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Railway defect detection based on track geometry using supervised and unsupervised machine learning

Author(s): (University of Birmingham, UK)
ORCID (University of Birmingham, UK)
Medium: journal article
Language(s): English
Published in: Structural Health Monitoring, , n. 4, v. 21
Page(s): 147592172110444
DOI: 10.1177/14759217211044492
Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.1177/14759217211044492.
  • About this
    data sheet
  • Reference-ID
    10658604
  • Published on:
    17/02/2022
  • Last updated on:
    20/06/2022
 
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