APPLICATION OF MACHINE LEARNING FOR REMOTE ELECTRONICS EXPERIMENTS AS THE MEAN OF INDENTIFICATION
DOI:
https://doi.org/10.17770/sie2021vol5.6371Keywords:
machine learning, LabVIEW, remote laboratoryAbstract
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.
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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