Thermoluminescence characteristics of calcite with a Gaussian process regression model of machine learning

Işık, Esme

doi:10.1002/bio.4298

Cited by 4 publications

(2 citation statements)

References 33 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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Research on Named Entity Recognition Based on Gated Interaction Mechanisms

Liu,

Chen,

Tao

et al. 2024

Applied Sciences

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Using long short-term memory (LSTM) networks to build a named entity recognition model is important for the task of named entity recognition. However, traditional memory networks lack a direct connection between input information and hidden states, leading to key feature information not being fully learned during training and causing information loss. This paper designs a bidirectional variant of the long short-term memory (BiLSTM) network called Mogrifier-BiGRU, which combines the BERT pre-trained model and the conditional random field (CRF) network model. The Mogrifier gating interaction unit is set with more hyperparameters to achieve deep interaction of gating information, changing the relationship between input and hidden states so that they are no longer independent. By introducing more nonlinear transformations, the model can learn more complex input–output mapping relationships. Then, by combining Bayesian optimization with the improved Mogrifier-BiGRU network, the optimal hyperparameters of the model are automatically calculated. Experimental results show that the model method based on the gating interaction mechanism can effectively combine feature information, improving the accuracy of Chinese-named entity recognition. On the dataset, an F1-score of 85.42% was achieved, which is 7% higher than traditional methods and 10% higher for the accuracy of some entity recognition.

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Thermoluminescence characteristics of calcite with a Gaussian process regression model of machine learning

Cited by 4 publications

References 33 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

Research on Named Entity Recognition Based on Gated Interaction Mechanisms

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Thermoluminescence characteristics of calcite with a Gaussian process regression model of machine learning

Cited by 4 publications

References 33 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

Research on Named Entity Recognition Based on Gated Interaction Mechanisms

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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