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    RADIO SIGNAL IDENTIFICATION, IDENTIFICATION SYSTEM LEARNING, AND IDENTIFIER DEPLOYMENT
    21.
    发明公开
    RADIO SIGNAL IDENTIFICATION, IDENTIFICATION SYSTEM LEARNING, AND IDENTIFIER DEPLOYMENT 审中-公开

    公开(公告)号：US20240104386A1

    公开(公告)日：2024-03-28

    申请号：US18376480

    申请日：2023-10-04

    Applicant: Virginia Tech Intellectual Properties, Inc.

    Inventor： Timothy James O`Shea

    IPC: G06N3/086 ,  G06N3/045 ,  G06N3/08 ,  G06N20/00 ,  H04B17/30 ,  H04W24/08

    CPC classification number: G06N3/086 ,  G06N3/045 ,  G06N3/08 ,  G06N20/00 ,  H04B17/30 ,  H04W24/08

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training and deploying machine-learned identification of radio frequency (RF) signals. One of the methods includes: determining an RF signal configured to be transmitted through an RF band of a communication medium; determining first classification information that is associated with the RF signal, and that includes a representation of a characteristic of the RF signal or a characteristic of an environment in which the RF signal is communicated; using at least one machine-learning network to process the RF signal and generate second classification information as a prediction of the first classification information; calculating a measure of distance between (i) the second classification information that was generated by the at least one machine-learning network, and (ii) the first classification information associated with the RF signal; and updating the at least one machine-learning network based on the measure of distance.

    AUTONOMOUS ROBOT FOR POWER LINE VIBRATION CONTROL AND INSPECTION
    26.
    发明公开
    AUTONOMOUS ROBOT FOR POWER LINE VIBRATION CONTROL AND INSPECTION 审中-公开

    公开(公告)号：US20230332660A1

    公开(公告)日：2023-10-19

    申请号：US17998735

    申请日：2021-06-04

    Applicant: VIRGINIA TECH INTELLECTUAL PROPERTIES, INC.

    Inventor： Oumar BARRY ,  Jiamin WANG ,  Paul-Camille KAKOU

    IPC: F16F7/10 ,  H02G1/02 ,  H02G7/14

    CPC classification number: F16F7/1005 ,  H02G1/02 ,  H02G7/14

    Abstract: Various embodiments of a system and method for reducing vibrations in and inspecting a suspended cable are described. In one embodiment, a vibration control robot includes a frame roller cage configured to roll open and closed around a cable, a drive system comprising a motor system to maneuver the robot along the cable, and a vibration absorption system to admit and absorb mechanical vibrations from the cable. The vibration absorption system can include a messenger cable segment of a predetermined length, where the messenger cable segment is mechanically coupled with the frame to admit mechanical vibrations on the cable. The vibration absorption system can also include an absorbing counterweight tip mass, a sliding mass, and a permanent magnet of an electromagnetic transducer device, to convert the mechanical vibrations in the messenger cable into electrical energy.

    REINFORCEMENT LEARNING WITH OPTIMIZATION-BASED POLICY
    27.
    发明公开
    REINFORCEMENT LEARNING WITH OPTIMIZATION-BASED POLICY 审中-公开

    公开(公告)号：US20230289612A1

    公开(公告)日：2023-09-14

    申请号：US18181048

    申请日：2023-03-09

    Applicant: VIRGINIA TECH INTELLECTUAL PROPERTIES, INC.

    Inventor： Ming Jin

    IPC: G06N3/092

    CPC classification number: G06N3/092

    Abstract: Concepts of using optimization-based policy in reinforcement learning (RL) are described. In one example, a method can include implementing an RL agent in a subsystem of the sequential decision-making system. The RL agent can be coupled to a prediction module and an optimization module of the subsystem. The method can also include defining a parameter value of the optimization module based on an observed state of the subsystem and/or a reward provided to the prediction module based on the observed state. The method can also include learning a policy that is defined by the optimization module based on the parameter value and a predicted future state of the subsystem that is predicted by the prediction module based on the reward. The policy can include a suggested action to be performed by the subsystem to achieve a goal. The method can also include implementing the policy to perform the suggested action.

    Learning approximate estimation networks for communication channel state information
    28.
    发明授权
    Learning approximate estimation networks for communication channel state information 有权

    公开(公告)号：US11699086B1

    公开(公告)日：2023-07-11

    申请号：US17732683

    申请日：2022-04-29

    Applicant: Virginia Tech Intellectual Properties, Inc.

    Inventor： Timothy James O'Shea ,  Kiran Karra ,  T. Charles Clancy

    IPC: G06N5/046 ,  H04B17/309 ,  G06N20/00 ,  H04B17/391 ,  G06N3/02

    CPC classification number: G06N5/046 ,  G06N3/02 ,  G06N20/00 ,  H04B17/309 ,  H04B17/3912

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training and deploying machine-learning estimation networks in a communications system. One of the methods includes: processing first information with ground truth information to generate a first RF signal by altering the first information by channel impairment having at least one channel effect, using a receiver to process the first RF signal to generate second information, training a machine-learning estimation network based on a network architecture, the second information, and the ground truth information, receiving by the receiver a second RF signal transmitted through a communication channel including the at least one channel effect, inferring by the trained estimation network the receiver to estimate an offset of the second RF signal caused by the at least one channel effect, and correcting the offset of the RF signal with the estimated offset to obtain a recovered RF signal.

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