Abstract
When steam and water powered engines started the first industrial revolution, this increased production rate and gave way to large production facilities, which transitioned into mass production of identical products in an assembly line with the introduction of electricity in the second revolution. Subsequently, the processing of different product categories along the same assembly line was facilitated with the help of automation and robotics. This has led to the third industrial period aided by the introduction of computers and the advancement in electronics. Currently, we are during the process of transitioning into an autonomous and intelligent manufacturing system where cyber and physical systems connects through data analytics and machine learning. In this article, a short overview of deep learning approaches in the detection and recognition of real time events is discussed.
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Awan, S., Trovati, M. (2022). Deep Learning Approaches to Detect Real Time Events Recognition in Smart Manufacturing Systems – A Short Survey. In: Barolli, L., Chen, HC., Miwa, H. (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2021. Lecture Notes in Networks and Systems, vol 312. Springer, Cham. https://doi.org/10.1007/978-3-030-84910-8_20
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DOI: https://doi.org/10.1007/978-3-030-84910-8_20
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