Hitachi, Ltd.

Fumiya Kudo

Tomoaki Akitomi

Susumu Serita

Hideki Hayashi

Toshihiro Kujirai

Yoshiyuki Tajima

Morio Matsumoto

Yu Kitano

An object of the present invention is to reduce an error between an actual machine and a simulation by removing the influence of overlearning of an adjustment by a mathematically-described function, and to optimize automatic operation control of the machine. An automatic operation control system for controlling an automatic operation of a machine sets a first model showing a relation between a control signal string input to the machine on the basis of a mathematically-described function and data output from the machine controlled in accordance with the control signal string. In a learning process including learning the automatic operation control of the machine, the system executes learning using the first model until a first condition is satisfied. After the first condition is satisfied, the learning is executed using a second model that is a model after the first model is changed one or more times until a second condition meaning overlearning is satisfied or the learning is finished without satisfying the second condition.

Automatic operation control method and system

Series data in a table form and which includes a plurality of records each having a value of a response variable, response variable series identification information, a value of an explanatory variable, and explanatory variable series identification information identifying series of the explanatory variable associated with one another is stored. An analytical record that is any of the records and that contains the value of the response variable or the value of the explanatory variable possibly influencing the response variable at a time of analyzing the response variable is identified; and an additional record having the value of the response variable or the value of the explanatory variable in the identified analytical record associated with the value of the response variable in a predetermined record that is any of the records is generated, and analytical series data obtained by adding the generated additional record to the series data is generated.

Data analysis support apparatus and data analysis support method

Provided is a technique for extracting a factor (event pattern) that has an influence on an objective index (objective variable). A data analysis device according to the present disclosure performs: a process of generating, with respect to explanatory variable data included in data to be analyzed, a time-series pattern in a predetermined range; a process of calculating a correlation value between the time-series pattern and at least one item of objective variable data included in the data to be analyzed; and a process of outputting, together with the correlation value, the time-series pattern corresponding to the correlation value as an analysis result.

Analysis server device, data analysis system, and data analysis method

This data conversion system is characterized by including: a storage unit that stores a column including a plurality of data elements; a range specification module that specifies the range of each of the data elements of the column; an information amount evaluation module that calculates the information amount of the data element within the specified range of the column; and a change point detection module that detects a point at which a change in the information amount according to a change in the specified range satisfies a predetermined condition.

Data conversion system and method of converting data

Provided is a data analysis support system, comprising a processor, and a storage device which is coupled to the processor. The storage device retains objective index information which associates primary key values with objective index values, and explanatory index information which associates values common to the primary keys with sets of values of explanatory indices of a plurality of items. The processor selects one or more items of the explanatory indices, clusters the values of the explanatory indices of the selected one or more items, identifies a range of the values of the explanatory indices of each of the items of each of the clusters which are obtained by the clustering, and outputs the identified value ranges.

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Tokyo, Japan

Fumiya Kudo