Abstract: The present invention provides a stable atomization heating unit, a heating assembly and an atomization device. The heating unit comprises an intermediate portion and end portions arranged respectively at two ends of the intermediate portion. The intermediate portion is connected to the end portions such that the heating unit heats when the end portions at the two ends are powered on, and an accommodating space is provided, in the heating unit, that runs through the end portions and the intermediate portion. The accommodating space has a smaller sectional area at the intermediate portion than at the end portions. The heating assembly comprises a liquid conducting body and the heating unit. The atomization device comprises a housing and the heating assembly. The housing is provided with a liquid channel and an air channel, and the liquid channel is communicated with the liquid conducting body.

Abstract: A method for detecting a human behavior includes: obtaining an image to be detected; obtaining a plurality of key points and a plurality of pieces of position information respectively corresponding to the plurality of key points by key-point recognition on the image to be detected; grouping the plurality of key points based on the plurality of pieces of position information to obtain a plurality of key-point groups, the plurality of key-point groups at least including a part of the plurality of key points; and determining a target human behavior based on key points in the plurality of key-point groups.

Abstract: A method of training a target object detection model includes: extracting a plurality of feature maps of a sample image according to a training parameter, fusing the plurality of feature maps to obtain at least one fused feature map, and obtaining an information of a target object based on the at least one fused feature map, by using the target object detection model; determining a loss of the target object detection model based on the information of the target object and a tag information of the sample image, and adjusting the training parameter according to the loss of the target object detection model. A method of detecting a target object and an apparatus are also provided.

Abstract: Provided are a method and apparatus for evaluating image relative definition, a device and a medium, relating to technologies such as computer vision, deep learning and intelligent medical. A specific implementation solution is: extracting a multi-scale feature of each image in an image set, where the multi-scale feature is used for representing definition features of objects having different sizes in an image; and scoring relative definition of each image in the image set according to the multi-scale feature by using a relative definition scoring model pre-trained, where the purpose for training the relative definition scoring model is to learn a feature related to image definition in the multi-scale feature.

Abstract: A heating mechanism for atomization by heating and an atomization device, the heating mechanism comprising heating circuits for evaporating liquid and electrodes for connecting to a power supply unit. All of the heating circuits are connected in parallel between contacts of two electrodes, two adjacent heating circuits are connected by a plurality of connections to form a planar integrated unit, and heat dissipation members extending laterally for dispersing heat from the heating circuits are provided in at least the intermediate of the outer heating circuits. The atomization device comprises a porous ceramic body and the heating mechanism, wherein the heating mechanism is embedded in and planarly attached to the bottom of the porous ceramic body. The heating mechanism has a good support strength and provide uniform heat distribution, the heating mechanism of the atomization device is not separated from the ceramic body and has a good heating uniformity.

Abstract: The present invention discloses a special hydraulic press for bulging pressing forming of an automobile axle housing and a pressing forming method. A hydraulic press body is a closed frame consisting of an upper cross beam, a lower cross beam, four stand columns I, II, III, and IV, four vertical pull rods I, II, III, and IV, and four transverse pull rods I, II, III, and IV, and is used for balancing a deformation force up to 3000 T in the vertical direction or the horizontal direction in the pressing forming process. During pressing forming, the downward speeds of a main hydraulic cylinder, small left and right vertical hydraulic cylinders are precisely controlled to prevent the left end, the right end and the middle of the tube blank from bending.

Abstract: A method for detecting a surface defect, a method for training model, an apparatus, a device, and a medium, are provided. The method includes: inputting a surface image of the article for detection into a defect detection model to perform a defect detection, and acquiring a defect detection result output by the defect detection model; inputting a surface image of a defective article determined to be defective into an image discrimination model based on the defect detection result to determine whether the surface image of the defective article is defective, wherein the image discrimination model is a trained generative adversarial networks model, and the generative adversarial networks model is obtained by training using a surface image of a defect-free good article; and adjusting the defect detection result of the surface image of the defective article according to a determination result of the image discrimination model.

