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    • 1. 发明授权
    • Anomaly detection method and system and maintenance method and system
    • 异常检测方法及系统及维护方法及系统
    • US08611228B2
    • 2013-12-17
    • US11914156
    • 2006-06-02
    • Yasuhiko MatsunagaJunichi TakeuchiTakayuki Nakata
    • Yasuhiko MatsunagaJunichi TakeuchiTakayuki Nakata
    • H04L12/26
    • H04L1/0019
    • A network management apparatus in a mobile communication network holds a communication quality index in a normal operation and periodically receives input of a communication quality measurement result from a radio base station control apparatus. When a connection failure count f with respect to a connection request count a of each radio cell is obtained as a measurement result, letting p0 be the call loss rate in the normal operation, an upper probability B of a binomial distribution representing the connection failure count becomes larger than f is obtained (step 524). The negative logarithm of the upper probability B is obtained as the score of the degree of abnormality (step 525). Anomaly of communication is detected when the score of the degree of abnormality exceeds a predetermined threshold value (steps 526 and 527). After that, maintenance control is executed in accordance with the calculated score of the degree of abnormality, thereby appropriately avoiding a fault of the communication system. This allows to calculate the degree of abnormality from the measurement result of the communication quality index in the mobile communication network in consideration of the statistical reliability and execute maintenance corresponding to the degree of abnormality.
    • 移动通信网络中的网络管理装置在正常操作中保持通信质量指标,并且从无线基站控制装置周期性地接收通信质量测量结果的输入。 当获得关于每个无线电小区的连接请求计数a的连接失败计数f作为测量结果时,将p0作为正常操作中的呼叫损失率,表示连接故障计数的二项式分布的较高概率B 变得大于f(步骤524)。 获得上位概率B的负对数作为异常程度的得分(步骤525)。 当异常程度的分数超过预定阈值时,检测到通信异常(步骤526和527)。 之后,根据异常程度的计算得出执行维护控制,从而适当地避免通信系统的故障。 考虑到统计可靠性并根据异常程度执行维护,可以从移动通信网络中的通信质量指标的测量结果计算出异常程度。
    • 2. 发明申请
    • ANOMALY DETECTION METHOD AND SYSTEM AND MAINTENANCE METHOD AND SYSTEM
    • 异常检测方法与系统及维护方法与系统
    • US20090052330A1
    • 2009-02-26
    • US11914156
    • 2006-06-02
    • Yasuhiko MatsunagaJunichi TakeuchiTakayuki Nakata
    • Yasuhiko MatsunagaJunichi TakeuchiTakayuki Nakata
    • H04W24/00H04L12/26
    • H04L1/0019
    • A network management apparatus in a mobile communication network holds a communication quality index in a normal operation and periodically receives input of a communication quality measurement result from a radio base station control apparatus. When a connection failure count f with respect to a connection request count a of each radio cell is obtained as a measurement result, letting p0 be the call loss rate in the normal operation, an upper probability B of a binomial distribution representing the connection failure count becomes larger than f is obtained (step 524). The negative logarithm of the upper probability B is obtained as the score of the degree of abnormality (step 525). Anomaly of communication is detected when the score of the degree of abnormality exceeds a predetermined threshold value (steps 526 and 527). After that, maintenance control is executed in accordance with the calculated score of the degree of abnormality, thereby appropriately avoiding a fault of the communication system. This allows to calculate the degree of abnormality from the measurement result of the communication quality index in the mobile communication network in consideration of the statistical reliability and execute maintenance corresponding to the degree of abnormality.
    • 移动通信网络中的网络管理装置在正常操作中保持通信质量指标,并且从无线基站控制装置周期性地接收通信质量测量结果的输入。 当获得关于每个无线电小区的连接请求计数a的连接失败计数f作为测量结果时,将p0作为正常操作中的呼叫损失率,表示连接故障计数的二项式分布的较高概率B 变得大于f(步骤524)。 获得较高概率B的负对数作为异常程度的得分(步骤525)。 当异常程度的分数超过预定阈值时,检测到通信异常(步骤526和527)。 之后,根据异常程度的计算得出执行维护控制,从而适当地避免通信系统的故障。 考虑到统计可靠性并根据异常程度执行维护,可以从移动通信网络中的通信质量指标的测量结果计算出异常程度。
    • 3. 发明申请
    • Travel-time prediction apparatus, travel-time prediction method, traffic information providing system and program
    • 旅行时间预测装置,旅行时间预测方法,交通信息提供系统和程序
    • US20080097686A1
    • 2008-04-24
    • US11907567
    • 2007-10-15
    • Junichi TakeuchiTakayuki NakataTakashi FujitaYasuhiro Sugisaki
    • Junichi TakeuchiTakayuki NakataTakashi FujitaYasuhiro Sugisaki
    • G05D3/20G07C1/10
    • G08G1/0104
    • Disclosed is a travel-time prediction apparatus that is capable of making a mid-term prediction of travel time accurately by combining present conditions and statistical information. The apparatus includes a travel-time transition pattern database storing travel-time transition patterns obtained by statistically processing past time-series data of each road link according to type of data. Upon accepting a travel-time transition pattern corresponding to a specified link and day type from the database, the apparatus calculates conversion parameters of a travel-time transition pattern for which an error between the travel-time transition pattern and a sequentially input travel-time time-series data will be reduced, and then makes a prediction using a prediction function obtained by converting the travel-time transition pattern by the calculated conversion parameters. The calculated predicted value and the conversion parameters are distributed as traffic information.
