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Development of data anomaly classification for structural health monitoring based on iterative trimmed loss minimization and human-in-the-loop learning

Author(s): ORCID (Department of Civil Engineering, National Chung-Hsing University, Taichung, Taiwan, R.O.C.)
(Department of Civil Engineering, National Chung-Hsing University, Taichung, Taiwan, R.O.C.)
Medium: journal article
Language(s): English
Published in: Structural Health Monitoring
DOI: 10.1177/14759217241242031
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: 10.1177/14759217241242031.
  • About this
    data sheet
  • Reference-ID
    10775642
  • Published on:
    29/04/2024
  • Last updated on:
    29/04/2024
 
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