Abstract: Disclosed is a method and apparatus for inspecting defects in a washer based on deep learning. According to an embodiment of the present disclosure, a method for inspecting defects in a washer based on deep learning gathers learning data while the washer operates and trains a first ANN model for diagnosing the condition of the washer and a second ANN model for securing the reliability of the result of inspection of the condition of the washer. Thereafter, the washer may make a diagnosis of whether the washer is defective based on the two pre-trained ANN models and are thereby able to continuously monitor whether the washer has an abnormal condition. According to an embodiment, the artificial intelligence (AI) module may be related to unmanned aerial vehicles (UAVs), robots, augmented reality (AR) devices, virtual reality (VR) devices, and 5G service-related devices.

Abstract: A washing machine includes a case; a tub; a drum; a vibration sensor provided in the tub and configured to output a factor of a current vibration result for sensing a vibration value of the tub; a motor configured to drive the drum to process laundry; a motor control module configured to control a drum RPM to reflect a request RPM by controlling a current of the motor and output a current vibration inducing factor; an AI module configured to receive inputs of the current vibration result factor and the vibration inducing factor and output a compensating variable for proactively deal with a future vibration result that is expected based on a current laundry distribution state, in a dry-spin cycle; and a processor configured to perform a dry-spin cycle through centrifugal power of the drum by compensating the request RPM by reflecting the compensating variable in a vibration expectation section.

Abstract: A washing machine include: a case; a tub; a drum; a vibration sensor provided in the tub and configured to output a factor of a current vibration result for sensing a vibration value of the tub; a motor configured to drive the drum to process laundry; a motor control module configured to control a drum RPM to reflect a request RPM by controlling a current value of the motor and output a current vibration inducing factor; an AI module configured to receive inputs of the current vibration result factor and the vibration inducing factor and output a compensating variable for proactively deal with a future vibration result that is expected based on a current laundry distribution state, corresponding to a current laundry distribution state; and a processor configured to perform a dry-spin cycle through centrifugal power of the drum by compensating the request RPM by reflecting the compensating variable.

Abstract: A laundry dewatering method using an intelligent washing machine is disclosed. A laundry dewatering method using an intelligent washing machine trains a ball balancer control model for acquiring first ball balancer control information related to position control in an inner tub of a ball balancer positioned in the inner tub, acquires first washing machine state information related to a dewatering operation of the washing machine, acquires the first ball balancer control information by inputting the first washing machine state information to the trained ball balancer control model, controls a position of the ball balancer in the inner tub on the basis of the acquired first ball balancer control information, and dewaters the laundry on the basis of the control result, thereby being able to reduce vibration and noise of the washing machine due to laundry that is non-uniformly received and dewatered in the inner tub.

Abstract: Disclosed is a method for drying laundry using an intelligent washer. According to an embodiment of the present disclosure, a method for drying laundry trains a time prediction model for obtaining first remaining time information related to complete dryness of the laundry, obtains first washer state information related to a state of a washer for drying the laundry, obtains the first remaining time information by inputting the first washer state information to the trained time prediction model, and dries the washer based on the obtained first remaining time information, thus precisely predicting the time necessary for complete dryness of wet laundry. According to an embodiment, the washer may be related to artificial intelligence (AI) modules, unmanned aerial vehicles (UAVs), robots, augmented reality (AR) devices, virtual reality (VR) devices, and 5G service-related devices.

Abstract: Disclosed is a method and apparatus for inspecting defects in a washer based on deep learning. According to an embodiment of the present disclosure, a method for inspecting defects in a washer based on deep learning gathers learning data while the washer operates and trains a first ANN model for diagnosing the condition of the washer and a second ANN model for securing the reliability of the result of inspection of the condition of the washer. Thereafter, the washer may make a diagnosis of whether the washer is defective based on the two pre-trained ANN models and are thereby able to continuously monitor whether the washer has an abnormal condition. According to an embodiment, the artificial intelligence (AI) module may be related to unmanned aerial vehicles (UAVs), robots, augmented reality (AR) devices, virtual reality (VR) devices, and 5G service-related devices.

