Application Of Neural Network In Optical Fiber Intelligent Security Signal Recognition

The Internet of Things is a network through which all kinds of objects with independent functions can be interconnected through carriers such as telecommunication networks and the Internet.Intelligent security is the combination of Internet of Things technology and security products to make security more humane,intelligent,modern,more accurate detection of intrusions,to better guarantee our personal and property security.Intelligent security combined with optical fiber sensing technology can make up for the shortcomings of traditional security system.Optical fiber vibration intrusion system is a modern intelligent security system for monitoring and alarming emergencies threatening public security.It is a new system based on optical fiber sensing technology and applied in perimeter prevention.In this paper,the principle of optical fiber vibration sensor system is studied,and an intrusion signal acquisition system is built.The intrusion signal is extracted.Then a convolution neural network model is built to recognize the intrusion signal.Finally,an optical fiber signal recognition system is compiled to realize interaction.This paper mainly carried out the following research work:1.The sensing principle of optical fiber sensing system and several typical types of interferometric optical fiber sensing systems are studied: Michelson interferometric optical fiber sensor,Mach-Zehnder interferometric optical fiber sensor and Segnac interferometric optical fiber sensor.The structure and signal acquisition principle of the Michelson interferometric fiber optic vibration sensor system are mainly studied.The intrusion signal is collected by the fiber optic vibration sensor system installed in the window of the family.The experimental data are divided into four situations: window opening,climbing,window knocking and non-invasive situation.2.he signal preprocessing method and two signal feature extraction methods are studied:short-time Fourier transform and wavelet transform.The signal features are extracted separately and the effects of the two feature extraction methods are compared.3.The principle of neural network is studied,and the convolution neural network and BP neural network are designed.The feature matrix obtained by short-time Fourier transform and the feature vector obtained by wavelet transform are used for pattern recognitionrespectively.The test results are compared,and it is concluded that the convolution neural network is better than BP neural network in pattern recognition.The total accuracy of convolution neural network can reach 93.0%.4.The optical fiber signal recognition system is compiled,and GUI is compiled with the Tkinter module of Python.The trained convolutional neural network model is used for pattern recognition of optical fiber vibration sensor signals.