An online rfid localization in the manufacturing shopfloor

Andri Ashfahani; Mahardhika Pratama; Edwin Lughofer; Qing Cai; Huang Sheng

doi:10.1007/978-3-030-05645-2_10

An online rfid localization in the manufacturing shopfloor

Andri Ashfahani^*, Mahardhika Pratama, Edwin Lughofer, Qing Cai, Huang Sheng

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

18 Citations (Scopus)

Abstract

Radio Frequency Identification technology has gained popularity for cheap and easy deployment. In the realm of manufacturing shopfloor, it can be used to track the location of manufacturing objects to achieve better efficiency. The underlying challenge of localization lies in the nonstationary characteristics of manufacturing shopfloor which calls for an adaptive life-long learning strategy in order to arrive at accurate localization results. This paper presents an evolving model based on a novel evolving intelligent system, namely evolving Type-2 Quantum Fuzzy Neural Network (eT2QFNN), which features an interval type-2 quantum fuzzy set with uncertain jump positions. The quantum fuzzy set possesses a graded membership degree which enables better identification of overlaps between classes. The eT2QFNN works fully in the evolving mode, where all parameters including the number of rules are automatically adjusted and generated on the fly. The parameter adjustment scenario relies on decoupled extended Kalman filter method. Our numerical study shows that eT2QFNN is able to deliver comparable accuracy compared to state-of-the-art algorithms.

Original language	English
Title of host publication	Predictive Maintenance in Dynamic Systems
Subtitle of host publication	Advanced Methods, Decision Support Tools and Real-World Applications
Publisher	Springer International Publishing
Pages	287-309
Number of pages	23
ISBN (Electronic)	9783030056452
ISBN (Print)	9783030056445
DOIs	https://doi.org/10.1007/978-3-030-05645-2_10
Publication status	Published - 1 Jan 2019
Externally published	Yes

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10.1007/978-3-030-05645-2_10

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title = "An online rfid localization in the manufacturing shopfloor",

abstract = "Radio Frequency Identification technology has gained popularity for cheap and easy deployment. In the realm of manufacturing shopfloor, it can be used to track the location of manufacturing objects to achieve better efficiency. The underlying challenge of localization lies in the nonstationary characteristics of manufacturing shopfloor which calls for an adaptive life-long learning strategy in order to arrive at accurate localization results. This paper presents an evolving model based on a novel evolving intelligent system, namely evolving Type-2 Quantum Fuzzy Neural Network (eT2QFNN), which features an interval type-2 quantum fuzzy set with uncertain jump positions. The quantum fuzzy set possesses a graded membership degree which enables better identification of overlaps between classes. The eT2QFNN works fully in the evolving mode, where all parameters including the number of rules are automatically adjusted and generated on the fly. The parameter adjustment scenario relies on decoupled extended Kalman filter method. Our numerical study shows that eT2QFNN is able to deliver comparable accuracy compared to state-of-the-art algorithms.",

author = "Andri Ashfahani and Mahardhika Pratama and Edwin Lughofer and Qing Cai and Huang Sheng",

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An online rfid localization in the manufacturing shopfloor. / Ashfahani, Andri; Pratama, Mahardhika; Lughofer, Edwin et al.
Predictive Maintenance in Dynamic Systems: Advanced Methods, Decision Support Tools and Real-World Applications. Springer International Publishing, 2019. p. 287-309.

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

TY - CHAP

T1 - An online rfid localization in the manufacturing shopfloor

AU - Ashfahani, Andri

AU - Pratama, Mahardhika

AU - Lughofer, Edwin

AU - Cai, Qing

AU - Sheng, Huang

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Radio Frequency Identification technology has gained popularity for cheap and easy deployment. In the realm of manufacturing shopfloor, it can be used to track the location of manufacturing objects to achieve better efficiency. The underlying challenge of localization lies in the nonstationary characteristics of manufacturing shopfloor which calls for an adaptive life-long learning strategy in order to arrive at accurate localization results. This paper presents an evolving model based on a novel evolving intelligent system, namely evolving Type-2 Quantum Fuzzy Neural Network (eT2QFNN), which features an interval type-2 quantum fuzzy set with uncertain jump positions. The quantum fuzzy set possesses a graded membership degree which enables better identification of overlaps between classes. The eT2QFNN works fully in the evolving mode, where all parameters including the number of rules are automatically adjusted and generated on the fly. The parameter adjustment scenario relies on decoupled extended Kalman filter method. Our numerical study shows that eT2QFNN is able to deliver comparable accuracy compared to state-of-the-art algorithms.

AB - Radio Frequency Identification technology has gained popularity for cheap and easy deployment. In the realm of manufacturing shopfloor, it can be used to track the location of manufacturing objects to achieve better efficiency. The underlying challenge of localization lies in the nonstationary characteristics of manufacturing shopfloor which calls for an adaptive life-long learning strategy in order to arrive at accurate localization results. This paper presents an evolving model based on a novel evolving intelligent system, namely evolving Type-2 Quantum Fuzzy Neural Network (eT2QFNN), which features an interval type-2 quantum fuzzy set with uncertain jump positions. The quantum fuzzy set possesses a graded membership degree which enables better identification of overlaps between classes. The eT2QFNN works fully in the evolving mode, where all parameters including the number of rules are automatically adjusted and generated on the fly. The parameter adjustment scenario relies on decoupled extended Kalman filter method. Our numerical study shows that eT2QFNN is able to deliver comparable accuracy compared to state-of-the-art algorithms.

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BT - Predictive Maintenance in Dynamic Systems

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An online rfid localization in the manufacturing shopfloor

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