IEICE TRANSACTIONS on Information
Efficient RFID Data Cleaning in Supply Chain Management
Summary :
Despite the improvement of the accuracy of RFID readers, there are still erroneous readings such as missed reads and ghost reads. In this letter, we propose two effective models, a Bayesian inference-based decision model and a path-based detection model, to increase the accuracy of RFID data cleaning in RFID based supply chain management. In addition, the maximum entropy model is introduced for determining the value of sliding window size. Experiment results validate the performance of the proposed method and show that it is able to clean raw RFID data with a higher accuracy.
- Publication
- IEICE TRANSACTIONS on Information Vol.E96-D No.7 pp.1557-1560
- Publication Date
- 2013/07/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E96.D.1557
- Type of Manuscript
- LETTER
- Category
- Artificial Intelligence, Data Mining
Authors
Hua FAN
National University of Defense Technology
Quanyuan WU
National University of Defense Technology
Jianfeng ZHANG
National University of Defense Technology
Keyword
Latest Issue
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Hua FAN, Quanyuan WU, Jianfeng ZHANG, "Efficient RFID Data Cleaning in Supply Chain Management" in IEICE TRANSACTIONS on Information,
vol. E96-D, no. 7, pp. 1557-1560, July 2013, doi: 10.1587/transinf.E96.D.1557.
Abstract: Despite the improvement of the accuracy of RFID readers, there are still erroneous readings such as missed reads and ghost reads. In this letter, we propose two effective models, a Bayesian inference-based decision model and a path-based detection model, to increase the accuracy of RFID data cleaning in RFID based supply chain management. In addition, the maximum entropy model is introduced for determining the value of sliding window size. Experiment results validate the performance of the proposed method and show that it is able to clean raw RFID data with a higher accuracy.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E96.D.1557/_p
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@ARTICLE{e96-d_7_1557,
author={Hua FAN, Quanyuan WU, Jianfeng ZHANG, },
journal={IEICE TRANSACTIONS on Information},
title={Efficient RFID Data Cleaning in Supply Chain Management},
year={2013},
volume={E96-D},
number={7},
pages={1557-1560},
abstract={Despite the improvement of the accuracy of RFID readers, there are still erroneous readings such as missed reads and ghost reads. In this letter, we propose two effective models, a Bayesian inference-based decision model and a path-based detection model, to increase the accuracy of RFID data cleaning in RFID based supply chain management. In addition, the maximum entropy model is introduced for determining the value of sliding window size. Experiment results validate the performance of the proposed method and show that it is able to clean raw RFID data with a higher accuracy.},
keywords={},
doi={10.1587/transinf.E96.D.1557},
ISSN={1745-1361},
month={July},}
Copy
TY - JOUR
TI - Efficient RFID Data Cleaning in Supply Chain Management
T2 - IEICE TRANSACTIONS on Information
SP - 1557
EP - 1560
AU - Hua FAN
AU - Quanyuan WU
AU - Jianfeng ZHANG
PY - 2013
DO - 10.1587/transinf.E96.D.1557
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E96-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2013
AB - Despite the improvement of the accuracy of RFID readers, there are still erroneous readings such as missed reads and ghost reads. In this letter, we propose two effective models, a Bayesian inference-based decision model and a path-based detection model, to increase the accuracy of RFID data cleaning in RFID based supply chain management. In addition, the maximum entropy model is introduced for determining the value of sliding window size. Experiment results validate the performance of the proposed method and show that it is able to clean raw RFID data with a higher accuracy.
ER -
English
