DATA-DRIVEN STREET FLOOD WARNING SYSTEM
    4.
    发明公开

    公开(公告)号:US20240135797A1

    公开(公告)日:2024-04-25

    申请号:US18485217

    申请日:2023-10-11

    IPC分类号: G08B21/10 G01W1/14

    CPC分类号: G08B21/10 G01W1/14

    摘要: A data-driven street flood warning system that employs distributed fiber optic sensing (DFOS)/distributed acoustic sensing (DAS) and machine learning (ML) technologies and techniques to provide a prediction of street flood status along a telecommunications fiber optic cable route using the DFOS/DAS data and ML models. Operationally, a DFOS/DAS interrogator collects and transmits vibrational data resulting from rain events while an online web server provides a user interface for end-users. Two machine learning models are built respectively for rain intensity prediction and flood level prediction. The machine learning models serve as predictive models for rain intensity and flood levels based on data provided to them, which includes rain intensity, rain duration, and historical data on flood levels.

    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

    IPC分类号: G01V1/00 G01C21/00 H04L41/14

    摘要: 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.

    INTENT-BASED NETWORK COMPUTING JOB ASSIGNMENT

    公开(公告)号:US20240129195A1

    公开(公告)日:2024-04-18

    申请号:US18481988

    申请日:2023-10-05

    IPC分类号: H04L41/0896 H04L41/122

    CPC分类号: H04L41/0896 H04L41/122

    摘要: Described is a novel framework, we call intent-based computing jobs assignment framework, for efficiently accommodating a clients' computing job requests in a mobile edge computing infrastructure. We define the intent-based computing job assignment problem, which jointly optimizes the virtual topology design and virtual topology mapping with the objective of minimizing the total bandwidth consumption. We use the Integer Linear Programming (ILP) technique to formulate this problem, and to facilitate the optimal solution. In addition, we employ a novel and efficient heuristic algorithm, called modified Steiner tree-based (MST-based) heuristic, which coordinately determines the virtual topology design and the virtual topology mapping. Comprehensive simulations to evaluate the performance of our solutions show that the MST-based heuristic can achieve an efficient performance that is close to the optimal performance obtained by the ILP solution.

    FIBER SENSING BY MONITORING POLARIZATION FUNCTION OF LIGHT ON SUPERVISORY PATH OF CABLES

    公开(公告)号:US20240103215A1

    公开(公告)日:2024-03-28

    申请号:US18369041

    申请日:2023-09-15

    IPC分类号: G02B6/024 G02B6/44

    CPC分类号: G02B6/024 G02B6/4427

    摘要: An advance in the art is made according to aspects of the present disclosure directed to methods for earthquake sensing that employ a supervisory system of undersea fiber optic cables. Earthquakes and other environmental disturbances are detected by monitoring the polarization of interrogation light instead of its phase. More specifically, our methods monitor the transfer matrix rather than just polarization and isolate disturbance location by monitoring eigenvalues of the polarization transfer matrix. From results obtained we have demonstrated experimentally that we can monitor disturbances that affect signal polarization on a span-by-span basis using High Loss Loop Back (HLLB) paths. It is shown that by measuring the polarization rotation matrix and determining the polarization rotation angle we can identify the span where the disturbance occurred with 35 dB extinction with no limitation on the magnitude of the disturbance and the number of affected spans.