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    • 1. 发明授权
    • Method for prohibiting unauthorized access in a non-contacting data carrier system
    • 用于在非接触式数据载体系统中禁止未授权访问的方法
    • US06823459B1
    • 2004-11-23
    • US09510573
    • 2000-02-22
    • Hideto HorikoshiNaoki AbeJun TanakaTomoki Maruichi
    • Hideto HorikoshiNaoki AbeJun TanakaTomoki Maruichi
    • G06F1130
    • G06F21/35
    • To provide a method whereby unauthorized data access by an RFID data processing system is prohibited without any degradation of performance being incurred. An RFID data processing system 30 comprises a CPU 35, a EEPROM 34, communication devices 31 and 32, and power controllers 40 and 41. When an RFID data processing system 30 in the power-ON state that does not have access authorization passes through a portal gate located at the entrance to an unauthorized data access protection area, the portal gate transmits a signal to set ON a tamper bit 44 in the EEPROM 34. When the tamper bit 44 has been set ON, a tamper bit interrupt request signal is output by the EEPROM 34. Upon receiving this signal, the power controllers 40 and 41 power off the RFID data processing system 30.
    • 提供一种可以防止RFID数据处理系统的未经授权的数据访问而不会导致性能下降的方法。 RFID数据处理系统30包括CPU35,EEPROM34,通信设备31和32以及功率控制器40和41.当处于不具有访问授权的电源接通状态的RFID数据处理系统30通过 门禁门位于未授权数据访问保护区域的入口处,门户门传输信号以将EEPROM 34中的篡改位44设置为ON。当篡改位44已经设置为ON时,输出篡改位中断请求信号 在接收到该信号时,功率控制器40和41关闭RFID数据处理系统30。
    • 4. 发明授权
    • Resource-light method and apparatus for outlier detection
    • 资源光法和异常检测装置
    • US08006157B2
    • 2011-08-23
    • US11863704
    • 2007-09-28
    • Naoki AbeJohn Langford
    • Naoki AbeJohn Langford
    • H04L1/00G06F11/30H03M13/00
    • G06N99/005Y10S707/99936Y10S707/99943
    • Outlier detection methods and apparatus have light computational resources requirement, especially on the storage requirement, and yet achieve a state-of-the-art predictive performance. The outlier detection problem is first reduced to that of a classification learning problem, and then selective sampling based on uncertainty of prediction is applied to further reduce the amount of data required for data analysis, resulting in enhanced predictive performance. The reduction to classification essentially consists in using the unlabeled normal data as positive examples, and randomly generated synthesized examples as negative examples. Application of selective sampling makes use of an underlying, arbitrary classification learning algorithm, the data labeled by the above procedure, and proceeds iteratively. Each iteration consisting of selection of a smaller sub-sample from the input data, training of the underlying classification algorithm with the selected data, and storing the classifier output by the classification algorithm. The selection is done by essentially choosing examples that are harder to classify with the classifiers obtained in the preceding iterations. The final output hypothesis is a voting function of the classifiers obtained in the iterations of the above procedure.
    • 异常值检测方法和装置具有较轻的计算资源需求,特别是对存储要求的要求,而且具有最先进的预测性能。 异常值检测问题首先降低到分类学习问题,然后应用基于预测不确定度的选择性抽样来进一步减少数据分析所需的数据量,从而提高预测性能。 归类分类主要在于使用未标记的正常数据作为正例,随机生成合成实例作为阴性实例。 选择性抽样的应用使用了基础的,任意的分类学习算法,由上述过程标记的数据,并且迭代地进行。 每个迭代包括从输入数据中选择较小的子样本,对所选数据训练底层分类算法,以及通过分类算法存储分类器输出。 选择是通过基本上选择难以对上述迭代中获得的分类器进行分类的示例来完成的。 最终输出假设是在上述过程的迭代中获得的分类器的投票函数。