Hitachi, Ltd.

Maxell, Ltd.

Hitachi-high-technologies Corporation

Kanako Esaki

Kiyoto Ito

Hiroyuki Mizuno

Kohsei Matsumoto

Tadayuki Matsumura

Akihiko Kandori

Yuko Sano

Hiroto Naito

Yuzo Shirakawa

Kenji Katsumata

Tatsuo Nakagawa

Kazuhiro Kaizaki

Takumi Okamura

Tomohiko Mizuguchi

Yuhua Zhang

Tomochika Yamashita

Tamotsu Nagabayashi

Taiki Fuji

Shinya Katsuki

Yasushi Saito

To act appropriately in consideration of uncertainty of a surrounding environment, it is provided a control system for generating an action for controlling a controlled device, comprising: a reception module configured to receive sensor data acquired by observing a state of a surrounding environment of the controlled device; a self-recognition module configured to derive, through use of a self-recognition prediction model that predicts a self-range being a range having a predictability and a controllability relating to the controlled device, a self-recognition block that defines the self-range from the sensor data; a target action prediction module configured to derive, through use of a target action prediction model that predicts a target action of the controlled device, the target action from the sensor data; and a switching module configured to select one of the self-recognition block or the target action in order to generate an action of the controlled device.

Control system and action generation method

An information processing apparatus and an information processing method capable of accurately recognizing an object to be sensed are provided. One or a plurality of learning models are selected from among learning models corresponding to a plurality of categories, a priority of each of the selected learning models is set, observation data obtained by sequentially compounding pieces of sensor data applied from a sensor is analyzed using the learning model and the priority of the learning model, a setting of sensing at a next cycle is selected based on an analysis result, predetermined control processing is executed in such a manner that the sensor performs sensing at the selected setting of sensing, and the learning model corresponding to the category estimated as the category to which a current object to be sensed belongs with a highest probability is selected based on the categories to each of which the previously recognized object to be sensed belongs.

Information processing apparatus and information processing method

To generate a prediction image in which an outline of an object is clear and existence of the object is easily recognized, the image prediction system that generates a future prediction image and includes a gaze unit, a working memory unit, a control unit, and a generation model unit. The gaze unit controls a region including an object included in an observation image as a first gaze region. The working memory unit controls the first gaze region as a second gaze region when a difference in the first gaze region between the observation image and a prediction image is equal to or less than a predetermined value. The generation model unit generates prediction images of the first gaze region and the second gaze region. The control unit integrates the prediction image of the first gaze region and the prediction image of the second gaze region to generate a prediction image.

Image prediction system

A retail agent determines a parameter of an activity proposal model by using data stored in a past record database, and determines parameters of an activity determination model and an activity value evaluation model by further using base activity simulation data. Consequently, it is possible to appropriately determine parameters of a subsystem control method in a complex system which cannot be embodied as a simulator and shows a significant change with respect to past record data.

Learning management system and learning management method

The present invention addresses the problem of limiting power consumption and radio wave interference when assessing the movements of multiple sites of a living body. The present invention is a movement function assessment system provided with: a movement function measurement apparatus for calculating movement data on the basis of the relative distances of pairs of transmission coils and reception coils attached to movable portions of a living body; and an assessment apparatus for assessing the movement function of the living body on the basis of the movement data received from the movement function measurement apparatus. A control unit of the movement function measurement apparatus performs the switching of the multiple transmission coils by a switching unit (1) and the switching of the multiple reception coils by a switching unit (2) so that the paired transmission coils and reception coils are operated sequentially.

Movement function assessment system and movement function measurement apparatus

According to one embodiment, a machine control system includes: a selecting unit which acquires a state quantity of a machine converted from data acquired by a sensor provided in the machine to select two or more learning models according to the acquired state quantity; a composing unit which inputs the state quantity acquired by the selecting unit to each of the two or more learning models selected by the selecting unit to calculate a composed value using a command value output from each of the learning models; and a learning unit which outputs a command value with respect to the machine in a range based on the composed value calculated by the composing unit, acquires a state quantity of the machine, searches for a command value of a specific condition from a combination of the output command value and the acquired state quantity, and outputs the searched command value to the machine, thereby creating a new learning model.

Machine control system

The control device includes: an observation unit that calculates an amount relating to a state of a control target detected by a sensor; an abnormality predictor detection unit that detects an abnormality predictor of the control target; a reward calculation unit that calculates a reward which is an amount obtained by evaluating pass or fail of an operation of the control target; a basic control amount calculation unit that calculates data relating to the state during an operation and a basic control amount serving as a reference to control the control target; a control amount search unit that searches for a control amount at which the reward satisfies a predetermined condition based on the basic control amount, the data relating to the state during the operation and the reward; and a control unit that generates a command value to the control target.

Control device, control method, and control program

A brain dysfunction assessment device () comprises: a body movement indicator () which presents a body movement to be performed to a subject with a body movement presentation device (); a body movement data acquisition unit () which acquires the subject's body movement data with a body movement detection sensor (); a body movement accuracy calculation unit () which calculates a positional accuracy and a time sequence accuracy of the subject's body movement from the acquired body movement data; and a cognitive dysfunction assessment unit () which assesses the level of cognitive dysfunction of the subject by comparing a value which indicates the accuracy of the subject's body movement that is obtained from the calculated positional accuracy and time sequence accuracy with statistical data which indicates the accuracy of body movement of healthy individuals.

Brain dysfunction assessment method, brain dysfunction assessment device, and program thereof

A detection device configured to detect each color of a plurality of components of a biological-sample is provided. The detection device executes the detection process with respect to the container which stores the sample containing the first and the second components. The detection device includes an image pickup device for picking up an image of the container, a background section serving as background of the image pickup section, and a detection section for detecting color of the first component of the sample. The container is disposed between the image pickup section and the background section. The detection section is configured to identify a first region of the first component having the label attached to the container as background, and a second region of the first component having the background section as background so as to detect color information on the first component from at least one region of the first and the second regions.

Growing community of inventors

Tokyo, Japan

Kanako Esaki