91 results about "Tensor representation" patented technology

The invention provides a spatial intelligent three-dimensional modeling method based on a two-dimensional structure design sketch image, and the method comprises the steps: carrying out the preprocessing of an input two-dimensional structure design sketch image, and obtaining a preprocessed design sketch image, extracting two-dimensional structure design features from the preprocessed design sketch image, and encoding the two-dimensional structure design features to generate structured tensor representation; deducing space components, function partitions and constraint logic based on structured tensor representation to construct a sketch semantic graph, nodes of the sketch semantic graph representing physical structure units, and edges of the sketch semantic graph representing connection relations and stress constraints among the physical structure units; determining a node embedding vector corresponding to each node in the sketch semantic graph so as to perform three-dimensional space coding on the node embedding vector to obtain a node potential vector, and generating a geometric reasoning sequence during three-dimensional structure geometric reasoning according to a mechanical logic definition on the basis of edges in the sketch semantic graph, and performing three-dimensional structure geometric reasoning based on the node potential vectors and the geometric reasoning sequence to generate a three-dimensional structure model.

The invention provides a smart home scene control method and system based on edge calculation, and relates to the technical field of Internet of Things. Multi-modal data of a camera, an audio collector, an environment sensor and the like are obtained and converted into tensor representation, a first data set is generated in combination with a priori rule, clustering analysis is executed for second text data to screen related information, and finally multi-round reasoning is performed in a large language model deployed on the edge side in a partitioned cache mode. And precise control of the household equipment is realized. The first partition reserves multi-mode core information, and the second partition can dynamically update user instructions or logs, so that key information is continuously concerned in a long-sequence context, and newly-added data is flexibly processed. According to the method, a real-time and efficient reasoning effect can still be maintained in a computing power limited environment by fully utilizing lightweight technologies such as pruning and quantification, and the accuracy and response speed of multi-modal scene understanding and home control are improved.

The invention discloses a hyperspectral fusion imaging method and device based on deep nonlinear transformation tensor low-rank representation, and the method comprises the following steps: collecting a priori multispectral image while collecting hyperspectral compression measurement in an observation scene, and taking the two data as input at the same time; based on tensor representation and deep learning theories, novel deep nonlinear transformation tensor low-rank regularization for the hyperspectral image is established, and global high-dimensional low-rank correlation of the hyperspectral image in a deep nonlinear transformation domain is fully described; taking the regularization item as a target function, modeling compression imaging and a spectrum degradation process into two data fidelity items, and constructing a fusion imaging model and a loss function; and minimizing a loss function through a learning algorithm to optimize the model, and fusing and reconstructing a complete hyperspectral image. According to the novel method and the novel device provided by the invention, high-precision fusion imaging of the observation scene can be realized without manual intervention and explicit intermediate steps.

In some example, a method for generating an input tensor representing image data to be encoded by an encoder comprises processing the image data using a selected global transformation to generate a first tensor comprising transformed image data, processing the first tensor by one or more layers of a selected neural network, to generate the input tensor, wherein the input tensor comprises a pre-processed tensor representation of the image data, providing at least one of: a set of configuration parameters for the selected global transformation, a first index representing the selected global transformation, and a second index representing the selected neural network to a decoder, and providing the input tensor for an encoder configured to generate a bitstream representing coded image data.

The invention relates to the technical field of corrugated pipe detection, and discloses a corrugated pipe defect rapid detection method and device.The corrugated pipe defect rapid detection method comprises the steps that physical state information of a corrugated pipe is obtained, and physical parameters of the corrugated pipe are collected at different time frequencies through a multi-parameter sensing network; a corrugated pipe multi-time scale physical parameter data matrix is generated; the corrugated pipe multi-time-scale physical parameter data matrix is converted into unified tensor representation, a multi-scale tensor set is generated through wavelet transform, and correlation strength between different scales is calculated; according to the method, a brand new technical path is provided for the field of corrugated pipe defect detection through the space-time physical multi-scale mapping technology, advanced prediction and accurate diagnosis of corrugated pipe defects are achieved by establishing the mapping relation from microscopic physical changes to macroscopic defect expressions, effective technical guarantee is provided for safe operation of corrugated pipes, and the method is suitable for popularization and application. And a remarkable economic value is created.

The invention relates to the technical field of financial data analysis and artificial intelligence, in particular to a multi-source heterogeneous financial data fusion and intelligent analysis system which comprises a data standardization processing module, a time sequence event fusion module, a knowledge graph construction module, a relation reasoning module and a self-adaptive anomaly detection module. The data standardization processing module is used for converting heterogeneous financial data from different sources into unified tensor representation; the time sequence event fusion module adopts a double-clue cooperation mechanism to establish a mapping relation between continuous time sequence data and discrete events; the knowledge graph construction module extracts financial entities and relationships thereof, and constructs a multi-level knowledge graph; the relation reasoning module performs deep reasoning based on a graph attention mechanism; and the adaptive anomaly detection module dynamically adjusts the detection threshold according to the market environment. According to the system, implicit association in heterogeneous financial data can be deeply mined, market anomalies are recognized in advance, and comprehensive support is provided for financial decision making.

