Abstract: Proposed are an energy management apparatus and an energy storage apparatus. The energy management apparatus includes an electricity generation amount forecast unit including an electricity generation amount forecast model, a consumption amount forecast unit including a consumption amount forecast model, and a control module configured to detect an abnormality of a solar panel based on a forecast electricity generation amount, which is forecasted on a per-time span basis, and on an actually generated electricity generation amount, wherein the control module corrects the forecast electricity generation amount based on the electricity generation amounts up to now and determines the presence or absence of the abnormality of the solar panel and the cause of a failure of the solar panel based on the corrected forecast electricity generation amount.

Abstract: Discussed herein is a method of managing product reception/release data of a refrigerator including obtaining internal image data of the refrigerator using a camera provided in the refrigerator, extracting high-reliability data from the internal image data of the refrigerator, updating product reception/release data based on the high-reliability data, and creating a user database based on the updated product reception/release data.

Abstract: According to an embodiment of the present disclosure, a refrigerator may include a storage compartment, an outer door, one or more cameras provided in the outer door, a global DB configured to store a plurality of default food identification items and a plurality of default product names respectively corresponding to the plurality of default food identification items, and a local DB configured to store edited product names and a food identification item corresponding to the edited product names, and a processor configured to photograph an internal image of the storage compartment through the one or more cameras, obtain a food identification item from the photographed internal image, determine whether the obtained food identification item is stored in the local DB, and when the food identification item is not stored in the local DB, determine whether the obtained food identification item is stored in the global DB.

Abstract: The present disclosure relates to an energy management apparatus and an energy management method. An energy management apparatus according to one embodiment includes an energy generation prediction unit having an energy generation prediction model, an energy consumption prediction unit having an energy consumption prediction model, and a control module to adjust a power purchase time point and a quantity of power to be purchased for each time zone based on the predicted energy generation output from the energy generation prediction model and the predicted energy consumption output from the energy consumption prediction model.

Abstract: A computer-implemented method includes obtaining a labeled data set of images including data from a rehearsal memory and new input data, augmenting the labeled data set to generate a first data set and a second data set, wherein each image of the first data set corresponds to a corresponding image of the second data set, inputting the first data set into a query encoder and inputting the second data set into a momentum encoder to obtain encodings output by the query encoder and the momentum encoder, obtaining a contrastive loss based on the encodings using a sum of a first contrastive loss function and a second contrastive loss function, updating parameters of the query encoder based on the obtained contrastive loss, and updating parameters of the momentum encoder based on parameters of the query encoder.

Abstract: A robot cleaner for cleaning in consideration of a floor state through artificial intelligence includes a cleaning unit including a suction unit and a mopping unit, a driving unit to drive the robot cleaner, and a processor to obtain a carpet area or a rug area based on cleaning data, and to control the driving unit to change an entrance direction of the robot cleaner when the robot cleaner enters the carpet area or the rug area.

Abstract: The present disclosure relates to an artificial intelligence apparatus capable of classifying and detecting an item of an unseen class having high visual similarity to an item of a seen class that has been learned and known in advance, and a method for detecting an unseen class items thereof, and when the item image is input, generate encoded data by encoding the item image, generate decoded data by decoding the encoded data using a codebook of a plurality of codebooks in which encoded data for each item class is stored, corresponding to a pre-learned item class, and detect the unknown item by classifying the class of the item image based on the generated decoded data.

Abstract: The present invention includes an artificial intelligence device and a method for operating the same. The artificial intelligence device includes a communication unit that receives indoor area map data and indoor area image data from at least one external device, and a processor that generates first furniture placement map data in which a placement position of at least one piece of furniture is mapped, provides the first furniture placement map data to a furniture placement model, and obtains second furniture placement map data in which a placement position of at least one piece of furniture is changed, based on indoor area map data and indoor area image data.

Abstract: An embodiment of the present invention provides an artificial intelligence (AI) robot for determining a cleaning route using sensor data, comprising: a sensor unit including at least one of an image sensor, a depth sensor or a shock sensor; a cleaning unit including at least one of a suction unit or a mopping unit; a driving unit configured to drive the AI robot; and a processor configured to: acquire the sensor data from the sensor unit, determine a complex area using the acquired sensor data, create a virtual wall for blocking an entry into the determined complex area, determine the cleaning route in consideration of the created virtual wall, and control the cleaning unit and the driving unit based on the determined cleaning route.

Abstract: An AI apparatus and an operating method are provided, the AI apparatus includes a communication interface to receive 3D sensor data and bumper sensor data from a first cleaner, a processor to generate surrounding situation map data based on the 3D sensor data and the bumper sensor data, and a learning processor to generate learning data by labeling area classification data for representing whether the surrounding situation map data corresponds to the stuck area, and to train a stuck area classification model based on the learning data. The processor transmits the trained stuck area classification model to a second cleaner through the communication interface.

