Abstract: A control method includes obtaining historical operation records for a plurality of indoor units of an air conditioner system, and determining control parameters of the at least two indoor units according to the historical operation records to control the at least two indoor units. The air conditioner system further includes an outdoor unit.

Abstract: Aspects of the subject disclosure may include, for example, a device that has a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, including downsampling a full point cloud to obtain a downsampled point cloud, wherein the downsampling reduces a data size of the full point cloud; and using a machine-learning model to assign labels for segmentation and object identification to points in the downsampled point cloud, wherein the machine-learning model is trained on the full point cloud. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a device that has a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, including downsampling a full point cloud to obtain a downsampled point cloud, wherein the downsampling reduces a data size of the full point cloud; and using a machine-learning model to assign labels for segmentation and object identification to points in the downsampled point cloud, wherein the machine-learning model is trained on the full point cloud. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a device that has a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, including downsampling a full point cloud to obtain a downsampled point cloud, wherein the downsampling reduces a data size of the full point cloud; and using a machine-learning model to assign labels for segmentation and object identification to points in the downsampled point cloud, wherein the machine-learning model is trained on the full point cloud. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a device that has a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, including downsampling a full point cloud to obtain a downsampled point cloud, wherein the downsampling reduces a data size of the full point cloud; and using a machine-learning model to assign labels for segmentation and object identification to points in the downsampled point cloud, wherein the machine-learning model is trained on the full point cloud. Other embodiments are disclosed.

Abstract: The present disclosure provides a control method, applied to a control device with a touch display unit, the control device is configured to control an air conditioner system to operate, the air conditioner system comprises one outdoor unit and at least two indoor units, and the control method includes following operations: obtaining historical operation records for the at least two indoor units; and determining control parameters of the at least two indoor units according to the historical operation records to control the at least two indoor units. The present disclosure also discloses an air conditioner system, a control device, and a computer-readable storage medium. The present disclosure solves the problem of how to facilitate the user to control an air conditioner system with multiple indoor units.

Abstract: Aspects of the subject disclosure may include, for example, a device that has a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, including downsampling a full point cloud to obtain a downsampled point cloud, wherein the downsampling reduces a data size of the full point cloud; and using a machine-learning model to assign labels for segmentation and object identification to points in the downsampled point cloud, wherein the machine-learning model is trained on the full point cloud. Other embodiments are disclosed.

Abstract: A method, apparatus, and computer program product are disclosed for determining, by a mobile wireless device, a likelihood that a first device ID of a target wireless device corresponds to a physical location of the first wireless device at a first measurement location, based on comparing a measured received signal strength of the target wireless device at the first and a second measurement locations and comparing the measured received signal strength of one or more other wireless devices at the first and second measurement locations. The mobile wireless device registers the first device ID of the target wireless device and associates the registered first device ID with the determined corresponding physical location of the target wireless device.

Abstract: A method, apparatus, and computer program product are disclosed for determining, by a mobile wireless device, a likelihood that a first device ID of a target wireless device corresponds to a physical location of the first wireless device at a first measurement location, based on comparing a measured received signal strength of the target wireless device at the first and a second measurement locations and comparing the measured received signal strength of one or more other wireless devices at the first and second measurement locations. The mobile wireless device registers the first device ID of the target wireless device and associates the registered first device ID with the determined corresponding physical location of the target wireless device.