CNN-based real-time video detection of plasma instability in nuclear fusion applications

In this paper a real-time detection of plasma instabilities, called MARFEs, is performed through a real-time image processing on plasma video sequences. These sequences are recorded by a vision system based on a 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 detection system allows 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 fully satisfactory.