Title: MLoC: A Cloud Framework adopting Machine Learning for Industrial Automation
Authors: Huang, Yu-Lun
Sun, Wen-Lin
Yeh, Kai-Wei
電機工程學系
Department of Electrical and Computer Engineering
Issue Date: 1-Jan-2019
Abstract: By leveraging the modern machine learning algorithms, we can build up more Artificial Intelligence (AI) systems, like self-driving cars, smart factories and financial analysis systems, to improve our daily life. In addition to building up an AI system, several prerequisites are required to drive the system, including data collection, data storage, machine learning models, training dataset, parameters tuning, and so on. To obtain the benefit of scalability and flexibility, most AI systems are built on a cloud platform, which shares resources with others in the same infrastructure. Though the above concept is trivial, the implementation faces big challenges when realizing it. In this paper, an easy-to-use cloud framework for machine learning as well as its implementation guideline is presented for building up a cloud-based development platform. We conduct several experiments on analyzing and monitoring the health condition of bearings of motors. We compare and analyze the feasibility of the proposed framework.
URI: http://hdl.handle.net/11536/153289
ISBN: 978-4-88898-300-6
ISSN: 2072-5639
Journal: 2019 12TH ASIAN CONTROL CONFERENCE (ASCC)
Begin Page: 1413
End Page: 1418
Appears in Collections:Conferences Paper