Similarity Measures for Visual Comparison and Retrieval of Test Data in Aluminum Production

Nikolina Jekic, Belgin Mutlu, Manuela Schreyer, Steffen Neubert, Tobias Schreck

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem Konferenzband

Abstract

Monitoring, analyzing and determining the production quality in a complex and long-running process such as in the aluminum production is a challenging task. The domain experts are often overwhelmed by the flood of data being generated and collected and have difficulties to analyze and interpret the results. Likewise, experts find it difficult to identify patterns in their data that may indicate deviations and anomalies that lead to unstable processes and lower product quality. We aim to support domain experts in the production data exploration and identifying meaningful patterns. The existing research covers a broad spectrum of pattern recognition methodologies that can be potentially applied to elicit patterns in data collected from industrial production. Hence, in this paper, we further analyze the applicability of different similarity measures to effectively recognize specific ultrasonic patterns which may indicate critical process deviations in aluminum production.

Originalspracheenglisch
TitelIVAPP
Redakteure/-innenChristophe Hurter, Helen C. Purchase, José Braz, Kadi Bouatouch
Herausgeber (Verlag)SciTePress 2013
Seiten210-218
Seitenumfang9
ISBN (elektronisch)9789897584886
DOIs
PublikationsstatusVeröffentlicht - 2021

Publikationsreihe

NameVISIGRAPP 2021 - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
Band3

ASJC Scopus subject areas

  • !!Computer Vision and Pattern Recognition
  • !!Computer Science Applications
  • !!Computer Graphics and Computer-Aided Design

Fields of Expertise

  • Information, Communication & Computing

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