Optimization of RFID reading performance based on YOLOv3 and elman neural network

As a non-contact automatic identification technology, Radio Frequency Identification (RFID) is of great significance to improve the simultaneous identification of multi-target. This paper designs a more efficient and accurate multi-tag reading performance measurement system based on the fusion of YOLOv3 and Elman neural network. In the machine vision subsystem, multi-tag images are collected by dual CCD and detected by neural network algorithm. The reading distance of 3D distributed multi-tag is measured by laser ranging to evaluate the reading performance of RFID system. Firstly, the multi-tag are detected by YOLOv3, which realizes the measurement of 3D coordinates, improves the prediction accuracy, enhances the recognition ability of small targets, and improves the accuracy of 3D coordinate detection. Secondly, the relationship between the 3D coordinates and the corresponding reading distance of RFID multi-tag are modelled by Elman recurrent neural network. Finally, the reading performance of RFID multi-tag is optimized. Compared with the state-of-the-arts, the multi-tag detection rate of YOLOv3 is 17.4% higher and the time is 3.27 times higher than that of the previous template matching algorithm. In terms of reading performance, the MAPE of Elman neural network is 1.46 %, which is at least 21.43 % higher than other methods. In running time, Elman only needs 1.69s, which is at least 28.40% higher than others. Thus, the system not only improves the accuracy, but also improves the speed, which provides a new insight for the measurement and optimization of RFID performance.

Download Full-text

An optimal measurement method for spatial distribution of radio frequency identification multi-tag based on image analysis and PSO

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331218823864 ◽

2019 ◽

Vol 41 (12) ◽

pp. 3331-3339

Author(s):

Xiaolei Yu ◽

Yujun Zhou ◽

Zhenlu Liu ◽

Zhimin Zhao

Keyword(s):

Neural Network ◽

Image Analysis ◽

Radio Frequency ◽

Radio Frequency Identification ◽

Template Matching ◽

Detection System ◽

Optimization Method ◽

Frequency Identification ◽

Edge Detection Method ◽

Reading Distance

In this paper, a multi-tag optimization method based on image analysis and particle swarm optimization (PSO) neural network is proposed to verify the effect of radio frequency identification (RFID) multi-tag distribution on the performance of the system. A RFID tag detection system is proposed with two charge coupled device (CCD). This system can automatically focus on the tag according to its position, so it can obtain the image information more accurately by template matching and edge detection method. Therefore, the spatial structure of multi-tag and the corresponding reading distance can be obtained for training. Because of its excellent performance in multi-objective optimization, the PSO neural network is used to train and predict multi-tag distribution at the maximum reading distance. Compared with other neural networks, PSO is more accurate and its uptime is shorter for RFID multi-tag analysis.

Download Full-text

Automatic identification of atrial fibrillation based on the modified Elman neural network with exponential moving average algorithm

Measurement ◽

10.1016/j.measurement.2021.109806 ◽

2021 ◽

pp. 109806

Author(s):

Zhanjie Song ◽

Jibin Wang

Keyword(s):

Neural Network ◽

Atrial Fibrillation ◽

Moving Average ◽

Automatic Identification ◽

Elman Neural Network

Download Full-text

A hybrid elman neural network predictor for time series prediction

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.20.12799 ◽

2018 ◽

Vol 7 (2.20) ◽

pp. 159 ◽

Cited By ~ 3

Author(s):

N Mohana Sundaram ◽

S N. Sivanandam

Keyword(s):

Neural Network ◽

Time Series ◽

Time Delay ◽

Adaptive Learning ◽

Time Series Data ◽

Time Series Prediction ◽

Series Data ◽

Elman Neural Network ◽

Dynamic Processing ◽

Elman Recurrent Neural Network

Artificial Neural Networks have become popular in the world of prediction and forecasting due to their nonlinear nonparametric adaptive-learning property. They become an important tool in data analysis and data mining applications. Elman neural network due to its recurrent nature and dynamic processing capabilities can perform the prediction process with a good range of accuracy. In this paper an Elman recurrent Neural Network is hybridised with a time delay called a tap delay line for time series prediction process to improve its performance. The Elman neural network with the time delay inputs is trained tested and validated using the solar sun spot time series data that contains the monthly mean sunspot numbers for a 240 year period having 2899 data values. The results confirm that the proposed Elman network hybridised with time delay inputs can predict the time series with more accurately and effectively than the existing methods.

Download Full-text

Rotor Resistance Online Identification of Vector Controlled Induction Motor Based on Neural Network

Mathematical Problems in Engineering ◽

10.1155/2014/831839 ◽

2014 ◽

Vol 2014 ◽

pp. 1-10 ◽

Cited By ~ 3

Author(s):

Bo Fan ◽

Zhixin Yang ◽

Wei Xu ◽

Xianbo Wang

Keyword(s):

Neural Network ◽

Induction Motor ◽

High Performance ◽

Nonlinear Function ◽

Training Dataset ◽

Variable Frequency ◽

Elman Neural Network ◽

Factors Affecting ◽

Rotor Resistance ◽

Elman Recurrent Neural Network

Rotor resistance identification has been well recognized as one of the most critical factors affecting the theoretical study and applications of AC motor’s control for high performance variable frequency speed adjustment. This paper proposes a novel model for rotor resistance parameters identification based on Elman neural networks. Elman recurrent neural network is capable of performing nonlinear function approximation and possesses the ability of time-variable characteristic adaptation. Those influencing factors of specified parameter are analyzed, respectively, and various work states are covered to ensure the completeness of the training samples. Through signal preprocessing on samples and training dataset, different input parameters identifications with one network are compared and analyzed. The trained Elman neural network, applied in the identification model, is able to efficiently predict the rotor resistance in high accuracy. The simulation and experimental results show that the proposed method owns extensive adaptability and performs very well in its application to vector controlled induction motor. This identification method is able to enhance the performance of induction motor’s variable-frequency speed regulation.

Download Full-text