1. A comparative study of a traditional localization algorithm and a deep learning model for radioactive particle tracking application.
- Author
-
Dam, Roos Sophia de Freitas, Affonso, Renato Raoni Werneck, Salgado, William Luna, Schirru, Roberto, and Salgado, César Marques
- Subjects
- *
DEEP learning , *MACHINE learning , *TRACKING algorithms , *ARTIFICIAL intelligence , *MONTE Carlo method , *CONCRETE mixers , *CIVIL engineering - Abstract
Radioactive particle tracking is a nuclear technique that tracks a sealed radioactive particle inside a volume through a mathematical location algorithm, which is widely applied in many fields such as chemical and civil engineering in hydrodynamics flows. It is possible to reconstruct the trajectory of the radioactive particle using a traditional mathematical algorithm or artificial intelligence methods. In this paper, the traditional algorithm is based on solving a minimization problem between the simulated events and a calibration dataset, and it was written using C++ language. The artificial intelligence method is represented by a deep neural network, in which hyperparameters were defined using a Python optimization library called Optuna. This paper aims to compare the potentiality of both methods to evaluate the accuracy of the radioactive particle tracking technique. This study proposes a simplified model of a concrete mixer, six NaI(Tl) detectors, and a137Cs sealed radioactive particle. The simulated measurement geometry and the dataset (3615 patterns) were developed using the MCNPX code, which is a mathematical code based on the Monte Carlo Method. The results show a mean absolute percentage error (MAPE) of 20.81%, 10.33%, and 16.84% for x, y and z coordinates, respectively, for the traditional algorithm. For the deep neural network, MAPE is 6.87%, 2.70%, and 22.79% respectively for x, y and z coordinates. In addition, an investigation is carried out to analyze whether the size of the calibration dataset influences the performance of both methods. • Comparison of location algorithms applied in a radioactive particle tracking system. • RPT system is a simplified concrete mixer, six NaI(Tl) detectors and 137Cs source. • Traditional algorithm was written in C++ and is based on solving a minimization problem. • Deep learning model is represented by a deep neural network developed in Python. • Dataset with 3615 patterns was developed using the MCNPX code. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF