Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Automated UHF RFID-based book positioning and monitoring method in smart libraries
In this study, a method is proposed for ultra high frequency radio frequency identification (UHF RFID)-based book positioning and counting developed for smart libraries. In the experimental setup created, RFID tags placed in books were automatically detected using three RFID antennas. Using received signal strength indicator information from each antenna for each book, the locations of the books are determined. In addition, classification was made by using machine learning approaches for the study. For this purpose, the best result for sequence determination in the classification study using ensemble trees, K nearest neighbours (KNN), and support vector machine algorithms was obtained with the ensemble subspace KNN algorithm with 94.1%. The best result for cabinet detection was obtained in the study using the ensemble subspace KNN algorithm and a 78.5% accuracy rate was achieved. The best result for rack detection was obtained with the ensemble subspace KNN algorithm with 95.4%. The study is thought to be useful in the automatic determination of the row, cabinet, and rack of books in smart libraries.
Automated UHF RFID-based book positioning and monitoring method in smart libraries
In this study, a method is proposed for ultra high frequency radio frequency identification (UHF RFID)-based book positioning and counting developed for smart libraries. In the experimental setup created, RFID tags placed in books were automatically detected using three RFID antennas. Using received signal strength indicator information from each antenna for each book, the locations of the books are determined. In addition, classification was made by using machine learning approaches for the study. For this purpose, the best result for sequence determination in the classification study using ensemble trees, K nearest neighbours (KNN), and support vector machine algorithms was obtained with the ensemble subspace KNN algorithm with 94.1%. The best result for cabinet detection was obtained in the study using the ensemble subspace KNN algorithm and a 78.5% accuracy rate was achieved. The best result for rack detection was obtained with the ensemble subspace KNN algorithm with 95.4%. The study is thought to be useful in the automatic determination of the row, cabinet, and rack of books in smart libraries.
Automated UHF RFID-based book positioning and monitoring method in smart libraries
Yaman, Orhan (Autor:in) / Ertam, Fatih (Autor:in) / Tuncer, Turker (Autor:in) / Firat Kilincer, Ilhan (Autor:in)
IET Smart Cities ; 2 ; 173-180
15.10.2020
8 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
RFID tags , ensemble trees , smart libraries , rack detection , received signal strength indicator information , K nearest neighbours , radiofrequency identification , library automation , trees (mathematics) , learning (artificial intelligence) , support vector machines , nearest neighbour methods , classification study , support vector machine algorithms , ensemble subspace KNN algorithm , sequence determination , UHF antennas , UHF RFID-based book monitoring , RFID antennas , UHF RFID-based book positioning , RSSI , pattern classification
Metadata by IET is licensed under CC BY 3.0
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