Hitachi, Ltd.

Kazuaki Tokunaga

Toshiyuki Ukai

Takeshi Uehara

Kunihiko Harada

Atsushi Ito

Shinji Nakagawa

Yasuharu Namba

Toshiko Aizono

Masato Kunitomo

Yutaro Kato

Wenpeng Wei

Yuto Kojima

Naoki Tabaru

Soichiro Tanaka

Syunsuke Monai

Takuya Mogawa

Kazuyuki Oota

Shimei Ko

A highly accurate feature extraction is performed on a signal with temporal variation in amplitude, and this signal is restored to detect a state of a transmission source (output source) of this signal to be normal or abnormal. A signal processing method includes: separating a signal X into an oscillation signal with a constant amplitude Xand a signal with temporal variation in amplitude X, the separating performed by a signal separator; performing processing of dimensionality reduction, compression, or the like, on the oscillation signal Xso as to extract a feature value (information) included in the oscillation signal X; and outputting a restored signal X' that is restored from the oscillation signal Xby performing processing inverse to the processing of dimensionality reduction, compression, or the like, based on the extracted feature value, performing the processing, the inverse processing, and the outputting performed by a signal Xrestorer.

Signal processing system and signal processing method

A system performs operation monitoring in which a learning model in operation is monitored. In the operation monitoring, the system performs a first certainty factor comparison to determine, each time the learning model in operation to which input data is input outputs output data, whether or not a certainty factor of the learning model is below a first threshold. In a case where a result of the first certainty factor comparison is true, the system replaces the learning model in operation with any of candidate learning models having a certainty factor higher than the certainty factor of the learning model in operation in which the result of true is obtained among one or more candidate learning models (one or more learning models each having a version different from a version of the learning model in operation), as a learning model of an operation target.

Operational support system and method

A computer system determines an allocation of resources in a task formed of processes. The task includes a transition between processes corresponding to rework. The computer system comprises: at least one predictor configured to calculate predicted values of an inflow amount and an outflow amount of the items of each of the processes forming the task; and a resource allocation determining unit configured to determine an allocation of the resources to each of the processes. The resource allocation determining unit uses the at least one predictor to form a simulator configured to calculate the predicted values of the inflow amount and the outflow amount of the items of each of the processes in any allocation of the resources, in a case of receiving a request including a constraint condition of the resources and an optimization condition; and determines the allocation of the resources to each of the processes.

Computer system and method for determining of resource allocation

A system calculates for each frontage space which is held by a plurality of shelves and in which commodities are placed, a recommended capacity value of the commodity based on a predicted shipment volume from demand prediction of the commodity placed in the frontage space. The system determines exchange pairs (frontage space pairs) in each of which exchange of the commodities are performed based on a current capacity value and the recommended capacity value of each frontage space. Each exchange pair satisfies the following: The current capacity of a first frontage space is smaller than the recommended capacity, or the current capacity of a second frontage space is larger than the recommended capacity. The recommended capacity of the second frontage space satisfies the current capacity of the first frontage space. The recommended capacity of the first frontage space satisfies the current capacity of the second frontage space.

Article placement optimization system and article placement optimization method

A data analysis device that analyzes data having a record including an objective variable and a plurality of explanatory variables includes a node generating unit that generates a node specified by a condition of the explanatory variable on the basis of the objective variable and the explanatory variable of the record and associating the record with the node, an evaluation value generating unit that generates a proportion of the number of records whose target value is the objective variable among a plurality of records associated with the node as an evaluation value, and a parameter extracting unit that selects a node on the basis of the evaluation value and extracts and outputs the condition of the explanatory variable related to the selected node.

Data analysis device and analysis method

An analysis system is configured to acquire data of a first numeric feature combination by referring to the parent population data. The data of the first numeric feature combination includes a score of a target indicator for each of category combinations. Each category combination of the category combinations is composed of categories of the numeric features of the first numeric feature combination. The analysis system is configured to determine a score at coordinates corresponding to each of the category combinations in a space having each of the numeric features of the first numeric feature combination as an axis, calculate a gradient feature vector representing a gradient for the score in the space, and determine, based on the gradient feature vector, whether to include the first numeric feature combination in numeric feature combinations to be extracted from the parent population data.

Analysis system

When multiple explanatory variables are automatically created, a huge amount of output suggestions causes a heavy burden on selecting the suggestion. A marketing support system is configured to include a suggestion extraction unit that accepts purchase data and analyzes a correlation between the purchase data to output a composite variable, a restriction filtering unit that accepts the composite variable and a restriction table to exclude the composite variable based on a restriction condition defined in the restriction table, and a result filtering unit that uses a measure result defined in the past to estimate an anticipated effect when a measure based on the composite variable is performed, and selects a plurality of explanatory variables.

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Tokyo, Japan

Kazuaki Tokunaga