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The results of detection in the real and simulated scenarios confirmed the efficiency of the proposed automatic threshold adjustment.
The main novelty of the method are: (1) Intelligent data analysis and monitoring, without a threshold adjustment; (2) Fast identification of an internal fault conditions and high coverage of turn-to-turn faults; (3) Secutity during transients such as inrush currents, external faults and overexcitation; (4) Simplicity.
Thanks to those two properties, the algorithm follows an iterative cycle of interval enumeration, FWER estimation and significance threshold adjustment which continues until all intervals belonging to a certain Σ k * have been enumerated.
A threshold adjustment method based on Monte-Carlo simulations (AFNI program Alphasim) was used to guard against identifying false positive areas of activation similar to Study 1 [30].
Figure 6(a) shows The packet dropping rate of the CBR, MVBR, and UVBR for disferent CBR arrivaloweres.
A threshold adjustment method based on Monte-Carlo simulations as implemented in AFNI function Alphasim was used to guard against identifying false positive areas of activation [30].
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