Two kinds of neural network algorithms suitable for fiber optic sensing array signal processing

1996 ◽  
Vol 35 (8) ◽  
pp. 2196 ◽  
Author(s):  
Yaqing Tu
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Array Signal Processing ◽  
Fiber Optic ◽  
Network Algorithms ◽  
Fiber Optic Sensing

Method for tunnel cross-section deformation monitoring based on distributed fiber optic sensing and neural network

2021 ◽  
Vol 67 ◽  
pp. 102704
Author(s):  
Gong-yu Hou ◽  
Zi-xiang Li ◽  
Zhi-yu Hu ◽  
Dong-xing Feng ◽  
Hang Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Cross Section ◽  
Fiber Optic ◽  
Deformation Monitoring ◽  
Fiber Optic Sensing ◽  
Tunnel Cross Section

A surveillance system for urban buried pipeline subject to third-party threats based on fiber optic sensing and convolutional neural network

2020 ◽  
pp. 147592172093064
Author(s):  
Suzhen Li ◽  
Renzhu Peng ◽  
Zelong Liu
Keyword(s):  
Neural Network ◽  
False Alarm ◽  
Convolutional Neural Network ◽  
Surveillance System ◽  
Fiber Optic ◽  
Third Party ◽  
The Third ◽  
Time Space ◽  
Fiber Optic Sensing ◽  
Space Matrix

Third-party threats, such as construction activities and man-made sabotage, have become the main cause of pipeline accidents in recent years. This article proposes a surveillance system for protecting the buried municipal pipelines from third-party damage based on distributed fiber optic sensing and convolutional neural network (CNN). Due to the ability of detecting very small perturbation, the phase-sensitive optical time-domain reflectometry (φ-OTDR) is employed for distributed vibration measurements along the pipelines. A two-layer classifier based on CNN is developed: one layer is used to discriminate the third-party activities from the environmental disturbance; the other is to determine the specific type of the third-party events. Meanwhile, a time-space matrix is introduced to reduce the false alarm and correct possible errors by taking into account the continuity of the signals in time and space. Field tests are carried out to validate the effectiveness of the proposed surveillance system. The recognition results show that the CNN-based classifiers achieve the accuracy of over 97%, which is 14.8% higher than that of the traditional feature-based machine learning method using random forest (RF) algorithm. It also indicates that the time-space matrix can dramatically reduce the false alarm and enhance the recognition accuracy.

scholarly journals Implementation of a Load Sensitizing Bridge Spherical Bearing Based on Low-Coherent Fiber-Optic Sensors Combined with Neural Network Algorithms

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 37
Author(s):  
Jingjing Guo ◽  
Tiesuo Geng ◽  
Huaizhi Yan ◽  
Lize Du ◽  
Zhe Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Temperature Compensation ◽  
Fiber Optic ◽  
Fiber Optic Sensors ◽  
Network Algorithms ◽  
Load Sensing ◽  
The Neural Network ◽  
Spherical Bearing ◽  
Neural Network Algorithm ◽  
Set Up

Low-coherent fiber-optic sensors combined with neural network algorithms were designed to carry out a load-sensitizing spherical bearing. Four sensing fibers were wound around the outside of the pot support of the spherical bearing uniformly deployed from upper to bottom. The upper three were configured in a distributed way to respond to the applied load as a function of the three strain sensors. The bottom one was employed as a temperature compensation sensor. A loading experiment was implemented to test the performance of the designed system. The results showed that there was a hysteresis in all the three sensors between loading and unloading process. The neural network algorithm is proposed to set up a function of the three sensors, treated as a set of input vectors to establish the input-output relationship between the applied loads and the constructed input vectors, in order to overcome the hysteresis existing in each sensor. An accuracy of 6% for load sensing was approached after temperature compensation.

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