Abstract: An embodiment of this application provides an image object detection method. The method may include obtaining a detection image, an n-level deep feature map framework, and an m-level non-deep feature map framework. The method may further include extracting deep feature from an (i?1)-level feature of the detection image using an i-level deep feature map framework, to obtain an i-level feature of the detection image. The method may further include extracting non-deep feature from a (j?1+n)-level feature of the detection image using a j-level non-deep feature map framework, to obtain a (j+n)-level feature of the detection image. The method may further include performing information regression operation on an a-level feature to an (m+n)-level feature of the detection image, to obtain an object type information and an object position information of an object in the detection image. The a is an integer less than n and greater than or equal to 2.

Abstract: A data processing method includes receiving a message related to performance of a storage device, the message including an indicator value regarding the performance in a first time period, and a timestamp associated with the first time period. A status record of the storage device, including the number of received indicator values in a second time period including the first time period, is determined based on the timestamp, wherein the number of the received indicator values is less than a threshold number and can be updated based on the indicator value. The performance in the second time period can be determined based on the indicator value and the received indicator values in response to determining that the updated number of the received indicator values reaches the threshold number. Thus, the performance of the storage device can be quickly and accurately determined, and the consumption of computing resources is reduced.

Abstract: An intelligent continuous polishing machine and an intelligent continuous polishing process therefor are provided. The intelligent continuous polishing machine includes a base platen, a rotary platen, a multi-beam bridge mechanism, two workpiece holding mechanisms, a lap measuring mechanism, a lap cutting mechanism, a small-conditioner holding mechanism, a large-conditioner holding mechanism, and a control computer. The intelligent continuous polishing process includes a normal polishing procedure, a lap surface shape measuring procedure, and three procedures for correcting the lap surface shape error: a lap cutting procedure, a sub-aperture correcting procedure, and a full-aperture correcting procedure. All of these procedures are controlled by the control computer through CNC programs.

Abstract: This application discloses a method, an apparatus, and a storage medium for gesture movement recognition.

Abstract: An embodiment of this application provides an image object detection method. The method may include obtaining a detection image, an n-level deep feature map framework, and an m-level non-deep feature map framework. The method may further include extracting deep feature from an (i?1)-level feature of the detection image using an i-level deep feature map framework, to obtain an i-level feature of the detection image. The method may further include extracting non-deep feature from a (j?1+n)-level feature of the detection image using a j-level non-deep feature map framework, to obtain a (j+n)-level feature of the detection image. The method may further include performing information regression operation on an a-level feature to an (m+n)-level feature of the detection image, to obtain an object type information and an object position information of an object in the detection image. The a is an integer less than n and greater than or equal to 2.

Abstract: A hand key-point recognition model training method is provided for a model training device. The method includes: converting a sample virtual image into an emulation image through a Cycle-GAN model, the sample virtual image being an image generated through three-dimensional modeling, and the sample virtual image including key-point coordinates corresponding to hand key-points, and the emulation image being used for emulating an image acquired in a real scenario; extracting a hand image in the emulation image; and training a hand key-point recognition model according to the hand image in the emulation image and the key-point coordinates, the hand key-point recognition model being used for outputting hand key-point coordinates of a hand in a real image according to the inputted real image.

Abstract: The present invention discloses a permanent magnet for magnetic resonance, which is used for a magnetic resonance imaging apparatus for medical diagnosis. The permanent magnet of the present comprises permanent magnetic material; pole heads; plates for eliminating vortex (i.e. eddy currents); rings for homogenizing the magnetic field; gradient coils; and a yoke, said yoke of the permanent magnet has an integral and substantially C-shaped structure formed with two columns and is an open type configuration. This invention fully ensures the parallelism of the lower and upper poles, greatly improves the mechanical strength of the yoke, and results in a good magnetic uniformity and a consistent magnetic current.

Abstract: The present invention discloses a permanent magnet for magnetic resonance, which is used for a magnetic resonance imaging apparatus for medical diagnosis. The permanent magnet of the present comprises a yoke; magnetic material; pole heads; plates for eliminating vortex; rings for uniformising magnetic field; and gradient coils, said yoke of the magnet has an integral and substantially C-shaped structure with two columns and is open type. This invention fully ensures the parallelism of the lower and upper poles, greatly improves the mechanical strength of the yoke, and it can offer good magnetic uniformity and fluent magnetic current.