Abstract: An inflatable cot includes: a main body, where the top of the main body is provided with a cot surface, the main body is internally provided with an inflation cavity, the inflation cavity is internally provided with several fabric strands, two ends of each of the fabric strands are respectively connected to the top and the bottom of the inflation cavity, and the bottom of the main body is provided with connection portions; and a support frame, where the support frame includes connection rods and support legs, the connection rods are detachably inserted into the connection portions, and the support legs are detachably connected to the connection rods. In the inflatable cot, support performance of the cot can be improved, thereby improving comfortableness.

Abstract: An air mattress, comprising: a mattress body, where the mattress body comprises at least a bed surface, a first air chamber and a second air chamber are formed inside the mattress body, the first air chamber and the second air chamber are isolated from each other, the first air chamber is located below the bed surface, the second air chamber is located on an outer side of the bed surface, and the second air chamber surrounds the bed surface; and an air charging/discharging valve, where there are at least two air charging/discharging valves, and the two air charging/discharging valves respectively communicate with the first air chamber and the second air chamber. In the present invention, a support requirement for the air mattress can be satisfied during softness adjustment of the air mattress.

Abstract: An image processing method includes performing additional image feature extraction on a training source face image to obtain a source additional image feature, performing identity feature extraction on the training source face image to obtain a source identity feature, inputting a training template face image into an encoder in a to-be-trained face swapping model to obtain a face attribute feature, inputting the source additional image feature, the source identity feature, and the face attribute feature into a decoder in the face swapping model for decoding to obtain a decoded face image, obtaining a target model loss value based on an additional image difference between the decoded face image and a comparative face image, and adjusting the model parameters of the encoder and the decoder based on the target model loss value to obtain the trained face swapping model.

Abstract: The present disclosure relates to a system and method for digital radiography. The system may include an X-ray generation module, an X-ray acquisition module, a control module, a support module and a power supply module. The system may include one or more moving components. The X-ray acquisition module may have different configurations, such as a vertical configuration, a horizontal configuration and a free-style configuration. The control module may be configured for controlling the motion of the moving components, the selection of an X-ray acquisition module of a specific configuration, and parameters of the X-ray exposure and image acquisition. The support module may include a system of guiding rails. The power supply module may include a capacitor.

Abstract: A method, an apparatus, and a device for image processing and a training method thereof are provided. The training method includes obtaining a sample image set, the sample image set comprising a first number of sample images; constructing an image feature set based on the sample image set, the image feature set comprising an image feature extracted from each of the sample images in the sample image set; obtaining a training image set, the training image set comprising a second number of training images; constructing multiple training image pairs based on the training image set and the image feature set; and training the image processing model based on the multiple training image pairs.

Abstract: A face image processing method and apparatus, a face image display method and apparatus, and a device are provided, belonging to the technical field of image processing. The method includes: acquiring a first face image of a person; invoking an age change model to predict a texture difference map of the first face image at a specified age, the texture difference map being used for reflecting a texture difference between a face texture in the first face image and a face texture of a second face image of the person at the specified age; and performing image processing on the first face image based on the texture difference map to obtain the second face image.

Abstract: A chip system integrated with MCU and electromagnetic driving includes: a power management module, a signal acquisition module, a controller processing module, an electromagnetic driving module, an audio and video output module, a temperature measurement and control module, and a GPIO module; where the power management module includes: a voltage increasing/decreasing conversion module, a charging management module, a battery protection module, and a power output module; the signal acquisition module includes: a voltage acquisition module, a current acquisition module, a temperature acquisition module, a biological signal acquisition module, and an electromagnetic driving load acquisition module; the controller processing module includes: a signal processing module, a signal transmission module, and a signal communication module; and the audio and video output module includes: a voice playing module, an LED module, and an LCD module.

Abstract: The present disclosure relates to a system and method for digital radiography. The system may include an X-ray generation module, an X-ray acquisition module, a control module, a support module and a power supply module. The system may include one or more moving components. The X-ray acquisition module may have different configurations, such as a vertical configuration, a horizontal configuration and a free-style configuration. The control module may be configured for controlling the motion of the moving components, the selection of an X-ray acquisition module of a specific configuration, and parameters of the X-ray exposure and image acquisition. The support module may include a system of guiding rails. The power supply module may include a capacitor.

Abstract: A target tracking method is provided for a computer device. The method includes determining a target candidate region of a current image frame; capturing a target candidate image matching the target candidate region from the current image frame; determining a target region of the current image frame according to an image feature of the target candidate image; determining motion prediction data of a next image frame relative to the current image frame by using a motion prediction model and according to the image feature of the target candidate image; and determining a target candidate region of the next image frame according to the target region and the motion prediction data.

Abstract: The present disclosure relates to a system and method for digital radiography. The system may include an X-ray generation module, an X-ray acquisition module, a control module, a support module and a power supply module. The system may include one or more moving components. The X-ray acquisition module may have different configurations, such as a vertical configuration, a horizontal configuration and a free-style configuration. The control module may be configured for controlling the motion of the moving components, the selection of an X-ray acquisition module of a specific configuration, and parameters of the X-ray exposure and image acquisition. The support module may include a system of guiding rails. The power supply module may include a supercapacitor.

