Automatic identification and categorize zone of RFID reading in warehouse management system
Radio Frequency Identification (RFID) technology has improved the operational efficiency and process flow in the distribution of warehouse management system (WMS) around the globe. Nonetheless, a moving or missing tag as well as known and unknown tag’s location that may occur in the detection could...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
Springer Nature
2020
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/28907/ http://umpir.ump.edu.my/id/eprint/28907/7/Automatic%20Identification%20and%20Categorize%20Zone1.pdf http://umpir.ump.edu.my/id/eprint/28907/8/Automatic%20Identification%20and%20Categorize%20Zone.pdf |
| _version_ | 1848823164791947264 |
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| author | Choong, Chun Sern Ahmad Fakhri, Ab. Nasir Anwar, P. P. Abdul Majeed Muhammad Aizzat, Zakaria Mohd Azraai, M. Razman |
| author_facet | Choong, Chun Sern Ahmad Fakhri, Ab. Nasir Anwar, P. P. Abdul Majeed Muhammad Aizzat, Zakaria Mohd Azraai, M. Razman |
| author_sort | Choong, Chun Sern |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Radio Frequency Identification (RFID) technology has improved the operational efficiency and process flow in the distribution of warehouse management system (WMS) around the globe. Nonetheless, a moving or missing tag as well as known and unknown tag’s location that may occur in the detection could reduce the efficiency of process flow. This study aims at identifying the location of goods in between two
RFID reading zones by means of machine learning, particularly Support Vector Machine (SVM). A total of seven statistical features are extracted from the received signal strength (RSS) value from the raw RFID readings. SVM classifier are evaluated by considering the combination of different statistical features namely COMBINE to produce a more effective classification in comparison to individual statistical
feature. The performance of the classifier demonstrated a classification accuracy of approximately 94% by considering all features whereas the performance of the classifier by considering individual features alone is below than 90%. This preliminary study establishes the applicability of the proposed automatic identification is able to provide the management of goods as well as supply chain reasonably well without
human intervention. |
| first_indexed | 2025-11-15T02:52:47Z |
| format | Conference or Workshop Item |
| id | ump-28907 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T02:52:47Z |
| publishDate | 2020 |
| publisher | Springer Nature |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-289072020-10-06T04:07:37Z http://umpir.ump.edu.my/id/eprint/28907/ Automatic identification and categorize zone of RFID reading in warehouse management system Choong, Chun Sern Ahmad Fakhri, Ab. Nasir Anwar, P. P. Abdul Majeed Muhammad Aizzat, Zakaria Mohd Azraai, M. Razman TK Electrical engineering. Electronics Nuclear engineering Radio Frequency Identification (RFID) technology has improved the operational efficiency and process flow in the distribution of warehouse management system (WMS) around the globe. Nonetheless, a moving or missing tag as well as known and unknown tag’s location that may occur in the detection could reduce the efficiency of process flow. This study aims at identifying the location of goods in between two RFID reading zones by means of machine learning, particularly Support Vector Machine (SVM). A total of seven statistical features are extracted from the received signal strength (RSS) value from the raw RFID readings. SVM classifier are evaluated by considering the combination of different statistical features namely COMBINE to produce a more effective classification in comparison to individual statistical feature. The performance of the classifier demonstrated a classification accuracy of approximately 94% by considering all features whereas the performance of the classifier by considering individual features alone is below than 90%. This preliminary study establishes the applicability of the proposed automatic identification is able to provide the management of goods as well as supply chain reasonably well without human intervention. Springer Nature 2020-08 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/28907/7/Automatic%20Identification%20and%20Categorize%20Zone1.pdf pdf en http://umpir.ump.edu.my/id/eprint/28907/8/Automatic%20Identification%20and%20Categorize%20Zone.pdf Choong, Chun Sern and Ahmad Fakhri, Ab. Nasir and Anwar, P. P. Abdul Majeed and Muhammad Aizzat, Zakaria and Mohd Azraai, M. Razman (2020) Automatic identification and categorize zone of RFID reading in warehouse management system. In: Advances in Mechatronics, Manufacturing, and Mechanical Engineering: Selected articles from MUCET 2019 , 19-22 November 2019 , Bukit Gambang Resort City, Pahang, Malaysia. pp. 194-206.. ISBN 978-981-15-7309-5 (Published) https://doi.org/10.1007/978-981-15-7309-5_20 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Choong, Chun Sern Ahmad Fakhri, Ab. Nasir Anwar, P. P. Abdul Majeed Muhammad Aizzat, Zakaria Mohd Azraai, M. Razman Automatic identification and categorize zone of RFID reading in warehouse management system |
| title | Automatic identification and categorize zone of RFID reading in warehouse management system |
| title_full | Automatic identification and categorize zone of RFID reading in warehouse management system |
| title_fullStr | Automatic identification and categorize zone of RFID reading in warehouse management system |
| title_full_unstemmed | Automatic identification and categorize zone of RFID reading in warehouse management system |
| title_short | Automatic identification and categorize zone of RFID reading in warehouse management system |
| title_sort | automatic identification and categorize zone of rfid reading in warehouse management system |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/28907/ http://umpir.ump.edu.my/id/eprint/28907/ http://umpir.ump.edu.my/id/eprint/28907/7/Automatic%20Identification%20and%20Categorize%20Zone1.pdf http://umpir.ump.edu.my/id/eprint/28907/8/Automatic%20Identification%20and%20Categorize%20Zone.pdf |