会员体验
专利管家(专利管理)
工作空间(专利管理)
风险监控(情报监控)
数据分析(专利分析)
侵权分析(诉讼无效)
联系我们
交流群
官方交流:
QQ群: 891211   
微信请扫码    >>>
现在联系顾问~
热词
    • 7. 发明授权
    • Learning method and apparatus for a causal network
    • 因果网络的学习方法和设备
    • US06681215B2
    • 2004-01-20
    • US09681336
    • 2001-03-20
    • Vinay Bhaskar Jammu
    • Vinay Bhaskar Jammu
    • G06F1518
    • G06N7/005
    • A system and method for improving a causal network is provided. A new apriori probability is determined for a repair or a configuration factor within the causal network and compared to an old apriori probability. If the new apriori probability differs from the old apriori probability by more than a predetermined amount, the causal network is updated. Further, in another aspect, a causal network result is stored for a causal network, wherein the causal network includes a plurality of root causes with a symptom being associated with each of said root causes. An existing link probability is related to the symptom and root cause. An expert result or an actual data result related to each of the symptoms is stored. A new link probability is computed based on the stored causal network result, and expert result or the actual data result.
    • 提供了一种改善因果网络的系统和方法。 对因果网络中的修复或配置因素确定新的先验概率,并与旧的先验概率进行比较。 如果新的先验概率与旧的先验概率不同超过预定量,则更新因果网络。 此外,在另一方面,为因果网络存储因果网络结果,其中所述因果网络包括具有与每个所述根本原因相关联的症状的多个根本原因。 现有的链接概率与症状和根本原因有关。 存储与每个症状相关的专家结果或实际数据结果。 基于存储的因果网络结果,专家结果或实际数据结果计算新的链路概率。
    • 8. 发明申请
    • SYSTEMS AND METHODS FOR PREDICTION OF TRIPS
    • 用于预测TRIPS的系统和方法
    • US20140244567A1
    • 2014-08-28
    • US13600387
    • 2012-08-31
    • Ravi Yoganatha BabuVinay Bhaskar JammuAchalesh Kumar Pandey
    • Ravi Yoganatha BabuVinay Bhaskar JammuAchalesh Kumar Pandey
    • G06N5/04
    • G06N5/04F01D21/00F02C9/00F05D2260/80F05D2270/44G05B23/0283
    • A system is disclosed. The system includes a processing subsystem that receives component data signals corresponding to a plurality of parameters of a device, wherein the processing subsystem generates one or more sets of state category component data by allocating the component data signals into respective one or more sets of state category component data, determines a plurality of first dynamic thresholds and a plurality of second dynamic thresholds corresponding to at least one of the one or more sets of state category component data based upon a respective set of state category component data in the one or more sets of state category component data and a respective parameter in the plurality of parameters, and determines an impending trip of the device utilizing the plurality of first dynamic thresholds and the plurality of second dynamic thresholds.
    • 公开了一种系统。 该系统包括处理子系统,其接收对应于设备的多个参数的分量数据信号,其中处理子系统通过将分量数据信号分配到相应的一组或多组状态类别中来生成一组或多组状态类别分量数据 组件数据,基于一组或多组状态类别组件数据中的相应集合,确定与所述一组或多组状态类别分量数据中的至少一个对应的多个第一动态阈值和多个第二动态阈值 状态类别分量数据和多个参数中的相应参数,并且使用多个第一动态阈值和多个第二动态阈值来确定设备的即将跳闸。