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公开(公告)号:US20190236352A1
公开(公告)日:2019-08-01
申请号:US16376827
申请日:2019-04-05
Applicant: CHEVRON U.S.A. INC.
Inventor: Paul Duke , Shuxing Cheng
CPC classification number: G06K9/00476 , G06K9/00456 , G06K9/6218 , G06K9/6256 , G06K9/6262 , G06K9/6271 , G06K2209/01 , G06N3/04 , G06N3/0454 , G06N3/08 , G06N5/003 , G06N20/10 , G06N20/20
Abstract: Systems and methods for identifying patterns of symbols in standardized system diagrams are disclosed. Disclosed implementations obtain or synthetically generate a symbol recognition training data set including multiple training images, generate a symbol recognition model based on the symbol recognition training data set, obtain an image comprising a pattern of symbols, group symbols into process loops based on the logical relationships captured by process loop identification algorithm, apply a character classification model to image contours to identify the characters and group characters into tags via hierarchical clustering, and store the identified tags, symbols and identified process loops in a relational database.
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公开(公告)号:US11320357B2
公开(公告)日:2022-05-03
申请号:US16724830
申请日:2019-12-23
Applicant: Chevron U.S.A. Inc.
Abstract: A method is described for training a model that refines estimated parameter values within core images is disclosed. The method includes receiving multiple training image pairs wherein each training image pair includes: (i) an unrefined core image of a rock sample to be used for estimating rock properties, and (ii) a refined core image of the same rock sample; generating a training dataset from the multiple training image pairs; receiving an initial core model; generating a conditioned core model by training, using the multiple training image pairs, the initial core model; and storing the conditioned core model in electronic storage. The conditioned core model may be applied to an initial target core image data set to generate a refined target sore image dataset. The method may be executed by a computer system.
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公开(公告)号:US20190080164A1
公开(公告)日:2019-03-14
申请号:US16131946
申请日:2018-09-14
Applicant: CHEVRON U.S.A. INC.
Inventor: Paul Duke , Shuxing Cheng
Abstract: Systems and methods for categorizing patterns of characters in a document by utilizing machine based learning techniques include generating character classification training data, building a character classification model based on the character classification training data; obtaining an image that includes a pattern of characters, the characters including one or more contours, applying the character classification model to the image to classify the contours, and applying the labels to clusters of the contours.
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公开(公告)号:US11295123B2
公开(公告)日:2022-04-05
申请号:US16131946
申请日:2018-09-14
Applicant: CHEVRON U.S.A. INC.
Inventor: Paul Duke , Shuxing Cheng
Abstract: Systems and methods for categorizing patterns of characters in a document by utilizing machine based learning techniques include generating character classification training data, building a character classification model based on the character classification training data; obtaining an image that includes a pattern of characters, the characters including one or more contours, applying the character classification model to the image to classify the contours, and applying the labels to clusters of the contours.
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公开(公告)号:US11195007B2
公开(公告)日:2021-12-07
申请号:US16376827
申请日:2019-04-05
Applicant: CHEVRON U.S.A. INC.
Inventor: Paul Duke , Shuxing Cheng
Abstract: Systems and methods for identifying patterns of symbols in standardized system diagrams are disclosed. Disclosed implementations obtain or synthetically generate a symbol recognition training data set including multiple training images, generate a symbol recognition model based on the symbol recognition training data set, obtain an image comprising a pattern of symbols, group symbols into process loops based on the logical relationships captured by process loop identification algorithm, apply a character classification model to image contours to identify the characters and group characters into tags via hierarchical clustering, and store the identified tags, symbols and identified process loops in a relational database.
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