Please use this identifier to cite or link to this item: doi:10.22028/D291-37542
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Title: GUM2ALA – Uncertainty Propagation Algorithm for the Adaptive Linear Approximation According to the GUM
Author(s): Dorst, Tanja
Schneider, Tizian
Schütze, Andreas
Eichstädt, Sascha
Language: English
Title: SMSI 2021 : Sensor and Measurement Science International : proceedings
Pages: 314-315
Publisher/Platform: AMA Service GmbH
Year of Publication: 2021
Place of publication: Wunstorf
Place of the conference: Online
Free key words: measurement uncertainty
uncertainty propagation
feature extraction
adaptive linear approximation,
machine learning
DDC notations: 600 Technology
Publikation type: Conference Paper
Abstract: In machine learning, many feature extraction algorithms are available. To obtain reliable features from measured data, a propagation of measurement uncertainty is necessary for these algorithms. In this contribution, the Adaptive Linear Approximation (ALA) as one feature extraction algorithm is considered, and analytical formulas are developed for an uncertainty propagation in line with the Guide to the Expression of Uncertainty in Measurement (GUM). This extends the set of uncertainty-aware feature extraction methods, which already contains the discrete Fourier and Wavelet transform.
DOI of the first publication: 10.5162/SMSI2021/D1.1
URL of the first publication:
Link to this record: urn:nbn:de:bsz:291--ds-375421
ISBN: 978-3-9819376-4-0
Date of registration: 11-Oct-2022
Faculty: NT - Naturwissenschaftlich- Technische Fakultät
Department: NT - Systems Engineering
Professorship: NT - Prof. Dr. Andreas Schütze
Collections:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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