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    • 13. 发明授权
    • Method and system for efficient data collection and storage
    • 高效数据采集和存储的方法和系统
    • US08116936B2
    • 2012-02-14
    • US11860626
    • 2007-09-25
    • John Erik HersheyJeanette Marie BrunoBrock Estel OsbornNaresh Sundaram IyerCharles Larry AbernathyMichael Dean Fullington
    • John Erik HersheyJeanette Marie BrunoBrock Estel OsbornNaresh Sundaram IyerCharles Larry AbernathyMichael Dean Fullington
    • F02D45/00
    • F02D41/249F02D41/28F02D2041/285F02D2041/288
    • A system for collecting and storing performance data for an engine is provided. The system includes one or more sensors configured to generate sensor data signals representative of one or more engine data performance parameters. The system further includes a data sampling component, a data quantizing component, a data storage sampling rate component, a data encoding component and a data storage component. The data sampling component is configured to sample the sensor data signals at a data sampling rate. The data quantizing component is configured to generate quantized data samples corresponding to the sampled sensor data signals. The data storage sampling rate component is configured to determine a data storage sampling rate for the quantized data samples, based on an analysis of at least a subset of the quantized data samples. The data encoding component is configured to encode the quantized data samples according to the data storage sampling rate, and the data storage component is configured to store the encoded data samples from the encoding component.
    • 提供了一种用于收集和存储发动机性能数据的系统。 该系统包括配置成产生代表一个或多个引擎数据性能参数的传感器数据信号的一个或多个传感器。 该系统还包括数据采样组件,数据量化组件,数据存储采样率组件,数据编码组件和数据存储组件。 数据采样组件被配置为以数据采样率对传感器数据信号进行采样。 数据量化部件被配置为产生对应于采样的传感器数据信号的量化数据采样。 数据存储采样速率分量被配置为基于对量化数据样本的至少一个子集的分析来确定量化数据采样的数据存储采样率。 数据编码部件被配置为根据数据存储采样率对量化的数据样本进行编码,并且数据存储部件被配置为存储来自编码部件的编码数据样本。
    • 14. 发明授权
    • System and process for a fusion classification for insurance underwriting suitable for use by an automated system
    • 用于融合分类的系统和过程,适用于自动化系统使用的保险承保
    • US07383239B2
    • 2008-06-03
    • US10425721
    • 2003-04-30
    • Piero Patrone BonissoneKareem Sherif AggourRajesh Venkat SubbuWeizhong YanNaresh Sundaram IyerAnindya Chakraborty
    • Piero Patrone BonissoneKareem Sherif AggourRajesh Venkat SubbuWeizhong YanNaresh Sundaram IyerAnindya Chakraborty
    • G06F17/00G06N5/02
    • G06Q40/08G06Q40/00
    • A method and system for fusing a collection of classifiers used for an automated insurance underwriting system and/or its quality assurance is described. Specifically, the outputs of a collection of classifiers are fused. The fusion of the data will typically result in some amount of consensus and some amount of conflict among the classifiers. The consensus will be measured and used to estimate a degree of confidence in the fused decisions. Based on the decision and degree of confidence of the fusion and the decision and degree of confidence of the production decision engine, a comparison module may then be used to identify cases for audit, cases for augmenting the training/test sets for re-tuning production decision engine, cases for review, or may simply trigger a record of its occurrence for tracking purposes. The fusion can compensate for the potential correlation among the classifiers. The reliability of each classifier can be represented by a static or dynamic discounting factor, which will reflect the expected accuracy of the classifier. A static discounting factor is used to represent a prior expectation about the classifier's reliability, e.g., it might be based on the average past accuracy of the model, while a dynamic discounting is used to represent a conditional assessment of the classifier's reliability, e.g., whenever a classifier bases its output on an insufficient number of points it is not reliable.
