Distributed intelligent SNAP informatics
AssigneeNEC Corporation
InventorsShaobo Han, Yuheng Chen, Ming-Fang Huang, Tingfeng Li
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Abstract
A fiber optic sensing technology for vehicle run-off-road incident automatic detection by an indicator of sonic alert pattern (SNAP) vibration patterns. A machine learning method is employed and trained and evaluated against a variety of heterogeneous factors using controlled experiments, demonstrating applicability for future field deployment. Extracted events resulting from operation of our system may be advantageously incorporated into existing management systems for intelligent transportation and smart city applications, facilitating real-time alleviation of traffic congestion and/or providing a quick response rescue and clearance operation.
Claims7
A method of operating a distributed fiber optic sensing (DFOS) system employing distributed intelligent sonic alert pattern (SNAP) informatics, the method comprising:
- operating the DFOS system by
- generating by an interrogator, interrogating light and sending it into a length of optical sensing fiber located proximate to a roadway;
- detecting backscattered light that results from the interrogating light sent into the optical sensing fiber;
- generating and outputting signals indicative of detected backscattered light;
- and
- analyzing, the outputted signals indicative of detected backscattered light and determining strain signals associated with location(s) along the length of the optical sensing fiber that are indicative of a vibration and acoustic environment of the optical sensing fiber at the location(s);
- and
- determining normal, baseline characteristics including baseline vibration levels from road traffic operating on the roadway under normal conditions of the optical sensing;
- and
- determining when a vehicle operating on the roadway passes through a SNAP area of the roadway;
- wherein a portion of the optical sensing fiber is located under grass and another portion of the optical sensing fiber is located under a portion of the roadway;
- wherein said DFOS system analyzing is performed by a two-stage model comprising an embedding network providing deep representation learning and a support vector machine classifier;
- wherein the embedding network comprises a convolutional neural network and detects patterns from two-dimensional waterfall images generated from the determined strain signals and locations.
Dependencies are inferred from the claim text.