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- Publisher Website: 10.1109/TSMC.2019.2956201
- Scopus: eid_2-s2.0-85113266560
- WOS: WOS:000685890800035
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Article: A Heterogeneous Data Analytics Framework for RFID-Enabled Factories
Title | A Heterogeneous Data Analytics Framework for RFID-Enabled Factories |
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Authors | |
Keywords | Data analytics framework heterogeneity radio-frequency identification (RFID) smart manufacturing |
Issue Date | 2021 |
Publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6221021 |
Citation | IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, v. 51 n. 9, p. 5567-5576 How to Cite? |
Abstract | As the wide use of various smart sensors in the manufacturing environment, traditional factories have been upgraded and transformed into an intelligent level. Smart manufacturing factory thus has been enabled by some advanced technologies, such as Internet of Things (IoT) which could facilitate production operations and decision-makings on the one hand. On the other hand, enormous data will be created by the IoT devices. Manufacturing companies are facing some challenges when attempting to make full use of the huge datasets which are heterogeneous in format, complex in logic, unstructured in storage, and abstract in interpretation. In order to address these challenges, this article proposes a data heterogeneous analytics framework for a radio-frequency identification (RFID) enabled factory. RFID captured data from a real-life company is used for validating the proposed framework. Specifically, the performance of machining processes, logistics operations, and inspection behavior are examined from the RFID captured data. |
Persistent Identifier | http://hdl.handle.net/10722/279983 |
ISSN | 2023 Impact Factor: 8.6 2023 SCImago Journal Rankings: 3.992 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Zhong, RY | - |
dc.contributor.author | Putnik, GD | - |
dc.contributor.author | Newman, ST | - |
dc.date.accessioned | 2019-12-23T08:24:34Z | - |
dc.date.available | 2019-12-23T08:24:34Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, v. 51 n. 9, p. 5567-5576 | - |
dc.identifier.issn | 2168-2216 | - |
dc.identifier.uri | http://hdl.handle.net/10722/279983 | - |
dc.description.abstract | As the wide use of various smart sensors in the manufacturing environment, traditional factories have been upgraded and transformed into an intelligent level. Smart manufacturing factory thus has been enabled by some advanced technologies, such as Internet of Things (IoT) which could facilitate production operations and decision-makings on the one hand. On the other hand, enormous data will be created by the IoT devices. Manufacturing companies are facing some challenges when attempting to make full use of the huge datasets which are heterogeneous in format, complex in logic, unstructured in storage, and abstract in interpretation. In order to address these challenges, this article proposes a data heterogeneous analytics framework for a radio-frequency identification (RFID) enabled factory. RFID captured data from a real-life company is used for validating the proposed framework. Specifically, the performance of machining processes, logistics operations, and inspection behavior are examined from the RFID captured data. | - |
dc.language | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6221021 | - |
dc.relation.ispartof | IEEE Transactions on Systems, Man, and Cybernetics: Systems | - |
dc.rights | IEEE Transactions on Systems, Man, and Cybernetics: Systems. Copyright © Institute of Electrical and Electronics Engineers. | - |
dc.rights | ©2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | - |
dc.subject | Data analytics | - |
dc.subject | framework | - |
dc.subject | heterogeneity | - |
dc.subject | radio-frequency identification (RFID) | - |
dc.subject | smart manufacturing | - |
dc.title | A Heterogeneous Data Analytics Framework for RFID-Enabled Factories | - |
dc.type | Article | - |
dc.identifier.email | Zhong, RY: zhongzry@hku.hk | - |
dc.identifier.authority | Zhong, RY=rp02116 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TSMC.2019.2956201 | - |
dc.identifier.scopus | eid_2-s2.0-85113266560 | - |
dc.identifier.hkuros | 308798 | - |
dc.identifier.volume | 51 | - |
dc.identifier.issue | 9 | - |
dc.identifier.spage | 5567 | - |
dc.identifier.epage | 5576 | - |
dc.identifier.isi | WOS:000685890800035 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 2168-2216 | - |