APPLICATION OF MACHINE LEARNING FOR REMOTE ELECTRONICS EXPERIMENTS AS THE MEAN OF INDENTIFICATION

Authors

  • Antoni Kozic Vilniaus kolegija/University of Applied Sciences (LT)
  • Andzej Lucun Vilniaus kolegija/University of Applied Sciences (LT)
  • Mindaugas Vingelis LUMO Advanced Lighting Company Expert (LT)
  • Eugenijus Macerauskas Vilniaus kolegija/University of Applied Sciences (LT)
  • Andrius Narmontas Vilniaus kolegija/University of Applied Sciences (LT)

DOI:

https://doi.org/10.17770/sie2021vol5.6371

Keywords:

machine learning, LabVIEW, remote laboratory

Abstract

We are increasingly faced with automated solutions that create a dynamic and constantly evolving scientific and technical environment, with new challenges in finding more accurate and efficient solutions for identification. This article introduces a machine learning application that allows you to automatically recognize and identify learners in a distance learning experiment without being explicitly programmed. Experimental methods and components have been transferred to a distance learning laboratory environment. A conceptual model of an electronics laboratory has been developed for remote experiments using identification methods. This article focuses on machine learning using the LabVIEW package, which can access data and use it for self-study. The article presents the application of machine learning software LabVIEW National Instruments and NI ELVIS hardware simulators for electronic laboratory remote experiments and perspectives in the educational process.

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References

Bonk, C.J., Graham, C.R. (2012). The Handbook of Blended Learning-Global Perspectives, Local Designs, Wiley Published. ISBN 978-0-7879-7758-0.

Borodin, S., Batovrin, V. & Romanov, A. (2009). LabVIEW Elektronikos praktiniai darbai (89 – 112). Learning materials: VPU Press. ISBN 978-9955-20-480-0.

Ertugrul, N. (2000). Towards virtual laboratories: A survey of LabVIEW-based teaching/learning tools and future trends. International Journal of Engineering Education, 16(3), 171-180.

Gamage, K.A.A., Wijesuriya, D.I., Ekanayake, S.Y., Rennie, A.E.W., Lambert, C.G., & Gunawardhana, N. (2020). Online Delivery of Teaching and Laboratory Practices: Continuity of University Programmes during COVID-19 Pandemic. Education Sciences, 10(10), 291. DOI: 10.3390/educsci10100291

Kozic, A., Macerauskas, E., & Sakalys, P. (2016, May). Remote laboratory as conceptual model of blended learning. In SOCIETY. INTEGRATION. EDUCATION. Proceedings of the International Scientific Conference, Vol. 2, 549-557.

National Instruments Corporation. (2018). LabVIEW 2018 Analytics and Machine Learning Toolkit Readme, NI 2009. Retrieved from https://www.ni.com/pdf/manuals/377061b.html

Nguyen, G., Dlugolinsky, S., Bobak, M., Tran, V., Garcia, Á.L., Heredia, I. & Hluchy, L. (2019). Machine learning and deep learning frameworks and libraries for large-scale data mining: a survey. Artificial Intelligence Review, 52(1), 77-124.

Relf, C.G. (2003). Image acquisition and processing with LabVIEW. CRC press.

Salzmann, C., Gillet, D., & Huguenin, P. (2000). Introduction to real-time control using LabVIEW with an application to distance learning. Int. J. Engng Ed, 16(5), 372-384.

Ursutiu, D., Cotfas, P., Samoila, C., Zamfira, S. & Auer M. (2004). NI-ELVIS in Remote Electronic Laboratory REL. Proceedings of the 1st International Symposium on Remote Engineering and Virtual Instrumentation, Villach, Austria, 28. / 29. September 2004. ISBN3-89958-090-7.

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Published

2021-05-28

How to Cite

Kozic, A., Lucun, A., Vingelis, M., Macerauskas, E., & Narmontas, A. (2021). APPLICATION OF MACHINE LEARNING FOR REMOTE ELECTRONICS EXPERIMENTS AS THE MEAN OF INDENTIFICATION. SOCIETY. INTEGRATION. EDUCATION. Proceedings of the International Scientific Conference, 5, 371-378. https://doi.org/10.17770/sie2021vol5.6371