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    • 1. 发明申请
    • Method for predicting the mobility in mobile ad hoc networks
    • 用于预测移动自组织网络中的移动性的方法
    • US20090046678A1
    • 2009-02-19
    • US11892031
    • 2007-08-17
    • Young-Koo LeeSungyoung LeeHui Xu
    • Young-Koo LeeSungyoung LeeHui Xu
    • H04Q7/24
    • H04W8/005H04W40/18H04W40/20H04W40/24
    • Disclosed are methods for determining the neighborhood local view of a mobile node in time which can facilitate the forwarding decision in the design of network protocols. In conventional mobile ad hoc networks nodes set up local topology view based on periodical received “Hello” messages. The conventional method is replaced with proactive and adaptive methods of predicting locations of nodes based on preserved historical information extracted from received “Hello” messages and constructing neighborhood view by aggregating predicted locations. This method is useful for providing updated and consistent topology local view that a network communication employs to determine optimal forward decisions and improve communication performance.
    • 公开了用于确定移动节点在时间上的邻近局部视图的方法,其可以促进网络协议的设计中的转发决定。 在传统的移动自组织网络节点中,基于周期性接收的“Hello”消息来建立本地拓扑视图。 传统方法被替换为基于从接收的“Hello”消息提取的保留的历史信息预测节点位置的主动和自适应方法,并通过聚合预测位置来构建邻域视图。 该方法对于提供网络通信采用的更新和一致的拓扑局部视图来确定最佳前向决策并提高通信性能是有用的。
    • 7. 发明申请
    • METHOD OF RECOGNIZING PATTERNS BASED ON MARKOV CHAIN HIDDEN CONDITIONAL RANDOM FIELD MODEL
    • 基于马尔可夫链条隐藏条件随机场模型识别图案的方法
    • US20130124438A1
    • 2013-05-16
    • US13307219
    • 2011-11-30
    • Sung-Young LEEYoung-Koo LeeLa The Vinh
    • Sung-Young LEEYoung-Koo LeeLa The Vinh
    • G06F15/18
    • G06K9/00302G06K9/00718G06N20/00G10L15/14
    • Provided is a method of recognizing patterns based on a hidden conditional random fields model to which full-Gaussian covariance has been applied. The method includes dividing a training input signal and outputting a frame sequence, extracting a feature vector from the frame sequence, calculating a parameter through a conditional random fields model to which Gaussian covariance has been applied using the feature vector, receiving, by the hidden conditional random fields model to which the parameter has been applied, a feature vector extracted from a test input signal measured for an actual pattern to infer a label indicating the actual pattern, and proposing a method of calculating gradient values for a conditional probability vector, a transition probability vector, a Gaussian mixture weight, a mean of Gaussian distributions, and covariance of the Gaussian distributions, as an analysis method.
    • 提供了一种基于已应用全高斯协方差的隐藏条件随机场模型识别模式的方法。 该方法包括分割训练输入信号并输出​​帧序列,从帧序列中提取特征向量,通过使用特征向量应用高斯协方差的条件随机场模型计算参数,通过隐藏条件 从应用了参数的随机场模型,从针对实际模式测量的测试输入信号提取的特征向量,推断出表示实际模式的标签,以及提出计算条件概率向量的梯度值的方法 概率向量,高斯混合权重,高斯分布的平均值和高斯分布的协方差作为分析方法。