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There exist several image analysis methods for defect detection, including global gray-level or gradient thresholding, simple background subtraction, statistical classification and color classification [16].
This paper provides the recent advances and researches about non-destructive testing (NDT) methods for defect characterization in engineering materials and composites.
Electro optical techniques (EOP: electro optical probing and EOFM: electro optical frequency mapping) are effective backside contactless methods for defect localization and design debug for VLSI.
A case study compared the telematic digital workbench against paper-based and Pocket PC-based methods for defect management in a controlled laboratory experiment.
We compared state-of-the-art methods for defect detection and the CNN-based method in order to verify the superior performance of the CNNs.
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There are several non-destructive and destructive methods for defects detection and evaluation.
There are several well-established methods for defects evaluation based on various principles (both destructive and non-destructive).
Overall good agreement between the proposed method and the FE results provides confidence in the use of the proposed method for defect assessment of components at elevated temperatures.
They showed that naive Bayes machine learning methods outperform rule-based or decision tree learning methods and they showed, on the other hand, that the choice of learning methods used for defect predictions can be much more important than used attributes.
Ultrasonic methods using guided waves offer a reliable and cost effective method for defects monitoring in advanced structures due to their long propagation range and their sensitivity to defects in their propagation path.
To the best of our knowledge, there is no effective comprehensive method for defect-tolerant CMOL cell mapping covering both stuck-at open and stuck-at close defects because the mechanism how stuck-at-close defects affect CMOL cell mapping has not been intensively explored.
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