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    • 6. 发明申请
    • SYSTEM ARCHITECTURE AND PROCESS FOR SEAMLESS ADAPTATION TO CONTEXT AWARE BEHAVIOR MODELS
    • 系统架构和无缝适应过程以突出特征行为模型
    • US20090210373A1
    • 2009-08-20
    • US12034164
    • 2008-02-20
    • Juan YuHasan Timucin OzdemirKuo Chu Lee
    • Juan YuHasan Timucin OzdemirKuo Chu Lee
    • G06N5/02
    • G06K9/00771A61B5/1128A61B5/16A61B5/7267G06K9/00785
    • A surveillance system implements an architecture and process to support real-time abnormal behavior assessment operations in a distributed scalable sensor network. An automated behavior model builder generates behavior models from sensor data. A plurality of abnormal behavior scoring engines operating concurrently to generate abnormal behavior assessment models by scoring the behavior models. An execution performance manager performs fast switching of behavior models for the abnormal behavior scoring engines. The execution performance manager performs detection of abnormal behavior score distribution characteristic deviation by comparing a current abnormal behavior assessment model to a pre-recorded abnormal behavior assessment model. The execution performance manager selects a pre-recorded behavior model for the abnormal behavior scoring engines when the deviation exceeds a predetermined threshold.
    • 监控系统实现一种架构和过程,以支持分布式可扩展传感器网络中的实时异常行为评估操作。 自动行为模型构建器从传感器数据生成行为模型。 多个异常行为评分引擎同时运行,通过评分行为模型来产生异常行为评估模型。 执行性能管理员可以快速切换异常行为评分引擎的行为模型。 执行绩效管理者通过将当前的异常行为评估模型与预先记录的异常行为评估模型进行比较来执行异常行为评分分布特征偏差的检测。 当偏差超过预定阈值时,执行性能管理器为异常行为评分引擎选择预先记录的行为模型。
    • 9. 发明授权
    • System architecture and process for seamless adaptation to context aware behavior models
    • 系统架构和过程,用于无缝适应上下文感知行为模型
    • US07962435B2
    • 2011-06-14
    • US12034164
    • 2008-02-20
    • Juan YuHasan Timucin OzdemirKuo Chu Lee
    • Juan YuHasan Timucin OzdemirKuo Chu Lee
    • G06F17/00G06N5/02G08B13/00
    • G06K9/00771A61B5/1128A61B5/16A61B5/7267G06K9/00785
    • A surveillance system implements an architecture and process to support real-time abnormal behavior assessment operations in a distributed scalable sensor network. An automated behavior model builder generates behavior models from sensor data. A plurality of abnormal behavior scoring engines operating concurrently to generate abnormal behavior assessment models by scoring the behavior models. An execution performance manager performs fast switching of behavior models for the abnormal behavior scoring engines. The execution performance manager performs detection of abnormal behavior score distribution characteristic deviation by comparing a current abnormal behavior assessment model to a pre-recorded abnormal behavior assessment model. The execution performance manager selects a pre-recorded behavior model for the abnormal behavior scoring engines when the deviation exceeds a predetermined threshold.
    • 监控系统实现一种架构和过程,以支持分布式可扩展传感器网络中的实时异常行为评估操作。 自动行为模型构建器从传感器数据生成行为模型。 多个异常行为评分引擎同时运行,通过评分行为模型来产生异常行为评估模型。 执行性能管理员可以快速切换异常行为评分引擎的行为模型。 执行绩效管理者通过将当前的异常行为评估模型与预先记录的异常行为评估模型进行比较来执行异常行为评分分布特征偏差的检测。 当偏差超过预定阈值时,执行性能管理器为异常行为评分引擎选择预先记录的行为模型。