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Data-driven modeling in metrology – A short introduction, current developments and future perspectives

  • Linda-Sophie Schneider is a Ph.D. student at the Pattern Recognition Lab, University of Erlangen-Nuremberg, focusing on deep learning and optimization-based techniques for CT trajectory optimization and CT reconstruction. She began her Ph.D. in August 2021, following an M.Sc. in Applied Mathematics from FAU (2018-2021). Her current research interests include machine learning in CT imaging and combinatorial optimization.

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Published/Copyright: July 8, 2024

Abstract

Mathematical models are vital to the field of metrology, playing a key role in the derivation of measurement results and the calculation of uncertainties from measurement data, informed by an understanding of the measurement process. These models generally represent the correlation between the quantity being measured and all other pertinent quantities. Such relationships are used to construct measurement systems that can interpret measurement data to generate conclusions and predictions about the measurement system itself. Classic models are typically analytical, built on fundamental physical principles. However, the rise of digital technology, expansive sensor networks, and high-performance computing hardware have led to a growing shift towards data-driven methodologies. This trend is especially prominent when dealing with large, intricate networked sensor systems in situations where there is limited expert understanding of the frequently changing real-world contexts. Here, we demonstrate the variety of opportunities that data-driven modeling presents, and how they have been already implemented in various real-world applications.

Zusammenfassung

Mathematische Modelle sind im Bereich der Messtechnik von entscheidender Bedeutung, da sie eine Schlüsselrolle bei der Ableitung von Messergebnissen und der Berechnung von Unsicherheiten aus Messdaten spielen, die auf einem Verständnis des Messprozesses beruhen. Diese Modelle stellen im Allgemeinen den Zusammenhang zwischen der gemessenen Größe und allen anderen relevanten Größen dar. Solche Beziehungen werden verwendet, um Messsysteme zu konstruieren, die Messdaten interpretieren können, um Schlussfolgerungen und Vorhersagen über das Messsystem selbst zu treffen. Klassische Modelle sind in der Regel analytisch und beruhen auf grundlegenden physikalischen Prinzipien. Das Aufkommen digitaler Technologien, umfangreicher Sensornetzwerke und leistungsstarker Computerhardware hat jedoch zu einer zunehmenden Verlagerung hin zu datengetriebenen Methoden geführt. Dieser Trend ist besonders ausgeprägt, wenn es um große, komplizierte vernetzte Sensorsysteme geht, bei denen nur ein begrenztes Expertenwissen über die sich häufig ändernden realen Zusammenhänge vorhanden ist. In diesem Artikel wird aufgezeigt, welche vielfältigen Möglichkeiten die datengestützte Modellierung bietet und wie sie bereits in verschiedenen realen Anwendungen umgesetzt wurde.


Corresponding author: Linda-Sophie Schneider, Friedrich-Alexander-University Erlangen-Nuremberg, Chair of Pattern Recognition, Erlangen, Germany, E-mail: 

Funding source: VDI/VDE/IT

Award Identifier / Grant number: DIK-2004-0009

Funding source: German Ministry of Education and Research (BMBF)

Award Identifier / Grant number: 033KI201

About the author

Linda-Sophie Schneider

Linda-Sophie Schneider is a Ph.D. student at the Pattern Recognition Lab, University of Erlangen-Nuremberg, focusing on deep learning and optimization-based techniques for CT trajectory optimization and CT reconstruction. She began her Ph.D. in August 2021, following an M.Sc. in Applied Mathematics from FAU (2018-2021). Her current research interests include machine learning in CT imaging and combinatorial optimization.

Acknowledgments

Our special thanks go to our author colleagues with whom we jointly prepared the manuscript „Modelling of Networked Measuring Systems – From White-Box Models to Data Based Approaches“ [92] within the EMPIR project 17IND12 Met4FoF, namely Andonovic, I., University of Strathclyde, UK; Dorst, T., ZeMA – Zentrum für Mechatronik und Automatisierungstechnik gGmbH, Saarbrücken; Eichstädt, S., Physikalisch-Technische Bundesanstalt, Braunschweig and Berlin; Füssl, R., Technische Universität Ilmenau; Gourlay, G., University of Strathclyde, UK; Harris, P. M., National Physical Laboratory, Teddington, UK; Heizmann, M., Karlsruhe Institute of Technology; Luo Y., National Physical Laboratory, Teddington, UK; Schütze, A., Saarland University, Saarbrücken;Sommer, K.-D., Ilmenau University of Technology; Tachtatzis, C., University of Strathclyde, UK.

  1. Research ethics: Not applicable.

  2. Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: The authors states no competing interests.

  4. Research funding: A main part of this work was financed by the „SmartCT – Artificial Intelligence Methods for an Autonomous Robotic CT System“ project (project nr. DIK-2004-0009). Additionally, part of this work was funded by German Ministry of Education and Research (BMBF) under grant number 033KI201. Also, part of this work has been developed within the Joint Research project 17IND12 Met4FoF of the European Metrology Programme for Innovation and Research (EMPIR). The EMPIR is jointly funded by the EMPIR participating countries within EURAMET and the European Union.

  5. Data availability: Not applicable.

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Received: 2024-01-11
Accepted: 2024-06-04
Published Online: 2024-07-08
Published in Print: 2024-09-25

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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