Optimization of Neural Network Parameters for Defect Characterization

dc.contributor.author Xie, G. X.
dc.contributor.author Chao, M.
dc.contributor.author Yeoh, C. H.
dc.contributor.author Mandayam, S.
dc.contributor.author Udpa, S.
dc.contributor.author Udpa, L.
dc.contributor.author Lord, William
dc.date 2018-02-14T07:30:40.000
dc.date.accessioned 2020-06-30T06:45:44Z
dc.date.available 2020-06-30T06:45:44Z
dc.date.copyright Mon Jan 01 00:00:00 UTC 1996
dc.date.issued 1996
dc.description.abstract <p>Natural gas, which is one of the nation’s cheapest forms of energy, is transported to consumer sites via a vast transmission pipeline network .Safety considerations and a desire to assure uninterrupted energy supply require that the pipelines be inspected periodically. The effective detection of defects in the pipeline is vital to assure the integrity of the transmission systems. A variety of nondestructive evaluation techniques (NDE), such as ultrasonic, eddy current, and magnetic flux leakage (MFL) methods have been employed to detect defects in gas pipelines [1]. Among these methods, the MFL method represents the commonly used technique.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/qnde/1996/allcontent/287/
dc.identifier.articleid 3331
dc.identifier.contextkey 5807658
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath qnde/1996/allcontent/287
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/60867
dc.language.iso en
dc.relation.ispartofseries Review of Progress in Quantitative Nondestructive Evaluation
dc.source.bitstream archive/lib.dr.iastate.edu/qnde/1996/allcontent/287/1996_Kennedy_BulkWave.pdf|||Fri Jan 14 23:11:49 UTC 2022
dc.source.uri 10.1007/978-1-4613-0383-1_287
dc.subject.keywords Electrical and Computer Engineering
dc.title Optimization of Neural Network Parameters for Defect Characterization
dc.type event
dc.type.genre article
dspace.entity.type Publication
relation.isSeriesOfPublication 289a28b5-887e-4ddb-8c51-a88d07ebc3f3
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