A laundry treatment device includes a washing module configured to perform operation related to washing, a camera configured to capture an image of a tag attached to a laundry, and a processor configured to acquire laundry information of a plurality of laundries, to convert the acquired laundry information into encoding data, and to acquire values of laundry control variables corresponding to the converted encoding data based on a laundry course learning model learned using a plurality of reference data through a deep learning algorithm.

A washing machine and a method for controlling the same are disclosed. The washing machine includes a plurality of washing devices, each of which includes a heater. Based on a target temperature of wash water heated by one heater of any one of the washing devices, the washing machine changes a target temperature setting environment of wash water heated by the remaining heater. The washing machine includes a first heater to heat wash water stored in a first tub, a second heater to heat wash water stored in a second tub, a control panel to provide a target temperature setting menu for setting a target temperature of the wash water stored in the first tub, and a controller to drive the first heater and the second heater. The controller changes the target temperature setting menu supplied from the control panel in response to the second target temperature.

In particular, a method performed by one or more devices is disclosed, the method comprising: obtaining a first intensity information item (210) representative of a spectral image (208) resulting from an soiling (202, 302) of a textile (200, 304); obtaining a second intensity information item (212, 214) representative of a spectral image (216) characteristic of at least one property of at least one part of the textile (200, 304); determining at least one treatment parameter, wherein the determination of the treatment parameter takes place dependent both on the composition of the soiling (202, 302) from the first intensity information item (210) and on the at least one property of at least the part of the textile (200, 304) from the second Intensity information item (212, 214); and outputting or triggering an outputting of the at least one treatment parameter.

Disclosed herein is a washing machine and a control method thereof, and more particularly, to a technology capable of controlling washing modes in various manners by manipulating a control panel of the washing machine. The washing machine includes a display configured to display a washing mode, an inputter configured to receive a washing mode select command, a storage configured to store a selected washing mode, and a controller configured to allow a washing mode, which has the largest number of times selected during a predetermined number of times of washings, to be displayed on a first screen of the display, and in response to the number of the washing mode having the largest number of selection times being plural, configured to allow a washing mode, which is the last selected among the plurality of washing modes, to be displayed on the first screen of the display.

A washing machine has a rotatable drum and drum drive and a drum container for it, an inlet device to introduce water into the drum onto laundry therein, a pump and water pipes, several temperature sensors on the drum container, a heater for heating water and a washing machine controller connected to everything. Laundry in the drum is moistened with a defined quantity of water, wherein the moisture penetration varies depending on the fiber type of the laundry. Then a predefined quantity of water with a predefined temperature is applied to this laundry. The washing machine controller can deduce the fiber type of the laundry from the way how quickly and to what extent heat is extracted from this quantity of water.

Disclosed is an artificial intelligence (AI)-based laundry treatment apparatus comprising: a washing module; a camera to acquire an image of laundry; a memory for storing therein a laundry recognition model, wherein the model is trained using a machine learning or deep learning algorithm, wherein the model is configured to recognize laundry information about the laundry; and a processor configured to apply image data from the acquired image to the laundry recognition model to acquire the laundry information.

Disclosed is an artificial intelligence (AI)-based self-control air conditioner. The AI-based self-control air conditioner includes a communication unit configured to receive an image including member data for identifying the member from an image acquisition apparatus corresponding to a group including at least one member, and a processor configured to recognize the member data from the received image, to acquire operation data including an operation condition of an air conditioner, which is desired by the member, based on the recognized data, to store member information including the member data and the operation data in a database, and to acquire and analyze the operation condition of the air conditioner, which is desired by the member, with respect to at least one member from a plurality of pieces of member information corresponding to the group stored in the database, wherein the air conditioner is autonomously driven according to control of the processor. Accordingly, the air conditioner learns members itself and controls an operation in an optimum state to reduce power consumption and is driven according to an operation condition set for each member to enhance personal convenience of the member.

A method includes receiving, at an artificial intelligence (AI) accelerator of a computing system, image data of one or more objects from an image sensor and performing an AI operation on the image data at the AI accelerator of the computing system using an AI model. The method further includes determining a custom cleaning cycle at the AI accelerator in response to performing the AI operation.

A laundry appliance includes a basket rotatably mounted within a tub, a motor assembly for selectively rotating the basket, and a wash fluid dispenser for selectively providing a flow of wash fluid into the tub. A controller is configured to receive a command to initiate a self-clean cycle, determine that a cycle adjustment condition exists based at least in part on historical data related to the performance of self-clean cycles, generate a set of adjusted self-clean cycle parameters by adjusting an operating parameter of the self-clean cycle, and perform the self-clean cycle in accordance with the adjusted self-clean cycle parameters.

A water pretreatment system connected to a water supply pipe for supplying water that has been preheated to a home appliance, including: a mixing valve having an inlet connected to the water supply pipe, and a pair of outlets; a water filter having an inlet communicated with one of the pair of outlets of the mixing valve, and having an outlet communicated with the home appliance; a bypass line having an inlet end connected to the other of the pair of outlets of the mixing valve, and having an outlet end connected to the home appliance, the mixing valve being configured to at least one of direct water to the water filter or direct water to the bypass line; and a controller configured to control the mixing valve such that preheated water supplied to the home appliance is one of unfiltered, mixed, or fully filtered by the water filter.