Kabushiki Kaisha Toshiba

Toshiba Infrastructure Systems & Solutions Corporation

Ken Ueno

Shigeru Maya

Shigeaki Sakurai

Ryohei Orihara

Akihiro Yamaguchi

Youichi Kitahara

Masaru Suzuki

Takeichiro Nishikawa

Myungsook Ko

Akihiro Suyama

Yasuto Ishitani

Ryusei Shingaki

Eiichi Sunagawa

Kaneharu Nishino

One embodiment of the present provides an apparatus and the like that uses a model that estimates shapelet deformation which is according to variations in an anticipated factor, and thereby suppresses a drop in classification performance even if the circumstances of the anticipated factor are different between training and testing. An information processing apparatus according to one embodiment of the present invention is provided with an adjuster and a feature calculator. The adjuster adjusts the shape of a reference waveform pattern corresponding to time series data and used to classify the time series data, on the basis of the value of a factor parameter corresponding to the time series data. The feature calculator calculates a feature of a waveform of the time series data on a basis of the shape of the reference waveform pattern after the adjustment.

Information processing apparatus, information processing method, and non-transitory storage medium

One embodiment of the present invention provides an information processing apparatus for precisely estimating a state of equipment. An information processing apparatus as one embodiment of the present invention includes: an acquirer; a calculator; and a determiner. The acquirer is configured to acquire first data about a predetermined target. The calculator is configured to calculate distribution of magnitude of a value included in the first data. The determiner is configured to determine a portion of a width of the distribution as a specific range used for estimating a state of the target based on the distribution.

State estimation apparatus, method, and non-transitory computer readable medium

An information processing device according to an embodiment estimates a probability distribution of a maximum value or a minimum value of time-series target sensor data in a designated estimation period after a base time. The information processing device includes one or more hardware processors that: calculate a reference feature amount representing a feature of related sensor data at a reference time before the base time based on a preset feature amount calculation model; estimate a reference distribution parameter in a reference period based on a preset parameter estimation model; update the feature amount calculation model and the parameter estimation model; calculate an estimation feature amount representing the feature of related sensor data at the base time based on the updated feature amount calculation model; and estimate an estimation distribution parameter based on the updated parameter estimation model.

Information processing device, information processing method, and computer program product

An information processing device includes processing circuitry configured to classify a plurality of partial waveform patterns that characterize a plurality of time series data into a plurality of classes based on the plurality of time series data classified into the plurality of classes, update shapes of the partial waveform patterns by fitting the partial waveform patterns to the time series data of the corresponding class, and reclassify the plurality of time series data into the plurality of classes based on the updated partial waveform patterns and difficulty levels that represent degrees of difficulty of classification and interpretation of the time series data.

Information processing device, information processing method, and recording medium

One embodiment of the present invention provides an apparatus and the like for improving the accuracy of deterioration estimation regarding a monitoring target. An information processing apparatus as one embodiment of the present invention includes a detector, a separator, a calculator, and an estimator. The detector detects a normal waveform pattern included in a waveform of time-series data of a target. The separator separates the waveform of the time-series data into components while removing the normal waveform pattern that is detected. The calculator calculates a feature of the time-series data based on at least one of the components. The estimator estimates deterioration of the target based on the feature.

Information processing apparatus, information processing method, and non-transitory computer readable medium

An information processing apparatus has a model construction unit that constructs a model represented using a plurality of variables corresponding to a plurality of classes, an evaluated value calculation unit that calculates an evaluated value of the model constructed by the model construction unit, a correlation specification unit that specifies a correlation between some variables among the plurality of variables based on the calculated evaluated value, a variable processing determination unit that determines whether to perform at least one of creation, integration, and stratification of at least some variables among the plurality of variables based on the correlation specified by the correlation specification unit, and a variable processing unit that performs at least one of creation processing, integration processing, and stratification processing of the variables when the at least one of the creation, the integration, and the stratification of the variables is determined to be performed.

Information processing apparatus, information processing method, and program

An apparatus according to one embodiment of the present invention detects an abnormality of a monitoring target on the basis of state data of the target and includes an estimated data calculator, a deviation degree calculator, an abnormality degree calculator, and an abnormality determiner. The estimated data calculator calculates estimated data of a second period on the basis of the state data of the first period. The deviation degree calculator calculates a degree of deviation of the second period on the basis of the state data and the estimated data of the second period. The abnormality degree calculator calculates a degree of abnormality of the second period on the basis of the degree of deviation of the second period. The abnormality determiner determines presence or absence of an abnormality of the target in the second period on the basis of the degree of abnormality in the second period.

Abnormality detection apparatus, abnormality detection method, and non-transitory computer readable medium

An information processing apparatus includes an input unit configured to input an annotation of at least one of an underline, a box, a character, a character string, a symbol and a symbol string to a displayed document, an annotation recognition unit configured to recognize a type of the annotation and a coverage of the annotation in the document, an intention estimation unit configured to estimate intention of a user based on the type of the annotation and information in the coverage, an action storage unit configured to store a plurality of actions, an action selection unit configured to select an action to be performed for the document from the action storage based on the intention estimated by the intention estimation unit, and an execution unit configured to execute the action selected by the action selection unit.

Information processing method and apparatus

A time series pattern generation unit extracts a key concept corresponding to a text of each data element of a plurality of time series data by referring to an extraction rule memory, and generates a plurality of time series patterns. A colony generation unit unifies a plurality of data elements having the same key concept and substantially the same time duration, and generates colonies of unified elements. In response to a selection of a first colony, a colony extraction unit specifies a second colony having a key concept contrary to the first colony, and extracts colonies earlier than the first colony and the second colony. A time reason extraction unit extracts a time reason as a divergence condition of the first colony from the second colony from the extracted colonies.

Time series pattern extraction apparatus and method

Constraining sequential data expressing sequential data which a sequential pattern to be extracted must include is specified in advance. Sequential pattern candidates with sequence lengthare initially determined from among a plurality of input sequential data. Next, a set of sequential pattern candidates is generated by determining a plurality of new sequential pattern candidates by elongating the sequence length of the sequential pattern candidates. In this sequential pattern candidate set, after sequential pattern candidates which can generate only a sequential pattern which does not include constraining sequential data are eliminated, sequential data which include constraining data, and frequently appear are extracted as a new sequential pattern.

Sequential pattern extracting apparatus

A time-series data analyzing apparatus which extracts a composite factor time-series pattern from time-series data. The apparatus includes a dividing device which divides the time-series data into pattern generation time-series data and pattern inspection time-series data which do not include pattern generation time-series data. A first generating device generates a transitional pattern including a support time data indicating a transition of support time and having a transition occurrence probability higher than a minimum occurrence probability in the pattern generation time-series data. A second generating device generates frequently appearing integrated transitional patterns. A second computing device computes cause-and-effect strength of each of the frequently appearing integrated transitional patterns using the pattern inspection time-series data. A display device displays the composite factor time-series pattern having the cause-and-effect strength higher than the minimum cause-and-effect strength given preliminarily.

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Kawasaki, Japan

Ken Ueno