Uncertainty of LiF thermoluminescence at low dose levels: Experimental results

'TLDetect': AI-Based Application for Detection and Correction of Anomalous TLD Glow Curves

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, GC review and correction in the lab were performed using an old in-house software, which uses the 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 the typical patterns of GCs, 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 a last class that contains all other unclassified anomalies. By this automatic filtering and classification, the algorithm substantially reduces the amount of the technician's time spent reviewing the GCs and makes the external dosimetry laboratory dose assessment process more repeatable, more accurate, and faster. Moreover, a database of the class anomalies distribution over time of GCs is saved along with all their relevant statistics, which can later assist with preliminary diagnosis of TLD reader hardware issues.

TL-SDA: A designed toolkit for the deconvolution analysis of thermoluminescence glow curves

In the present study, a graphical user interface (GUI) toolkit has been developed to analyze the thermoluminescence (TL) glow-curve and evaluate the trapping parameters using TL expression based on the one-trap one-recombination model. The basic idea of the deconvolution analysis in the developed toolkit is based on performing a sequence of successful fits, where the information provided by each fit is used by the next fit until the deconvolution of the entire glow curve approaches an optimum solution. The starting values and ranges of the fitting parameters can be controlled and adjusted to improve the deconvolution analysis of complex structure glow curves. The designed toolkit is also supported by the background-subtraction option to improve the analysis at low irradiation dose levels. The expanded uncertainty at the 95 % confidence level of the fitted trapping parameters is also provided. All the evaluations performed using the designed toolkit are allowed to be extracted into an Excel spreadsheet. The TL-SDA toolkit can be freely downloaded from: TLSDA_v1 - File Exchange - MATLAB Central (https://www.mathworks.com/matlabcentral/fileexchange/154136-tlsda_v1-1).