An example of abnormal glow curves identification in personnel thermoluminescent dosimetry

Piniella, V. Osorio; Stadtmann, H.; Lankmayr, Ernst

doi:10.1016/s0165-022x(02)00099-4

Cited by 6 publications

(1 citation statement)

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“…The sources of anomalies may be related to the dosimeter, reader system, handling of the TLD during field use and readout, and so on [1][2][3]. At lower dose levels, deviations in the shape of the GC are commonly attributable to non-radiation-induced TL signals (NRI-TL) originating from black body radiation from the TL element, the heated components of the TLD reader and dark current from the photomultiplier tube [4].…”

Section: Introductionmentioning

confidence: 99%

A machine learning approach for correcting glow curve anomalies in CaSO₄:Dy-based TLD dosimeters used in personnel monitoring

et al. 2023

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The study presents a novel approach to analyzing the thermoluminescence (TL) glow curves of CaSO4:Dy-based personnel monitoring dosimeters using machine learning. This study demonstrates the qualitative and quantitative impact of different types of anomalies on the TL signal and trains machine learning algorithms to estimate correction factors to account for these anomalies. The results show a good degree of agreement between the predicted and actual correction factors, with a coefficient of determination greater than 0.95, a root mean squared error less than 0.025, and a mean absolute error less than 0.015. The utilization of machine learning algorithms leads to a significant two-fold reduction in the coefficient of variation of TL counts from anomalous glow curves. This study proposes a promising approach to address anomalies caused by dosimeter, reader, and handling-related factors. Furthermore, it accounts for non-radiation-induced thermoluminescence at low dose levels towards improving the dosimetric accuracy in personnel monitoring.

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Section: Introductionmentioning

confidence: 99%

A machine learning approach for correcting glow curve anomalies in CaSO₄:Dy-based TLD dosimeters used in personnel monitoring

et al. 2023

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Glow curve deconvolution for the routine readout of LiF:Mg,Ti thermoluminescent detectors

Stadtmann

Wilding

2017

Radiation Measurements

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Automatic detection of anomalous thermoluminescent dosimeter glow curves using machine learning

Gal

Datz

2018

Radiation Measurements

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An example of abnormal glow curves identification in personnel thermoluminescent dosimetry

Cited by 6 publications

References 4 publications

A machine learning approach for correcting glow curve anomalies in CaSO₄:Dy-based TLD dosimeters used in personnel monitoring

A machine learning approach for correcting glow curve anomalies in CaSO₄:Dy-based TLD dosimeters used in personnel monitoring

Glow curve deconvolution for the routine readout of LiF:Mg,Ti thermoluminescent detectors

Automatic detection of anomalous thermoluminescent dosimeter glow curves using machine learning

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An example of abnormal glow curves identification in personnel thermoluminescent dosimetry

Cited by 6 publications

References 4 publications

A machine learning approach for correcting glow curve anomalies in CaSO4:Dy-based TLD dosimeters used in personnel monitoring

A machine learning approach for correcting glow curve anomalies in CaSO4:Dy-based TLD dosimeters used in personnel monitoring

Glow curve deconvolution for the routine readout of LiF:Mg,Ti thermoluminescent detectors

Automatic detection of anomalous thermoluminescent dosimeter glow curves using machine learning

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Product

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A machine learning approach for correcting glow curve anomalies in CaSO₄:Dy-based TLD dosimeters used in personnel monitoring

A machine learning approach for correcting glow curve anomalies in CaSO₄:Dy-based TLD dosimeters used in personnel monitoring