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    Method and system for competence monitoring and contiguous learning for control
    1.
    发明授权
    Method and system for competence monitoring and contiguous learning for control 有权

    公开(公告)号：US10921755B2

    公开(公告)日：2021-02-16

    申请号：US16222279

    申请日：2018-12-17

    Applicant: General Electric Company

    Inventor： Nurali Virani ,  Abhishek Srivastav

    IPC: G05B13/02

    Abstract: According to some embodiments a competence module is provided to: receive an objective; select a machine learning model associated with the objective; receive data from the at least one data source; determine at least one next input based on the received data; determine whether the at least one next input is in a competent region or is in an incompetent region of the machine learning model; when the at least one next input is inside the competent region, generate an output; determine an estimate of uncertainty for the generated output; when the uncertainty is below an uncertainty threshold, the machine learning model is competent and when the uncertainty is above the uncertainty threshold, the machine learning model is incompetent; and operate the physical asset based on one of the competent and incompetent state of the machine learning model. Numerous other aspects are provided.

    Autonomous reasoning and experimentation agent for molecular discovery
    2.
    发明授权
    Autonomous reasoning and experimentation agent for molecular discovery 有权

    公开(公告)号：US11158400B2

    公开(公告)日：2021-10-26

    申请号：US16739239

    申请日：2020-01-10

    Applicant: General Electric Company

    Inventor： Jason Nichols ,  Johan Michael Reimann ,  Nurali Virani ,  Naresh Sundaram Iyer

    IPC: G16C20/20 ,  G16C20/70 ,  G06N7/00 ,  G06N20/00

    Abstract: According to some embodiments, a system, method and non-transitory computer-readable medium are provided comprising a Hypothesis Generation Engine (HGE) to receive one or more property target values for a material; a memory for storing program instructions; an HGE processor, coupled to the memory, and in communication with the HGE, and operative to execute program instructions to: receive the one or more property target values for the material; analyze the one or more property target values as compared to one or more known values in a knowledge base; generate, based on the analysis, an initial set of hypothetical structures, wherein each hypothetical structure includes at least one property target value; execute a likelihood model for each candidate material to generate a likelihood probability for each hypothetical structure, wherein the likelihood probability is a measure of the likelihood that the hypothetical structure will have the target property value; convert each hypothetical structure into a natural language representation; execute an abduction kernel on the natural language representation with the at least one likelihood probability, to output at least one proposed structure that satisfies a likelihood threshold for having the property target value; and receive the output of the executed abduction kernel at a testing module to determine whether the output satisfies the property target values. Numerous other aspects are provided.

    SYSTEMS AND METHOD FOR DISTRIBUTED ENERGY RESOURCES POWER ESTIMATION
    3.
    发明申请
    SYSTEMS AND METHOD FOR DISTRIBUTED ENERGY RESOURCES POWER ESTIMATION 有权

    公开(公告)号：US20240405565A1

    公开(公告)日：2024-12-05

    申请号：US18327594

    申请日：2023-06-01

    Applicant: General Electric Company

    Inventor： Bojun Feng ,  Nurali Virani ,  Honggang Wang ,  Benoit Christophe ,  Kiran Kumar Pratapagiri

    IPC: H02J3/38 ,  H02J3/00

    Abstract: A system for predicting performance of electric power generation and delivery systems is provided. The system includes a computing device including at least one processor in communication with at least one memory. The at least one processor is programmed to store a first plurality of attribute data for a plurality of measured assets attached to a grid, store a plurality of constraints for matching measured assets to unmeasured assets, receive a second plurality of attribute data for an unmeasured asset attached to the grid, compare the first plurality of attribute data to the second plurality of attribute data and the plurality of constraints associated with the unmeasured asset, determine a measured asset of the plurality of measured assets to assign to the unmeasured asset based on the comparison, and determine a performance forecast for the unmeasured asset based on a power performance of the determined measured asset.

