A method to control a laundry drying machine having a rotatable drum, a motor for rotating the drum, and a hot air generator for supplying a drying airflow to the drum. The method includes: collecting physicals quantities during an initial phase of said drying cycle, performing an estimation of polynomial coefficients of a cubic polynomial model indicative of an estimated change of a laundry moisture value over the time based on respective linear polynomial models comprising the collected physicals quantities, performing an estimation of the laundry moisture value by the cubic polynomial model based on the estimated polynomial coefficients, and controlling the motor and hot air generator during a drying cycle, based on one or more physicals quantities associated with the drum loaded with laundry, the electric motor and the hot air generator.

A laundry treating appliance having a tub, rotatable drum, at least one transmitter coil, at least one ring shaped housing on the drum perimeter and having an annular channel therein and at least one balancing unit disposed in the channel. The balancing unit has a receiver coil configured to receive wireless power from the transmitter coil and an actuator powered by the receiver coil, a sensing element to detect a position of the balancing unit in the housing, a balancing control unit configured to control the positioning of the balancing unit by driving the transmitter coil to send power to the receiver coil to operate the actuator on a friction force element to change the friction between the housing and the balancing unit.

Disclosed is an intelligent washing machine. A control method for an intelligent washing machine according to an embodiment of the present invention may: predict a washing machine cycle from user information; control the washing machine according to the predicted washing machine cycle; acquire an image of the inside of the drum by means of a camera while the washing machine is being controlled; predict the contamination level inside the drum by using the acquired image; and perform an additional control operation according to the predicted result. The intelligent washing machine may be linked to an Artificial Intelligence module, an Unmanned Aerial Vehicle (UAV), a robot, an Augmented Reality (AR) device, a virtual reality (VR) device, a device related to a 5G service, or the like.

Disclosed are a method and apparatus for washing machine dehydration, and a washing machine. The method includes: step 1, detecting a current eccentricity value to serve as a first eccentricity value, and determining a first dehydration rotational speed value corresponding to the first eccentricity value; step 2, performing a corresponding dehydration operation; step 3, detecting another current eccentricity value to serve as a second eccentricity value, and determining a second dehydration rotational speed value corresponding to the second eccentricity value; step 4, if the second eccentricity value is smaller than the first eccentricity value, performing a corresponding dehydration operation, assigning the value of the second eccentricity value to the first eccentricity value and then performing the step 3; and step 5, if the second eccentricity value is greater than or equal to the first eccentricity value, performing a leveling operation, and then performing the step 3.

Provided are a method and an apparatus for analyzing a vibration of a deep-learning based washing machine. In the method for analyzing a vibration of a deep-learning based washing machine according to an embodiment of the present invention, a washing tub of the washing machine includes a specific shape pattern, an artificial neural network model is learned from a video image obtained by photographing the shape pattern through a camera and a vibration value sensed through the vibration sensor, and thus, by using the artificial neural network model, it is possible to predict a vibration value of the washing machine using the camera of the washing machine even without a vibration sensor. According to the present invention, a smart washing machine without the vibration sensor such as 6-axis gyro sensor can be implemented. The AI device of the present invention can be associated with an unmanned aerial vehicle (UAV), a robot, an augmented reality (AR) device, a virtual reality (VR) device, and a device related to a 5G service.

A laundry washing machine includes a weight sensing system including a weight sensor positioned to sense at least a portion of a weight associated with a wash tub and configured to output an associated pulse width modulated (PWM) force signal having a duty cycle that varies with applied force on the weight sensor. A controller of the laundry washing machine includes a state change sensing logic circuit coupled to receive the PWM force signal from the weight sensor and execute an interrupt service routine triggered by each state transition to capture a timestamp associated with each state transition such that the duty cycle of the PWM force signal may be determined using the captured timestamps.

A washing machine and a method of controlling the same are disclosed. The amount of laundry that is introduced into the washing machine is measured using gravity and inertia applied during the operation of the motor, whereby it is possible to precisely calculate the amount of laundry and to minimize the effects of the initial position of the laundry and the movement of the laundry. In addition, the current value of the motor that is operated is used to measure the amount of laundry without a sensor. Furthermore, the amount of laundry is measured at the rotational speed of the motor at which the laundry clings to the drum, whereby it is possible to minimize an error due to the movement of the laundry to thus improve accuracy. Moreover, it is possible to determine the amount of laundry within a short time. Consequently, it is easy to commence the spin-drying operation, thereby reducing washing time and saving energy.

A method for controlling a laundry washing machine. The method includes: performing a tub loading process comprising operating supply valve(s) to provide a first volume of water through a detergent supply system and into a tub to fill the tub with wash liquid; performing a washing process comprising rotating a drum to agitate laundry in in the presence of the wash liquid; performing a static rinsing process comprising operating a pump to remove any free volume of wash liquid from the tub without rotating the drum; and performing one or more rinsing processes comprising: operating the supply valve(s) to provide a volume of clean rinse water to the tub, operating the pump to remove the rinse water from the drum, and operating the motor to spin the drum at high speed to extract rinse water from the laundry, while continuing to operate the pump.

A washing machine and method for operating a washing machine having a suds container for holding washing liquid, a non-ribbed drum provided with stamped portions rotatably mounted in the suds container for holding laundry, a motor for driving the drum, and a control device. During rotation of the drum in a subcritical rotational speed range, the method includes sampling a variable related to the drum or the motor over a predetermined period of time to determine time signals of the variable, executing a frequency analysis of the time signal of the variable to determine frequency components of the variable in a predetermined frequency range, summing the frequency components at predetermined frequencies of the predetermined frequency range, and detecting, depending on the sum of the summed up frequency components, whether a loading situation where laundry is sliding in the drum is present.

A laundry appliance includes a camera assembly mounted within view of a chamber configured for receiving a load of clothes. A controller is operably coupled to the camera assembly for waking the camera only when certain conditions occur. Specifically, the controller is configured to detect an activity trigger indicative of interaction with the laundry appliance, obtain one or more images of the chamber using the camera assembly in response to detecting the activity trigger, and analyze the one or more images using a machine learning image recognition process to identify the occurrence of a predetermined condition or event.