False Positive RFID Detection Using Classification Models
| Autor: | Muhammad Syafrudin, Jongtae Rhee, Ganjar Alfian, Bohan Yoon |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
false positive
Business process Computer science RSS Supply chain 02 engineering and technology outlier detection computer.software_genre 01 natural sciences lcsh:Technology lcsh:Chemistry 0202 electrical engineering electronic engineering information engineering False positive paradox Radio-frequency identification General Materials Science Instrumentation lcsh:QH301-705.5 Fluid Flow and Transfer Processes RFID business.industry lcsh:T Process Chemistry and Technology 010401 analytical chemistry General Engineering computer.file_format Filter (signal processing) lcsh:QC1-999 0104 chemical sciences Computer Science Applications machine learning classification lcsh:Biology (General) lcsh:QD1-999 lcsh:TA1-2040 Outlier 020201 artificial intelligence & image processing Anomaly detection Data mining business lcsh:Engineering (General). Civil engineering (General) computer lcsh:Physics |
| Zdroj: | Applied Sciences, Vol 9, Iss 6, p 1154 (2019) Applied Sciences Volume 9 Issue 6 |
| ISSN: | 2076-3417 |
| Popis: | Radio frequency identification (RFID) is an automated identification technology that can be utilized to monitor product movements within a supply chain in real-time. However, one problem that occurs during RFID data capturing is false positives (i.e., tags that are accidentally detected by the reader but not of interest to the business process). This paper investigates using machine learning algorithms to filter false positives. Raw RFID data were collected based on various tagged product movements, and statistical features were extracted from the received signal strength derived from the raw RFID data. Abnormal RFID data or outliers may arise in real cases. Therefore, we utilized outlier detection models to remove outlier data. The experiment results showed that machine learning-based models successfully classified RFID readings with high accuracy, and integrating outlier detection with machine learning models improved classification accuracy. We demonstrated the proposed classification model could be applied to real-time monitoring, ensuring false positives were filtered and hence not stored in the database. The proposed model is expected to improve warehouse management systems by monitoring delivered products to other supply chain partners. |
| Databáze: | OpenAIRE |
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