知搜-全球专利检索

  1. Login
  2. Sign Up

Search Results

52 records (took 0.017 seconds)

    Transfer learning/dictionary generation and usage for tailored part parameter generation from coupon builds
    3.
    发明授权
    Transfer learning/dictionary generation and usage for tailored part parameter generation from coupon builds 有权

    公开(公告)号：US11079739B2

    公开(公告)日：2021-08-03

    申请号：US16284409

    申请日：2019-02-25

    Applicant: General Electric Company

    Inventor： Subhrajit Roychowdhury ,  Alexander Chen ,  Xiaohu Ping ,  John Erik Hershey

    IPC: G05B19/4099 ,  B33Y50/00 ,  B29C64/386 ,  B29C64/153 ,  B22F10/20 ,  B22F10/30

    Abstract: According to some embodiments, system and methods are provided comprising receiving, via a communication interface of a part parameter dictionary module comprising a processor, geometry data for a plurality of geometric structures forming a plurality of parts, wherein the parts are manufactured with an additive manufacturing machine; determining, using the processor of the part parameter dictionary module, a feature set for each geometric structure; generating, using the processor of the part parameter dictionary module, one of a coupon and a coupon set for the feature set; generating an optimized parameter set for each coupon, using the processor of the part parameter dictionary module, via execution of an iterative learning control process for each coupon; mapping, using the processor of the part parameter dictionary module, one or more parameters of the optimized parameter set to one or more features of the feature set; and generating a dictionary of optimized scan parameter sets to fabricate geometric structures with a material used in additive manufacturing. Numerous other aspects are provided.

    Dynamic, resilient virtual sensing system and shadow controller for cyber-attack neutralization
    8.
    发明授权
    Dynamic, resilient virtual sensing system and shadow controller for cyber-attack neutralization 有权

    公开(公告)号：US11468164B2

    公开(公告)日：2022-10-11

    申请号：US16710051

    申请日：2019-12-11

    Applicant: GENERAL ELECTRIC COMPANY

    Inventor： Subhrajit Roychowdhury ,  Masoud Abbaszadeh ,  Mustafa Tekin Dokucu

    IPC: G06F21/55 ,  G06N3/08 ,  G06N3/04 ,  G05B13/04 ,  G05B13/02

    Abstract: An industrial asset may have monitoring nodes (e.g., sensor or actuator nodes) that generate current monitoring node values. An abnormality detection and localization computer may receive the series of current monitoring node values and output an indication of at least one abnormal monitoring node that is currently being attacked or experiencing a fault. An actor-critic platform may tune a dynamic, resilient state estimator for a sensor node and output tuning parameters for a controller that improve operation of the industrial asset during the current attack or fault. The actor-critic platform may include, for example, a dynamic, resilient state estimator, an actor model, and a critic model. According to some embodiments, a value function of the critic model is updated for each action of the actor model and each action of the actor model is evaluated by the critic model to update a policy of the actor-critic platform.

    VIRTUAL SENSOR SUPERVISED LEARNING FOR CYBER-ATTACK NEUTRALIZATION
    10.
    发明申请
    VIRTUAL SENSOR SUPERVISED LEARNING FOR CYBER-ATTACK NEUTRALIZATION 有权

    公开(公告)号：US20210126943A1

    公开(公告)日：2021-04-29

    申请号：US16666807

    申请日：2019-10-29

    Applicant: GENERAL ELECTRIC COMPANY

    Inventor： Subhrajit Roychowdhury ,  Masoud Abbaszadeh ,  Mustafa Tekin Dokucu

    IPC: H04L29/06 ,  G06K9/62

    Abstract: An industrial asset may have monitoring nodes that generate current monitoring node values. A dynamic, resilient estimator may split a temporal monitoring node space into normal and one or more abnormal subspaces associated with different kinds of attack vectors. According to some embodiments, a neutralization model is constructed and trained for each attack vector using supervised learning and the associated abnormal subspace. In other embodiments, a single model is created using out-of-range values for abnormal monitoring nodes. Responsive to an indication of a particular abnormal monitoring node or nodes, the system may automatically invoke the appropriate neutralization model to determine estimated values of the particular abnormal monitoring node or nodes (e.g., by selecting the correct model or using out-of-range values). The series of current monitoring node values from the abnormal monitoring node or nodes may then be replaced with the estimated values.

Patent Agency Ranking