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- W2076289400 abstract "Oil- and gas-pipelines must be examined in regular intervals for defects like metal loss. For this reason the Pipetronix company has developed different probes which collect a high number of ultrasonic readings of the wall condition. Based on this measurement the research center for computer science has implemented the automatic inspection system NeuroPipe. The kernel of this inspection tool is a hybrid neural classifier which was trained using manually collected defect examples. The following paper focuses on the aspects of the successful use neural network learning technology for this industrial application. Furthermore the difficulties, when applying these techniques, are discussed." @default.
- W2076289400 created "2016-06-24" @default.
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- W2076289400 date "1996-12-19" @default.
- W2076289400 modified "2023-10-14" @default.
- W2076289400 title "<title>NeuroPipe: a neural-network-based automatic pipeline inspection system</title>" @default.
- W2076289400 doi "https://doi.org/10.1117/12.262509" @default.
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