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Machine learning models for detecting the causes of conditions of a satellite communication system

机译:用于检测卫星通信系统状况原因的机器学习模型

摘要

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training and using machine learning models to detect problems in a satellite communication system. In some implementations, one or more feature vectors that respectively correspond to different times are obtained. The feature vector(s) are provided as input to one or more machine learning models trained to receive at least one feature vector that includes feature values representing properties of the satellite communication system and output an indication of potential causes of a condition of the satellite communication system based on the properties of the satellite communication system. A particular cause that is indicated as being a most likely cause of the condition of the satellite communication system is determined based on one or more machine learning model outputs received from each of the one or more machine learning models.
机译:方法,系统和装置,包括编码在计算机存储介质上的计算机程序,用于训练和使用机器学习模型来检测卫星通信系统中的问题。在一些实施方式中,获得分别对应于不同时间的一个或多个特征向量。提供一个或多个特征向量作为一个或多个机器学习模型的输入,这些模型被训练为接收至少一个特征向量,该特征向量包括代表卫星通信系统特性的特征值并输出指示卫星通信状况的潜在原因基于卫星通信系统的属性的系统。基于从一个或多个机器学习模型中的每一个接收的一个或多个机器学习模型输出,确定指示为卫星通信系统状况的最可能原因的特定原因。

著录项

  • 公开/公告号US10594027B1

    专利类型

  • 公开/公告日2020-03-17

    原文格式PDF

  • 申请/专利权人 HUGHES NETWORK SYSTEMS LLC;

    申请/专利号US201816118836

  • 发明设计人 AMIT ARORA;ARCHANA GHARPURAY;JOHN KENYON;

    申请日2018-08-31

  • 分类号H01Q1/28;G01C21/20;G06K9/46;H04B7/185;

  • 国家 US

  • 入库时间 2022-08-21 11:30:23

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