Abstract: A light-emitting diode display panel, a manufacturing method thereof, and an organic light-emitting diode display device are provided. The light-emitting diode display panel includes: a base substrate including a display region and a peripheral region surrounding the display region; a plurality of sub-pixels located in the display region and located at a side of the base substrate; a color-resistance layer located at a side of a second electrode in the sub-pixel away from the base substrate; and a light-blocking structure located in the peripheral region and being an annular structure surrounding the plurality of sub-pixels. The light-blocking structure includes a first light-blocking structure and a second light-blocking structure. The first light-blocking structure includes at least one interval extending in a direction from the display region pointing to the peripheral region. The second light-blocking structure at least fully fills the interval.

Abstract: A face recognition method includes: obtaining a first feature image that describes a face feature of a target face image and a first feature vector corresponding to the first feature image; obtaining a first feature value that represents a degree of difference between a face feature in the first feature image and that in the target face image; obtaining a similarity between the target face image and a template face image according to the first feature vector, the first feature value, and a second feature vector and a second feature value corresponding to a second feature image of the template face image, the second feature value describing a degree of difference between a face feature in the second feature image and that in the template face image; and determining, when the similarity is greater than a preset threshold, that the target face image matches the template face image.

Abstract: Embodiments of this application disclose a face recognition method and apparatus, an electronic device and a storage medium. The embodiments of this application can obtain a face image feature of a resource transfer object and a device identification of a resource transfer device; search for a target face feature matching the face image feature, and determine a target object corresponding to the target face feature; search for a resource transfer device graph feature corresponding to the device identification and a target object graph feature corresponding to the target object; determine an initial resource transfer probability that the target object performs resource transfer at the resource transfer place; generate a fused resource transfer probability according to a similarity between the face image feature and the target face feature and the initial resource transfer probability; and determine a resource transfer verification manner of the resource transfer object.

Abstract: Embodiments of this application provide a data processing method and apparatus, a device, and a medium.

Abstract: A data processing method is provided. In the method, a first model that includes N network layers is obtained. The first model is trained with a first data set that includes first data and training label information of the first data, N being a positive integer. The first model is trained with a second data set. The second data set including second data and training label information of the second data, the second data being quantized. A first unquantized target network layer of the N network layers is quantized. Further, an updated first model that includes the quantized first target network layer is trained with the second data set to obtain a second model.

Abstract: The present disclosure relates to data processing method and apparatus. The method includes: acquiring local model parameters respectively corresponding to N local recognition models; acquiring M parameter fusion modes associated with a local model parameter set, and performing parameter fusion on the local model parameter set respectively according to each parameter fusion mode, so as to obtain M alternative global models; and acquiring evaluation indexes of the M alternative global models respectively in a multimedia verification data set, determining a target global model in the M alternative global models according to the evaluation indexes, and transmitting the target global model to N clients, the N clients updating parameters of a local recognition model associated with the target global model according to the target global model respectively, so as to obtain an object recognition model.

Abstract: A brushless direct-current (BLDC) motor is provided including: a stator including a stator core having an aperture extending therethrough and a series of stator windings; and a rotor including a rotor core having a substantially cylindrical body, permanent magnets secured to an inner surface of the rotor core, a rotor shaft extending through the stator, and an overmold structure. The overmold structure includes: a radially body extending adjacent an axial end of the stator and coupled to the rotor shaft via a bushing, and a peripheral body formed around an outer surface and axial end surfaces of the rotor core and structurally securing the permanent magnets to the inner surface of the rotor core.

Abstract: A power tool is provided including a tool housing, a transmission, and a brushless motor disposed in the tool housing that includes an inner stator, an outer rotor, and a rotor shaft. A stator mount assembly is provided including a stator mount including an axial member secured to the stator, and an integrated mounting member including a radial member secured to the stator mount, a ring gear mount extending from the radial member away from the motor, and a ring gear supported by the ring gear mount. The transmission is secured to the tool housing, and it includes a transmission housing having a generally cylindrical body, and a planetary gear set including a carrier and at least one planet gear rotatably mounted to the carrier. The ring gear meshes with the at least one planet gear. In an embodiment, the transmission housing is integrated with the stator mount assembly.

