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• W4361275861 endingPage "1617" @default.
• W4361275861 startingPage "1617" @default.
• W4361275861 abstract "This pattern recognition method can effectively identify vibration signals collected by a phase-sensitive optical time-domain reflectometer (Φ-OTDR) and improve the accuracy of alarms. An alignment-free end-to-end multi-vibration event detection method based on Φ-OTDR is proposed, effectively detecting different vibration events in different frequency bands. The pulse accumulation and pulse cancellers determine the location of vibration events. The local differential detection method demodulates the vibration event time-domain variation signals. After the extraction of the signal time-frequency features by sliding window, the convolution neural network (CNN) further extracts the signal features. It analyzes the temporal relationship of each group of signal features using a bidirectional long short-term memory network (Bi-LSTM). Finally, the connectionist temporal classification (CTC) is used to label the unsegmented sequence data to achieve single detection of multiple vibration targets. Experiments show that using this method to process the collected 8563 data, containing 5 different frequency bands of multi-vibration acoustic sensing signal, the system F1 score is 99.49% with a single detection time of 2.2 ms. The highest frequency response is 1 kHz. It is available to quickly and efficiently identify multiple vibration signals when a single demodulated acoustic sensing signal contains multiple vibration events." @default.
• W4361275861 created "2023-03-31" @default.
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• W4361275861 date "2023-03-29" @default.
• W4361275861 modified "2023-09-25" @default.
• W4361275861 title "Using Phase-Sensitive Optical Time Domain Reflectometers to Develop an Alignment-Free End-to-End Multitarget Recognition Model" @default.
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• W4361275861 doi "https://doi.org/10.3390/electronics12071617" @default.
• W4361275861 hasPublicationYear "2023" @default.
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