HIGHLIGHTS
- who: Guanyu Piao and collaborators from the Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan, USA have published the paper: Classification and characterization of coexisting defects from magnetic flux leakage data using deep learning method, in the Journal: (JOURNAL)
- what: The authors propose a convolutional neural network (CNN) based deep learning method to differentiate between single defect and coexisting defects scenarios and estimate the defect sizes including length width and depth. Finite-element-method (FEM) simulation models are developed to investigate the effect of coexisting defects on the measured MFL data . . .
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