Real time monitoring of radiation instabilities in TOKAMAK machines via CNNs

In this paper a new strategy for real time detection of plasma instabilities, called MARFEs, is proposed through real-time image processing on plasma video sequences. These sequences are recorded by a vision system based on a charge coupled device (CCD) camera installed at Frascati Tokamak Upgrade (FTU). The strategy used to perform the task is based on a new family of nonlinear analog processors, digitally programmable, implemented into the so-called Cellular Neural Network Universal Machine (CNN-UM). The basic idea deals with a system capable to carry out safer nuclear fusion experiments, preventing the plant from excessive mechanical and thermal stress which occurs during plasma instability phenomena (i.e., disruptions). Experimental results, obtained on the FTU machine, are reckoned fully satisfactory.