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    • 1. 发明申请
    • Assesssing biometric sample quality using wavelets and a boosted classifier
    • 使用小波和增强分类器评估生物特征样本质量
    • US20100111376A1
    • 2010-05-06
    • US12457959
    • 2009-06-26
    • Weizhong YanFrederick W. WheelerPeter H. TuXiaoming Liu
    • Weizhong YanFrederick W. WheelerPeter H. TuXiaoming Liu
    • G06K9/00
    • G07C9/00158G06K9/00268G06K9/036G06K9/6255
    • A biometric sample training device, a biometric sample quality assessment device, a biometric fusion recognition device, an integrated biometric fusion recognition system and example processes in which each may be used are described. Wavelets and a boosted classifier are used to assess the quality of biometric samples, such as facial images. The described biometric sample quality assessment approach provides accurate and reliable quality assessment values that are robust to various degradation factors, e.g., such as pose, illumination, and lighting in facial image biometric samples. The quality assessment values allow biometric samples of different sample types to be combined to support complex recognition techniques used by, for example, biometric fusion devices, resulting in improved accuracy and robustness in both biometric authentication and biometric recognition.
    • 描述了生物特征样本训练装置,生物特征样本质量评估装置,生物测定融合识别装置,集成生物测定融合识别系统以及其中可以使用每一种的实例过程。 小波和增强分类器用于评估生物特征样本的质量,如面部图像。 所描述的生物特征样本质量评估方法提供对各种降解因素(例如面部图像生物特征样本中的姿态,照明和照明)可靠的质量评估值。 质量评估值允许组合不同样本类型的生物特征样本,以支持例如生物测定融合装置使用的复杂识别技术,从而提高生物特征认证和生物识别识别两者的精度和鲁棒性。
    • 2. 发明授权
    • Assessing biometric sample quality using wavelets and a boosted classifier
    • 使用小波和增强分类器评估生物特征样本质量
    • US08442279B2
    • 2013-05-14
    • US12457959
    • 2009-06-26
    • Weizhong YanFrederick W WheelerPeter H TuXiaoming Liu
    • Weizhong YanFrederick W WheelerPeter H TuXiaoming Liu
    • G06K9/00
    • G07C9/00158G06K9/00268G06K9/036G06K9/6255
    • A biometric sample training device, a biometric sample quality assessment device, a biometric fusion recognition device, an integrated biometric fusion recognition system and example processes in which each may be used are described. Wavelets and a boosted classifier are used to assess the quality of biometric samples, such as facial images. The described biometric sample quality assessment approach provides accurate and reliable quality assessment values that are robust to various degradation factors, e.g., such as pose, illumination, and lighting in facial image biometric samples. The quality assessment values allow biometric samples of different sample types to be combined to support complex recognition techniques used by, for example, biometric fusion devices, resulting in improved accuracy and robustness in both biometric authentication and biometric recognition.
    • 描述了生物特征样本训练装置,生物特征样本质量评估装置,生物测定融合识别装置,集成生物测定融合识别系统以及其中可以使用每一种的实例过程。 小波和增强分类器用于评估生物特征样本的质量,如面部图像。 所描述的生物特征样本质量评估方法提供对各种降解因素(例如面部图像生物特征样本中的姿态,照明和照明)可靠的质量评估值。 质量评估值允许组合不同样本类型的生物特征样本,以支持例如生物测定融合装置使用的复杂识别技术,从而提高生物特征认证和生物识别识别两者的精度和鲁棒性。
    • 3. 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。
    • 5. 发明授权
    • Method for switching route and network device thereof
    • 交换路由及其网络设备的方法
    • US07898943B2
    • 2011-03-01
    • US10591218
    • 2006-01-09
    • Weizhong Yan
    • Weizhong Yan
    • G01R31/08
    • H04L45/28H04L45/00H04L45/22H04L45/54H04L49/3009H04L49/309H04L49/55
    • A method for switching route and a network device are disclosed, wherein the method comprises: setting a relationship between a port number of each destination port and a port number of the transmitting port, the port number of each transmitting port is the port number of the corresponding destination port; when there is a service failure in any destination port, modifying the port number of the transmitting port corresponding to a fault destination port into the port number of the backup port corresponding to the fault destination port in the set relationship, and saving the modified relationship; after receiving a data packet, the network device transmitting the data packet based on the saved relationship. The network device comprises a CPU, a first routing unit and a second routing unit. In accordance with the present invention, the time consumed by modifying routing data can be reduced, enabling the network device to switch route quickly and the user services to recover quickly.
    • 公开了一种交换路由和网络设备的方法,其特征在于,该方法包括:设置每个目的端口的端口号与发送端口的端口号之间的关系,每个发送端口的端口号是 对应目的地端口; 当目的端口出现业务故障时,将与故障目的端口对应的发送端口的端口号修改为与该关系中的故障目的端口对应的备份端口的端口号,并保存修改后的关系; 接收到数据包后,网络设备根据保存的关系发送数据包。 网络设备包括CPU,第一路由单元和第二路由单元。 根据本发明,可以减少修改路由数据消耗的时间,使网络设备能够快速切换路由,快速恢复用户业务。
    • 9. 发明申请
    • SYSTEM AND METHOD FOR DEFINING NORMAL OPERATING REGIONS AND IDENTIFYING ANOMALOUS BEHAVIOR OF UNITS WITHIN A FLEET, OPERATING IN A COMPLEX, DYNAMIC ENVIRONMENT
    • 用于定义正常操作区域的系统和方法,并识别单元中的单个异常行为,复杂动态环境中的操作
    • US20080091630A1
    • 2008-04-17
    • US11755924
    • 2007-05-31
    • Piero BonissoneWeizhong YanNaresh IyerKai GoebelAnil Varma
    • Piero BonissoneWeizhong YanNaresh IyerKai GoebelAnil Varma
    • G06N5/00
    • G05B23/024G06K9/6284G06N99/005
    • Monitoring dynamic units that operate in complex, dynamic environments, is provided in order to classify and track unit behavior over time. When domain knowledge is available, feature-based models may be used to capture the essential state information of the units. When domain knowledge is not available, raw data is relied upon to perform this task. By analyzing logs of event messages (without having access to their data dictionary), embodiments allow the identification of anomalies (novelties). Specifically, a Normalized Compression Distance (such as one based on Kolmogorov Complexity) may be applied to logs of event messages. By analyzing the similarity and differences of the event message logs, units are identified that did not experience any abnormality (and locate regions of normal operations) and units that departed from such regions. Of particular interest is the detection and identification of units' epidemics, which is defined as sustained/increasing numbers of anomalies over time.
    • 提供了监控在复杂,动态环境中运行的动态单元,以便对时间段内的单元行为进行分类和跟踪。 当领域知识可用时,可以使用基于特征的模型来捕获单位的基本状态信息。 当领域知识不可用时,依靠原始数据来执行此任务。 通过分析事件消息的日志(不访问其数据字典),实施例允许识别异常(新奇事物)。 具体来说,归一化压缩距离(例如基于Kolmogorov复杂度的距离)可以应用于事件消息的日志。 通过分析事件消息日志的相似性和差异,识别出没有经历任何异常(并定位正常操作的区域)的单位和离开这些区域的单位。 特别感兴趣的是检测和识别单位的流行病,其定义为持续/越来越多的异常随时间变化。