Skip to main content
SpringerLink
Log in

Marko Esche, Reinhard Meyer, Martin Nischwitz

Conformity assessment of measuring instruments with artificial intelligence

A case study on the application of machine learning algorithms

  • Aufsätze
  • Published:
Datenschutz und Datensicherheit - DuD Aims and scope Submit manuscript

Zusammenfassung

Feedback from industry suggests that AI will soon gain traction in the regulated economic sector known as legal metrology. Driven by innovation, conformity assessment of measuring instruments equipped with machine learning algorithms needs to be standardized to ensure unbiased results. Although there is still no AI legislation, communications from the European Commission already contain fundamental principles and rules for AI development in order to ensure and improve the quality of life for all Europeans. In this context, several classes of use cases for the application of AI in measuring instruments and for conformity assessment are presented and investigated here.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

References

  1. EC, Directive 2014/32/EU of the European Parliament and of the Council of 26 February 2014on the harmonisation of the laws of the Member States relating to the making available on the market of measuring instruments; European Union, Council of the European Union ; European Parliament, Directive, February 2014

  2. IT-Grundschutz-Kompendium, Glossar: Datensicherheit; Bundesamt für Sicherheit in der Informationstechnik; 2020; https://www.bsi.bund.de/DE/Themen/ITGrundschutz/ITGrundschutzKompendium/vorkapitel/Glossar_.html

  3. M. Esche and F. Thiel, Software risk assessment for measuring instruments in legal metrology; in: Proceedings of the Federated Conference on Computer Science and Information Systems, Lodz, Poland,September 2015, pp. 1113–1123

  4. N. O’Mahony, S. Campbell, A. Carvalho, S. Harapanahalli, G. Hernandez, L. Krpalkova, D. Riordan and J. Walsh, Deep Learning vs. Traditional Computer Vision; in: Advances in Computer Vision, Springer Verlag, 2020, https://doi.org/10.1007/978-3-030-17795-9_10

    Book  Google Scholar 

  5. DIN SPEC 92001-1:2019-04, Artificial Intelligence – Life Cycle Processes and Quality Requirements – Part 1: Quality Meta Model; Beuth Verlag GmbH, DOI: 10.31030/ 3050203

  6. J. Su, D. V. Vargas and K. Sakurai, One Pixel Attack for Fooling Deep Neural Networks; in: IEEE Transactions on Evolutionary Computation, vol. 23, no. 5, pp. 828-841, Oct. 2019, https://doi.org/10.1109/TEVC.2019.2890858

    Article  Google Scholar 

  7. Rohde, M., Schindler, W., Künstliche Intelligenz in Evaluierung und Zulassung. Datenschutz und Datensicherheit (DuD) 43, 627–630 (2019). https://doi.org/10.1007/s11623-019-1177-4

    Article  Google Scholar 

  8. L. Nørgaard, M. Lagerholm and M. Westerhaus, Artificial Neural Networks and Near Infrared Spectroscopy – A case study on protein content in whole wheat grain; in: Dedicated Analytical Solutions, Issue 1, April 2013

  9. Joint Committee for Guides in Metrology, JCGM 100: Evaluation of Measurement Data – Guide to the Expression of Uncertainty in Measurement; JCGM, 2008

  10. K. Shridhar, F. Laumann and M. Liwicki, A Comprehensive guide to Bayesian Convolutional Neural Network; arXiv preprint arXiv:1901.02731, 2019

  11. European Commission, COM/2020/65 On Artificial Intelligence – A European approach to excellence and trust; White Paper, February 2020

  12. G. Kumar, K. Kumar & M. Sachdeva, The use of artificial intelligence based techniques for intrusion detection: a review; Artif Intell Rev 34, p. 369–387 (2010), https://doi.org/10.1007/s10462-010-9179-5

    Article  Google Scholar 

  13. A. C. Mutlu, I. H. Boyaci, H. E. Genis, R. Ozturk, N. Basaran-Akgul, T. Sanal, A. K. Evlice, Prediction of wheat quality parameters using near-infrared spectroscopy and artificial neural networks; in: Eureopean Food Research and Technology, Springer-Verlag, August 2011, https://doi.org/10.1007/s00217-011-1515-8

    Book  Google Scholar 

  14. Verordnung über das Inverkehrbringen und die Bereitstellung von Messgeräten auf dem Markt sowie über ihre Verwendung und Eichung (Mess- und Eichverordnung – MessEV); Bundesgesetzblatt 2014 Part 1, No. 58, December 2014, last change on 2020-04-28

  15. C. Blundell, J. Cornebise, K. Kavukcuoglu, D. Wierstra, Weight uncertainty in neural networks; in: Proceedings of the 32nd International Conference on Machine Learning, July 2015

  16. A. Krizhevsky and I. Sutskever and G. E. Hinton, ImageNet Classification with Deep Convolutional Neural Networks; in: Advances in Neural Information Processing Systems 25, p. 1097-1105 (2012)

    Google Scholar 

  17. K. Shridhar, F. Laumann and M. Liwicki, Uncertainty estimations by softplus normalization in bayesian convolutional neural networks with variational inference; arXiv preprint arXiv: 1806.05978, 2018

Download references

Author information

Authors and Affiliations

  1. PTB, Berlin, Deutschland

    Marko Esche, Reinhard Meyer & Martin Nischwitz

Authors
  1. Marko Esche
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Reinhard Meyer
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Martin Nischwitz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marko Esche.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Esche, M., Meyer, R. & Nischwitz, M. Conformity assessment of measuring instruments with artificial intelligence . Datenschutz Datensich 45, 184–189 (2021). https://doi.org/10.1007/s11623-021-1415-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11623-021-1415-4

Search

Navigation