    • 公开了一种行进时间预测装置,其能够通过组合当前条件和统计信息来准确地进行旅行时间的中期预测。 该装置包括行进时间过渡模式数据库,其存储通过根据数据类型统计处理每个道路链路的过去时间序列数据而获得的行进时间过渡模式。 在从数据库接收到与指定的链接和日期类型相对应的旅行时间过渡模式时,该装置计算行驶时间过渡模式的转换参数,行驶时间过渡模式的行驶时间过渡模式和顺序输入的行驶时间 时间序列数据将被减少,然后使用通过将所述行进时间转变模式转换为所计算的转换参数而获得的预测函数进行预测。 计算出的预测值和转换参数作为交通信息分配。
    • 4. 发明授权
    • Travel-time prediction apparatus, travel-time prediction method, traffic information providing system and program
    • 旅行时间预测装置,旅行时间预测方法,交通信息提供系统和程序
    • US08090523B2
    • 2012-01-03
    • US11907567
    • 2007-10-15
    • Junichi TakeuchiTakayuki NakataTakashi FujitaYasuhiro Sugisaki
    • Junichi TakeuchiTakayuki NakataTakashi FujitaYasuhiro Sugisaki
    • G06F19/00
    • G08G1/0104
    • Disclosed is a travel-time prediction apparatus that is capable of making a mid-term prediction of travel time accurately by combining present conditions and statistical information. The apparatus includes a travel-time transition pattern database storing travel-time transition patterns obtained by statistically processing past time-series data of each road link according to type of data. Upon accepting a travel-time transition pattern corresponding to a specified link and day type from the database, the apparatus calculates conversion parameters of a travel-time transition pattern for which an error between the travel-time transition pattern and a sequentially input travel-time time-series data will be reduced, and then makes a prediction using a prediction function obtained by converting the travel-time transition pattern by the calculated conversion parameters. The calculated predicted value and the conversion parameters are distributed as traffic information.
    • 公开了一种行进时间预测装置,其能够通过组合当前条件和统计信息来准确地进行旅行时间的中期预测。 该装置包括行进时间过渡模式数据库,其存储通过根据数据类型统计处理每个道路链路的过去时间序列数据而获得的行进时间过渡模式。 在从数据库接收到与指定的链接和日期类型相对应的旅行时间过渡模式时,该装置计算行驶时间过渡模式的转换参数,行驶时间过渡模式的行驶时间过渡模式和顺序输入的行驶时间 时间序列数据将被减少,然后使用通过将所述行进时间转变模式转换为所计算的转换参数而获得的预测函数进行预测。 计算出的预测值和转换参数作为交通信息分配。
    • 5. 发明申请
    • Time series analysis system, time series analysis method, and time series analysis program
    • 时间序列分析系统,时间序列分析方法和时间序列分析程序
    • US20060217939A1
    • 2006-09-28
    • US11389086
    • 2006-03-27
    • Takayuki NakataJunichi Takeuchi
    • Takayuki NakataJunichi Takeuchi
    • G06F15/00
    • G06F17/18
    • A time series analysis system can perform computation with a less amount of computation than before when a long-term trend component and a long-term cyclic component are handled, or when a plurality of cyclic components is handled. From input time series data including a plurality of cyclic components, long-term time series data of a different series using a plurality of time spans is prepared for each of the time spans. Then, the long-term time series data for each of the time spans is removed from the original time series data, thereby preparing short-term time series data. Then, by learning through probability statistic processing that uses the long-term time series data and the short-term time series data, a model having a time span optimal for prediction of the time series data is selected as an optimal model, and is used for prediction of the time series data.
    • 当处理长期趋势分量和长期循环分量时,或者当处理多个循环分量时,时间序列分析系统可以以比以前更少的计算量执行计算。 从包括多个循环分量的输入时间序列数据,为每个时间跨度准备使用多个时间跨度的不同系列的长期时间序列数据。 然后,从原始时间序列数据中除去每个时间跨度的长期时间序列数据,由此制作短期时间序列数据。 然后,通过使用长期时间序列数据和短期时间序列数据的概率统计处理进行学习,选择具有用于预测时间序列数据的最佳时间范围的模型作为最优模型,并且被使用 用于预测时间序列数据。