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    METHODS AND SYSTEMS FOR NETWORK-BASED DETECTION OF COMPONENT WEAR
    23.
    发明申请
    METHODS AND SYSTEMS FOR NETWORK-BASED DETECTION OF COMPONENT WEAR 审中-公开

    公开(公告)号：US20170090458A1

    公开(公告)日：2017-03-30

    申请号：US14868856

    申请日：2015-09-29

    Applicant: General Electric Company

    Inventor： Ser Nam Lim ,  David Scott Diwinsky ,  Russell Robert Irving

    IPC: G05B19/4065

    CPC classification number: G05B23/0232 ,  G05B19/406 ,  G05B19/4065 ,  G05B2219/50276

    Abstract: A monitoring system for monitoring a plurality of components is provided. The monitoring system includes a plurality of client systems. The plurality of client systems is configured to generate a plurality of component status reports. The plurality of component status reports is associated with the plurality of components. The monitoring system also includes a component wear monitoring (CWM) computer device configured to receive the plurality of component status reports from the plurality of client systems, generate component status information based on a plurality of component status reports, aggregate the component status information to identify a plurality of images associated with a first component, and compare the plurality of images associated with the first component. The plurality of images represents the first component at different points in time. The CWM computer device is also configured to determine a state of the first component based on the comparison.

    Image analysis neural network systems
    26.
    发明授权
    Image analysis neural network systems 有权

    公开(公告)号：US10546242B2

    公开(公告)日：2020-01-28

    申请号：US15495313

    申请日：2017-04-24

    Applicant: General Electric Company

    Inventor： Arpit Jain ,  Swaminathan Sankaranarayanan ,  David Scott Diwinsky ,  Ser Nam Lim ,  Kari Thompson

    IPC: G06N7/00 ,  G06K9/62 ,  G06N3/08

    Abstract: A method includes determining object class probabilities of pixels in a first input image by examining the first input image in a forward propagation direction through layers of artificial neurons of an artificial neural network. The object class probabilities indicate likelihoods that the pixels represent different types of objects in the first input image. The method also includes selecting, for each of two or more of the pixels, an object class represented by the pixel by comparing the object class probabilities of the pixels with each other, determining an error associated with the object class that is selected for each pixel of the two or more pixels, determining one or more image perturbations by back-propagating the errors associated with the object classes selected for the pixels of the first input image through the layers of the neural network without modifying the neural network, and modifying a second input image by applying the one or more image perturbations to one or more of the first input image or the second input image prior to providing the second input image to the neural network for examination by the neurons in the neural network for automated object recognition in the second input image.

    Optical flow determination system
    27.
    发明授权
    Optical flow determination system 有权

    公开(公告)号：US10482609B2

    公开(公告)日：2019-11-19

    申请号：US15478784

    申请日：2017-04-04

    Applicant: General Electric Company

    Inventor： Ser Nam Lim ,  Mustafa Devrim Kaba ,  Mustafa Uzunbas ,  David Diwinsky

    IPC: G06T7/215 ,  G06N3/04 ,  G06N3/08 ,  G06T7/277

    Abstract: A generative adversarial network (GAN) system includes a generator sub-network configured to examine images of an object moving relative to a viewer of the object. The generator sub-network also is configured to generate one or more distribution-based images based on the images that were examined. The system also includes a discriminator sub-network configured to examine the one or more distribution-based images to determine whether the one or more distribution-based images accurately represent the object. A predicted optical flow of the object is represented by relative movement of the object as shown in the one or more distribution-based images.

    Robotic sensing apparatus and methods of sensor planning
    28.
    发明授权
    Robotic sensing apparatus and methods of sensor planning 有权

    公开(公告)号：US10265850B2

    公开(公告)日：2019-04-23

    申请号：US15342500

    申请日：2016-11-03

    Applicant: General Electric Company

    Inventor： Ser Nam Lim ,  David Scott Diwinsky ,  Xiao Bian ,  Wayne Ray Grady ,  Mustafa Gokhan Uzunbas ,  Mustafa Devrim Kaba

    IPC: B25J9/16 ,  G01B11/24 ,  G05B19/401 ,  G05D1/00 ,  G06T7/00

    Abstract: The present disclosure is directed to a computer-implemented method of sensor planning for acquiring samples via an apparatus including one or more sensors. The computer-implemented method includes defining, by one or more computing devices, an area of interest; identifying, by the one or more computing devices, one or more sensing parameters for the one or more sensors; determining, by the one or more computing devices, a sampling combination for acquiring a plurality of samples by the one or more sensors based at least in part on the one or more sensing parameters; and providing, by the one or more computing devices, one or more command control signals to the apparatus including the one or more sensors to acquire the plurality of samples of the area of interest using the one or more sensors based at least on the sampling combination.

    Neural network point cloud generation system
    29.
    发明授权
    Neural network point cloud generation system 有权

    公开(公告)号：US10262243B2

    公开(公告)日：2019-04-16

    申请号：US15604012

    申请日：2017-05-24

    Applicant: General Electric Company

    Inventor： Ser Nam Lim ,  Jingjing Zheng ,  Jiajia Luo ,  David Scott Diwinsky

    IPC: G06K9/62 ,  G06K9/66 ,  G06T3/40 ,  G06T7/00

    Abstract: A system includes one or more processors and a memory that stores a generative adversarial network (GAN). The one or more processors are configured to receive a low resolution point cloud comprising a set of three-dimensional (3D) data points that represents an object. A generator of the GAN is configured to generate a first set of generated data points based at least in part on one or more characteristics of the data points in the low resolution point cloud, and to interpolate the generated data points into the low resolution point cloud to produce a super-resolved point cloud that represents the object and has a greater resolution than the low resolution point cloud. The one or more processors are further configured to analyze the super-resolved point cloud for detecting one or more of an identity of the object or damage to the object.

    MACHINE LEARNING SYSTEM FOR IN-SITU RECOGNITION OF COMMON LOCATIONS IN A ROTATABLE BODY WITH REPEATING SEGMENTS
    30.
    发明申请
    MACHINE LEARNING SYSTEM FOR IN-SITU RECOGNITION OF COMMON LOCATIONS IN A ROTATABLE BODY WITH REPEATING SEGMENTS 审中-公开

    公开(公告)号：US20190095765A1

    公开(公告)日：2019-03-28

    申请号：US15714208

    申请日：2017-09-25

    Applicant: General Electric Company

    Inventor： Ser Nam Lim ,  David Scott Diwinsky

    IPC: G06K9/68 ,  F01D5/00 ,  F01D21/00 ,  G06K9/62

    Abstract: A system includes one or more processors configured to automatically identify different distressed portions in repeating segments of a rotating body. At least one of a size and/or a shape of one or more of the distressed portions changes with respect to time. The one or more processors also are configured to determine a pattern of the different distressed portions in the repeating segments of the rotating body during rotation of the rotating body based on identifying the different distressed portions. The one or more processors also are configured to subsequently automatically identify locations of individual segments of the repeating segments in the rotating body using the pattern of the distressed portions that is determined.

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