Abstract: A device and a method for nondestructively detecting a transient characteristic of a conductive screw of a turbo-generator rotor are provided. The device includes a personal computer (PC), an extremely-steep pulse generator, an ultra-high-frequency double-isolation transformer, and a pulse emitting and coupling module, which are connected in sequence. The pulse emitting and coupling module is connected to a load. A synchronous pulse receiving non-inductive divider circuit synchronously receives a characteristic waveform from the load, and the synchronous pulse receiving non-inductive divider circuit is connected to an ultra-high-speed analog/digital (A/D) module through a nonlinear saturation amplifying circuit that amplifies a signal. The PC receives a signal from the ultra-high-speed A/D module. The load includes a positive or negative excitation lead loop that is in a 180° symmetrical and instantaneous short-circuit state and a rotor shaft.

Abstract: The method includes: obtaining parameter data of a first data shape of an AI network, where the first data shape is a data length in each dimension that is supported by the AI network for processing, the parameter data includes combination information of at least two calculating units, data that is supported for processing by each calculating unit is data having a second data shape, and a data length in any dimension obtained after the second data shape of each calculating unit is combined based on the combination information greater than or equal to a data length of the first data shape in a same dimension (S171); and invoking the at least two calculating units to perform calculation on first target data having the first data shape (S172).

Abstract: A multi-dimensional flight release efficiency evaluation method. The method comprises: obtaining air flow control production and operation data which mainly comprises airspace capacity information, flight scheduling basic information, flight four-dimensional trajectory information and the like through a business information comprehensive processing platform, identifying a flight object affected by flow control and a flight object restricted by flow control through processing the operation data, analyzing a flight release time-hopping degree, calculating flight release delay distribution, evaluating controlled flight release fairness, predicting a controlled flight release normal rate, comparing airspace flow capacity matching situations, establishing a multi-dimensional flight release efficiency evaluation index set, and visually displaying evaluation indexes in modes of list, histogram, line chart, radar chart and the like.

Abstract: A multi-dimensional flight release efficiency evaluation method. The method comprises: obtaining air flow control production and operation data which mainly comprises airspace capacity information, flight scheduling basic information, flight four-dimensional trajectory information and the like through a business information comprehensive processing platform, identifying a flight object affected by flow control and a flight object restricted by flow control through processing the operation data, analyzing a flight release time-hopping degree, calculating flight release delay distribution, evaluating controlled flight release fairness, predicting a controlled flight release normal rate, comparing airspace flow capacity matching situations, establishing a multi-dimensional flight release efficiency evaluation index set, and visually displaying evaluation indexes in modes of list, histogram, line chart, radar chart and the like.

Abstract: A face image processing method includes: performing face detection on an image to be processed, and obtaining at least one face region image included in the image to be processed and face attribute information in the at least one face region image; and for the at least one face region image, processing an image corresponding to a first region and/or an image corresponding to a second region in the face region image at least according to the face attribute information in the face region image, wherein the first region is a skin region, and the second region includes at least a non-skin region.

Abstract: A face image processing method includes: performing face detection on an image to be processed, and obtaining at least one face region image included in the image to be processed and face attribute information in the at least one face region image; and for the at least one face region image, processing an image corresponding to a first region and/or an image corresponding to a second region in the face region image at least according to the face attribute information in the face region image, wherein the first region is a skin region, and the second region includes at least a non-skin region.

Abstract: A face image processing method includes: performing face detection on a to-be-processed image, to obtain at least one face region image included in the to-be-processed image and face key point information in the at least one face region image; and processing, for the at least one face region image, an image corresponding to a first region and/or an image corresponding to a second region in the face region image at least according to the face key point information in the face region image, wherein the image corresponding to the second region and the image corresponding to the first region are at least partially non-overlapping.

Abstract: An image processing method includes: obtaining a facial skin tone area in an image to be processed; filtering the image to be processed to obtain a filtered smooth image; obtaining a high-frequency image based on the smooth image and the image to be processed; obtaining a facial skin tone high-frequency image based on the high-frequency image and a facial skin tone mask; and superimposing the high-frequency image and the image to be processed based on the facial skin tone mask and preset first superimposition strength in a luma channel, and superimposing a luma channel signal of the facial skin tone high-frequency image onto a luma channel signal of the image to be processed, to obtain a first image.

Abstract: An image processing method includes: obtaining a facial skin tone area in an image to be processed; filtering the image to be processed to obtain a filtered smooth image; obtaining a high-frequency image based on the smooth image and the image to be processed; obtaining a facial skin tone high-frequency image based on the high-frequency image and a facial skin tone mask; and superimposing the high-frequency image and the image to be processed based on the facial skin tone mask and preset first superimposition strength in a luma channel, and superimposing a luma channel signal of the facial skin tone high-frequency image onto a luma channel signal of the image to be processed, to obtain a first image.

Abstract: A face image processing method includes: performing face detection on an image to be processed, and obtaining at least one face region image included in the image to be processed and face attribute information in the at least one face region image; and for the at least one face region image, processing an image corresponding to a first region and/or an image corresponding to a second region in the face region image at least according to the face attribute information in the face region image, wherein the first region is a skin region, and the second region includes at least a non-skin region.

Abstract: A face image processing method includes: performing face detection on a to-be-processed image, to obtain at least one face region image included in the to-be-processed image and face key point information in the at least one face region image; and processing, for the at least one face region image, an image corresponding to a first region and/or an image corresponding to a second region in the face region image at least according to the face key point information in the face region image, wherein the image corresponding to the second region and the image corresponding to the first region are at least partially non-overlapping.