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Open image in new window Fig. 6 Flow chart of the proposed EMS algorithm for active power control.

The flow chart of the proposed EMS algorithm is shown in Fig. 6 and that is described in detail in the following sections respectively.

Average segmentation errors for the proposed algorithm, the graph cut algorithm (Boykov and Jolly, 2001), and the Expectation Maximization Segmentation (EMS) algorithm Van Leemput et al., 2001 in terms of Dice coefficients are found to be (3.72 ± 1.12)%, (14.88 ± 1.69)%, and (11.95 ± 5.2)%, respectively.

MEME used the multiple EMs algorithm [ 4].

Parameters Estimation (EM Algorithm).

The EM algorithm is an iterative procedure.

The EM algorithm is an algorithm implementing maximum likelihood estimation.

We have employed the EM algorithm to train mSVDD.

We can implement expectation-maximization (EM) algorithm in variational inference.

The expectation-maximum (EM) algorithm is used for GMM training.

The EM algorithm can be implemented exactly as before.