Version 1
: Received: 28 August 2024 / Approved: 28 August 2024 / Online: 28 August 2024 (12:34:22 CEST)
How to cite:
Amit, G.; Revayev, O.; Vagner, R. ‘TLDetect’ – AI-based Application for the Detection and Correction of Anomalous TLD Glow Curves. Preprints2024, 2024082061. https://doi.org/10.20944/preprints202408.2061.v1
Amit, G.; Revayev, O.; Vagner, R. ‘TLDetect’ – AI-based Application for the Detection and Correction of Anomalous TLD Glow Curves. Preprints 2024, 2024082061. https://doi.org/10.20944/preprints202408.2061.v1
Amit, G.; Revayev, O.; Vagner, R. ‘TLDetect’ – AI-based Application for the Detection and Correction of Anomalous TLD Glow Curves. Preprints2024, 2024082061. https://doi.org/10.20944/preprints202408.2061.v1
APA Style
Amit, G., Revayev, O., & Vagner, R. (2024). ‘TLDetect’ – AI-based Application for the Detection and Correction of Anomalous TLD Glow Curves. Preprints. https://doi.org/10.20944/preprints202408.2061.v1
Chicago/Turabian Style
Amit, G., Oran Revayev and Roy Vagner. 2024 "‘TLDetect’ – AI-based Application for the Detection and Correction of Anomalous TLD Glow Curves" Preprints. https://doi.org/10.20944/preprints202408.2061.v1
Abstract
This research reviews a novel artificial intelligence (AI) based application called ‘TLDetect’, which filters and classifies anomalous glow curves (GCs) of thermoluminescent dosimeters (TLDs). Until recently, GCs review and correction in the lab were performed using an old in-house software, which uses Microsoft Access database and allows the laboratory technician to manually review and correct almost all GCs without any filtering. The newly developed application ‘TLDetect’ uses a modern SQL database and filters out only the necessary GCs for technician review. TLDetect first uses an Artificial Neural Network (ANN) model to filter out all regular GCs. Afterwards, it automatically classifies the rest of the GCs into five different anomaly classes. These five classes are defined by GCs typical patterns, i.e. high noise at either low or high temperature channels, untypical GC width (either wide or narrow), shifted GCs whether to the low or to the high temperatures, spikes, and the last class contains all other unclassified anomalies. By this automatic filtering and classification, the algorithm substantially reduces the amount of technician’s time of reviewing the GCs and makes the external dosimetry laboratory dose assessment process more repeatable, accurate and fast. Moreover, a database of GCs class anomalies distribution over time is saved along with all their relevant statistics, which can later assist with preliminary diagnosis of TLD reader hardware issues.
Physical Sciences, Nuclear and High Energy Physics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