Abstract: An object area measurement method and an apparatus are provided, relating to the computer vision and deep learning technology. The method includes acquiring an original image with a spatial resolution, the original image including a target object; acquiring an object identification model including at least two sets of classification models; generating one or more original image blocks based on the original image; performing operations on each original image block: scaling each original image block at at least two scaling levels to obtain scaled image blocks with at least two sizes, the scaled image blocks respectively corresponding to the at least two sets of classification models, and inputting the scaled image blocks into the object identification model to obtain an identification result of the target object; and determining an area of the target object based on the respective identification results of the one or more original image blocks and the spatial resolution.

Abstract: The present invention discloses an atomizer and an atomizing assembly thereof. The atomizing assembly includes an atomizing body; the atomizing body includes an air passage and a liquid inlet groove; the air passage at least includes a first passage communicated with a bottom of the atomizing body and at least one second passage communicated with the first passage, a first included angle is formed between the second passage and the first passage; the liquid inlet groove is spaced from the air passage. During the upward transmission of the atomized e-liquid, the incompletely atomized e-liquid will contact the sidewall of the air passage under the action of gravity when passing through the second passage, and will be readsorbed by the atomizing body, so as to conduct secondary atomization and reduce the phenomenon of insufficient e-liquid atomization, thereby avoiding the user from inhaling the incompletely atomized e-liquid and the condensate.

Abstract: A method for recognizing a category of an image includes: acquiring a spectral image; training an image recognition model based on the spectral image, in which the image recognition model acquires a spectral semantic feature of each pixel, a minimum distance between each pixel and each category, and a spectral distance between a first spectrum of each pixel and a second spectrum of each category; splices them; and performs classification and recognition based on the spliced feature to output a recognition probability of each pixel under each category; determining a loss function of the image recognition model, adjusting the image recognition model based on the loss function, and returning to training the adjusted image recognition model based on the spectral image until training ends; recognizing a maximum recognition probability, output from a target image recognition model, and using a category corresponding to the maximum recognition probability as a target category.

Abstract: A training method for a human body attribute detection model includes: acquiring positive sample sub-images and negative sample sub-images respectively corresponding to a plurality of human body attribute categories; determining a plurality of first annotation attributes respectively corresponding to the plurality of positive sample sub-images; and a plurality of second annotation attributes respectively corresponding to the plurality of negative sample sub-images; and training an artificial intelligence model according to the plurality of positive sample sub-images, the plurality of negative sample sub-images, the plurality of first annotation attributes and the plurality of second annotation attributes to obtain the human body attribute detection model, so that the human body attribute detection model obtained by training can effectively model fine-grained attributes of the human body.

Abstract: The present invention provides an atomizing unit and an atomizing device. The atomizing unit includes a tubular heating assembly and a liquid conducting member. The liquid conducting member is wrapped around an outer periphery of the tubular heating assembly or fitted to an inner peripheral surface of the tubular heating assembly. The tubular heating assembly includes an annular connecting portion, at least two heating portions connected to one end surface of the connecting portion and arranged around the end surface, and electrode portions connected to one end of the heating portions away from the connecting portion. Each side of two opposite sides of one heating portion faces to a corresponding side of its adjacent heating portion with a gap therebetween. The at least two heating portions are connected in series through the connecting portion. The atomizing unit has a high structural strength and is convenient for assembly.

Abstract: The invention provides a heating member of an atomizing device with different heating effects at different portions and an atomizing device. The heating member is tubular and includes electrode portions and a heating circuit portion. The heat per unit time of the radial side A of the heating member is less than that of another side B. The atomizing device includes the heating member and a liquid conducting member. When the heating member is applied in the atomizing device, the airflow can pass by the side B, so that no large temperature difference exists, thereby improving the smoke flavor, and preventing the carbon deposition due to high temperature. Besides, the liquid can enter the liquid conducting member from the gap at the side A, thereby avoiding the scorched flavor caused by insufficient liquid supply due to long liquid flow path caused by the liquid feeding at the two ends.

