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: The disclosure provides a method for recognizing an image, an apparatus for recognizing an image, an electronic device and a storage medium. An image to be processed is obtained. The number of first channels of the image is greater than the number of second channels of a red-green-blue (RGB) image. For each pixel of the image, a semantic type of the pixel is determined based on a value of the pixel on each channel. A recognition result of the image is generated based on the image and the semantic type of each pixel.

Abstract: An object detection model training method, object detection method and related apparatus, relate to the field of artificial intelligence technologies such as computer vision, deep learning. An implementation includes: obtaining training sample data including a first remote sensing image and position annotation information of an anchor box of a subject to be detected in the first remote sensing image, where the position annotation information includes angle information of the anchor box relative to a preset direction; obtaining an object feature map of the first remote sensing image based on an object detection model, performing object detection on the subject to be detected based on the object feature map to obtain an object bounding box, and determining loss information between the anchor box and the object bounding box based on the angle information; updating a parameter of the object detection model based on the loss information.

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: 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: The present disclosure discloses a method and apparatus for generating an image, a device, a storage medium and a program product, relates to the field of artificial intelligence, and particularly to computer vision and deep learning technologies, and may be applied in smart cloud and power grid inspection scenarios. A particular implementation of the method comprises: acquiring an original insulator image; performing an image transformation on the original insulator image to obtain a composite insulator image; and inputting the original insulator image and the composite insulator image into a pre-trained generative adversarial network to generate a target insulator image. According to the implementation, the image transformation is performed on the original insulator image, and then, massive target insulator images are generated through the generative adversarial network.

Abstract: The present disclosure discloses a method and apparatus for training a model, a method and apparatus for predicting a mineral, a device and a storage medium, and relates to the fields of computer vision and deep learning technologies. An implementation of the method may include: acquiring a target hyperspectral image of a target area, the target hyperspectral image including at least one pixel point annotated with a mineral category; determining a mask image corresponding to the target hyperspectral image; determining a sample hyperspectral image according to the target hyperspectral image and the mask image; determining an annotation vector of each pixel point according to the at least one pixel point annotated with the mineral category; and training a model according to the sample hyperspectral image and the annotation vector of the each pixel point.

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 header model for instance segmentation includes a target box branch having a first branch and a second branch, where the first branch is configured to process an inputted first feature map to obtain class information and confidence of a target box, and the second branch is configured to process the first feature map to obtain location information of the target box. The header model also includes a mask branch configured to process an inputted second feature map to obtain mask information, wherein the second feature map is a feature map outputted by an ROI extraction module, and the first feature map is a feature map resulting from a pooling performed on the second feature map.

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: 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 device for preventing cables against external damage based on sound source localization comprises a power supply unit, and a sound source sensor unit, a camera unit, a signal processing unit and a wireless communication unit which are electrically connected to the power supply unit. The signal processing unit is connected to the sound source sensor unit, the camera unit and the wireless communication unit. The camera unit is associated with the sound source sensor unit. When the sound source sensor unit recognizes a target signal, the signal processing unit sends a trigger signal to the camera unit, and then the camera unit is triggered to replay a surveillance video to determine whether or not a target really exists. Compared with the prior art, the device has the advantages of being good in safety, high in reliability and the like.

Abstract: A device for preventing cables against external damage based on sound source localization comprises a power supply unit, and a sound source sensor unit, a camera unit, a signal processing unit and a wireless communication unit which are electrically connected to the power supply unit. The signal processing unit is connected to the sound source sensor unit, the camera unit and the wireless communication unit. The camera unit is associated with the sound source sensor unit. When the sound source sensor unit recognizes a target signal, the signal processing unit sends a trigger signal to the camera unit, and then the camera unit is triggered to replay a surveillance video to determine whether or not a target really exists. Compared with the prior art, the device has the advantages of being good in safety, high in reliability and the like.

Abstract: The present disclosure discloses a method and device for invoking a JAVA card object. The method comprises: receiving, by a JCRE, a service request message transmitted by an application Applet1; acquiring, by the JCRE, interaction information between the Applet1 and the Applet2 according to an ID of the Applet1 and an ID of the Applet2; acquiring, by the JCRE, a trust degree between the Applet1 and the Applet2 according to the interaction information between the Applet1 and the Applet2, information about one interaction comprising service class information for this interaction and information about whether a service request task of the Applet1 succeeds after this interaction; and determining, by the JCRE, that the Applet1 invokes a shared interface service of the Applet2, when the trust degree reaches a trust degree threshold.

Abstract: The present disclosure discloses a method and device for invoking a JAVA card object. The method comprises: receiving, by a JCRE, a service request message transmitted by an application Applet1; acquiring, by the JCRE, interaction information between the Applet1 and the Applet2 according to an ID of the Applet1 and an ID of the Applet2; acquiring, by the JCRE, a trust degree between the Applet1 and the Applet2 according to the interaction information between the Applet1 and the Applet2, information about one interaction comprising service class information for this interaction and information about whether a service request task of the Applet1 succeeds after this interaction; and determining, by the JCRE, that the Applet1 invokes a shared interface service of the Applet2, when the trust degree reaches a trust degree threshold.