Abstract: A method for controlling a home appliance in consideration of a state of a surrounding environment is disclosed. A method for controlling a home appliance according to an exemplary embodiment of the present disclosure includes operating a home appliance using a first operation profile, identifying at least one of a behavior type of a user or a type of sound around the user, using Internet of Things (IoT) communication using 5G communication and artificial intelligence (AI), and changing an operation profile for operating a home appliance to a second operation profile, based on at least one of the identified behavior type of the user or type of sound around the user. According to the present disclosure, in accordance with the state of the surrounding environment, the operation noise can be reduced or the operation time of the home appliance can reduced without increasing the discomfort of the user, and therefore the discomfort of the user due to the operation noise can be relieved.

Abstract: Disclosed is a method for drying laundry using an intelligent washer. According to an embodiment of the present disclosure, a method for drying laundry trains a time prediction model for obtaining first remaining time information related to complete dryness of the laundry, obtains first washer state information related to a state of a washer for drying the laundry, obtains the first remaining time information by inputting the first washer state information to the trained time prediction model, and dries the washer based on the obtained first remaining time information, thus precisely predicting the time necessary for complete dryness of wet laundry. According to an embodiment, the washer may be related to artificial intelligence (AI) modules, unmanned aerial vehicles (UAVs), robots, augmented reality (AR) devices, virtual reality (VR) devices, and 5G service-related devices.

Abstract: An intelligent vibration prediction method and apparatus are disclosed. An intelligent vibration prediction method according to an embodiment of the present disclosure inputs washing machine operation data to an input deviation correction model, acquires corrected washing machine operation data from the input deviation correction model, inputs the corrected washing machine operation data to a vibration prediction model, and acquires vibration prediction data from the vibration prediction model, thereby configuring a vibration prediction model optimized for an actual use environment. One or more of the vibration prediction method, the intelligent computing device and the server of the present disclosure can be associated with artificial intelligence (AI) modules, unmanned aerial vehicle (UAV) robots, augmented reality (AR) devices, virtual reality (VR) devices, 5G service related devices, etc.

Abstract: A laundry dewatering method using an intelligent washing machine is disclosed. A laundry dewatering method using an intelligent washing machine trains a ball balancer control model for acquiring first ball balancer control information related to position control in an inner tub of a ball balancer positioned in the inner tub, acquires first washing machine state information related to a dewatering operation of the washing machine, acquires the first ball balancer control information by inputting the first washing machine state information to the trained ball balancer control model, controls a position of the ball balancer in the inner tub on the basis of the acquired first ball balancer control information, and dewaters the laundry on the basis of the control result, thereby being able to reduce vibration and noise of the washing machine due to laundry that is non-uniformly received and dewatered in the inner tub.

Abstract: A washing machine include: a case; a tub; a drum; a vibration sensor provided in the tub and configured to output a factor of a current vibration result for sensing a vibration value of the tub; a motor configured to drive the drum to process laundry; a motor control module configured to control a drum RPM to reflect a request RPM by controlling a current value of the motor and output a current vibration inducing factor; an AI module configured to receive inputs of the current vibration result factor and the vibration inducing factor and output a compensating variable for proactively deal with a future vibration result that is expected based on a current laundry distribution state, corresponding to a current laundry distribution state; and a processor configured to perform a dry-spin cycle through centrifugal power of the drum by compensating the request RPM by reflecting the compensating variable.

Abstract: A washing machine includes a case; a tub; a drum; a vibration sensor provided in the tub and configured to output a factor of a current vibration result for sensing a vibration value of the tub; a motor configured to drive the drum to process laundry; a motor control module configured to control a drum RPM to reflect a request RPM by controlling a current of the motor and output a current vibration inducing factor; an AI module configured to receive inputs of the current vibration result factor and the vibration inducing factor and output a compensating variable for proactively deal with a future vibration result that is expected based on a current laundry distribution state, in a dry-spin cycle; and a processor configured to perform a dry-spin cycle through centrifugal power of the drum by compensating the request RPM by reflecting the compensating variable in a vibration expectation section.

Abstract: A method for controlling a home appliance in consideration of a state of a surrounding environment is disclosed. A method for controlling a home appliance according to an exemplary embodiment of the present disclosure includes operating a home appliance using a first operation profile, identifying at least one of a behavior type of a user or a type of sound around the user, using Internet of Things (IoT) communication using 5G communication and artificial intelligence (AI), and changing an operation profile for operating a home appliance to a second operation profile, based on at least one of the identified behavior type of the user or type of sound around the user. According to the present disclosure, in accordance with the state of the surrounding environment, the operation noise can be reduced or the operation time of the home appliance can reduced without increasing the discomfort of the user, and therefore the discomfort of the user due to the operation noise can be relieved.