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Titlebook: New Paradigm of Industry 4.0; Internet of Things, Srikanta Patnaik Book 2020 Springer Nature Switzerland AG 2020 Machine Learning.Human Ma

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書目名稱New Paradigm of Industry 4.0
副標題Internet of Things,
編輯Srikanta Patnaik
視頻videohttp://file.papertrans.cn/666/665540/665540.mp4
概述Presents contributions on the study, implementation, and standardization of Industry 4.0.Demonstrates how IoT, Big Data and Cyber-Physical Systems can accelerate the implementation of Industry 4.0.Wri
叢書名稱Studies in Big Data
圖書封面Titlebook: New Paradigm of Industry 4.0; Internet of Things,  Srikanta Patnaik Book 2020 Springer Nature Switzerland AG 2020 Machine Learning.Human Ma
描述.The book provides readers with an overview of the state of the art in the field of Industry 4.0 and related research advancements. The respective chapters identify and discuss new dimensions of both risk factors and success factors, along with performance metrics that can be employed in future research work. They also discuss a number of real-time issues, problems and applications with corresponding solutions and suggestions.. .Sharing new theoretical findings, tools and techniques for Industry 4.0, and covering both theoretical and application-oriented approaches, the book offers a valuable asset for newcomers to the field and practicing professionals alike..
出版日期Book 2020
關鍵詞Machine Learning; Human Machine Interaction; Industry 4; 0; Internet of Things; Big Data; Cyber Physical S
版次1
doihttps://doi.org/10.1007/978-3-030-25778-1
isbn_softcover978-3-030-25780-4
isbn_ebook978-3-030-25778-1Series ISSN 2197-6503 Series E-ISSN 2197-6511
issn_series 2197-6503
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

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Imparting Hands-on Industry 4.0 Education at Low Cost Using Open Source Tools and Python Eco-Systemh students. It is expected that this will help create Industry 4.0 laboratory infrastructure at affordable cost. The approach used is to report findings from experience gained over several years at an institute of higher learning where laboratories were set up to provide hands-on experiments in the
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A Review Study of Condition Monitoring and Maintenance Approaches for Diagnosis Corrosive Sulphur Dn to reduce the probability of failures? The major result is the described gaps between the currently applied CM and selection of relevant CM suggested for providing definitely indication of corrosion. The currently CM implies two techniques; oil analysis based only on evaluation the quality of the
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Principal Components Based Multivariate Statistical Process Monitoring of Machining Process Using Mng PCA, followed by process monitoring using Hotelling T2 multivariate statistical control chart based on principal component scores. The approach has a potential to provide an industry-ready solution to automated, economic and 100% process monitoring.
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