Abstract: A power tool is provided including a tool housing, a brushless direct-current (BLDC) motor disposed in the tool housing, and a transmission. The motor includes a stator including a stator core having an aperture extending therethrough and stator windings, a rotor including a cylindrical rotor core supporting at least one permanent magnet around an outer surface of the stator core, a rotor shaft rotatably coupled to the rotor, and a stator mount including a radial member near a front end of the stator and an axial member secured to the stator. The transmission is secured to the tool housing and includes an input member coupled to the rotor shaft, and an output member configured to be driven by rotation of the input member. The radial member of the stator mount is secured to the transmission to support the stator at least radially within the rotor.

Abstract: In a method for generating a cartoon face image that is performed by a computing device, a to-be-processed face image is obtained. Face feature information is recognized from the to-be-processed face image, and cartoon materials corresponding to feature parts of a face in the to-be-processed face image are obtained according to the face feature information. The obtained cartoon materials are cartoon face materials. The cartoon face materials include cartoon facial feature materials and another cartoon material other than the cartoon facial feature materials. Each of the cartoon facial feature materials are adjusted by using an adjustment parameter, and the other cartoon material are combined with the cartoon facial feature materials after adjustment to generate the cartoon face image.

Abstract: This application relates to a model training method.

Abstract: A face recognition method includes: obtaining a first feature image that describes a face feature of a target face image and a first feature vector corresponding to the first feature image; obtaining a first feature value that represents a degree of difference between a face feature in the first feature image and that in the target face image; obtaining a similarity between the target face image and a template face image according to the first feature vector, the first feature value, and a second feature vector and a second feature value corresponding to a second feature image of the template face image, the second feature value describing a degree of difference between a face feature in the second feature image and that in the template face image; and determining, when the similarity is greater than a preset threshold, that the target face image matches the template face image.

Abstract: This application relates to a face recognition method performed at a computer server. After obtaining a to-be-recognized face image, the server inputs the to-be-recognized face image into a classification model. The server then obtains a recognition result of the to-be-recognized face image through the classification model. The classification model is obtained by inputting a training sample marked with class information into the classification model, outputting an output result of the training sample, calculating a loss of the classification model in a training process according to the output result, the class information and model parameters of the classification model, and performing back propagation optimization on the classification model according to the loss.

Abstract: In a method for generating a cartoon face image that is performed by a computing device, a to-be-processed face image is obtained. Face feature information is recognized from the to-be-processed face image, and cartoon materials corresponding to feature parts of a face in the to-be-processed face image are obtained according to the face feature information. The obtained cartoon materials are cartoon face materials. The cartoon face materials include cartoon facial feature materials and another cartoon material other than the cartoon facial feature materials. Each of the cartoon facial feature materials are adjusted by using an adjustment parameter, and the other cartoon material are combined with the cartoon facial feature materials after adjustment to generate the cartoon face image.

Abstract: This application relates to a model training method.

Abstract: The data ETL (Extraction-Transformation-Loading) dispatching method of the present invention is applied to a real-time data ETL system, wherein a main ETL dispatching engine executes tasks according to preset sequence. In the execution, some key information are memorized by the main ETL dispatching engine, such as lscycle (latest successful data cycle), curcycle (current processing data cycle), and endcycle (processing end data cycle), etc., and are transferred to called programs. After the execution of the called programs, the main ETL dispatching engine records and keeps the updated dispatching information. In the data re-extraction, the data ETL dispatching method is adopted for the automatic re-extraction of some tasks and some cycles therein.

Abstract: As for the memory dispatching method applied to real-time data ETL system, the main ETL dispatching program executes one task according to preset sequence. In the execution, some key information are memorized by dispatching engine, such as lscycle (latest successful data cycle), curcycle (current processing data cycle), and endcycle (processing end data cycle), etc., are transferred to the called program. After the execution of the called program, the dispatching engine records and keeps the updated dispatching information; in the data re-extraction, memory dispatching method is adopted for the automatic re-extraction of some tasks and some cycles therein.