Hitachi, Ltd.

Toshiko Aizono

Wenpeng Wei

Yuya Okadome

Ryo Sakai

Hiroyuki Kaji

Yasuharu Namba

Yasutsugu Morimoto

Atsuko Koizumi

Kazuaki Tokunaga

Masaru Takeuchi

Takuya Mogawa

A robot control system includes a state candidate generation unit that generates a state candidate that is a state transition destination of a robot at next time, a control amount estimation unit that estimates a control amount for transitioning to the state candidate, a state candidate evaluation unit that calculates a distance between the target state of the robot and the state candidate, calculates a coincidence degree between (i) a state at next time estimated from a state at current time of the robot and the control amount and (ii) the state candidate, and sets a sum of the distance and the coincidence degree to be an evaluation value, and a selection unit that selects a state candidate with a minimum evaluation value from state candidates and generate a motion corresponding to the selected state candidate.

Robot control system and robot control method

An error in a demand prediction of items is reduced. A data processing apparatus for processing data on a demand for multiple items includes a model learning unit that generates a prediction model for predicting the demand for the multiple items, and a demand prediction unit that predicts the demand for the multiple items by using the prediction model. The model learning unit inputs actual data of the demand for each item to a first neural network of each item, extracts a feature quantity of each item, and combines the feature quantity of each item to generate a second neural network that is a prediction model.

Data processing apparatus and data processing method

In view of the relevant background art, the present invention is to provide a system or apparatus which enables data in use for learning to be sorted out, thereby reducing the amount of data to be handled. Presenting one example of the control system according to the present invention, the control system including a first processing device to generate a learnt model; and a second processing device to operate employing the generated learnt model and provided with input means, wherein the second processing device transmits information input from the input means to the first processing device; and where the input information as transmitted is information belonging to an area different from an area designated with the learnt model, the first processing device generates an updated learnt model based on the information belonging to the different area as received and the learnt model.

Control system and control method

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.

Marketing support system

A data item name constituting a source data schema is extracted (), a data item name constituting a target data schema is extracted (), element concepts included in the extracted data item names of the source data schema and the target data schema are extracted (), a distinctive relation between the data item names is set based on the extracted element concepts to be stored in an ontology (), the data item names of the source data schema and the target data schema are read, a correlation is set between the two read data item names based on the distinctive relation to be stored in a mapping definition file ().

Program for mapping of data schema

For each word occurring in Japanese text, a set of words co-occurring with it and their co-occurrence frequencies are extracted, where two words are regarded as co-occurring with each other when they occur in the same sentence. Likewise, for each word occurring in an English text that corresponds to the Japanese text, a set of words co-occurring with it and their co-occurrence frequencies are extracted. A correlation is calculated between a Japanese word and an English word based upon the co-occurrent word set of the Japanese word and that of the English word, with the assistance of a Japanese-English bilingual dictionary of basic words. The correlation is defined as the ratio of the number of possible correspondences between the two co-occurrent word sets to the total of the co-occurrence frequencies in the two co-occurrent word sets. Pairs of words having a mutually maximum correlation are selected as candidate translation pairs of words, and displayed on a display device. Finally, user-selected pairs are registered in the bilingual dictionary. Thus, the bilingual dictionary is augmented incrementally.

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Tokyo, Japan

Toshiko Aizono