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Automatic IRNDT inspection applying sparse PCA-based clustering
- Source :
- CCECE
- Publication Year :
- 2017
- Publisher :
- IEEE, 2017.
-
Abstract
- Recent progress in Thermal and infrared Non-Destructive Testing (IRNDT) in different fields have provided interesting defect detection solutions. Principal Component Analysis (PCA) based K-means clustering have been successfully introduced and used in many clustering applications. However, PCA suffers from being relatively more sensitive to the noise due to having a linear transformation. On the other hand, Sparse Principal Component Analysis (SPCA) has a superior performance in relation to noise because of l 1 and l 2 norm additional terms which increase its robustness. As such, we propose SPCA based K-means clustering for defect segmentation. Principal Component Thermography (PCT) and Candid Covariance-Free Incremental Principal Component Thermography (CCIPCT) are also used as a pretreatment of data in order to reduce the number of components. Three types of specimens (CFRP, Plexiglass and Aluminum) have been used for comparative and quantitative benchmarking even in the case of adding Gaussian noise (0%– 35%). The results conclusively indicate the promising performance and confirmed the outlined properties.
- Subjects :
- Engineering
business.industry
Sparse PCA
020206 networking & telecommunications
Pattern recognition
02 engineering and technology
Image segmentation
computer.software_genre
01 natural sciences
010104 statistics & probability
symbols.namesake
Robustness (computer science)
Gaussian noise
Thermography
Principal component analysis
0202 electrical engineering, electronic engineering, information engineering
symbols
Segmentation
Artificial intelligence
Data mining
0101 mathematics
business
Cluster analysis
computer
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE)
- Accession number :
- edsair.doi...........ad6989c9b58ccfe6565e9a6c6e629685