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Lookup NU author(s): Professor Bin Gao,
Professor Gui Yun TianORCiD
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© 2017 IEEE. In this paper, an innovative Compressed Sensing (CS) framework has been proposed with the purpose of providing a sub-Nyquist sampling mechanism to Electromagnetic Acoustic Transducer (EMAT) testing. Particularly, a novel sparse representation method based on dictionary learning was introduced into the CS framework aimed at a more efficient signal sparse representation and more accurate signal reconstruction. The proposed CS framework was examined through a process of thickness measurement of an 813-X70 pipeline. Experimental results indicated that the thickness feature of the specimen could be precisely extracted from the recovered data with 20% of compression ratio compared with conventional sampling method. It demonstrates that the CS theorem can significantly reduce the required sampling rate of EMAT testing system without loss of signal resolution, thus improves the overall testing efficiency.
Author(s): Yan Y, Gao B, Tian G-Y
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: Far East NDT New Technology and Application Forum (FENDT 2017)
Year of Conference: 2017
Online publication date: 24/12/2018
Acceptance date: 02/04/2016
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