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    • 21. 发明授权
    • Monitoring driving safety using semi-supervised sequential learning
    • 使用半监督顺序学习监测驾驶安全
    • US07860813B2
    • 2010-12-28
    • US12184221
    • 2008-07-31
    • Jinjun WangShenghuo ZhuYihong Gong
    • Jinjun WangShenghuo ZhuYihong Gong
    • G06F15/18
    • G09B9/052
    • A computer-implemented method and system for predicting operation risks of a vehicle. The method and system obtains a training data stream of vehicular dynamic parameters and logging crash time instances; partitions the data stream into units representing dimension vectors, labels the units that overlap the crash time instances as most dangerous; labels the units, which are furthest from the units that are labeled as most dangerous, as most safe; propagates the most dangerous and the most safe labeling information of the labeled units to units which are not labeled; estimates parameters of a danger-level function using the labeled and unlabeled units; and applies the danger-level function to an actual data stream of vehicular dynamic parameters to predict the operation risks of the vehicle.
    • 一种用于预测车辆操作风险的计算机实现的方法和系统。 该方法和系统获取车辆动态参数和日志崩溃时间实例的训练数据流; 将数据流划分为表示维度向量的单位,将与崩溃时间实例重叠的单位标记为最危险的; 标记与最危险的单位最远的单位,最安全; 将标记单位的最危险和最安全的标签信息传播到未标记的单位; 使用标记和未标记的单位估计危险度函数的参数; 并将危险度函数应用于车辆动态参数的实际数据流,以预测车辆的运行风险。
    • 25. 发明授权
    • Continuous linear dynamic systems
    • 连续线性动态系统
    • US08917907B2
    • 2014-12-23
    • US13406011
    • 2012-02-27
    • Jinjun WangJing Xiao
    • Jinjun WangJing Xiao
    • G06K9/00G06K9/62
    • G06K9/00765G06K9/00335G06K9/6297
    • Aspects of the present invention include systems and methods for segmentation and recognition of action primitives. In embodiments, a framework, referred to as the Continuous Linear Dynamic System (CLDS), comprises two sets of Linear Dynamic System (LDS) models, one to model the dynamics of individual primitive actions and the other to model the transitions between actions. In embodiments, the inference process estimates the best decomposition of the whole sequence into continuous alternating between the two set of models, using an approximate Viterbi algorithm. In this way, both action type and action boundary may be accurately recognized.
    • 本发明的方面包括用于分割和识别动作原语的系统和方法。 在实施例中,被称为连续线性动态系统(CLDS)的框架包括两组线性动态系统(LDS)模型,其中一个模型用于对各个原始动作的动力学进行建模,另一组模型对动作之间的转换进行建模。 在实施例中,推理过程使用近似维特比算法来估计整个序列的最佳分解到两组模型之间的连续交替。 以这种方式,可以准确地识别动作类型和动作边界。
    • 26. 发明申请
    • Continuous Linear Dynamic Systems
    • 连续线性动态系统
    • US20120219186A1
    • 2012-08-30
    • US13406011
    • 2012-02-27
    • Jinjun WangJing Xiao
    • Jinjun WangJing Xiao
    • G06K9/62
    • G06K9/00765G06K9/00335G06K9/6297
    • Aspects of the present invention include systems and methods for segmentation and recognition of action primitives. In embodiments, a framework, referred to as the Continuous Linear Dynamic System (CLDS), comprises two sets of Linear Dynamic System (LDS) models, one to model the dynamics of individual primitive actions and the other to model the transitions between actions. In embodiments, the inference process estimates the best decomposition of the whole sequence into continuous alternating between the two set of models, using an approximate Viterbi algorithm. In this way, both action type and action boundary may be accurately recognized.
    • 本发明的方面包括用于分割和识别动作原语的系统和方法。 在实施例中,被称为连续线性动态系统(CLDS)的框架包括两组线性动态系统(LDS)模型,其中一个模型用于对各个原始动作的动力学进行建模,另一组模型对动作之间的转换进行建模。 在实施例中,推理过程使用近似维特比算法来估计整个序列的最佳分解到两组模型之间的连续交替。 以这种方式,可以准确地识别动作类型和动作边界。
    • 30. 发明申请
    • MONITORING DRIVING SAFETY USING SEMI-SUPERVISED SEQUENTIAL LEARNING
    • 使用半监督的顺序学习监控驾驶安全
    • US20090191513A1
    • 2009-07-30
    • US12184221
    • 2008-07-31
    • Jinjun WangShenghuo ZuYihong Gong
    • Jinjun WangShenghuo ZuYihong Gong
    • G09B9/04
    • G09B9/052
    • A computer-implemented method and system for predicting operation risks of a vehicle. The method and system obtains a training data stream of vehicular dynamic parameters and logging crash time instances; partitions the data stream into units representing dimension vectors, labels the units that overlap the crash time instances as most dangerous; labels the units, which are furthest from the units that are labeled as most dangerous, as most safe; propagates the most dangerous and the most safe labeling information of the labeled units to units which are not labeled; estimates parameters of a danger-level function using the labeled and unlabeled units; and applies the danger-level function to an actual data stream of vehicular dynamic parameters to predict the operation risks of the vehicle.
    • 一种用于预测车辆操作风险的计算机实现的方法和系统。 该方法和系统获取车辆动态参数和日志崩溃时间实例的训练数据流; 将数据流划分为表示维度向量的单位,将与崩溃时间实例重叠的单位标记为最危险的; 标记与最危险的单位最远的单位,最安全; 将标记单位的最危险和最安全的标签信息传播到未标记的单位; 使用标记和未标记的单位估计危险度函数的参数; 并将危险度函数应用于车辆动态参数的实际数据流,以预测车辆的运行风险。