Estimation of Actual Dose Based on Bayesian Probabilistic Approach Using Personnel Monitoring Dose Records

Abstract

In this study, the Bayesian probabilistic approach is applied for the estimation of the actual dose using personnel monitoring dose records of occupational workers. To implement the Bayesian approach, the probability distribution of the uncertainty in the reported dose as a function of the actual dose is derived. Using the uncertainty distribution function of reported dose and prior knowledge of dose levels generally observed in a monitoring period, the posterior probability distribution of the actual dose is estimated. The posterior distributions of each monitoring period in a year are convoluted to arrive at actual annual dose distribution. The estimated actual doses distributions show a significant deviation from reported annual doses particularly for low annual doses.

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

“…In our ongoing pursuit to enhance dosimetric precision and operational efficiency within the routine TLD personnel monitoring, we have initiated the implementation of an ML-based GC screening algorithm. The GC screening ML models has been incorporated into a user-friendly graphical interface created through the 'R-Shiny' application [38]. The present research plays a vital role in advancing this initiative, contributing to the improvement of a more robust and precise dose estimation module within a system intended to optimize and streamline operations in TLD personnel monitoring laboratories.…”