Search research articles

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Flow rate determination in a two-phase system using radioactive particle tracking and deep learning.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine·2026

Same author

Effectiveness of Elastodontic Devices for Correcting Sagittal Malocclusions in Mixed Dentition Patients: A Scoping Review.

Dentistry journal·2024

Same author

Prediction of fluids volume fraction and barium sulfate scale in a multiphase system using gamma radiation and deep neural network.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine·2023

Same author

Application of deep neural network and gamma radiation to monitor the transport of petroleum by-products through polyducts.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine·2023

Same author

Thickness prediction in metal alloys using nuclear techniques and artificial neural network: Modelling.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine·2022

Same author

Application of deep neural network and gamma-ray scattering in eccentric scale calculation regardless of the fluids volume fraction inside a pipeline.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine·2022

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Jul 6, 2025

Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut

Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut

Published on: June 15, 2020

A comparative study of a traditional localization algorithm and a deep learning model for radioactive particle

Roos Sophia de Freitas Dam¹, Renato Raoni Werneck Affonso², William Luna Salgado²

¹Programa de Engenharia Nuclear, Universidade Federal Do Rio de Janeiro, Avenida Horácio de Macedo 2030, Bloco G - Sala 206, Zip Code 21941-914, Cidade Universitária, RJ, Brazil; Instituto de Engenharia Nuclear, Rua Hélio de Almeida 75, Zip Code 21941-906, Cidade Universitária, RJ, Brazil.

Applied Radiation and Isotopes : Including Data, Instrumentation and Methods for Use in Agriculture, Industry and Medicine

|December 29, 2023

Summary

This study compares traditional algorithms and deep neural networks for radioactive particle tracking. Deep neural networks show higher accuracy in tracking particle trajectories, especially with larger datasets.

Keywords:

Deep neural network Gamma radiation Location algorithm MCNPX code Radioactive particle tracking

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Related Experiment Videos

Last Updated: Jul 6, 2025

Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut

Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut

Published on: June 15, 2020

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Area of Science:

Nuclear Engineering
Computational Physics
Data Science

Background:

Radioactive particle tracking is crucial for hydrodynamics in chemical and civil engineering.
Traditional algorithms and artificial intelligence (AI) methods can reconstruct particle trajectories.

Purpose of the Study:

To compare the accuracy of traditional algorithms versus deep neural networks (DNNs) for radioactive particle tracking.
To evaluate the influence of calibration dataset size on tracking performance.

Main Methods:

Developed a simplified concrete mixer model with six NaI(Tl) detectors and a 137Cs source.
Utilized MCNPX code for simulating measurement geometry and generating a dataset of 3615 patterns.
Implemented a traditional C++ minimization algorithm and a DNN with hyperparameters optimized by Optuna (Python library).

Main Results:

Traditional algorithm yielded Mean Absolute Percentage Errors (MAPE) of 20.81% (x), 10.33% (y), and 16.84% (z).
Deep neural network achieved lower MAPE: 6.87% (x), 2.70% (y), and 22.79% (z).
Preliminary analysis suggests larger calibration datasets improve performance for both methods.

Conclusions:

Deep neural networks demonstrate superior accuracy for radioactive particle tracking compared to traditional algorithms.
The choice of method and dataset size significantly impacts the precision of trajectory reconstruction.
Further research on dataset optimization is warranted for enhanced radioactive particle tracking applications.