Mitsubishi Electric Corporation

Genta Yoshimura

Takashi Matsumoto

Wataru Matsumoto

Yasushi Sato

Takuji Morimoto

Toshihiro Mega

Yoshinori Nakajima

Atsushi Kawashima

Toshisada Mariyama

Fuyuki Sato

Kimika Ikegami

Natsuki Tahara

Shinichiro Otani

Hiroki Kawano

Kazuyuki Nagahiro

Tomohiro Narui

Xiongxin Zhao

Hiroaki Hokari

Shuichi Murayama

Shan Gao

Naoki Taguchi

Keisuke Sugiura

Takao Endo

A management device includes: an acquisition unit that acquires operating history data for an equipment device in a prescribed time period; and a classification processing unit that classifies, based on the operating history data acquired by the acquisition unit, an operating pattern of the equipment device in the prescribed time period. The classification processing unit includes: an operating pattern calculation unit that calculates a first operating pattern in a first time period and a second operating pattern in a remaining second time period when the prescribed time period is divided into unit time periods; a change calculation unit that calculates a degree of change in the operating pattern using the first operating pattern and the second operating pattern when the prescribed time period is divided into unit time periods; a split point calculation unit that calculates, as a split time point, a time point where the degree of change satisfies a prescribed condition for the prescribed time period; and a split processing unit that splits the prescribed time period at the calculated split time point.

Management device, management system, and management method for classifying an operation pattern

A driver availability detection device includes an information acquisition unit to acquire information related to an occupant and a threshold setting unit to set an abnormal-state determination threshold for estimating the abnormal state of the occupant on a basis of an abnormal state score obtained by inputting the information related to the occupant acquired by the information acquisition unit to a machine learning model within a first threshold setting time.

Driver availability detection device and driver availability detection method

An inference device obtains an inference value of a time taken to melt frost for defrosting temperature information representing a temperature of an outdoor-side heat exchanger included by an outdoor unit of an air-conditioning apparatus or a state of change in the temperature. In the inference device, the time taken to melt frost is a period during which the temperature of the outdoor-side heat exchanger is stable within a first range. The inference device includes a first data acquisition unit that acquires the defrosting temperature information of the air-conditioning apparatus, and an inference unit that obtains, by using a trained model that infers a time taken to melt frost from the defrosting temperature information and in accordance with the defrosting temperature information acquired by the first data acquisition unit, an inference value of the time taken to melt frost for the defrosting temperature information.

Inference device and learning device

An information collection unit collects information indicating a driving operation of a vehicle by a driver and a state of the vehicle changed by the driving operation, and outputs, among the collected information, information in a period from a start point at which a driving operation contributing to stop of the vehicle is performed to a stop start point in a stop state where a stop time of the vehicle continues for a predetermined time or more, as driving operation information. A state estimation unit acquires driver state information indicating a state of the driver from a learned learner on which machine learning has been performed so as to output the driver state information when the driving operation information is input, by inputting the driving operation information output from the information collection unit to the learner and performing arithmetic processing of the learner.

Driver state estimation device, driver state estimation method, and learning method

A development assistance device includes: multiple conversion units each converting either an inputted signal value or inputted meta information into either a signal value or meta information, and outputting either the signal value after conversion or the meta information after conversion as output information; a pipeline unit causing, on the basis of pipeline information showing a relationship of mutual connections between the multiple conversion units, the multiple conversion units to sequentially perform the corresponding converting processes; an inference unit making an inference by using the output information outputted by a final conversion unit which the pipeline unit causes, on the basis of the pipeline information, to finally perform the converting process, out of the multiple conversion units; and an image generation unit generating image information for visualizing the connection relationship shown by the pipeline information, and a relationship of a connection between the final conversion unit and the inference unit.

Development assistance device, development assistance system, and development assistance method

A power consumption estimation device () which estimates power consumption of each of one object facility () or more whose operation statuses can be monitored includes: a total power vector generation unit () to acquire time-series data of total power consumption; a status matrix generation unit () to acquire time-series data of an operation parameter of the one object facility () or more; a reference signal generation unit () to generate one reference signal or more; a contribution degree estimation unit () to perform multiple regression analysis by taking the total power consumption as an objective variable, and the operation parameter of the one object facility () or more and a component value of the one reference signal or more, as explanatory variables, to thereby calculate a contribution degree of each of the one object facility () or more; and a breakdown calculation unit () to multiply the contribution degree of the object facility () and the operation parameter of the object facility (), to thereby calculate power consumption of the object facility ().

Power consumption estimation device

Growing community of inventors

Tokyo, Japan

Genta Yoshimura