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    Safety of Radioactive Materials Transportation and Storing Assisted by Internet of Things

    Source: Journal of Nuclear Engineering and Radiation Science:;2022:;volume( 008 ):;issue: 003::page 31901-1
    Author:
    Zorkany
    ,
    M.;Morsi
    ,
    Hassan F.
    DOI: 10.1115/1.4048113
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Nuclear newcomers need to verify the support infrastructure development keeps steady with the power plant construction and radioactive materials transportation. European power reactor's (EPR) spent fuel (SF) had been chosen in this paper for newcomers to determine the SF levels. Radioactive materials are causing harm to the environment, people, and property. These materials may be subject to terrorism, particularly during transportations. Therefore, solutions for the safety of storing and transporting radioactive materials will be proposed in this paper. Recently, the world is suffering from a new type of terrorist—known as “Lone Wolves”—which can attack these materials. Lone wolves harmful attack detection is extremely difficult at any attack stage. Existing surveillance systems are weak to counter the lone wolves, where they respond after the happening of the incident. Therefore, this paper proposed a design for Lone Wolves counter-system (LWCS) based on deep learning and assisted by internet of things to prevent these attacks. Also, an embedded system solution will be proposed based on existing infrastructure networks. The proposed embedded system can monitor and track the radioactive materials during storing and transportation in real-time. Simulation results for the proposed system will show improved performance in the transport of radioactive materials.
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      Safety of Radioactive Materials Transportation and Storing Assisted by Internet of Things

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4287398
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    • Journal of Nuclear Engineering and Radiation Science

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    contributor authorZorkany
    contributor authorM.;Morsi
    contributor authorHassan F.
    date accessioned2022-08-18T13:04:49Z
    date available2022-08-18T13:04:49Z
    date copyright5/27/2022 12:00:00 AM
    date issued2022
    identifier issn2332-8983
    identifier otherners_008_03_031901.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4287398
    description abstractNuclear newcomers need to verify the support infrastructure development keeps steady with the power plant construction and radioactive materials transportation. European power reactor's (EPR) spent fuel (SF) had been chosen in this paper for newcomers to determine the SF levels. Radioactive materials are causing harm to the environment, people, and property. These materials may be subject to terrorism, particularly during transportations. Therefore, solutions for the safety of storing and transporting radioactive materials will be proposed in this paper. Recently, the world is suffering from a new type of terrorist—known as “Lone Wolves”—which can attack these materials. Lone wolves harmful attack detection is extremely difficult at any attack stage. Existing surveillance systems are weak to counter the lone wolves, where they respond after the happening of the incident. Therefore, this paper proposed a design for Lone Wolves counter-system (LWCS) based on deep learning and assisted by internet of things to prevent these attacks. Also, an embedded system solution will be proposed based on existing infrastructure networks. The proposed embedded system can monitor and track the radioactive materials during storing and transportation in real-time. Simulation results for the proposed system will show improved performance in the transport of radioactive materials.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleSafety of Radioactive Materials Transportation and Storing Assisted by Internet of Things
    typeJournal Paper
    journal volume8
    journal issue3
    journal titleJournal of Nuclear Engineering and Radiation Science
    identifier doi10.1115/1.4048113
    journal fristpage31901-1
    journal lastpage31901-13
    page13
    treeJournal of Nuclear Engineering and Radiation Science:;2022:;volume( 008 ):;issue: 003
    contenttypeFulltext
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