Daniel Collishaw-Schepman
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Daniel Collishaw-Schepman has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
Eurofusion Tokamak Exploitation Team
1 shared publication- Jacob Connors
UKAEA (United Kingdom Atomic Energy Authority) , Culham Campus, Abingdon, Oxfordshire OX14 3DB
1 shared publication Jet Contributors
1 shared publication- Matthew Field
UKAEA (United Kingdom Atomic Energy Authority) , Culham Campus, Abingdon, Oxfordshire OX14 3DB
1 shared publication - Pedro Carvalho
UKAEA, Culham Campus, Abingdon OX14 3DB, United Kingdom of Great Britain and Northern Ireland
1 shared publication - Scott Silburn
UKAEA (United Kingdom Atomic Energy Authority), Culham Campus, Abingdon, Oxfordshire OX14 3DB, United Kingdom of Great Britain and Northern Ireland
1 shared publication
Latest Publications
- Open Access
- 1 April 2025
- Published by AIP Publishing in Physics of Plasmas
- Vol. 32 (4)
Using deep learning for the detection of UFOs within the JET tokamak
Components on the inner wall of fusion reactors are exposed to extremely high heat loads, and significant care must be taken to minimize the damage to these components during operation. UFOs, also known as transient impurity events, are small particles of dust within the tokamak vessel that can lead to plasma disruptions. These can cause serious damage to the device and remain a significant challenge for safe operations. This study presents a novel approach to track these UFO events by utilizing visual camera data from the Joint European Torus and a Convolutional Neural Network to identify UFOs within the tokamak. The model attained an accuracy of 95.1%, a precision of 95.4% for UFO detection, a recall of 95.1%, and a receiver operating characteristic curve area of 0.99. These metrics underscore the model's competence in reliably identifying UFOs with minimal false identifications. Consequently, this suggests the model's suitability for integration into UFO detection systems in tokamak environments, improving monitoring and efficiency, and potentially providing an avenue for real-time UFO detection in future research.
- 1 April 2025
- Published by AIP Publishing in Physics of Plasmas
- Vol. 32 (4)
Open AccessUsing deep learning for the detection of UFOs within the JET tokamak
Components on the inner wall of fusion reactors are exposed to extremely high heat loads, and significant care must be taken to minimize the damage to these components during operation. UFOs, also known as transient impurity events, are small particles of dust within the tokamak vessel that can lead to plasma disruptions. These can cause serious damage to the device and remain a significant challenge for safe operations. This study presents a novel approach to track these UFO events by utilizing visual camera data from the Joint European Torus and a Convolutional Neural Network to identify UFOs within the tokamak. The model attained an accuracy of 95.1%, a precision of 95.4% for UFO detection, a recall of 95.1%, and a receiver operating characteristic curve area of 0.99. These metrics underscore the model's competence in reliably identifying UFOs with minimal false identifications. Consequently, this suggests the model's suitability for integration into UFO detection systems in tokamak environments, improving monitoring and efficiency, and potentially providing an avenue for real-time UFO detection in future research.