Automatic detection of anomalous thermoluminescent dosimeter glow curves using machine learning

Gal, Amit; Datz, H.

doi:10.1016/j.radmeas.2018.07.014

Cited by 11 publications

(3 citation statements)

References 13 publications

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“…Recently, researchers have explored the feasibility of using ML algorithms for identifying anomalous GCs, to study the characteristics of TL emission and for the estimation of elapsed time after exposure [3,[8][9][10][11][12][13][14][15]. As mentioned earlier, we demonstrated the effectiveness of ML algorithms in identifying abnormal GCs and classifying them based on the associated abnormalities [3].…”

Section: Introductionmentioning

confidence: 52%

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: 52%

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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“…One of the earliest demonstrations of the potential of ML in TL dosimetry was by Moscovitch et al [14], who used an artificial neural network (ANN) to estimate doses in LiF:Mg:Ti-based four-element dosimeters. More recently, researchers have demonstrated the applicability of ML algorithms in TL dating, identification of anomalies in TL glow curves (GC), and classification of thermoluminescence features of natural halite [15][16][17][18][19][20][21][22][23]. In the present work, we aimed to develop an algorithm for estimating the average photon energy and the dose in terms of H p (d) from TL readouts of a three-element CaSO 4 :Dy dosimeter.…”

Section: Introductionmentioning

confidence: 99%

A multi-stage machine learning algorithm for estimating personal dose equivalent using thermoluminescent dosimeter

Pathan,

Pradhan,

Selvam

et al. 2024

Mach. Learn.: Sci. Technol.

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In the present age, marked by data-driven advancements in various fields, the importance of machine learning holds a prominent position. The ability of machine learning algorithms to resolve complex patterns and extract insights from large datasets has solidified its transformative potential in various scientific domains. This paper introduces an innovative application of machine learning techniques in the domain of radiation dosimetry. Specifically, it shows the applicability of machine learning in estimating the radiation dose received by occupational workers. This estimation is expressed in terms of personal dose equivalent, and it involves the utilization of thermoluminescence signals emitted by CaSO4:Dy–based personnel monitoring badges. To estimate personal dose equivalent, three-stage algorithm driven by machine learning models is proposed. This algorithm systematically identifies the photon energy ranges, calculates the average photon energy, and determines personal dose equivalent. By implementing this approach to the conventional three-element dosimeter, the study overcomes existing limitations and enhances accuracy in dose estimation. The algorithm demonstrates 97.8% classification accuracy in discerning photon energy ranges and achieves a coefficient of determination of 0.988 for estimating average photon energy. Importantly, it also reduces the coefficient of variation of relative deviations by up to 6% for estimated personal dose equivalent, compared to existing algorithms. The study improves accuracy and establishes a new methodology for evaluating radiation exposure to occupational workers using conventional thermoluminescent dosimeter badge.

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“…Nhằm xác định các thành phần, cấu trúc của vật liệu đã có một số nghiên cứu liên quan đến các thông số động học của bẫy TL của phổ TL (Peng, Dong, & Han, 2016;Nguyen, 2017;Nguyen, Tran, Nguyen & Nguyen, 2017. Một số nghiên cứu khác liên quan đến hiệu ứng fading của phổ TL (sự suy giảm tín hiệu TL theo thời gian) có sử dụng máy học và phần mềm Python (Amit & Datz, 2018;Kröninger, Mentzel, Theinert, & Walbersloh, 2019;Theinert et al, 2018). Ngoài ra, gần đây, nhiều công trình khác đã nghiên cứu phân tích và xử lí phổ TL của nhiều loại vật liệu khác nhau (Aşlar, Şahiner, Polymeris, & Meriç, 2021;Bassinet & Le Bris, 2020;Peng, Kitis, Sadek, Karsu Asal, & Li, 2021).…”

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Nghiên cứu XỬ LÍ phổ NHIỆT PHÁT QUANG (TL) BẰNG PHẦN MỀM PYTHON

Sang¹,

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2021

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Phổ nhiệt phát quang (TL) là những đường cong phức tạp không tuân theo những phân bố thông thường mà theo mô hình bậc một, hai, tổng quát, một bẫy một tâm tái hợp hoặc mô hình trộn. Bài báo này đưa ra phương pháp sử dụng phần mềm Python để mô phỏng và làm khớp đường cong thực nghiệm của phổ TL theo các mô hình khác nhau. Phương pháp mô phỏng phổ TL dựa vào thông số bẫy hoặc thông số đỉnh phổ theo các phương trình động học. Việc xử lí và phân tích phổ TL tìm ra được các thông số đặc trưng của phổ TL của vật liệu như năng lượng bẫy, tần số thoát và thời gian sống ở bẫy. Kết quả cho thấy, phổ TL được mô phỏng và làm khớp phù hợp nhất với mô hình bậc tổng quát. Hệ số khớp phổ nhỏ cho thấy phổ TL mô phỏng và thực nghiệm là tương đồng nhau.

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Automatic detection of anomalous thermoluminescent dosimeter glow curves using machine learning

Cited by 11 publications

References 13 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

A multi-stage machine learning algorithm for estimating personal dose equivalent using thermoluminescent dosimeter

Nghiên cứu XỬ LÍ phổ NHIỆT PHÁT QUANG (TL) BẰNG PHẦN MỀM PYTHON

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Automatic detection of anomalous thermoluminescent dosimeter glow curves using machine learning

Cited by 11 publications

References 13 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

A multi-stage machine learning algorithm for estimating personal dose equivalent using thermoluminescent dosimeter

Nghiên cứu XỬ LÍ phổ NHIỆT PHÁT QUANG (TL) BẰNG PHẦN MỀM PYTHON

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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