An appliance management system includes a plurality of appliances connected to a network and a portable terminal. Each of the appliances includes a sensor configured to detect an abnormality of the appliance, and a first near field wireless communication module configured to broadcast an identifier of the appliance when the sensor detects the abnormality. The portable terminal includes a second near field wireless communication module and a controller. The controller is configured to control the second near field wireless communication module to transmit an access request to an abnormal appliance upon the second near field wireless communication module receiving an identifier of the abnormal appliance. The terminal controller is also configured to control a display to display an abnormality screen including the identifier of the abnormal appliance upon the second near field wireless communication module receiving abnormality information from the abnormal appliance.

A home appliance and a control method for the home appliance, which is operable in an IoT environment through a 5G communication network and uses a neural network model generated according to machine learning is provided. The home appliance may include a home appliance main body; a container mounted within the home appliance main body to accommodate a treatment target; a camera arranged to photograph the inside of the container; and one or more processors configured to control an operation of the home appliance, wherein the processor is configured to determine an amount of a treatment target based on feature shapes of the container identified in an image of the inside of the container photographed by the camera.

A method for the process programming of a household appliance includes the following steps: receiving a sequential program by the household appliance; checking the sequential program with respect to a predetermined device condition by the household appliance; and releasing the sequence of the sequential program in the household appliance as a function of the result of the check.

An appliance control method includes: acquiring a user action of a user; predicting a plurality of action information items about future user actions of the user based on the user action, the plurality of action information items including a plurality of actions and predicted times at which the plurality of actions are predicted to be performed; determining appliance control operations corresponding to the plurality of actions based on the plurality of actions and the predicted times; and performing the appliance control operations based on the predicted times.

An electronic device and method for predicting whether a manufactured product will exhibit a potential defect by providing measurement information of a home appliance as input to a first learning network model and a second learning network model trained to predict whether the home appliance will exhibit a potential defect, applying a first weight to first prediction information output from the first learning network model and a second weight to second prediction information output from the second learning network model, identifying a probability that the home appliance will exhibit the potential defect based on weighted first prediction information of the first prediction information to which the first weight is applied and second prediction information of the second prediction information to which the second weight is applied. The first learning network model is a supervised learning network model and the second learning network model is an unsupervised learning network model.

A home appliance and a control method for the home appliance, which is operable in an IoT environment through a 5G communication network and uses a neural network model generated according to machine learning is provided. The home appliance may include a home appliance main body; a container mounted within the home appliance main body to accommodate a treatment target; a camera arranged to photograph the inside of the container; and one or more processors configured to control an operation of the home appliance, wherein the processor is configured to determine an amount of a treatment target based on feature shapes of the container identified in an image of the inside of the container photographed by the camera.

An apparatus and method are used for disaggregating and classifying energy consumption data from the overall energy consumption data of a building. Both high frequency sampling data classified by a convolutional neural network running on a cloud server and low frequency sampling data analyzed by a convolutional neural net running locally on an electric smart meter are used in the method. A control circuit switches between high and low frequency sampling modes based on the activity level of the building. Individual devices can also be controlled through a mobile application connected to the control circuit.

An appliance management system includes a plurality of appliances connected to a network and a portable terminal. Each of the appliances includes a sensor configured to detect an abnormality of the appliance, and a first near field wireless communication module configured to broadcast an identifier of the appliance when the sensor detects the abnormality. The portable terminal includes a second near field wireless communication module and a controller. The controller is configured to control the second near field wireless communication module to transmit an access request to an abnormal appliance upon the second near field wireless communication module receiving an identifier of the abnormal appliance. The terminal controller is also configured to control a display to display an abnormality screen including the identifier of the abnormal appliance upon the second near field wireless communication module receiving abnormality information from the abnormal appliance.

A system comprising a physical sphere, a digital sphere and a fusion system. The physical sphere including physical spatial elements and temporal elements. The fusion system comprising a foreplane including physical fabric, a perceptor subsystem, and an actuator subsystem, and a backplane including a communication infrastructure, computing and storage infrastructure, power infrastructure, redundancy, and cloud connections. The digital sphere including an artificial intelligence system tethered to the physical sphere, the artificial intelligence system comprising a subsystem of observation configured to receive data from the perceptor subsystem, a subsystem of thinking configured to learn from, model, and determine a state of an enclosure based on the received data, and a subsystem of activity configured to generate decisions with the actuator subsystem based on the state of the enclosure according to a predetermined objective for the enclosure. Computations performed by the system are spatial tethered where operations require spatial signatures to ensure information is contained within the enclosure.

A home appliance, a home appliance system, and a controlling method thereof are disclosed. The home appliance includes an input interface, a communication interface, a processor, and a motor, wherein the processor is configured to receive input of an instruction regarding whether to activate a noise control mode from a user through the input interface, receive notification information including at least one of a received call or a received message from a terminal device interlocked with the home appliance through the communication interface, and based on the noise control mode being in an inactivated state, ignore the notification information and maintain the driving speed of the motor.