Abstract: Road traffic volume information is acquired, and CO2 recovery devices 30, 30a for recovering CO2 exhausted into the air from vehicles on a road and drifting around the road are installed. Activation of the CO2 recovery devices 30, 30a is controlled based on the road traffic volume information.

Abstract: The machine learning device comprises a training part configured to train a machine learning model used in a vehicle; and a detecting part configured to detect replacement of a vehicle part mounted in the vehicle. The training part is configured to retrain the machine learning model using training data sets corresponding to a vehicle part after replacement when a vehicle part relating to input data of the machine learning model is replaced.

Abstract: A replacement confirmation system includes: a retaining unit that retains a learning model, which is generated by machine learning and outputs a parameter used for vehicle control; an acquirer that acquires a result of confirmation with a driver of a vehicle regarding permission for replacement of a parameter output function other than a learning model used in vehicle control with a learning model; and a determination unit that determines to perform replacement with a learning model when the acquirer has acquired permission from a driver.

Abstract: A device for predicting power consumption in a predetermined target area includes: an attribute specifying unit for specifying an attribute of a person within the predetermined target area; and a prediction unit for calculating a predicted power consumption in the target area based on the specified attribute such that the predicted power consumption in the target area differs when the attribute of the person differs.

Abstract: A model learning system includes a device in which a learning model is used, a data acquisition device that acquires data used for creating a training data set for the learning model, and a model learning device that automatically learns the learning model using the training data set for the learning model created using the data. The model learning device is configured to, when a model accuracy after automatically learning the learning model decreases or does not increase compared to before learning, exclude the learning model from a target to be automatically learned.

Abstract: A management server performs processing including updating map information with change of a placement location of a power feed mat corresponding to a power feed control device being determined as not being required (S204) when the management server determines that it has received power feed information from the power feed control device (YES in S200) and when the management server determines an integrated value of an amount of power feed as being equal to or larger than a threshold value (YES in S202), updating the map information with change of the placement location of the power feed mat corresponding to the power feed control device being determined as being required (S206) when the management server determines the integrated value of the amount of power feed as being smaller than the threshold value (NO in S202), and notifying a terminal (S208).

Abstract: A data collection device collects data from a data acquisition device for acquiring data relating to a person located within a predetermined target area. The data collection device includes a processor for: determining whether data acquired by the data acquisition device is data relating to a resident within the target area; and controlling transmission of data from the data acquisition device to the data collection device. The processor causes the data acquisition device to transmit data of parameters that are at least partially different between a case where data acquired by the data acquisition device relates to a resident and a case where data acquired by the data acquisition device does not relate to a resident.

Abstract: A server includes a processor that: acquires a learning condition for newly generating a learned model; identifies AI ethics that the learned model needs to satisfy; and determines whether the learned model newly generated satisfies the AI ethics based on the learning condition and the AI ethics.

Abstract: A server includes a processor that: acquires a learning condition of a learned model; identifies AI ethics that the learned model needs to satisfy; and determines whether the learned model satisfies the AI ethics based on the learning condition and the AI ethics.

Abstract: An information management system provided with a plurality of external devices able to send and receive information and a server configured to be able to communicate with the external devices. The external devices are configured to send to the server the amounts of CO2 recovery recovered by the vehicles provided with the CO2 recovery devices. The server is configured to add up and manage the amounts of CO2 recovery sent from the external device.

Abstract: A control system includes a motor-generator, an electric power generation system, and a battery. An EV operation, in which the electric motor is operated while the electric power generation system is stopped, is performed, when an SOC of the battery is higher than a first value, and an HV operation, in which the electric motor is operated while the electric power generation system is operated, is performed, when the SOC is lower than the first value. At the time of the EV operation, in response to a predicted value of the SOC being equal to or higher than a second set value which is lower than the first set value, the EV operation is continued even if the SOC falls below the first value. The predicted value is a predicted value of the SOC assuming continuation of the EV operation from a current location to a destination.

