee Sisakht, Reza, ABBASI DAVANI, FEREYDOUN, & Ghaderi, Rouhollah. (2020). The use of artificial neural networks to distinguish naturally occurring radioactive materials from unauthorized radioactive materials using a plastic scintillation detector. RADIATION PHYSICS AND ENGINEERING, 1(2), 23-26. SID. https://sid.ir/paper/355237/en

    Vancouver: Copy

    Ziyaee Sisakht Reza, ABBASI DAVANI FEREYDOUN, Ghaderi Rouhollah. The use of artificial neural networks to distinguish naturally occurring radioactive materials from unauthorized radioactive materials using a plastic scintillation detector. RADIATION PHYSICS AND ENGINEERING[Internet]. 2020;1(2):23-26. Available from: https://sid.ir/paper/355237/en

    IEEE: Copy

    Reza Ziyaee Sisakht, FEREYDOUN ABBASI DAVANI, and Rouhollah Ghaderi, “The use of artificial neural networks to distinguish naturally occurring radioactive materials from unauthorized radioactive materials using a plastic scintillation detector,” RADIATION PHYSICS AND ENGINEERING, vol. 1, no. 2, pp. 23–26, 2020, [Online]. Available: https://sid.ir/paper/355237/en

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