研究目的
To achieve the detection and the recognition events at the real time, an effective two-level vibration recognition method and a technique are proposed and presented in this article.
研究成果
The proposed two-level pattern recognition scheme effectively identifies event types along the fenced fiber optic cable with an accuracy of 99%. The system's real-time performance is ensured with a data processing time of 0.2839 s for a 10 km detection range.
研究不足
The article does not explicitly mention limitations, but future work includes detecting and classifying underground fence disturbances, fence cutting, dragging, etc.
1:Experimental Design and Method Selection:
The article proposes a two-level vibration recognition method using signal characteristics (short-term energy and short-time over-threshold) compared to a dynamic threshold to judge the type of event. The power distribution features on the frequency domain are extracted through power spectral estimation and combined with time-domain characteristics as a feature vector through Support Vector Machine (SVM).
2:Sample Selection and Data Sources:
Data samples include intrusion and non-intrusion events collected under various environmental conditions.
3:List of Experimental Equipment and Materials:
Optical fiber sensor, F-OTDR (phase-sensitive optical time-domain reflectometry), SVM for classification.
4:Experimental Procedures and Operational Workflow:
Signal characteristics are extracted and compared to dynamic thresholds. Power spectral estimation is performed on suspected intrusion signals, and features are combined for SVM classification.
5:Data Analysis Methods:
Modern power spectrum estimation (AR model based) is used for frequency-domain feature extraction. SVM is used for classification and prediction.
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