8 results about "Tensor form" patented technology

A system and method of encoding at least a plurality of tensors forming a hierarchical representation for a single frame into a bitstream. The method comprises deriving a first unit of information from a plurality of tensors forming the hierarchical representation, the plurality of tensors including at least a first tensor and a second tensor, feature maps of the first tensor having a larger spatial resolution than feature maps of the second tensor and encoding, in a first mode, at least the first unit of information into the bitstream. In a second mode, the method also comprises deriving a second unit of information derived from the first tensor; and encoding, the second unit of information and the first unit of information into the bitstream.

This invention discloses an intelligent dynamic gesture recognition method, comprising: constructing initial node features of a dynamic gesture sequence obtained based on Leap Motion into a tensor form as subsequent input; designing a spatial feature extraction module based on a hypergraph neural network to learn the spatial features of the dynamic gesture sequence; designing a temporal feature extraction module based on a hypergraph neural network to learn the temporal features of the dynamic gesture sequence; constructing a spatiotemporal feature fusion classification module based on node spatial and temporal features to effectively fuse the spatiotemporal features of nodes and obtain the final gesture recognition result; and using a cross-entropy loss function to constrain network training based on the spatial feature extraction module, temporal feature extraction module, and spatiotemporal feature fusion classification module of the hypergraph neural network to obtain a dynamic gesture recognition model based on the hypergraph neural network. This invention utilizes the hypergraph neural network to effectively mine the potential higher-order correlations of gesture data, achieving more accurate dynamic gesture recognition.

This invention presents a method for establishing large deformation viscoelastic constitutive relations based on Prony technology. It addresses the problems of existing viscoelastic constitutive models lacking universality, and the complexity and programming difficulties associated with their mathematical relationships. The invention includes: decomposing the stress tensor into the sum of the deviatoric stress tensor and the mean stress; decomposing the strain tensor into the sum of the deviatoric strain tensor and the mean strain; and providing a convolutional expression for the deviatoric stress tensor and the mean stress. The increment of the deviatoric stress tensor is expressed as a function of the deviatoric strain increment, and the increment of the mean stress is expressed as a function of the mean strain increment. The tensor form of the incremental constitutive relation is written in matrix form. This method has been programmed, and the numerical results obtained from the program agree well with the experimental results in specimen-level experiments on solid rocket motor propellant grains.

The present application belongs to the technical field of grain informatization processing, and particularly relates to a method and system for identifying imperfect grains based on spectral spatial coupling features. The method comprises inputting visible light images and hyperspectral data of the grains into a recognition model; a visible light image branch in the recognition model processes the visible light images of the grains to obtain the output of the branch; a hyperspectral data branch in the recognition model processes the hyperspectral data of the grains to obtain the output of the branch; the outputs of the visible light image branch and the hyperspectral data branch are used to obtain the recognition result; the recognition model is trained using grain sample images; the processing of the hyperspectral data branch comprises: expanding the hyperspectral data represented by a high-order tensor into a tensor factor multiplication mode containing spectral and spatial information features, and each mode is a tensor form in which all different spectral information features are superimposed; and the tensor form is subjected to feature extraction to obtain the branch output.

3999397_2 Abstract METHOD, APPARATUS AND SYSTEM FOR ENCODING AND DECODING A TENSOR An apparatus and method for decoding at least a plurality of tensors forming a hierarchical representation of feature maps for a single frame from a bitstream. The method comprises: decoding a first unit of information from the bitstream; decoding a second unit of information from the bitstream; and determining a first plurality of tensors from the first unit of information, feature maps of at least one tensor of the first plurality of tensors having a different spatial resolution from feature maps of other tensor(s) of the first plurality of tensors. The method also comprises determining a second plurality of tensors from the second unit of information, feature maps of at least one tensor of the second plurality of tensors having a different spatial resolution from feature maps of other tensor(s) of the second plurality of tensors. Feature maps of each tensor of the first plurality of tensors have different spatial resolution from feature maps of each tensor of the second plurality of tensors, and the tensors of the first plurality of tensors and the second plurality of tensors correspond to the hierarchical representation of feature maps for the single frame Abstract 20 26 21 40 59 07 A ug 2 02 6 A b s t r a c t 2 0 2 6 2 1 4 0 5 9 0 7 2 0 2 6 A u g

The application discloses an incomplete multi-view clustering method fusing consensus and difference information, and belongs to the technical field of multi-view clustering methods. The method firstly constructs an incomplete graph by using the cross-order neighborhood relation of non-missing samples, so as to mine potential comprehensive and complementary neighborhood information in data. Subsequently, a logarithm-based tensor rank approximation model is introduced to perform low-rank recovery on a tensor formed by the incomplete graph, so that a complete fine-grained affinity tensor is obtained. On this basis, the application further proposes an adaptive weight fusion mechanism, which learns the weights of different views to extract a consistent affinity matrix from the recovered tensor. The mechanism can fully utilize the difference and consensus of cross-views, and effectively solves the clustering problem caused by the missing of multi-view data.

The invention relates to the technical field of motor adaptive control, and discloses a motor adaptive control method and system based on artificial intelligence, and the method comprises the steps: collecting multi-source operation data in a motor operation process; constructing a tensor representation with at least a three-order structure based on the collected multi-source operation data; carrying out high-dimensional feature compression, and extracting a low-dimensional hidden space feature vector representing the running state of the motor; constructing a control objective function, and calculating the optimal control quantity of the motor; driving a motor to execute control operation based on the calculated optimal control quantity; on the basis of the feedback data, carrying out online updating on a tensor auto-encoder model; the system comprises a data acquisition module, a tensor construction module, a tensor auto-encoder module, a control optimization module, a control execution module and an online updating module. The method introduces a tensor form with a three-order structure to express the multivariable operation data of the motor, and can capture the coupling characteristics of a time sequence, a space and variables at the same time.

The invention provides a community search method and system oriented to new hardware. The method comprises the following steps: acquiring an original image to be subjected to community search and converting the original image into a tensor form; constructing an index based on-truss hierarchical decomposition for the original image in the tensor form through a tensor operator; and performing community search on the index based on the-truss hierarchical decomposition by a community search algorithm based on tensor calculation to obtain a community search result of the original image. According to the method, the original image is converted into the tensor format, and the efficient index based on-truss hierarchical decomposition is constructed, so that efficient community search based on new hardware is realized.