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On Similarity in Case-Based Reasoning for Structural Health Monitoring

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Computer Aided Systems Theory - EUROCAST 2009 (EUROCAST 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5717))

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Abstract

This contribution deals with the importance of similarity in Case-based Reasoning and the application for problems in the field of Structural Health Monitoring. Case-based Reasoning and different methodologies for the retrieval of knowledge stored in a case base are presented. Furthermore, different approaches for object representation and applicable similarity measures including indexing techniques to reduce the number of required distance-calculations are introduced, particularly the M-tree approach. Finally, a prototype for a Case-based Decision Support System for Structural Health Monitoring is described.

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Authors and Affiliations

  1. FAW – Institute for Applied Knowledge Processing, University of Linz, Altenbergerstr. 69, 4040, Linz, Austria

    Reinhard Stumptner, Bernhard Freudenthaler & Josef Küng

Authors
  1. Reinhard Stumptner
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  2. Bernhard Freudenthaler
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  3. Josef Küng
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Editor information

Editors and Affiliations

  1. Instituto Universitario de Ciencias y Tecnologías Cibernéticas, Universidad de Las Palmas de Gran Canaria, Campus de Tafira, 35017, Las Palmas de Gran Canaria, Las Palmas, Spain

    Roberto Moreno-Díaz  & Alexis Quesada-Arencibia  & 

  2. Institute of Systems Science, Johannes Kepler University Linz, Altenbergerstrasse 69, 4040, Linz, Austria

    Franz Pichler

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Stumptner, R., Freudenthaler, B., Küng, J. (2009). On Similarity in Case-Based Reasoning for Structural Health Monitoring. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory - EUROCAST 2009. EUROCAST 2009. Lecture Notes in Computer Science, vol 5717. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04772-5_31

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  • DOI: https://doi.org/10.1007/978-3-642-04772-5_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04771-8

  • Online ISBN: 978-3-642-04772-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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