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公开(公告)号：US10268913B2
公开(公告)日：2019-04-23
申请号：US15477517
申请日：2017-04-03
Applicant: General Electric Company
Inventor： Ser Nam Lim , Arpit Jain , David Diwinsky , Sravanthi Bondugula , Yen-Liang Lin , Xiao Bian
Abstract: A generative adversarial network (GAN) system includes a generator sub-network configured to examine one or more images of actual damage to equipment. The generator sub-network also is configured to create one or more images of potential damage based on the one or more images of actual damage that were examined. The GAN system also includes a discriminator sub-network configured to examine the one or more images of potential damage to determine whether the one or more images of potential damage represent progression of the actual damage to the equipment.
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公开(公告)号：US20180322366A1
公开(公告)日：2018-11-08
申请号：US15584129
申请日：2017-05-02
Applicant: General Electric Company
Inventor： Ser Nam Lim , Arpit Jain , David Scott Diwinsky , Sravanthi Bondugula
CPC classification number: G06K9/6259 , G06K9/6256 , G06K9/66 , G06T7/0004 , G06T7/0008 , G06T2207/20081 , G06T2207/20084 , G06T2207/30156 , G06T2207/30164
Abstract: A system that generates training images for neural networks includes one or more processors configured to receive input representing one or more selected areas in an image mask. The one or more processors are configured to form a labeled masked image by combining the image mask with an unlabeled image of equipment. The one or more processors also are configured to train an artificial neural network using the labeled masked image to one or more of automatically identify equipment damage appearing in one or more actual images of equipment and/or generate one or more training images for training another artificial neural network to automatically identify the equipment damage appearing in the one or more actual images of equipment.
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公开(公告)号：US20180286034A1
公开(公告)日：2018-10-04
申请号：US15477517
申请日：2017-04-03
Applicant: General Electric Company
Inventor： Ser Nam Lim , Arpit Jain , David Diwinsky , Sravanthi Bondugula , Yen-Liang Lin , Xiao Bian
IPC: G06T7/00
CPC classification number: G06K9/00973 , G06K9/00771 , G06K9/6284 , G06K9/6296 , G06N3/0454 , G06T2207/20081 , G06T2207/20084 , G06T2207/30164
Abstract: A generative adversarial network (GAN) system includes a generator sub-network configured to examine one or more images of actual damage to equipment. The generator sub-network also is configured to create one or more images of potential damage based on the one or more images of actual damage that were examined. The GAN system also includes a discriminator sub-network configured to examine the one or more images of potential damage to determine whether the one or more images of potential damage represent progression of the actual damage to the equipment.
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公开(公告)号：US10262236B2
公开(公告)日：2019-04-16
申请号：US15584129
申请日：2017-05-02
Applicant: General Electric Company
Inventor： Ser Nam Lim , Arpit Jain , David Scott Diwinsky , Sravanthi Bondugula
Abstract: A system that generates training images for neural networks includes one or more processors configured to receive input representing one or more selected areas in an image mask. The one or more processors are configured to form a labeled masked image by combining the image mask with an unlabeled image of equipment. The one or more processors also are configured to train an artificial neural network using the labeled masked image to one or more of automatically identify equipment damage appearing in one or more actual images of equipment and/or generate one or more training images for training another artificial neural network to automatically identify the equipment damage appearing in the one or more actual images of equipment.
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