Abstract: A machine learning method performs training of values of model parameters forming a machine learning model, and is performed in a machine learning system including a vehicle having the machine learning model and a server able to communicate with the vehicle. The machine learning method includes: detecting, by the server, a current processing load of the server; determining, by the server, processing amounts of training respectively performed by the server and the vehicle, based on the processing load; performing, by the server, training of the values of the model parameters in accordance with the determined processing amount in the server; and performing, by the vehicle, training of the values of the model parameters in accordance with the determined processing amount in the vehicle. The server decreases a processing amount at the server when the processing load is relatively high compared to when it is relatively low.

Abstract: A data collection system has a data acquisition device located in a predetermined target area and a data collection device communicable with the data acquisition device, and collects data necessary for use or training of a machine learning model from the data acquisition device to the data collection device. The data collection system includes a processor configured to: determine whether a collection promotion condition is satisfied; and control data transmission. The processor controls data transmission from the data acquisition device to the data collection device so that an amount of data transmission per unit time is larger when it is determined that the collection promotion condition is satisfied, compared to when it is determined that the collection promotion condition is not satisfied.

Abstract: A data management device includes an information acquisition unit that acquires information indicating a resident's degree of acceptance of data collection in a residential unit, and a sensor setting unit that determines an activating mode of each of a group of sensors in the residential unit based on the information acquired by the information acquisition unit.

Abstract: A CO2 capture system for efficiently capturing CO2 from emissions emitted from buildings. The CO2 capture system comprises: a CO2 capture device that captures CO2; a flow path that delivers an exhaust gas emitted from a building to the CO2 capture device; a flow rate regulator that regulates a flow rate of the exhaust gas; and a control unit that controls the flow rate regulator to regulate the flow rate of the exhaust gas.

Abstract: An elevating operation apparatus includes: an obtaining device configured to obtain a first information and a second information, wherein the first information indicates whether or not one vehicle, which has a power receiving unit for wireless electric power supply, includes an elevating mechanism for moving the power receiving unit in a height direction of the one vehicle, and the second information indicates whether or not a power supply apparatus, which is installed outside the one vehicle and which has a power transmitting unit for wireless electric power supply, includes an elevating mechanism for moving the power transmitting unit in the height direction; and a determining device configured to determine to move at least one of the power receiving unit and the power transmitting unit in the height direction on the basis of the first information and the second information.

Abstract: A data collection system collects data, for training of a machine learning model for classifying into a plurality of classes, from a data acquisition device to a data collection device. The system includes a processor configured to specify an occurrence probability of occurrence of a low occurrence class for each condition; set a target transmission amount per unit time; specify a current condition; and transmit data from the data acquisition device to the data collection device, in accordance with a target transmission amount corresponding to the specified current condition. The amount of data transmission is larger in a condition having a relatively higher occurrence probability of the low occurrence class, compared to a condition having a relatively lower occurrence probability of occurrence of the low occurrence class.

Abstract: A method and a system of properly utilizing CO2 captured from the atmosphere as an agricultural fertilizer and a fuel for electric generation. The recycling method comprises: collecting information relating to demand for the CO2 to be utilized as the fertilizer and demand for the CO2 to be utilized as the fuel; calculating a ratio between an amount of the CO2 to be utilized as the fertilizer and an amount of the CO2 to be utilized as the fuel, based on the collected information; and thereafter utilizing the CO2 as the fertilizer and as the fuel based on the calculated ratio.

Abstract: A data collection apparatus collects data used for a process, based on image data representing a group of consecutive images received from an imaging device. A processor of the data collection apparatus is configured to: control transmission of image data to the data collection apparatus; detect an object included in each image of an image group represented by the image data; track a detected object between consecutive images; store object image data relating to each object tracked; and calculate a number of objects included in an image represented by the image data. The control of transmission of the image data or the detection of the objects is performed such that an amount of data per unit number of objects is smaller when the calculated number of objects is relatively larger, as compared to when the number is relatively smaller.

Abstract: A model management device includes a communication execution unit that transmits, when a first machine learning model having an accuracy of a predetermined value or more is generated in a first target area, information about the first machine learning model to at least one target area different from the first target area.