    SYSTEMS AND METHOD FOR MASKED MULTI-STEP MULTIVARIATE TIME SERIES POWER FORCASTING AND ESTIMATION
    4.
    发明公开
    SYSTEMS AND METHOD FOR MASKED MULTI-STEP MULTIVARIATE TIME SERIES POWER FORCASTING AND ESTIMATION 审中-公开

    公开(公告)号：US20240054348A1

    公开(公告)日：2024-02-15

    申请号：US18327619

    申请日：2023-06-01

    Applicant: General Electric Company

    Inventor： Yiwei Fu ,  Nurali Virani ,  Honggang Wang ,  Benoit Christophe

    IPC: G06N3/0895

    CPC classification number: G06N3/0895

    Abstract: A system includes a computing device including at least one processor in communication with at least one memory. The at least one processor is programmed to (a) store a plurality of historical time series data; (b) randomly select a sequence; (c) randomly select a mask length for a mask for the selected sequence; (d) apply the mask to the selected sequence, wherein the mask is applied to the plurality of forecast variables in the selected sequence; (e) execute a model with the masked selected sequence to generate predictions for the masked forecast variables; (f) compare the predictions for the masked forecast variables to the actual forecast variables in the selected sequence; (g) determine if convergence occurs based upon the comparison; and (h) if convergence has not occurred, update one or more parameters of the model and return to step b.

    Fast identification of trustworthy deep neural networks
    6.
    发明授权
    Fast identification of trustworthy deep neural networks 有权

    公开(公告)号：US11625483B2

    公开(公告)日：2023-04-11

    申请号：US16887623

    申请日：2020-05-29

    Applicant: General Electric Company

    Inventor： Johan Reimann ,  Nurali Virani ,  Naresh Iyer ,  Zhaoyuan Yang

    IPC: G06F21/56 ,  G06N7/00 ,  G06N3/08 ,  G06K9/62 ,  G06N3/084 ,  G06N20/10

    Abstract: A system and method including receiving a set of deep neural networks (DNN) including DNNs trained with an embedded trojan and DNNs trained without any embedded trojan, each of the trained DNNs being represented by a mathematical formulation learned by the DNNs and expressing a relationship between an input of the DNNs and an output of the DNNs; extracting at least one characteristic feature from the mathematical formulation of each of the trained DNNs; statistically analyzing the at least one characteristic feature to determine whether there is a difference between the DNNs trained with the embedded trojan and the DNNs trained without any embedded trojan; generating, in response to the determination indicating there is a difference, a detector model to execute the statistical analyzing on deep neural networks; and storing a file including the generated detector model in a memory device.

    Method for controlling operation of a wind turbine
    7.
    发明授权
    Method for controlling operation of a wind turbine 有权

    公开(公告)号：US10605228B2

    公开(公告)日：2020-03-31

    申请号：US16105076

    申请日：2018-08-20

    Applicant: General Electric Company

    Inventor： Scott Charles Evans ,  Sara Simonne Louisa Delport ,  Samuel Davoust ,  Nurali Virani ,  Samuel Bryan Shartzer

    IPC: F03D7/04 ,  F03D17/00 ,  F03D7/02

    Abstract: A method for controlling operation of a wind turbine includes collecting training data, training a machine learning model, obtaining recent data, and applying the machine learning model the recent data to output a reference power or reference power differential corresponding to the recent data. The machine learning model is then applied to the recent data to output at least one of estimated power or estimated power differential corresponding to values of the pitch setpoints and the tip speed ratio setpoints which differ from the recent data. A turbine setting is determined by comparing the estimated power or estimated power differential to the reference power or reference power differential, and then applying the turbine setting to the wind turbine if the estimated power or estimated power differential is greater than or equal to a threshold amount above the reference power or reference power differential.

    Systems and methods for continuous machine learning based control of wind turbines
    8.
    发明授权
    Systems and methods for continuous machine learning based control of wind turbines 有权

    公开(公告)号：US11060504B1

    公开(公告)日：2021-07-13

    申请号：US16784798

    申请日：2020-02-07

    Applicant: General Electric Company

    Inventor： Nurali Virani ,  Scott Charles Evans ,  Samuel Davoust ,  Samuel Bryan Shartzer ,  Dhiraj Arora

    IPC: F03D7/04 ,  F03D7/02 ,  F03D17/00 ,  G06N3/08 ,  G06N20/00

    Abstract: A control system is disclosed. The control system includes a wind turbine, at least one sensor configured to detect at least one property of the wind turbine to generate measurement data, and a controller communicatively coupled to the wind turbine and the at least one sensor. The controller includes at least one processor in communication with at least one memory device. The at least one processor is configured to control, during a training phase, the wind turbine according to at least one test parameter, receive, from the at least one sensor, during the training phase, first measurement data, generate, based on the at least one test parameter and the received first measurement data, a control model, receive, during an operating phase, second measurement data from the at least one sensor, and update the control model continuously based on the second measurement data.

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