Browsing by Author "Sun, Wen-Lin"

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An AHP-based Risk Assessment for an Industrial IoT Cloud
(2018-01-01)
Huang, Yu-Lun
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Sun, Wen-Lin
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電機工程學系
;
Department of Electrical and Computer Engineering
An Industrial Internet-of-Things (IIoT) Cloud builds up a platform to accelerate the realization of factories running Industry 4.0 services. The IIoT cloud interconnects machines, robotic arms, controllers and drivers to form a collaborative platform [1], which brings the convenience of developing new features, for example, remote controlling, automatic producing, etc. However, the interconnected network may suffer from new security issues and risks. Hence, a method for assessing risks of the IIoT network is required, especially for the core running cloud services for the IoT devices. In this research, a novel AHP (Analytic Hierarchy Process) [2]-based risk assessment model is presented for an IIoT cloud to self-check its own health.
MLoC: A Cloud Framework adopting Machine Learning for Industrial Automation
(2019-01-01)
Huang, Yu-Lun
;
Sun, Wen-Lin
;
Yeh, Kai-Wei
;
電機工程學系
;
Department of Electrical and Computer Engineering
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.