Prediction of Plasma Discharge Video by Convolutional Long Short-Term Memory

In the National Institute for Fusion Science, high-temperature plasmas have been produced in the Large Helical Device (LHD). A large number of videos having plasma discharge are captured during nuclear fusion operations. An observation of these recorded videos can lead to a new discovery and guarantee the safe operation of the experiments. In order to optimize the operational parameter of the experiment, the future emission states of plasmas should be estimated. Thus, this paper proposes a method to make a prediction of sequential future frames containing plasma light emission using the Convolutional Long Short-Term Memory (ConvLSTM). We confirmed that the future frames of plasma discharge could be well predicted with the satisfactory results of Mean Squared Error as 2.78×10
^-5
and Cosine Similarity as 0.9997.