Abstract: The present disclosure discloses a method for quantifying a bearing capacity of foundation containing shallow-hidden spherical cavities, comprising: in Step 1, constructing a spatial axisymmetric calculation model for stability analysis of the foundation containing shallow-hidden spherical cavities; in Step 2, solving the model to obtain a general solution which reflects the spatial stress distribution of surrounding rock containing shallow-hidden spherical cavities; in Step 3, obtain a mathematical expression by derivation for calculating the bearing capacity of the foundation containing shallow-hidden spherical cavities; and in Step 4: completing the determination of the foundation bearing capacity. Benefits: This method has many advantages such as comprehensive consideration, high accuracy and reliability of calculation results, and may provide the scientific basis for the development of prevention and control against the instability of the foundation containing shallow-hidden cavities.

Abstract: The present disclosure provides a method and device for intelligent control of heating furnace combustion based on a big data cloud platform, which relates to the technical field of artificial intelligence control. The method includes: construction of big data cloud platform based on production and operation parameters of the heating furnace; identification of key factors in the production process of the heating furnace by using big data mining technology; independent deployment of traditional heating furnace combustion control systems based on the mechanism model; and integration of cloud platform big data expert knowledge base and the heating furnace combustion intelligent control system.

Abstract: A computing device obtains a to-be-optimized image. The device aligns the to-be-optimized image to obtain a to-be-optimized aligned image. The to-be-optimized aligned image includes a target region having points of objects that are distributed in a standard position. The computing device uses the to-be-optimized aligned image as an inputs to a generation network. The device performs feature extraction on the to-be-optimized aligned image using the generation network, to obtain an optimized image. The generation network is obtained by training a to-be-trained generative adversarial deep neural network model according to a low-quality image pair and a joint loss function. The low-quality image pair includes a target image and a low-quality image corresponding to the target image.

Abstract: Disclosed is a method for training an expression transfer model performed by a computer device, the method including: obtaining a source domain facial image of a first object, a target domain facial image of a second object and a facial feature image associated with the target domain facial image; applying the facial feature image and the source domain facial image to an expression transfer model to obtain a synthesized facial image of the first object; applying the synthesized facial image and the target domain facial image to a discriminative network model obtain two discrimination results; applying the synthesized facial image and the target domain facial image to an image classification model to obtain a category feature vector for identifying a difference between the synthesized facial image and the target domain facial image; and updating the expression transfer model according to the category feature vector and the discrimination results.

Abstract: An embodiment of this application discloses an artificial intelligence-based image generation method performed by a computer device. The method includes: acquiring a source image including a target object whose pose is to be transformed, and a target image including a reference object presenting a target pose; determining a pose transition matrix according to a model pose corresponding to the pose of the target object and a model pose corresponding to the target pose of the reference object; extracting a basic appearance feature of the target object from the source image; processing the basic appearance feature based on the pose transition matrix, to obtain a target appearance feature of the target object in the target pose; and generating a target synthetic image of the target object in the target pose based on the target appearance feature.