Zobrazeno 1 - 8
of 8
pro vyhledávání: '"James Wingate"'
Autor:
Sheldon McCall, Shina Samuel Kolawole, Afreen Naz, Liyun Gong, Syed Waqar Ahmed, Pandey Shourya Prasad, Miao Yu, James Wingate, Saeid Pourroostaei Ardakani
Publikováno v:
IEEE Access, Vol 12, Pp 28798-28809 (2024)
Falls bring about significant risks to individuals’ well-being and independence, prompting widespread public health concerns. Swift detection and even predicting the risk of falls are crucial for implementing effective measures to alleviate the adv
Externí odkaz:
https://doaj.org/article/9976af289f504788ad964f5e9a5d68e1
Autor:
Antonios Papaoikonomou, James Wingate, Vasudha Verma, Aiden Durrant, George Ioannou, Tasos Papagiannis, Miao Yu, Georgios Alexandridis, Abdelhamid Dokhane, Georgios Leontidis, Stefanos Kollias, Andreas Stafylopatis
Publikováno v:
Annals of Nuclear Energy. 178:109373
The use of machine learning in the field of reactor safety and noise diagnostics has recently seen great potential given the advancements made in computational tools, hardware and noise simulations. In this work we demonstrate howdeep neural networks
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::604e4d536607842271f992a2a21de65a
https://doi.org/10.1016/j.anucene.2022.109373
https://doi.org/10.1016/j.anucene.2022.109373
Autor:
Stefanos Kollias, Miao Yu, James Wingate, Aiden Durrant, Georgios Leontidis, Georgios Alexandridis, Andreas Stafylopatis, Antonios Mylonakis, Paolo Vinai, Christophe Demaziere
Publikováno v:
Annals of Nuclear Energy (0306-4549) vol.177(2022)
Machine Learning is used in this paper for noise-diagnostics to detect defined anomalies in nuclear plant reactor cores solely from neutron detector measurements. The proposed approach leverages advanced diffusion-based core simulation tools to gener
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bac996266b8d1f322ec19dc47105094a
https://doi.org/10.1016/j.anucene.2022.109293
https://doi.org/10.1016/j.anucene.2022.109293
Publikováno v:
Electronics, Vol 10, Iss 482, p 482 (2021)
Electronics
Volume 10
Issue 4
Electronics
Volume 10
Issue 4
A novel machine learning approach is presented in this paper, based on extracting latent information and using it to assist decision making on ambulance attendance and conveyance to a hospital. The approach includes two steps: in the first, a forward
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eaa288a1a349637409b2be60ea13df87
https://www.mdpi.com/2079-9292/10/4/482
https://www.mdpi.com/2079-9292/10/4/482
The study presents a novel approach, based on deep learning, for diagnosis of Parkinson's disease through medical imaging. The approach includes analysis and use of the knowledge extracted by deep convolutional and recurrent neural networks when trai
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5a3ffb1c27e6f920734f3e1d087b09f5
https://eprints.lincoln.ac.uk/id/eprint/41398/1/IET_Image_Processing___Parkinson_s__lastsubmission.pdf
https://eprints.lincoln.ac.uk/id/eprint/41398/1/IET_Image_Processing___Parkinson_s__lastsubmission.pdf
Autor:
Aiden Durrant, Christophe Demaziere, James Wingate, Antonios Mylonakis, Georgios Leontidis, Fabio De Sousa Ribeiro, Paolo Vinai, Stefanos Kollias
Publikováno v:
International Conference on Physics of Reactors: Transition to a Scalable Nuclear Future, PHYSOR 2020
EPJ Web of Conferences, Vol 247, p 21004 (2021)
EPJ Web of Conferences, Vol 247, p 21004 (2021)
A methodology is proposed in this paper allowing the classification of anomalies and subsequently their possible localization in nuclear reactor cores during operation. The method relies on the monitoring of the neutron noise recorded by in-core neut
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::efddc213c38462b505b407ef41448c44
https://research.chalmers.se/en/publication/32ba77c8-132f-4c28-9a5c-37e22d929ca7
https://research.chalmers.se/en/publication/32ba77c8-132f-4c28-9a5c-37e22d929ca7
Publikováno v:
The FASEB Journal. 34:1-1
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::583792cd54d1e5af0e6ca9c77fbc3ef0
https://doi.org/10.1096/fasebj.2020.34.s1.02391
https://doi.org/10.1096/fasebj.2020.34.s1.02391