Abstract: The present application discloses a method and an apparatus for detecting wearing of a safety helmet, a device and a storage medium. The method for detecting wearing of a safety helmet includes: acquiring a first image collected by a camera device, where the first image includes at least one human body image; determining the at least one human body image and at least one head image in the first image; determining a human body image corresponding to each head image in the at least one human body image according to an area where the at least one human body image is located and an area where the at least one head image is located; and processing the human body image corresponding to the at least one head image according to a type of the at least one head image.

Abstract: A method for generating a road annotation, a device, and a storage medium are provided. The method may include: generating a road quantity and a road width in a tag picture; generating, for each road in the tag picture, a start point and an end point of the road; generating at least one point between the start point and the end point; drawing, for two adjacent points, a line segment from a previous point to a next point, where a width of the line segment is equal to the road width; and generating slanted box annotation information based on a coordinate of the previous point and a coordinate of the next point, where the slanted box annotation information includes an intersection point of diagonal lines, a width, a height and a slant angle of a slanted box.

Abstract: A fuel cell control method and system based on model prediction control are provided. The method includes: (1) obtaining data required for control; (2) determining whether the data required for control are received completely; (3) estimating an internal state of a fuel cell based on outlet pressure of an air compressor and a voltage of the fuel cell to obtain a state estimation result; (4) calculating a target outlet flow of the air compressor and a target current of the fuel cell with a model prediction control algorithm based on the state estimation result; (5) calculating a control voltage of the air compressor, and a target outlet flow of the air compressor; and (6) tracking power of the fuel cell based on the target current of the fuel cell, and controlling air supply of the fuel cell based on the control voltage of the air compressor.

Abstract: A method of training an image-text retrieval model, a method of multimodal image retrieval, an electronic device and a storage medium, each relating to the technical field of artificial intelligence, and in particular, to fields of computer vision and deep learning technologies. Sample data including a sample text and a sample image is acquired. The sample text includes a sample text in a first language and a sample text in a second language. The sample text in the first language and the sample text in the second language are processed by using the text encoding sub-model to obtain a sample text feature of the sample data. The sample image is processed by using the image encoding sub-model to obtain a sample image feature of the sample data. The image-text retrieval model is trained according to the sample text feature and the sample image feature.

Abstract: A method for generating a sample image includes: obtaining an initial image size of an initial image; obtaining a plurality of reference images by processing the initial image based on different reference processing modes; obtaining an image to be processed by fusing the plurality of reference images; and determining a target sample image from images to be processed based on the initial image size.

Abstract: Provided are a training method and apparatus for a target detection model, a device and a storage medium. The training method is described below. A feature map of a sample image is processed through a classification network of an initial model and a heat map and a classification prediction result of the feature map are obtained, a classification loss value is determined according to the classification prediction result and classification supervision data of the sample image, and a category probability of pixels in the feature map is determined according to the heat map of the feature map and a probability distribution map of the feature map is obtained; the feature map is processed through a regression network of the initial model and a regression prediction result is obtained, and a regression loss value is determined.

Abstract: The disclosure provides a method for detecting a change of a building, an apparatus for detecting a change of a building, an electronic device, a storage medium and a computer program product. The method includes: obtaining a remote-sensing image sequence of a to-be-detected region; obtaining a building probability map corresponding to each remote-sensing image in the remote-sensing image sequence; determining a sub-region located by each building in the to-be-detected region based on the building probability map corresponding to each remote-sensing image; for each building, determining an existence probability of the building in each remote-sensing image based on the sub-region located by the building and the building probability map corresponding to each remote-sensing image; and determining a change condition of the building based on the existence probability of the building in each remote-sensing image.