Artificial intelligence device mounted on wine refrigerator

    公开(公告)号:US10976715B2

    公开(公告)日:2021-04-13

    申请号:US16574692

    申请日:2019-09-18

    Abstract: An artificial intelligence device mounted on a wine refrigerator including one or more divided spaces includes an input unit, a processor, and an output unit. The input unit is configured to recognize a wine label of each space and recognize an image for determining opening or non-opening of a wine. The processor is configured to acquire wine information by using an artificial intelligence model that receives image data acquired from the input unit as an input value, create a wine list table of each space by using the acquired information, and group wines having the same storage condition into at least one group according to the wine list table, and perform a control such that a temperature of each space is set based on the storage condition of the group. The output unit is configured to output a signal received from the processor.

    Device including battery
    14.
    发明授权

    公开(公告)号:US10803385B2

    公开(公告)日:2020-10-13

    申请号:US16181271

    申请日:2018-11-05

    Abstract: A method of controlling a battery is disclosed. The method includes training an artificial neural network to calculate an internal characteristic parameter value of the battery corresponding to a sensed input/output parameter value using training data, sensing the input/output parameter value of the battery, acquiring the characteristic parameter value corresponding to the sensed input/output parameter value using the trained artificial neural network, and controlling charging or discharging of the battery based on the acquired characteristic parameter value.

    DEVICE INCLUDING BATTERY
    15.
    发明申请

    公开(公告)号:US20200074297A1

    公开(公告)日:2020-03-05

    申请号:US16181271

    申请日:2018-11-05

    Abstract: A method of controlling a battery is disclosed. The method includes training an artificial neural network to calculate an internal characteristic parameter value of the battery corresponding to a sensed input/output parameter value using training data, sensing the input/output parameter value of the battery, acquiring the characteristic parameter value corresponding to the sensed input/output parameter value using the trained artificial neural network, and controlling charging or discharging of the battery based on the acquired characteristic parameter value.

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