AI-DRIVEN CABLE MAPPING SYSTEM (CMS) EMPLOYING FIBER SENSING AND MACHINE LEARNING

    公开(公告)号:US20240134074A1

    公开(公告)日:2024-04-25

    申请号:US18485187

    申请日:2023-10-11

    CPC classification number: G01V1/001 G01C21/3848 H04L41/145

    Abstract: An AI-driven cable mapping system that employs distributed fiber optic sensing (DFOS) fiber sensing and machine learning that provides autonomous determination of fiber optic cable location and mapping of same. Designed Al algorithms operating within our inventive systems and methods provide an easy solution for cable mapping in a GIS system; automatically maps using landmarks and manhole locations; and employs a supervised learning algorithm. A vehicle-assist operation is employed wherein a vehicle carries a Global Positioning System (GPS) device and drives along a roadway thereby following the fiber optic cable route; data paring that provides further significant locational information wherein time synchronizes between the DFOS system and vehicle GPS device from which we automatically pair the data of fiber length from traffic trajectories and GPS coordinates by time series.

    ROAD SURFACE CONDITIONS DETECTION BY DISTRIBUTED OPTIC FIBER SYSTEM

    公开(公告)号:US20230152150A1

    公开(公告)日:2023-05-18

    申请号:US17987007

    申请日:2022-11-15

    CPC classification number: G01H9/004 G01P3/36 G06N20/10 G06N3/08

    Abstract: A fiber optic sensing cable located along a side of a paved road and runs parallel to a driving direction is monitored by distributed fiber optic sensing (DFOS) using Rayleigh backscattering generated along the length of the optical sensor fiber cable under dynamic vehicle loads. The interaction of vehicles with roadway locations exhibiting distressed pavement generates unique patterns of localized signals that are identified/distinguished from signals resulting from vehicles operating on roadway exhibiting a smooth pavement surface. Machine learning methods are employed to estimate an overall road surface quality as well as localizing pavement damage. Power spectral density estimation, principal component analysis, support vector machine (SVM) combined with principal component analysis (PCA), local binary pattern (LBP), and convolutional neural network (CNN) are applied to develop the machine learning models.

    MAPPING USING OPTICAL FIBER SENSING

    公开(公告)号:US20220333956A1

    公开(公告)日:2022-10-20

    申请号:US17720245

    申请日:2022-04-13

    Abstract: Distributed fiber optic sensing (DFOS) systems and methods that automatically detect vibration signal patterns from waterfall data recorded by DFOS system operations in real- time and associate the detected vibration signal patterns to GPS location coordinates without human intervention or interpretation. When embodied as a computer vision-based operation according to aspects of the present disclosure, our inventive systems and method provide accurate, cost-efficient, and objective determination without relying on humans and their resulting bias' and inconsistencies.

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