An Intelligent Perceptual Method of Fixed Assets Based on RFID and LSTM
To solve the disadvantages of traditional RFID-based fixed asset perception methods, such as vulnerability of radio signals to environmental interference, multiple tag interferences with each other, and low recognition accuracy, an intelligent perceptual method of fixed assets based on long and short-term memory neural networks (LSTM) and radio frequency identification (RFID) is proposed. The sequence analysis is introduced to deal with the low recognition accuracy caused by the discontinuity of RFID signal reception , and a LSTM-based model is developed to learn the pattern of RFID signal sequences received over a period of time, which can realize the two-category identification of the location of fixed assets. The proposed model has been validated by real experiments carried out in a university laboratory. The results show that the recognition accuracy of the fixed asset state using time sequence analysis can reach 99.26％, which is 11.05％ higher than that of the traditional RFID perception model based on discrete moments, and the recognition accuracy of multiple tags can reach 93.0％.
LSTMRFIDperceptual recognitiontime sequencesreal time fixed assets monitoring
万方数据
维普
