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  1. Artikel ; Online: Deep TL: progress of a machine learning aided personal dose monitoring system.

    Derugin, Evelin / Kröninger, Kevin / Mentzel, Florian / Nackenhorst, Olaf / Walbersloh, Jörg / Weingarten, Jens

    Radiation protection dosimetry

    2023  Band 199, Heft 8-9, Seite(n) 767–774

    Abstract: Personal dosemeters using thermoluminescence detectors can provide information about the irradiation event beyond the pure dose estimation, which is valuable for improving radiation protection measures. In the presented study, the glow curves of the ... ...

    Abstract Personal dosemeters using thermoluminescence detectors can provide information about the irradiation event beyond the pure dose estimation, which is valuable for improving radiation protection measures. In the presented study, the glow curves of the novel TL-DOS dosemeters developed by the Materialprüfungsamt NRW in cooperation with the TU Dortmund University are analysed using deep learning approaches to predict the irradiation date of a single-dose irradiation of 10 mGy within a monitoring interval of 41 d. In contrast of previous work, the glow curves are measured using the current routine read-out process by pre-heating the detectors before the read-out. The irradiation dates are predicted with an accuracy of 2-5 d by the deep learning algorithm. Furthermore, the importance of the input features is evaluated using Shapley values to increase the interpretability of the neural network.
    Mesh-Begriff(e) Humans ; Algorithms ; Heating ; Machine Learning ; Neural Networks, Computer ; Radiation Protection
    Sprache Englisch
    Erscheinungsdatum 2023-05-16
    Erscheinungsland England
    Dokumenttyp Journal Article
    ZDB-ID 225912-6
    ISSN 1742-3406 ; 0144-8420
    ISSN (online) 1742-3406
    ISSN 0144-8420
    DOI 10.1093/rpd/ncad078
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  2. Buch ; Online: No more glowing in the dark

    Mentzel, Florian / Derugin, Evelin / Jansen, Hannah / Kröninger, Kevin / Nackenhorst, Olaf / Walbersloh, Jörg / Weingarten, Jens

    How deep learning improves exposure date estimation in thermoluminescence dosimetry

    2021  

    Abstract: The time- or temperature-resolved detector signal from a thermoluminescence dosimeter can reveal additional information about circumstances of an exposure to ionizing irradiation. We present studies using deep neural networks to estimate the date of a ... ...

    Abstract The time- or temperature-resolved detector signal from a thermoluminescence dosimeter can reveal additional information about circumstances of an exposure to ionizing irradiation. We present studies using deep neural networks to estimate the date of a single irradiation with 12 mSv within a monitoring interval of 42 days from glow curves of novel TL-DOS personal dosimeters developed by the Materialpr\"ufungsamt NRW in cooperation with TU Dortmund University. Using a deep convolutional network, the irradiation date can be predicted from raw time-resolved glow curve data with an uncertainty of roughly 1-2 days on a 68% confidence level without the need for a prior transformation into temperature space and a subsequent glow curve deconvolution. This corresponds to a significant improvement in prediction accuracy compared to a prior publication, which yielded a prediction uncertainty of 2-4 days using features obtained from a glow curve deconvolution as input to a neural network.
    Schlagwörter Physics - Medical Physics ; Computer Science - Machine Learning
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2021-06-14
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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