Optical Fiber Intrusion Signal Recognition Based on Improved Mel Frequency Cepstrum Coefficient

2018 ◽  
Author(s):  
Yuan Zhang ◽  
Lu Zhao ◽  
Qing Tian ◽  
Jun Fan
Keyword(s):  
Optical Fiber ◽  
Signal Recognition ◽  
Mel Frequency Cepstrum Coefficient

Optical fiber intrusion signal recognition method based on TSVD-SCN

2019 ◽  
Vol 48 ◽  
pp. 270-277 ◽  
Author(s):  
Zhiyong Sheng ◽  
Zhiqiang Zeng ◽  
Hongquan Qu ◽  
Yuan Zhang
Keyword(s):  
Optical Fiber ◽  
Signal Recognition ◽  
Recognition Method

Optical Fiber Perimeter Vibration Signal Recognition Based on Multifractal Spectrum

2019 ◽  
Vol 48 (2) ◽  
pp. 206001
Author(s):  
熊兴隆 XIONG Xing-long ◽  
张琬童 ZHANG Wan-tong ◽  
冯磊 FENG Lei ◽  
李猛 LI Meng ◽  
马愈昭 MA Yu-zhao ◽  
...  
Keyword(s):  
Optical Fiber ◽  
Multifractal Spectrum ◽  
Vibration Signal ◽  
Signal Recognition

AdaBoost-SCN algorithm for optical fiber vibration signal recognition

2019 ◽  
Vol 58 (21) ◽  
pp. 5612
Author(s):  
Hongquan Qu ◽  
Tingliang Feng ◽  
Yanping Wang ◽  
Yuan Zhang
Keyword(s):  
Optical Fiber ◽  
Vibration Signal ◽  
Signal Recognition

scholarly journals Signal Recognition Basing on Optical Fiber Vibration Sensor

2013 ◽  
Vol 347-350 ◽  
pp. 743-747
Author(s):  
Hai Yan Xu ◽  
Zhuo Zhang ◽  
Xue Wu Zhang
Keyword(s):  
Optical Fiber ◽  
Pattern Matching ◽  
Optical Fiber Sensor ◽  
Continuous Distribution ◽  
Vibration Sensor ◽  
Signal Recognition ◽  
Long Distance ◽  
Signal Characteristics ◽  
Distributed Optical Fiber Sensor ◽  
Distributed Optical Fiber

Distributed optical fiber sensor can acquire the information of physical field along time and spatial continuous distribution. It plays an important role in long-distance oil and electricity transmission and security. In this paper, the author introduced the universal steps in triggering pattern recognition, which includes signal characteristics extracting by accurate endpoint detecting, templates establishing by training, and pattern matching. By training the samples acquired in the laboratory, three templates are established. And pattern matching had been done between templates and all the samples. The results show that, 87.5 percent of the samples are matched correctly with the triggering patterns they are belonging to.

scholarly journals Ensemble Learning with Stochastic Configuration Network for Noisy Optical Fiber Vibration Signal Recognition

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3293 ◽  
Author(s):  
Hongquan Qu ◽  
Tingliang Feng ◽  
Yuan Zhang ◽  
Yanping Wang
Keyword(s):  
Optical Fiber ◽  
Ensemble Learning ◽  
Oil And Gas ◽  
Recognition Performance ◽  
Small Sample ◽  
Signal Recognition ◽  
Bootstrap Sampling ◽  
Vibration Signals ◽  
Uniform Noise ◽  
Noisy Signals

Optical fiber pre-warning systems (OFPS) based on Φ-OTDR are applied to many different scenarios such as oil and gas pipeline protection. The recognition of fiber vibration signals is one of the most important parts of this system. According to the characteristics of small sample set, we choose stochastic configuration network (SCN) for recognition. However, due to the interference of environmental and mechanical noise, the recognition effect of vibration signals will be affected. In order to study the effect of noise on signal recognition performance, we recognize noisy optical fiber vibration signals, which superimposed analog white Gaussian noise, white uniform noise, Rayleigh distributed noise, and exponentially distributed noise. Meanwhile, bootstrap sampling (bagging) and AdaBoost ensemble learning methods are combined with original SCN, and Bootstrap-SCN, AdaBoost-SCN, and AdaBoost-Bootstrap-SCN are proposed and compared for noisy signals recognition. Results show that: (1) the recognition rates of two classifiers combined with AdaBoost are higher than the other two methods over the entire noise range; (2) the recognition for noisy signals of AdaBoost-Bootstrap-SCN is better than other methods in recognition of noisy signals.

scholarly journals RVFL-Based Optical Fiber Intrusion Signal Recognition With Multi-Level Wavelet Decomposition as Feature

2018 ◽  
Vol 8 (3) ◽  
pp. 234-241 ◽  
Author(s):  
Yanping Wang ◽  
Dianjun Gong ◽  
Liping Pang ◽  
Dan Yang
Keyword(s):  
Optical Fiber ◽  
Wavelet Decomposition ◽  
Signal Recognition ◽  
Multi Level
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