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Patent Agency Ranking
公开(公告)号：US20150001420A1
公开(公告)日：2015-01-01
申请号：US13927543
申请日：2013-06-26
Applicant: General Electric Company
Inventor： Rajesh Veera Venkata Lakshmi Langoju , Abhijit Vishwas Patil , Sridhar Dasaratha , Vignesh Thirukazhukundram Subrahmaniam , Dmitry Vladimirovich Dylov , Siavash Yazdanfar , Stephen B. Solomon
IPC: G01N21/64
CPC classification number: G01N21/31 , A61B5/0075 , A61B5/0084 , A61B5/14546 , A61B5/1455 , A61B5/6849
Abstract: A method for classifying a tissue sample of a biopsy specimen into one of a plurality of classes is presented. The method includes receiving a light from at least one location of the tissue sample including a plurality of chromophores, wherein the received light comprises at least one of an attenuated illumination light and a re-emitted light. The method further includes processing a spectrum of the received light to determine a feature for each of the chromophores in the at least one location of the tissue sample. In addition, the method includes estimating a Z-score for each of the chromophores based on the determined feature. Also, the method includes classifying the tissue sample into one of the plurality of classes based on the estimated Z-score for each of the chromophores.
Abstract translation: 提出了将活检标本的组织样本分类为多个类别之一的方法。 该方法包括从包括多个发色团的组织样本的至少一个位置接收光，其中所接收的光包括衰减照明光和再发射光中的至少一个。 该方法还包括处理所接收的光的光谱以确定组织样本的至少一个位置中的每个发色团的特征。 此外，该方法包括基于所确定的特征来估计每个发色团的Z分数。 此外，该方法包括基于每个发色团的估计的Z分数将组织样本分类为多个类中的一个。
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公开(公告)号：US10443577B2
公开(公告)日：2019-10-15
申请号：US15198749
申请日：2016-06-30
Applicant: General Electric Company
Inventor： Krishna Kumar Swaminathan , Deepak Raj Sagi , Pritesh Jain , Sridhar Dasaratha , Nitika Bhaskar , Rahul Kumar Srivastava , Milesh Shrichandra Gogad
Abstract: A wind power generation system includes one or both of a memory or storage device storing one or more processor-executable executable routines, and one or more processors configured to execute the one or more executable routines which, when executed, cause acts to be performed. The acts include receiving weather data, wind turbine system data, or a combination thereof; transforming the weather data, the wind turbine system data, or the combination thereof, into a data subset, wherein the data subset comprises a first time period data; selecting one or more wind power system models from a plurality of models; transforming the one or more wind power system models into one or more trained models at least partially based on the data subset; and executing the one or more trained models to derive a forecast, wherein the forecast comprises a predicted electrical power production for the wind power system.
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公开(公告)号：US10975846B2
公开(公告)日：2021-04-13
申请号：US15210008
申请日：2016-07-14
Applicant: General Electric Company
Inventor： Rahul Kumar Srivastava , Krishna Kumar Swaminathan , Sridhar Dasaratha , Shishir Goel , Milesh Shrichandra Gogad , Nitika Bhaskar , Pritesh Jain
Abstract: The present discussion relates to generating power generation forecasts both on-site and remote to a wind farm, or other intermittent power generation asset, so as to increase the reliability of providing a forecast to interested parties, such as regulatory authorities. Forecasts may be separately generated at both the on-site and remote locations and, if both are available, one is selected for transmission to interested parties, such as regulatory authorities. If, due to circumstances, one forecast is unavailable, the other forecast may be used in its place locally and remotely, communications permitting.
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4.发明申请公开(公告)号：US20160178414A1
公开(公告)日：2016-06-23
申请号：US14574234
申请日：2014-12-17
Applicant: General Electric Company
Inventor： Angshuman Saha , Sridhar Dasaratha , Nitika Bhaskar , Janna Lindenberg , Richard Vagliani
CPC classification number: G01D21/00 , G05B23/0216
Abstract: Embodiments allow data cleaning of industrial data gathered from at least one sensor. The data cleaning utilizes a workflow that defines at least one cleaning step to be performed. Each cleaning step comprises detecting defects based on at least one constraint such as various models and/or statistics. Potential defects are presented to a user for feedback. The data is cleaned based on the feedback. Multiple copies of the data are stored to track all the various cleaning choices. All choices can be rolled back at will so that cleaning decisions made can be eliminated and different choices applied. Intermediate data is captured to allow reporting and auditing of the cleaning process.
Abstract translation: 实施例允许从至少一个传感器收集的工业数据的数据清洁。 数据清理利用了定义要执行的至少一个清洁步骤的工作流程。 每个清洁步骤包括基于诸如各种模型和/或统计的至少一个约束来检测缺陷。 将潜在的缺陷提交给用户进行反馈。 数据根据反馈进行清理。 存储数据的多个副本以跟踪所有各种清洁选择。 所有选择都可以随意回滚，从而可以消除所做出的清洁决定并应用不同的选择。 捕获中间数据以允许对清洁过程进行报告和审核。
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公开(公告)号：US08912512B1
公开(公告)日：2014-12-16
申请号：US13927543
申请日：2013-06-26
Applicant: General Electric Company
Inventor： Rajesh Veera Venkata Lakshmi Langoju , Abhijit Vishwas Patil , Sridhar Dasaratha , Vignesh Thirukazhukundram Subrahmaniam , Dmitry Vladimirovich Dylov , Siavash Yazdanfar , Stephen B. Solomon
IPC: G01N21/64
CPC classification number: G01N21/31 , A61B5/0075 , A61B5/0084 , A61B5/14546 , A61B5/1455 , A61B5/6849
Abstract: A method for classifying a tissue sample of a biopsy specimen into one of a plurality of classes is presented. The method includes receiving a light from at least one location of the tissue sample including a plurality of chromophores, wherein the received light comprises at least one of an attenuated illumination light and a re-emitted light. The method further includes processing a spectrum of the received light to determine a feature for each of the chromophores in the at least one location of the tissue sample. In addition, the method includes estimating a Z-score for each of the chromophores based on the determined feature. Also, the method includes classifying the tissue sample into one of the plurality of classes based on the estimated Z-score for each of the chromophores.
Abstract translation: 提出了将活检标本的组织样本分类为多个类别之一的方法。 该方法包括从包括多个发色团的组织样本的至少一个位置接收光，其中所接收的光包括衰减照明光和再发射光中的至少一个。 该方法还包括处理所接收的光的光谱以确定组织样本的至少一个位置中的每个发色团的特征。 此外，该方法包括基于所确定的特征来估计每个发色团的Z分数。 此外，该方法包括基于每个发色团的估计的Z分数将组织样本分类为多个类中的一个。
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