Quantitative Identification of Pipeline Crack Based on BP Neural Network

E-ISSN： 1662-9795|2017|737|477-480

ISSN： 1013-9826

Source： Key Engineering Materials, Vol.2017, Iss.737, 2017-07, pp. : 477-480

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