DIVERSE TIME-FREQUENCY DISTRIBUTIONS INTEGRATED TO AN ART2 NETWORK FOR NON-DESTRUCTIVE TESTING
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| Natura: | Artículo científico |
| Lingua: | en |
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Universidad Nacional Autónoma de México
2008
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| _version_ | 1876470130539495424 |
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| author | H. Benítez-Pérez |
| author_facet | H. Benítez-Pérez |
| contents | DIVERSE TIME-FREQUENCY DISTRIBUTIONS INTEGRATED TO AN ART2 NETWORK FOR NON-DESTRUCTIVE TESTING H. Benítez-Pérez L. Medina-Gómez Ingeniería Non Neural Networks destructive Testing Time Frequency Distribution Approach The use of several techniques for non-destructive testing is a common strategy for detecting and classifying flaws in aluminium material. Techniques like multiresolution data analysis and data classifiers are valuable for obtaining as much information as possible from the flaws. The combination of both techniques allows the clear definition of several characteristics like localization, size and form. In this study, localization using Time-Frequency Distribution feature extraction and an ART2 neural network as a classifier is the main goal. 2008 artículo científico 1665-6423 https://www.redalyc.org/articulo.oa?id=47413019002 en http://www.redalyc.org/revista.oa?id=474 Journal of Applied Research and Technology application/pdf Universidad Nacional Autónoma de México Journal of Applied Research and Technology (México) Num.1 Vol.6 |
| format | Artículo científico |
| id | redalyc_47413019002 |
| institution | Redalyc |
| language | en |
| publishDate | 2008 |
| publisher | Universidad Nacional Autónoma de México |
| spellingShingle | DIVERSE TIME-FREQUENCY DISTRIBUTIONS INTEGRATED TO AN ART2 NETWORK FOR NON-DESTRUCTIVE TESTING H. Benítez-Pérez Ingeniería Non Neural Networks destructive Testing Time Frequency Distribution Approach DIVERSE TIME-FREQUENCY DISTRIBUTIONS INTEGRATED TO AN ART2 NETWORK FOR NON-DESTRUCTIVE TESTING H. Benítez-Pérez L. Medina-Gómez Ingeniería Non Neural Networks destructive Testing Time Frequency Distribution Approach The use of several techniques for non-destructive testing is a common strategy for detecting and classifying flaws in aluminium material. Techniques like multiresolution data analysis and data classifiers are valuable for obtaining as much information as possible from the flaws. The combination of both techniques allows the clear definition of several characteristics like localization, size and form. In this study, localization using Time-Frequency Distribution feature extraction and an ART2 neural network as a classifier is the main goal. 2008 artículo científico 1665-6423 https://www.redalyc.org/articulo.oa?id=47413019002 en http://www.redalyc.org/revista.oa?id=474 Journal of Applied Research and Technology application/pdf Universidad Nacional Autónoma de México Journal of Applied Research and Technology (México) Num.1 Vol.6 |
| title | DIVERSE TIME-FREQUENCY DISTRIBUTIONS INTEGRATED TO AN ART2 NETWORK FOR NON-DESTRUCTIVE TESTING |
| topic | Ingeniería Non Neural Networks destructive Testing Time Frequency Distribution Approach |
| url | https://www.redalyc.org/articulo.oa?id=47413019002 |