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RFID-enabled indoor positioning method for a real-time manufacturing execution system using OS-ELM
Yang Z.; Zhang P.; Chen L.
2016
Source PublicationNeurocomputing
ISSN9252312
Volume174Pages:121
Abstract

Manufacturing execution systems (MES) have recently been introduced to monitor various manufacturing objects (MOs) in dynamic shop floors; they can leverage the efficiency of information flow across functional layers for planning and control. However, current MES practices using traditional indoor positioning algorithms face several difficulties in tracking MOs for wireless manufacturing: (1) inefficient wireless data acquisition in the shop-floor environment, (2) lack of a reliable and accurate real-time signal processing method for handling massive signal data, and (3) the positions of reference objects cannot be treated as in a static environment when unknown manufacturing orders arrive in a streaming manner. This paper proposes to handle the first challenge by adopting RFID technology that can constantly capture the wireless signals sent from tags mounted on various MOs. The second difficulty can be solved by applying the online sequential extreme learning machine (OS-ELM) that inherits the elegant properties of ELM in terms of extremely fast learning speed and high generalization performance. The OS-ELM based positioning method also addresses the third issue in which an online localization model has been constructed in a streaming manner. The proposed method can greatly reduce the training time without costly retraining of the previously trained data together with the newly arrived data. With the novel OS-ELM based RFID positioning framework, the MOs are upgraded to smart manufacturing objects (SMOs), and the processes are enhanced with real-time signal processing and intelligent tracking capabilities. The experimental results verify that the proposed positioning method is superior to other state-of-the-art algorithms in terms of accuracy, efficiency, and robustness. © 2015 Elsevier B.V..

KeywordIndoor Positioning Manufacturing Execution System (Mes) Online Sequential Extreme Learning Machine (Os-elm) Real-time Signal Processing Rfid Smart Manufacturing Objects (Smos)
DOI10.1016/j.neucom.2015.05.120
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000367276700012
The Source to ArticleScopus
Fulltext Access
Citation statistics
Cited Times [WOS]:37   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF ELECTROMECHANICAL ENGINEERING
Corresponding AuthorYang Z.
AffiliationDepartment of Electromechanical Engineering, Faculty of Science and Technology, University of Macau, Macau SAR, China
First Author AffilicationFaculty of Science and Technology
Corresponding Author AffilicationFaculty of Science and Technology
Recommended Citation
GB/T 7714
Yang Z.,Zhang P.,Chen L.. RFID-enabled indoor positioning method for a real-time manufacturing execution system using OS-ELM[J]. Neurocomputing,2016,174:121.
APA Yang Z.,Zhang P.,&Chen L..(2016).RFID-enabled indoor positioning method for a real-time manufacturing execution system using OS-ELM.Neurocomputing,174,121.
MLA Yang Z.,et al."RFID-enabled indoor positioning method for a real-time manufacturing execution system using OS-ELM".Neurocomputing 174(2016):121.
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