Abstract

In the present study, machine learning (ML) methods for the identification of abnormal glow curves (GC) of CaSO₄:Dy-based thermoluminescence dosimeters in individual monitoring are presented. The classifier algorithms, random forest (RF), artificial neural network (ANN) and support vector machine (SVM) are employed for identifying not only the abnormal glow curve but also the type of abnormality. For the first time, the simplest and computationally efficient algorithm based on RF is presented for GC classifications. About 4000 GCs are used for the training and validation of ML algorithms. The performance of all algorithms is compared by using various parameters. Results show a fairly good accuracy of 99.05% for the classification of GCs by RF algorithm. Whereas 96.7% and 96.1% accuracy is achieved using ANN and SVM, respectively. The RF-based classifier is recommended for GC classification as well as in assisting the fault determination of the TLD reader system.

中文翻译：

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基于Caso4：Dy的人员监控剂量计中异常辉光曲线和相关异常的识别的机器学习算法。

摘要

在本研究中，机器学习（ML）方法用于识别CaSO ₄的异常辉光曲线（GC）：提出了基于dy的热致发光剂量计的单独监测方法。分类器算法，随机森林（RF），人工神经网络（ANN）和支持向量机（SVM）不仅用于识别异常辉光曲线，而且用于识别异常类型。首次提出了基于RF的最简单且计算效率高的GC分类算法。大约4000个GC用于训练和验证ML算法。通过使用各种参数比较所有算法的性能。结果表明，采用RF算法对GC进行分类的准确度高达99.05％。而使用ANN和SVM分别可达到96.7％和96.1％的精度。