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dc.contributor.authorHuang, Yu-Lunen_US
dc.contributor.authorSun, Wen-Linen_US
dc.contributor.authorYeh, Kai-Weien_US
dc.date.accessioned2019-12-13T01:12:51Z-
dc.date.available2019-12-13T01:12:51Z-
dc.date.issued2019-01-01en_US
dc.identifier.isbn978-4-88898-300-6en_US
dc.identifier.issn2072-5639en_US
dc.identifier.urihttp://hdl.handle.net/11536/153289-
dc.description.abstractBy 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.en_US
dc.language.isoen_USen_US
dc.titleMLoC: A Cloud Framework adopting Machine Learning for Industrial Automationen_US
dc.typeProceedings Paperen_US
dc.identifier.journal2019 12TH ASIAN CONTROL CONFERENCE (ASCC)en_US
dc.citation.spage1413en_US
dc.citation.epage1418en_US
dc.contributor.department電機工程學系zh_TW
dc.contributor.departmentDepartment of Electrical and Computer Engineeringen_US
dc.identifier.wosnumberWOS:000490720700247en_US
dc.citation.woscount0en_US
Appears in Collections:Conferences Paper