This paper presents an integrated technical framework to protect pipelines against both malicious intrusions and piping degradation using a distributed fiber sensing technology and artificial intelligence. A distributed acoustic sensing (DAS) system based on phase-sensitive optical time-domain reflectometry (φ-OTDR) was used to detect acoustic wave propagation and scattering along pipeline structures consisting of straight piping and sharp bend elbow. Signal to noise ratio of the DAS system was enhanced by femtosecond induced artificial Rayleigh scattering centers. Data harnessed by the DAS system were analyzed by neural network-based machine learning algorithms. The system identified with over 85% accuracy in various external impact events, and over 94% accuracy for defect identification through supervised learning and 71% accuracy through unsupervised learning.

This paper reports on the use of machine learning to delineate data harnessed by fiber-optic distributed acoustic sensors (DAS) using fiber with enhanced Rayleigh backscattering to recognize vibration events induced by human locomotion. The DAS used in this work is based on homodyne phase-sensitive optical time-domain reflectometry (φ-OTDR). The signal-to-noise ratio (SNR) of the DAS was enhanced using femtosecond laser-induced artificial Rayleigh scattering centers in single-mode fiber cores. Both supervised and unsupervised machine-learning algorithms were explored to identify people and specific events that produce acoustic signals. Using convolutional deep neural networks, the supervised machine learning scheme achieved over 76.25% accuracy in recognizing human identities. Conversely, the unsupervised machine learning scheme achieved over 77.65% accuracy in recognizing events and human identities through acoustic signals. Through integrated efforts on both sensor device innovation and machine learning data analytics, this paper shows that the DAS technique can be an effective security technology to detect and to identify highly similar acoustic events with high spatial resolution and high accuracies.

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This study examined the effect of sacral neuromodulation on persistent bladder underactivity induced by prolonged pudendal nerve stimulation (PudNS). In 10 α‐chloralose anesthetized cats, repetitive application of 30-min PudNS induced bladder underactivity evident as an increase in bladder capacity during a cystometrogram (CMG). S1 or S2 dorsal root stimulation (15 or 30 Hz) at 1- or 1.5-times threshold intensity (T) for inducing reflex hindlimb movement (S1) or anal sphincter twitch (S2) was applied during a CMG to determine if the stimulation can reverse the bladder underactivity. Persistent (>3 hours) bladder underactivity consisting of a significant increase in bladder capacity to 163.1±11.3% of control was induced after repetitive (1-10 times) application of 30-min PudNS. S2 but not S1 dorsal root stimulation at 15 Hz and 1T intensity reversed the PudNS -induced bladder underactivity by significantly reducing the large bladder capacity to 124.3±12.9% of control. Other stimulation parameters were not effective. After the induction of persistent PudNS underactivity, recordings of reflex bladder activity under isovolumetric conditions revealed that S2 dorsal root stimulation consistently induced the largest bladder contraction at 15 Hz and 1T when compared to other frequencies (5-40 Hz) or intensities (0.25-1.5T). This study provides basic science evidence consistent with the hypothesis that abnormal pudendal afferent activity contributes to the bladder underactivity in Fowler's syndrome and that sacral neuromodulation treats this disorder by reversing the bladder inhibition induced by pudendal nerve afferent activity.