Detection and tracking of turbulent structures in the edge of tokamak plasmas using machine learning

This repository contains the data used in the study entitled "Detection and tracking of turbulent structures in the edge of tokamak plasmas: use of an ultra-fast camera and comparison of machine learning and Kalman filter methods". The data was collected using an ultra-fast camera on the COMPASS device.

This repository includes:

Fast passive imaging data: High-frequency captures of turbulent structures present in the edge of tokamak plasmas.Labels : Annotation of turbulent structures for training and validation of detection and tracking algorithms.Detection and tracking results using YOLO: Outputs from the YOLO (You Only Look Once) model applied to turbulent data.ObjectiveThis dataset is intended to provide a complete and reproducible set for research into the detection and tracking of turbulent structures in tokamak plasmas. It can be used to compare the performance of machine learning methods with traditional techniques, and to encourage further research in this crucial area for plasma physics and nuclear fusion.

Researchers are encouraged to use these data to:

Replicate the results of the original study.Develop and test new techniques for detecting and tracking turbulent structures.Compare the performance of machine learning algorithms with conventional methods.

References Please cite the repository as follows: S. Chouchene et al., (2024). Detection and tracking of turbulent structures in the edge of tokamak plasmas using machine learning. Zenodo. https://doi.org/10.5281/zenodo.12608068