Abstract: According to an embodiment of the present disclosure, a refrigerator may include a storage compartment, a door, one or more cameras provided in the door, a global DB configured to store a plurality of default food identification items and a plurality of default product names respectively corresponding to the plurality of default food identification items, and a local DB configured to store edited product names and a food identification item corresponding to the edited product names, and a processor configured to photograph an internal image of the storage compartment, obtain one or more food identification items from the photographed internal image, and if the obtained one or more food identification items are stored in the local DB, recognize one or more product names of the obtained one or more food identification items as product names of the food identification items stored in the local DB.

Abstract: According to an embodiment of the present disclosure, a refrigerator may include a storage compartment, an outer door, one or more cameras provided in the outer door, a global DB configured to store a plurality of default food identification items and a plurality of default product names respectively corresponding to the plurality of default food identification items, and a local DB configured to store edited product names and a food identification item corresponding to the edited product names, and a processor configured to photograph an internal image of the storage compartment through the one or more cameras, obtain a food identification item from the photographed internal image, determine whether the obtained food identification item is stored in the local DB, and when the food identification item is not stored in the local DB, determine whether the obtained food identification item is stored in the global DB.

Abstract: According to an embodiment of the present disclosure, a refrigerator may include a storage compartment, a door, one or more cameras provided in the door, a global DB configured to store a plurality of default food identification items and a plurality of default product names respectively corresponding to the plurality of default food identification items, and a local DB configured to store edited product names and a food identification item corresponding to the edited product names, and a processor configured to photograph an internal image of the storage compartment, obtain one or more food identification items from the photographed internal image, and if the obtained one or more food identification items are stored in the local DB, recognize one or more product names of the obtained one or more food identification items as product names of the food identification items stored in the local DB.

Abstract: Disclosed herein is an artificial intelligence cleaner. The artificial intelligence cleaner includes a memory configured to store a simultaneous localization and mapping (SLAM) map for a cleaning space; a driving unit configured to drive the artificial intelligence cleaner; and a processor configured to collect a plurality of cleaning logs for the cleaning space, divide the cleaning space into a plurality of cleaning areas using the SLAM map and the collected plurality of cleaning logs, determine a cleaning route of the artificial intelligence cleaner in consideration of the divided cleaning areas, and control the driving unit according to the determined cleaning route.

Abstract: A robot cleaner for recognizing a stuck situation through artificial intelligence includes a driving unit to drive the robot cleaner, a sensing unit configured to acquire three-dimensional (3D) image data and a bumper event, a memory configured to store a stuck situation recognition model for inferring the stuck situation of the robot cleaner, and a processor configured to convert the 3D image data and the bumper event into surrounding map image data, infer the stuck situation of the robot cleaner from the 3D image data and the bumper event using the stuck situation recognition model, and control the driving unit according to an inference result.

Abstract: Disclosed herein is a robot cleaner including a driving motor, a communication interface configured to receive, from an external cleaner, first cleaning record information including cleaning path information generated based on location information of the external cleaner, a memory configured to store second cleaning record information including the cleaning path information generated based on location information of the robot cleaner, and a processor configured to generate a cleaning plan of the robot cleaner based on the first cleaning record information and the second cleaning record information and control the driving motor such that the driving motor travels according to the cleaning plan.

Abstract: A robot cleaner to avoid a stuck situation through artificial intelligence (AI) may acquire a surrounding map, based on the sensing information, determine escape path factors based on the surrounding map by using the compensation model, if the stuck situation of the robot cleaner is detected, and control the driving motor such that the robot cleaner travels, based on the determined escape path factors.

Abstract: An artificial intelligence (AI) device for guiding an arrangement location of an air cleaning device includes a memory to store an air cleaning device arrangement model to infer the arrangement location of the air cleaning device based on information on fine dust in a house and a processor configured to acquire information on a map of the house and information on fine dust distribution in the house, and to determine the arrangement location of the air cleaning device based on the information on the map and the information on the fine dust distribution, by using the air cleaning device arrangement model.

Abstract: A robot cleaner for avoiding a stuck situation using artificial intelligence includes a sensing unit configured to detect the stuck situation of the robot cleaner, a driving unit to drive the robot cleaner, and a processor configured to determine a rotation angle of the robot cleaner when the stuck situation is detected through the sensing unit, control the driving unit such that the robot cleaner rotates by the determined rotation angle, and control the driving unit such that the robot cleaner reverses by a certain distance after rotating by the rotation angle.

Abstract: Disclosed herein is an artificial intelligence cleaner. The artificial intelligence cleaner includes a memory configured to store a simultaneous localization and mapping (SLAM) map for a cleaning space; a driving unit configured to drive the artificial intelligence cleaner; and a processor configured to collect a plurality of cleaning logs for the cleaning space, divide the cleaning space into a plurality of cleaning areas using the SLAM map and the collected plurality of cleaning logs, determine a cleaning route of the artificial intelligence cleaner in consideration of the divided cleaning areas, and control the driving unit according to the determined cleaning route.