Estimation of Plasma Emission Transition Using Hidden Markov Model

抄録

This study proposes a method for estimating plasma-emission transitions from plasma-emission videos using a hidden Markov model (HMM). The proposed method retrieves similar videos and learns model parameters from them. The plasma-emission characteristics that we have employed are color, brightness, position, shape, and the speed at which the brightness of a plasma emissions changes. Multiple HMMs based on these plasma-emission characteristics are employed to represent the plasma-emission patterns. The anticipated plasma-emission transitions are estimated using state-transition probabilities from the generated model. Experimental results are used to confirm that the proposed methods are effective in identifying similar plasma videos and estimating probable future states of the plasma.