  In a typical fusion experiment, the plasma can have several possible
confinement modes. At the TCV tokamak, aside from the Low (L) and High (H)
confinement modes, an additional mode, dithering (D), is frequently observed.
Developing methods that automatically detect these modes is considered to be
important for future tokamak operation. Previous work with deep learning
methods, particularly convolutional recurrent neural networks (Conv-RNNs),
indicates that they are a suitable approach. Nevertheless, those models are
sensitive to noise in the temporal alignment of labels, and that model in
particular is limited to making individual decisions taking into account only
its own hidden state and its input at each time step. In this work, we propose
an architecture for a sequence-to-sequence neural network model with attention
which solves both of those issues. Using a carefully calibrated dataset, we
compare the performance of a Conv-RNN with that of our proposed
sequence-to-sequence model, and show two results: one, that the Conv-RNN can be
improved upon with new data; two, that the sequence-to-sequence model can
improve the results even further, achieving excellent scores on both train and
test data.

Plasma Physics