9results about How to "Well represented" patented technology

The invention discloses a static model of a SiC MOSFET device based on physical characteristics, including the drain current I. d Model, channel voltage drop V ds Model, gate-source effective voltage V gs The model and the model of the inversion layer channel carrier mobility μ. The drain current I d The model is an improvement on the standard long-channel device model, characterized by the representation of physical processes such as channel modulation effect and depletion layer bulk charge effect; the gate-source effective voltage model introduces the gate-source effective voltage V. gs The interface state trap is characterized by an asymptotic function as a function of the external gate-source voltage V. GS The process by which charge carriers gradually fill the space between the MOSFET and the charge carriers. This model is highly versatile and has good characterization effects on different types of SiC MOSFET devices. The model has high accuracy, and the model parameters all have certain physical meanings, which is conducive to further research based on this model. At the same time, the model is compact, the simulation speed is fast, and it is suitable for circuit simulation.

The invention discloses a three-dimensional terrain recognition method, device and equipment and a computer readable storage medium, and belongs to the technical field of computers. The method comprises the following steps: acquiring a first depth image of a target area acquired by a first image acquisition module at a first moment, a first view image of the target area acquired by a second image acquisition module at the first moment, and first inertial data acquired by an inertial sensor; performing dynamic compensation on the first depth image and the first view image based on the first inertial data to obtain a compensated second depth image and a compensated second view image; generating point cloud data of the target area according to the second depth image and the second view image; and according to the point cloud data, topographic features of the target area are extracted, and the topographic features are used for representing the topography of the target area. The accuracy of the topographic features of the target area extracted by the method is higher.

The application discloses a WUPyV infection model and a construction method and application thereof, and the construction method comprises the following steps: (1) culturing proximal differentiated lung organoids; (2) removing collagen from the lung organoids to prepare lung organoid ball suspension, mixing the lung organoid ball suspension with a WUPyV sample, and co-incubating and infecting; and (3) re-embedding the lung organoids in basement glue after infection, and continuing to culture in a basic culture medium to obtain the WUPyV infection model. The WUPyV infection model is constructed by using three-dimensional human lung organoids, WUPyV can be separated and cultured, the infection process of WUPyV in human lung organs can be better simulated and characterized, and a new infection model is provided for WUPyV infection pathogenesis and body innate immune response mechanism and interaction between viruses and host cells.

The application discloses a gait recognition method, system, device and medium based on point cloud, which comprises the following steps: performing point cloud projection on gait point cloud data to obtain a depth image sequence; encoding the depth image sequence to obtain initial features, inputting the initial features into a large-scale spatial attention module and a small-scale spatial attention module to extract multi-scale enhanced features, and performing feature fusion on the initial features and the multi-scale enhanced features to obtain first depth features; inputting the first depth features into a multi-scale space-time convolution network module to perform space dimension and time dimension feature extraction and fusion through a pseudo 3D convolution chain of several levels to obtain second depth features, and performing aggregation on the second depth features to obtain target depth features; and inputting real-time acquired gait data into a preset gait recognition model to determine a recognition object. The application can improve the accuracy and efficiency of gait recognition.

The application relates to a building three-dimensional reconstruction method based on multi-coplanar geometry and a graph neural network, which comprises the following steps: pre-processing aerial images and ground images and obtaining an initial point matching set; iteratively extracting multiple groups of local homography matrices between the image pairs; scaling the aerial images and the ground images and constructing a multi-scale pyramid representation; performing matching by using a deep matching network; after the matching results are converged, the inverse transformation of the homography matrix is used to project back to the original image coordinate system to form a candidate cross-platform corresponding point line set; and verified cross-platform control point set is used to replace the matching input of the reconstruction process to output a final building three-dimensional model. The application can obtain sufficient, uniform and geometrically consistent cross-platform control points in cross-platform image pairs with large angle differences, scale changes and local occlusions, thereby replacing or reinforcing the matching input of traditional three-dimensional reconstruction, and realizing high-precision, high-completeness three-dimensional reconstruction and detail recovery of a city building scene.

The application discloses a kind of knowledge distillation methods, devices and equipment of mask autoencoder and storage medium, the method is by respectively establishing the teacher model and student model of mask autoencoder, wherein the teacher model and the student model are all visual transformation model, and the size of the teacher model is greater than the student model;The teacher model is pre-trained;The student model is pre-trained based on the pre-trained teacher model, so that the student model learns data generalization ability from the pre-trained teacher model, and obtains better image features representing ability;The student model is fine-tuned based on the pre-trained downstream task training, and the student model can be deployed in the power edge side lacking of computing resources, while ensuring that the model accuracy does not decrease, the model parameter is reduced, and the real-time inference speed is accelerated.

This invention provides a precise characterization method for manufactured sand particle shape. The manufactured sand of all particle sizes is divided into two parts with different particle size ranges: one part with particle size and the other with mass and mass , respectively, and a particle size less than . The particle shape of the manufactured sand with particle size is characterized using the unit flow time method, while introducing flow rate to transform the particle shape result into a dimensionless value. The particle shape of the manufactured sand with particle size is accurately and three-dimensionally characterized using the sphericity method. Based on the mass ratio of the two parts, the particle shape results of the two parts are converted into a particle shape index, thus transforming the abstract quality of manufactured sand particle shape into a concrete numerical value that can be mathematically calculated. This invention combines the flow time method and the sphericity method to form a new detection method. The new method effectively overcomes the shortcomings of single detection methods while leveraging their advantages of precise detection for a specific particle size range.

This invention discloses a method for characterizing multi-stage carbonate reservoirs based on time-varying Gaussian window transient extraction transform, used for characterizing multi-stage carbonate reservoirs. The method includes: inputting the seismic signal to be analyzed, where is time; constructing a short-time Fourier transform containing the time-varying window width parameter to be optimized, where represents frequency; determining the optimal window width parameter at each time step based on the local Rényi entropy minimization criterion; substituting and calculating its time partial derivative; constructing a transient extraction operator based on the time partial derivative results, including a local maximum criterion, an energy derivative zero-point criterion, and a group delay migration criterion; and proposing a time-varying Gaussian window transient extraction transform under the action of this operator for characterizing multi-stage carbonate reservoirs.

The application relates to the field of agricultural production and environment monitoring technology, and particularly discloses a soil chaotic data identification method based on multi-agent decision, which comprises the following steps: acquiring sample data of soil and constructing a data set; constructing a multi-agent expert pool and a joint representation network, and training the same by using the data set to obtain a soil heavy metal prediction model; wherein the multi-agent expert pool comprises agents of multiple algorithms; the joint representation network comprises a representation encoder, an expert recognizer and a classification discriminator; collecting observable variable data of soil and inputting the same into the soil heavy metal prediction model to obtain a heavy metal concentration prediction value, a confidence, a data type and resampling suggestions; the multi-agent expert pool and the joint representation network can give point estimation, resampling suggestions, sample-level cognitive divergence, knowledge missing estimation and causal sensitivity and other explanatory indexes, and directly support agricultural management decisions such as whether to resample, whether to manually review and the like.