Name
Title | ||
|---|---|---|
Defect Characterization With Eddy Current Testing Using Nonlinear-Regression Feature Extraction and Artificial Neural Networks. |
Abstract | ||
|---|---|---|
The estimation of the parameters of defects from eddy current nondestructive testing data is an important tool to evaluate the structural integrity of critical metallic parts. In recent years, several works have reported the use of artificial neural networks (ANNs) to deal with the complex relation between the testing data and the defect properties. To extract relevant features used by the ANN, pr... |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/TIM.2012.2236729 | IEEE Transactions on Instrumentation and Measurement |
Keywords | Field | DocType |
Feature extraction,Probes,Artificial neural networks,Current measurement,Coils,Neurons,Eddy currents | Eddy-current testing,Pattern recognition,Nondestructive testing,Feature extraction,Synthetic data,Test data,Artificial intelligence,Eddy current,Artificial neural network,Mathematics,Principal component analysis | Journal |
Volume | Issue | ISSN |
62 | 5 | 0018-9456 |
Citations | PageRank | References |
11 | 0.85 | 3 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luis S. Rosado | 1 | 15 | 2.77 |
| Fernando M. Janeiro | 2 | 47 | 6.74 |
| Pedro M. Ramos | 3 | 115 | 17.91 |
| Moisés Piedade | 4 | 58 | 8.92 |