    • 描述用于融合用于自动保险承保系统的分类器集合和/或其质量保证的方法和系统。 具体来说,分类器的集合的输出被融合。 数据的融合通常会导致一些共识和分类器之间的一些冲突。 共识将被测量并用于估计融合决策的信心程度。 根据融合的决定和信心程度以及生产​​决策引擎的决策和决策程度,然后可以使用比较模块来识别审计案例,增加用于重新调整生产的培训/测试集的案例 决策引擎,审查案例,或者可以简单地触发其发生记录以进行跟踪。 融合可以补偿分类器之间的潜在相关性。 每个分类器的可靠性可以由静态或动态折扣因子表示,这将反映分类器的预期准确性。 静态折扣因子用于表示对分类器的可靠性的先前期望,例如,可以基于模型的平均过去精度,而使用动态贴现来表示分类器的可靠性的条件评估,例如,每当 分类器的输出基于不可靠的点数不足。
    • 17. 发明授权
    • System and process for a fusion classification for insurance underwriting suitable for use by an automated system
    • 用于融合分类的系统和过程,适用于自动化系统使用的保险承保
    • US08214314B2
    • 2012-07-03
    • US12131545
    • 2008-06-02
    • Piero Patrone BonissoneKareem Sherif AggourRajesh Venkat SubbuWeizhong YanNaresh Sundaram IyerAnindya Chakraborty
    • Piero Patrone BonissoneKareem Sherif AggourRajesh Venkat SubbuWeizhong YanNaresh Sundaram IyerAnindya Chakraborty
    • G06F17/00G06N5/02
    • G06Q40/08G06Q40/00
    • A method and system for fusing a collection of classifiers used for an automated insurance underwriting system and/or its quality assurance is described. Specifically, the outputs of a collection of classifiers are fused. The fusion of the data will typically result in some amount of consensus and some amount of conflict among the classifiers. The consensus will be measured and used to estimate a degree of confidence in the fused decisions. Based on the decision and degree of confidence of the fusion and the decision and degree of confidence of the production decision engine, a comparison module may then be used to identify cases for audit, cases for augmenting the training/test sets for re-tuning production decision engine, cases for review, or may simply trigger a record of its occurrence for tracking purposes. The fusion can compensate for the potential correlation among the classifiers. The reliability of each classifier can be represented by a static or dynamic discounting factor, which will reflect the expected accuracy of the classifier. A static discounting factor is used to represent a prior expectation about the classifier's reliability, e.g., it might be based on the average past accuracy of the model, while a dynamic discounting is used to represent a conditional assessment of the classifier's reliability, e.g., whenever a classifier bases its output on an insufficient number of points it is not reliable.
    • 描述用于融合用于自动保险承保系统的分类器集合和/或其质量保证的方法和系统。 具体来说,分类器的集合的输出被融合。 数据的融合通常会导致一些共识和分类器之间的一些冲突。 共识将被测量并用于估计融合决策的信心程度。 根据融合的决定和信心程度以及生产​​决策引擎的决策和决策程度,然后可以使用比较模块来识别审计案例,增加用于重新调整生产的培训/测试集的案例 决策引擎,审查案例,或者可以简单地触发其发生记录以进行跟踪。 融合可以补偿分类器之间的潜在相关性。 每个分类器的可靠性可以由静态或动态折扣因子表示,这将反映分类器的预期准确性。 静态折扣因子用于表示对分类器的可靠性的先前期望,例如,可以基于模型的平均过去精度,而使用动态贴现来表示分类器的可靠性的条件评估,例如,每当 分类器的输出基于不可靠的点数不足。
    • 19. 发明申请
    • STORAGE MODEL FOR MAINTAINING STRUCTURED DOCUMENT FIDELITY
    • 用于维护结构化文档的存储模式
    • US20100228827A1
    • 2010-09-09
    • US12396472
    • 2009-03-03
    • Dana B. BirkbyAlexey GalataNaresh SundaramKarim M. BatthishVinayak Morada
    • Dana B. BirkbyAlexey GalataNaresh SundaramKarim M. BatthishVinayak Morada
    • G06F15/16
    • H04L51/066
    • Architecture that introduces storage of an extra (skeleton) property of a document as well as default document properties on a server. In a specific messaging implementation, a MIME skeleton property is stamped on an arriving MIME messages. An incoming MIME message is shredded and all content that is currently saved to MAPI properties continues to be saved. The remaining message content that is not saved to the MAPI properties is stored in the skeleton property. The skeleton property includes all body part headers and any body part content that was not saved as a property on the item by the server. On retrieval of this message by a MIME client, the MIME message is regenerated in full fidelity by using the default set of properties in combination with the skeleton (or extra) property and the stored body content.
    • 引入文档的额外(骨架)属性的存储以及服务器上的默认文档属性的体系结构。 在特定的消息传递实现中,MIME骨架属性在到达的MIME消息上被标记。 传入的MIME消息被切割,并且当前保存到MAPI属性的所有内容继续保存。 未保存到MAPI属性的剩余消息内容存储在骨架属性中。 骨架属性包括所有身体部位标题和服务器未被保存为项目上的属性的任何正文部分内容。 在通过MIME客户端检索此消息时,通过使用与骨架(或额外)属性和存储的身体内容相结合的默认属性集,完全保真地重新生成MIME消息。