TY - GEN
T1 - Automatic identification and categorize zone of rfid reading in warehouse management system
AU - Choong, Chun Sern
AU - Nasir, Ahmad Fakhri Ab
AU - Abdul Majeed, Anwar P.P.
AU - Zakaria, Muhammad Aizzat
AU - Razman, Mohd Azraai Mohd
N1 - Publisher Copyright:
© Springer Nature Singapore Pte Ltd. 2021.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85090530337&partnerID=8YFLogxK
U2 - 10.1007/978-981-15-7309-5_20
DO - 10.1007/978-981-15-7309-5_20
M3 - Conference Proceeding
AN - SCOPUS:85090530337
SN - 9789811573088
T3 - Lecture Notes in Mechanical Engineering
SP - 194
EP - 206
BT - Advances in Mechatronics, Manufacturing, and Mechanical Engineering - Selected articles from MUCET 2019
A2 - Zakaria, Muhammad Aizzat
A2 - Abdul Majeed, Anwar P.P.
A2 - Hassan, Mohd Hasnun Arif
PB - Springer Science and Business Media Deutschland GmbH
T2 - 11th Malaysian Technical Universities Conference on Engineering and Technology,MUCET 2019
Y2 - 19 November 2019 through 22 November 2019
ER -