Nec Corporation

Nec Soft, Ltd.

Takayuki Nakata

Kenji Aoki

Ryohei Fujimaki

Junichi Takeuchi

Takashi Fujita

Takeshi Ono

Yasuhiko Matsunaga

Hidenori Tsukahara

Kazushi Suzuki

Akinori Satou

Yasuhiro Sugisaki

Syunsuke Hirose

Takayuki Kaneso

An information processing device calculates a mean and a variance of a response variable for each range determined based on a partition candidate being information on partitioning a domain of an explanatory variable into a plurality of ranges based on observed data and the mean and the variance are dependent on the explanatory variable, selects a specific partition candidate among a plurality of the partition candidates using an information criterion calculated based on the observed data and the calculated mean and variance of the response variable, and calculates a smooth function representing the variance of the response variable in the domain of the explanatory variable and a smooth function representing the mean of the response variable in the domain of the explanatory variable based on the mean and the variance calculated for each range determined based on the specific partition candidate.

Information processing device, method of processing information and storage medium

To detect an abnormal value in a satellite positioning system with high precision even when the observation environment changes or there is the time series correlation between data. An abnormal value index calculation unitcalculates an abnormal value index at each time of time-series data such as a pseudo distance between each artificial satellite and a receiver in the satellite positioning system or the like. A dynamic model forming unitdynamically forms a model from the abnormal value index in a predetermined period and calculates a change point index from the time-series abnormal value index based on the dynamic model. The change point index is an index for determining whether the time-series is a one-shot outlier from a dynamic model or the dynamic model itself of input data changes when the time-series value which suddenly increases and decreases exists. The abnormal value detection unitcompares the change point index with a threshold value set in advance and detects the abnormal value index corresponding to the change point index at that time as an abnormal value when the value of the change point index is greater than the threshold value.

Abnormal value detection apparatus for satellite positioning system, abnormal value detection method, and abnormal value detection program

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 pbe 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). The negative logarithm of the upper probability B is obtained as the score of the degree of abnormality (step). Anomaly of communication is detected when the score of the degree of abnormality exceeds a predetermined threshold value (stepsand). 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.

Anomaly detection method and system and maintenance method and system

Disclosed is an apparatus that generates automatically a characteristic pattern in time series data by clustering a plurality of time series subsequences generated from the time series data. The apparatus includes a time series subsequence generation unit that generates a plurality of time series subsequences from the time series data, a phase alignment unit that aligns a phase of the generated time series subsequence, a clustering unit that performs clustering of a plurality of the time series subsequences, each having a phase aligned, a storage apparatus that stores the pattern obtained by the clustering, and an output apparatus that outputs the stored pattern.

Pattern generation method, pattern generation apparatus, and program

A diagnostic device detects a fault and estimates its cause based on the degree of change of measured data attributes. A diagnostic object change degree pattern (CDP) generation unit calculates the degree of change of each attribute of data, including attributes of the object being diagnosed measured from the object being diagnosed to generate a diagnostic object CDP which is a combination of values of the degree of change of the respective attributes. A criterion CDP memory holds in store a criterion CDP, formed of a pattern of values of the degree of change of the attributes of data measured from the object being diagnosed, in association with event(s)-to-be-diagnosed on the fault sort basis or on the fault cause basis. A CDP diagnosis unit effects pattern matching between the diagnostic object CDP and the criterion CDP in the criterion CDP memory to diagnose the object being diagnosed.

Diagnostic device

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.

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Tokyo, Japan

Takayuki Nakata