Name
Title | ||
|---|---|---|
Agglomerative clustering of defects in ultrasonic non-destructive testing using hierarchical mixtures of independent component analyzers |
Abstract | ||
|---|---|---|
This paper presents a novel procedure to classify materials with different defects, such as holes or cracks, from mixtures of independent component analyzers. The data correspond to the ultrasonic echo recorded after an impact by several sensors on the surface of the material. These signals are modelled by independent component analysis mixture models (ICAMM) for every kind of defect. After the ICAMM model is estimated for every defect, these are merged according to a distance measure that is obtained from the Kullback-Leibler divergence. The hierarchy obtained from the impact-echo data and the learning process allow different kinds of defective materials to be grouped consistently. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/IJCNN.2014.6889826 | Neural Networks |
Keywords | Field | DocType |
independent component analysis,inspection,learning (artificial intelligence),pattern clustering,production engineering computing,ultrasonic materials testing,ICAMM,Kullback-Leibler divergence,agglomerative clustering,distance measure,independent component analysis mixture models,learning process,material classification,ultrasonic echo,ultrasonic nondestructive testing | Hierarchical clustering,Ultrasonic sensor,Correlation clustering,Pattern recognition,Computer science,Nondestructive testing,Artificial intelligence,Cluster analysis,Machine learning | Conference |
ISSN | Citations | PageRank |
2161-4393 | 0 | 0.34 |
References | Authors | |
9 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Addisson Salazar | 1 | 121 | 23.46 |
| Jorge Igual | 2 | 8 | 1.35 |
| Luis Vergara | 3 | 64 | 8.86 |