Biblio

Distributed acoustic sensing (DAS) systems based on fiber brag grating (FBG) have been widely used for distributed temperature and strain sensing over the past years, and function well in perimeter security monitoring and structural health monitoring. However, with relevant algorithms functioning with low accuracy, the DAS system presently has trouble in signal recognition, which puts forward a higher requirement on a high-precision identification method. In this paper, we propose an improved recognition method based on relative fundamental signal processing methods and convolutional neural network (CNN) to construct a mathematical model of disturbance FBG signal recognition. Firstly, we apply short-time energy (STE) to extract original disturbance signals. Secondly, we adopt short-time Fourier transform (STFT) to divide a longer time signal into short segments. Finally, we employ a CNN model, which has already been trained to recognize disturbance signals. Experimental results conducted in the real environments show that our proposed algorithm can obtain accuracy over 96.5%.

The following topics are dealt with: feature extraction; data mining; support vector machines; mobile computing; photovoltaic power systems; mean square error methods; fault diagnosis; natural language processing; control system synthesis; and Internet of Things.