Tracking of the plasma states in a nuclear fusion device using SOMs

Massimo Camplani, Barbara Cannas, Alessandra Fanni, Gabriella Pautasso, Giuliana Sias, Piergiorgio Sonato, The Asdex-Upgrade Team

Research output: Contribution to conferencePaper

3 Citations (Scopus)

Abstract

Knowledge discovery consists of finding new knowledge from data bases where dimension, complexity or amount of data is prohibitively large for human observation alone. The Self Organizing Map (SOM) is a powerful neural network method for the analysis and visualization of high-dimensional data. The need for efficient data visualization and clustering is often faced, for instance, in the analysis, monitoring, fault detection, or prediction of various engineering plants. In this paper, the use of a SOM based method for prediction of disruptions in experimental devices for nuclear fusion is investigated. The choice of the SOM size is firstly faced, which heavily affects the performance of the mapping. Then, the ASDEX Upgrade Tokamak high dimensional operational space is mapped onto the 2-dimensional SOM, and, finally, the current process state and its history in time has been visualized as a trajectory on the map, in order to predict the safe or disruptive state of the plasma. © 2009 Springer-Verlag.
Original languageEnglish
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event11th International Conference on Engineering Applications of Neural Networks, EANN 2009 - , United Kingdom
Duration: 1 Jan 2009 → …

Conference

Conference11th International Conference on Engineering Applications of Neural Networks, EANN 2009
CountryUnited Kingdom
Period1/1/09 → …

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All Science Journal Classification (ASJC) codes

  • Computer Science(all)

Cite this

Camplani, M., Cannas, B., Fanni, A., Pautasso, G., Sias, G., Sonato, P., & The Asdex-Upgrade Team (2009). Tracking of the plasma states in a nuclear fusion device using SOMs. Paper presented at 11th International Conference on Engineering Applications of Neural Networks, EANN 2009, United Kingdom. https://doi.org/10.1007/978-3-642-03969-0_40