Lg Electronics Inc.

Guntae Bae

Dongsoo Choung

Junkwan Lee

Sungjin Kim

Jongwoo Han

Jungho Lee

Suman Bae

Jongpil Won

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.

Method and apparatus for inspecting defects in washer based on deep learning

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.

Washing machine and a control method of the same

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.

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.

Intelligent washing machine and method for controlling ball balancer using the same

A terminal and operating method thereof includes receiving a plurality of contents, classifying the plurality of received contents into a plurality of groups based on recipient information of a recipient corresponding to a user of the terminal, and outputting the plurality of contents classified into the plurality of groups in order corresponding to the recipient information. A thumbnail image represents each of the plurality of groups. The thumbnail image is displayed in accordance with the recipient information, such as a thumbnail image of a group considered to be more of interest to a user displayed in a larger size.

Terminal receiving content based on network environment and controlling method thereof

Provided are a mobile terminal including an artificial intelligent agent and a method for controlling a mobile terminal using machine learning. A mobile terminal comprises a microphone, a learning data unit configured to identify and store keywords and a controller configured to: activate the microphone in response to an activation event, receive a voice input via the microphone, extract vocabulary information from the received voice input and execute a control corresponding to command information included in the received voice input, wherein the executed control is varied by utilizing the keywords and the extracted vocabulary information.

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Seoul, South Korea

Guntae Bae