The proposed model is demonstrated to estimate defect size with minimal speed related error, offset error, and standard deviation.
Some experimental methods like Raman spectroscopy, electron spin resonance, and optical absorption spectroscopy have been employed to estimate defect's type and concentration on the surface of nanotubes [5].
A repeated measures analysis of variance was conducted to explore any differences in estimating defect depth across all three algorithms for either total or pattern deviation probability plots.
In order to analyse differences between algorithms in estimating defect depth, each stimulus location was given a numerical value corresponding to the level of total or pattern deviation significance (0 = not significant, 1 = 5%, 2 = 2%, 3 = 1%, 4 = 0.5%).
The integrated intensity of D mode is usually normalized with respect to that of the G mode for estimating the defect concentration [49, 50].
Each segmented volume was inspected by producing a 3D model using the Slicer software package (www.slicer.org) and its quality rated: a) no or minor defects (estimated to be smaller than ∼0.5% of total volume) b) moderate defects (∼0.5 5% of total volume), major defects (greater than 5% of total volume).
In this study, we used the surface plasmon resonance (SPR) to estimate filling defects, including incomplete mold filling and trapped air bubble.
Multiple corrosion models and failure pressure models have been incorporated into a single flexible network to estimate corrosion defects and associated probability of failure (PoF).
Folate deficiencies result from unbalanced diets and are responsible for an estimated 250,000 birth defects annually.
The expected volumes of such positron traps (near ~90 Å3) can be simply estimated from corresponding defect-related positron lifetime τ 2 ≅ 0.36 ns in respect to Jensen's calculations [33].
