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After that, we introduce some defects in the developed model such us the eccentricity defect, profile error and cracked tooth.
A slow positron beam was used to probe the defect profile.
Some obtained contours of rail surface area defect are shown as Fig. 9. Fig. 9 Rail surface defect profile screenshot.
The resulting position-independent defect profile (for simplicity we keep the term "defect profile") consisting of influences of defect type and defect neighborhood only is shown in Fig. 2B.
We performed normalization of δI MM by division by the standard deviation σ profile (see Additional file 10B), or, alternatively, by division by the average of all MM hybridization signals of the corresponding MM defect profile.
The "defect profile" plots (plots of the normalized hybridization signal vs. defect position – e.g. in Fig. 2) show that the dominant parameter determining oligonucleotide probe-target-affinity – on the microarray surface – is the position of the defect.
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The reconstruction of defect profiles based on ultrasonic guided waves means the acquisition of defect profiles and parameters from ultrasonic guided wave inspection signals, and it is the key for the inversion of ultrasonic guided waves.
A computational method using a genetic algorithm is proposed for recovering internal defect profiles with HTS-SQUID data.
The irradiated samples were characterized by energy-variable slow-positron beams with Doppler broadening and positron lifetime measurements to investigate defect profiles.
Finally, experimental results indicate that proposed method possesses faster speed, lower computational complexity and better generalization performance, and it is a feasible and effective approach to reconstruct 2-D defect profiles.
Although those methods are well-known practical techniques, there exists a growing interest of developing the new and improved methods for evaluating the more accurate defect profiles of material systems.
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