Abstract: The present disclosure belongs to the technical field of welding materials, and in particular relates to a Fe—Ni based alloy welding wire for welding 800H alloy and a preparation method thereof and a method for welding 800H alloy. The Fe—Ni based alloy welding wire for welding 800H alloy provided by the present disclosure has a reasonable chemical components, and after being used to weld 800H alloy, the obtained weld has a tensile strength of 557.6 MPa and an elongation of 37.5% at ambient temperature, and has a tensile strength of 420 MPa and an elongation of 17.25% at a temperature of 650° C.

Abstract: The present disclosure discloses an infrared radiation slurry and an infrared radiation heating element based on the infrared radiation slurry. Raw materials of the infrared radiation slurry include high infrared radiance materials, a conductive material and a substrate adhesive. The raw materials are evenly mixed, coated on a quartz glass tube, and carbonized to obtain the infrared radiation heating element. The present disclosure utilizes the compounded infrared radiation slurry to form a coating on a glass substrate with uniform components, uniform and controllable resistance, high conversion efficiency of electrothermal radiation, and strong adhesion, thereby achieving excellent performance of the obtained infrared radiation heating element.

Abstract: An image super-resolution method includes performing frequency domain transformation on a first image to obtain a spectral feature of the first image, the spectral feature representing a distribution of a grayscale gradient in the first image. The method further includes performing blur kernel prediction based on the spectral feature to obtain a blur kernel of the first image, the blur kernel being a convolution kernel. The method also includes performing super-resolution processing on the first image based on the blur kernel to generate a super-resolved image, a definition of the super-resolved image being higher than a definition of the first image.

Abstract: A facial image processing method includes: obtaining first optical flow information; obtaining, based on the first optical flow information, second optical flow information through an optical flow information prediction sub-model of an image processing model; generating, based on the first facial sample image and the second optical flow information, a predicted image of the first facial sample image through a generator of the image processing model; discriminating the predicted image and the second facial sample image through a discriminator of the image processing model to obtain a first discrimination result and a second discrimination result; and training the image processing model based on the first discrimination result, the second discrimination result, the predicted image, the second facial sample image, the first optical flow information, and the second optical flow information.

Abstract: Embodiments of this application disclose an image style conversion method performed by an electronic device. The method includes: performing quality enhancement on a first target style image to obtain a second target style image; performing feature extraction on the second target style image to obtain a target style feature; performing migration training on a preset target style conversion model by using a full style conversion model and the target style feature to obtain a target style conversion model; inputting a full style feature, the target style feature, and a to-be-converted image into the target style conversion model, and performing style conversion on the to-be-converted image using the target style conversion model to obtain a target image conforming to a target style.

Abstract: The present disclosure belongs to the technical field of welding materials, and in particular relates to a Fe—Ni based alloy welding wire for welding 800H alloy and a preparation method thereof and a method for welding 800H alloy. The Fe—Ni based alloy welding wire for welding 800H alloy provided by the present disclosure has a reasonable chemical components, and after being used to weld 800H alloy, the obtained weld has a tensile strength of 557.6 MPa and an elongation of 37.5% at ambient temperature, and has a tensile strength of 420 MPa and an elongation of 17.25% at a temperature of 650° C.

Abstract: An image processing method includes performing additional image feature extraction on a training source face image to obtain a source additional image feature, performing identity feature extraction on the training source face image to obtain a source identity feature, inputting a training template face image into an encoder in a to-be-trained face swapping model to obtain a face attribute feature, inputting the source additional image feature, the source identity feature, and the face attribute feature into a decoder in the face swapping model for decoding to obtain a decoded face image, obtaining a target model loss value based on an additional image difference between the decoded face image and a comparative face image, and adjusting the model parameters of the encoder and the decoder based on the target model loss value to obtain the trained face swapping model.

Abstract: A face image processing method and apparatus, a face image display method and apparatus, and a device are provided, belonging to the technical field of image processing. The method includes: acquiring a first face image of a person; invoking an age change model to predict a texture difference map of the first face image at a specified age, the texture difference map being used for reflecting a texture difference between a face texture in the first face image and a face texture of a second face image of the person at the specified age; and performing image processing on the first face image based on the texture difference map to obtain the second face image.

Abstract: A method, an apparatus, and a device for image processing and a training method thereof are provided. The training method includes obtaining a sample image set, the sample image set comprising a first number of sample images; constructing an image feature set based on the sample image set, the image feature set comprising an image feature extracted from each of the sample images in the sample image set; obtaining a training image set, the training image set comprising a second number of training images; constructing multiple training image pairs based on the training image set and the image feature set; and training the image processing model based on the multiple training image pairs.

Abstract: This application relates to an image processing method and apparatus, a facial recognition method and apparatus, a computer device, and a readable storage medium.