Abstract: A validation method determining device includes a data acquiring unit to acquire training data to be used for training of a predictor and test data to be used for measurement of prediction accuracy of the predictor, a validation method recommending unit to recommend a validation method for extracting the training data and validation data corresponding to a prediction target using data type information indicating data types of the training data, the test data, and column data constituting both the data, and a validation method determining unit to determine a validation method to be used for estimation of prediction performance of the predictor from a recommended validation method.

Abstract: 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.

Abstract: 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

Abstract: An awakening level estimation device includes: processing circuitry configured to acquire two or more types of occupant state information different from each other, the occupant state information indicating a current state value of an occupant; acquire occupant basic state information indicating a basic state value of the occupant when the occupant is in an awakening state; acquire difference information indicating a difference between the current state value and the basic state value, estimate an awakening level of the occupant on the basis of the two or more types of difference information, and input the two or more types of difference information to a learned model corresponding to a leaning result by machine learning, and generating and outputting awakening level information indicating an awakening level on the basis of an estimation result output from the leaned model.

Abstract: An energy conservation assistance apparatus includes an event occurrence time extraction unit, a degree of change calculation unit, and a display control unit. The event occurrence time extraction unit refers to facility operation history information included in a reference period and a designated period to extract occurrence times at which a predetermined event has occurred in each of the reference period and the period of interest. The degree of change calculation unit calculates a degree of change in an operation pattern of a facility obtained from a distribution of the occurrence times at which the predetermined event has occurred in the period of interest, relative to an operation pattern of the facility obtained from a distribution of the occurrence times at which the predetermined event has occurred in the reference period. The display control unit presents information regarding a degree of change to a user.

Abstract: 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.

Abstract: The feature amount extraction device includes: a data acquisition unit to acquire time-sequential data as input time-sequential data; a multiple-filter application unit, including multiple digital filters, to apply each of the digital filters to the input time-sequential data acquired by the data acquisition unit, and output, for each of the digital filters, filter response time-sequential data that is time-sequential data including a time-sequential feature or a frequency feature after having undergone the application; and a feature amount extracting unit to extract feature amounts for a plurality of pieces of the filter response time-sequential data output from the multiple-filter application unit for each of the plurality of pieces of the filter response time-sequential data, and output the extracted feature amounts as feature amount data.

Abstract: 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.

Abstract: A learning device includes: a learning data acquiring unit to acquire a plurality of pieces of learning data in which one piece of learning data is a combination of first information based on one of one or a plurality of pieces of time-series data including observation values in time series, second information based on one of a plurality of prediction periods including at least two prediction periods different from each other, and third information based on observation values after a lapse of the prediction period; and a learning unit to perform learning using the plurality of pieces of learning data acquired using information obtained by combining the first information and the second information in the learning data as an explanatory variable and using the third information as a response variable, and generate a learned model capable of inferring inference observation values after a lapse of a designated prediction period.

Abstract: A power consumption estimation device (10) which estimates power consumption of each of one object facility (100) or more whose operation statuses can be monitored includes: a total power vector generation unit (32) to acquire time-series data of total power consumption; a status matrix generation unit (34) to acquire time-series data of an operation parameter of the one object facility (100) or more; a reference signal generation unit (36) to generate one reference signal or more; a contribution degree estimation unit (38) to perform multiple regression analysis by taking the total power consumption as an objective variable, and the operation parameter of the one object facility (100) 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 (100) or more; and a breakdown calculation unit (42) to multiply the contribution degree of the object facility (100) and the operation parameter of the objec

Abstract: A development assistance device includes: multiple conversion units each for converting either an inputted signal value or inputted meta information into either a signal value or meta information, and for outputting either the signal value after conversion or the meta information after conversion as output information; a pipeline unit for 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 for 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 for generating image information for visualizing the connection relationship shown by the pipeline information, and a relationship of a connection between the final

Abstract: A power consumption estimation device (10) which estimates power consumption of each of one object facility (100) or more whose operation statuses can be monitored includes: a total power vector generation unit (32) to acquire time-series data of total power consumption; a status matrix generation unit (34) to acquire time-series data of an operation parameter of the one object facility (100) or more; a reference signal generation unit (36) to generate one reference signal or more; a contribution degree estimation unit (38) to perform multiple regression analysis by taking the total power consumption as an objective variable, and the operation parameter of the one object facility (100) 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 (100) or more; and a breakdown calculation unit (42) to multiply the contribution degree of the object facility (100) and the operation parameter of the objec

Abstract: A first intermediate layer activity level calculator refers to an index stored in a first intermediate layer storage, acquires, from among activity levels of respective nodes in an input layer calculated by an input layer activity level calculator and weight for respective edges and bias values stored in the first intermediate layer storage, an activity level of each node in the input layer that is connected with each node in the first intermediate layer and weight for each of edges and a bias value thereof, and calculates an activity level of each node in the first intermediate layer using the activity level of each node in the first input layer and the weight for each of the edges and the bias value having been acquired. This is capable of reducing the calculation amount and the memory amount upon performing inference, and also obtaining higher inference accuracy.