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ARAIM algorithm and fault probabilities were first discussed.
Then residuals are designed and fault probabilities are introduced for each fault candidate.
Diffraction peak profile analysis was applied to determine microstructural parameters such as stacking/twinning fault probabilities, dislocation density and stacking fault energy (SFE).
The additive saccharin eliminated the texture and yielded very fine microstructures with high dislocation densities and twin fault probabilities for both solution types.
In these deformed alloys, the dislocation density (ρe) was of the order of 1015 m−2 but the stacking fault probabilities were almost negligible.
After SP, domain sizes in both ferrite and austenite are refined, and microstrain, dislocation densities and compound fault probabilities are observed to increase sharply in the near surface layer.
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Consequently, that will lessen fault probability and improve reliability.
Research on fault probability reduction, fault identification and fault management are key issues to achieve reliable output from DMFB.
This component parameter gj, in conjunction with a priori fault probability, is used in a Bayesian framework to compute the posterior fault candidate probabilities.
The substance of the presented Bayesian identification-based FDI methodology is a probabilistic model determined by the fault probability table.
In spite of twinning, the stacking fault probability (Psf) of twinned austenite was remarkably low (∼10−4) at low strains, but increased moderately (to ∼10−3) up to failure strain.
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