A dryer appliance, a controller, and a method for operation are provided. The controller is configured to execute steps of a method including receiving an input indicative of a drying cycle end time; determining a moisture extraction rate based at least in part on the received input indicative of drying cycle end time, the moisture extraction rate indicative of a rate at which moisture is removed from articles within a chamber of the dryer appliance; determining a rate of change of moisture extraction rate corresponding to the drying cycle end time; and adjusting a condensation rate based at least on the rate of change of moisture extraction rate and the received input indicative of the drying cycle end time.

A dryer appliance, a controller, and a method for operation are provided. The controller is configured to execute steps of a method including receiving an input indicative of a drying cycle end time; determining a moisture extraction rate based at least in part on the received input indicative of drying cycle end time, the moisture extraction rate indicative of a rate at which moisture is removed from articles within a chamber of the dryer appliance; determining a rate of change of moisture extraction rate corresponding to the drying cycle end time; and adjusting a condensation rate based at least on the rate of change of moisture extraction rate and the received input indicative of the drying cycle end time.

A method of operating a dryer appliance includes activating a heating system of the dryer appliance and tracking an energy output of the heating system. The method also includes determining a load score of a load of articles to be dried in the dryer appliance based on the tracked energy output and determining a termination criterion for a dry operation of the dryer appliance based on the load score. The method further includes performing the dry operation in the dryer appliance and terminating the dry operation when the determined termination criterion is reached.

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 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.

A dryer appliance includes a microphone for monitoring sound generated during operation of the dryer appliance and a controller is operably coupled to the microphone. The controller is configured for obtaining a sound signal generated during operation of the dryer appliance and converting the sound signal into a spectrogram that represents a sound frequency and a sound amplitude over time. An artificial intelligence image recognition process is used to analyze the spectrogram to identify one or more sound signatures that are associated with particular operating conditions, and operation of the dryer appliance is adjusted based at least in part on the identification of the sound signature.

A dryer appliance includes a microphone for monitoring sound generated during operation of the dryer appliance and a controller is operably coupled to the microphone. The controller is configured for obtaining a sound signal generated during operation of the dryer appliance and converting the sound signal into a spectrogram that represents a sound frequency and a sound amplitude over time. An artificial intelligence image recognition process is used to analyze the spectrogram to identify one or more sound signatures that are associated with particular operating conditions, and operation of the dryer appliance is adjusted based at least in part on the identification of the sound signature.

A method and system for setting parameters of a treatment cycle in a household appliance is provided. First a model establishing a relationship between sets of configuration parameter values of achievable treatment performance is obtained. Then a target treatment performance for the treatment cycle is obtained. Values for a subset of a configuration parameter are fixed, the subset comprising at most all configuration parameters but a remaining configuration parameter. Then, a value for the remaining configuration parameter is determined. For this value, a difference between the target treatment performance and an achievable treatment performance predicted by the model using said value is below a predetermined threshold. Finally, the parameters of the treatment cycle, which can be setting parameters of the treatment cycle in the household appliance are output. The parameters comprise the determined value and the fixed values.

A method and system for setting parameters of a treatment cycle in a household appliance is provided. First a model establishing a relationship between sets of configuration parameter values of achievable treatment performance is obtained. Then a target treatment performance for the treatment cycle is obtained. Values for a subset of a configuration parameter are fixed, the subset comprising at most all configuration parameters but a remaining configuration parameter. Then, a value for the remaining configuration parameter is determined. For this value, a difference between the target treatment performance and an achievable treatment performance predicted by the model using said value is below a predetermined threshold. Finally, the parameters of the treatment cycle, which can be setting parameters of the treatment cycle in the household appliance are output. The parameters comprise the determined value and the fixed values.

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.