The invention provides a method for predicting miRNA-lncRNA-disease ternary correlation through deep tensor decomposition and hypergraph convolution, and relates to the technical field of miRNA-lncRNA-disease ternary correlation prediction. Comprising six steps of integration of multi-source heterogeneous data, generation of three-dimensional tensor representation, hypergraph convolution modeling high-order interaction, graph attention network feature refining, depth graph convolution network enhancement and correlation prediction. Node features in a graph attention self-adaptive refining similarity network are integrated, global structure learning is enhanced by adopting a depth graph convolutional network, and the combination can generate stable and information-rich embedding for final ternary correlation prediction, so that potential complex correlation among various biological entities such as diseases, genes and drugs can be accurately extracted, and the prediction accuracy is improved. The potential relation and mechanism between the biological entities are further disclosed, and comprehensive ternary correlation prediction is achieved.

The invention relates to a data processing method and system, in particular to a vocational education multi-source data federation treatment method and system, and belongs to the technical field of vocational education informatization and big data. Then constructing a knowledge graph in a triple form and mapping the knowledge graph to a multi-dimensional tensor space; secondly, multi-source data compatibility is analyzed and evaluated through topological features, and feature fusion maintaining topological invariants is executed; finally, knowledge reasoning and service are provided based on a quantum probability field model, the complex relation expression ability is improved through tensor representation, the heterogeneous data fusion quality is guaranteed through topology durability, the uncertainty in knowledge service is processed by introducing the quantum probability theory, and the reliability of the knowledge reasoning and service is improved. The problems of multi-source heterogeneous, complex relation, insufficient service accuracy and the like of vocational education data are effectively solved, and a comprehensive data federation governance solution is provided for the vocational education field.

The present application relates to a data processing method and system, in particular to a vocational education multi-source data federal governance method and system, belonging to the field of vocational education informatization and big data technology, which first acquires and preprocesses vocational education multi-source data through network crawler and OCR technology; then constructs a knowledge graph in the form of triplets and maps it to a multi-dimensional tensor space; then evaluates the compatibility of multi-source data through topological feature analysis, and performs feature fusion that maintains topological invariants; finally, based on a quantum probability field model, knowledge reasoning and services are provided, the present application uses tensor representation to improve the expression ability of complex relationships, ensures the quality of heterogeneous data fusion through topological persistence, and introduces quantum probability theory to handle uncertainty in knowledge services, effectively solving the problems of multi-source heterogeneous vocational education data, complex relationships and insufficient service accuracy, and providing a comprehensive data federal governance solution for the vocational education field.

The invention discloses an image and point cloud fusion multi-modal three-dimensional target detection method, which comprises the following steps: dynamically establishing bidirectional mapping of point cloud and voxels in a Bev view angle based on a voxelization method of double hash tables, and then extracting geometric features of each Bev voxel; after the point cloud is converted from a LiDAR coordinate system to an image coordinate system, the reflection intensity of the point cloud serves as a new channel to be attached to an image, four-channel image tensor representation is obtained, and global multi-level image semantic features are obtained through an adaptive feature extraction module based on perspective voxels; obtaining a regional semantic feature corresponding to the perspective voxel geometric feature through a perspective voxel projection method, and achieving the correlation alignment of the regional semantic feature and the point cloud geometric feature; a channel weight adaptive module based on a cross attention mechanism is adopted to weight geometric features and semantic features respectively, the weighted features are spliced to generate a feature tensor, and then a region proposal RPN network is adopted to carry out classification and regression tasks.

The invention provides a monocular depth estimation method and product based on convolution compensation dual-channel self-attention, and relates to the field of computer vision. Comprising the following steps: converting an event flow of a target scene into three-dimensional tensor representation; obtaining event image fusion multi-scale spatial features based on the image of the target scene and the three-dimensional tensor representation; modeling spatial context correlation in a spatial dimension by utilizing event image fusion multi-scale spatial features through a context modeling self-attention branch to obtain a context modeling self-attention result; through a modal fusion self-attention branch, using the event image to fuse the modal correlation of the multi-scale spatial features in the channel dimension modeling image and the event, and obtaining a modal fusion self-attention result; and pixel-level depth prediction is carried out by using a context modeling self-attention result and a modal fusion self-attention result to obtain a depth map, so that complementary characteristics between an event and an image are fully mined, fine-grained depth fusion expression is realized, and depth estimation precision and generalization ability are effectively improved.

The invention discloses a multi-source data driven intelligent charging stop resource matching method and system and a medium, and relates to the technical field of charging stop resource matching, and the method comprises the steps: respectively executing the multi-source data collection of a user side, a resource side and an environment side, and building a multi-source data set; mapping the multi-source data set to a multi-dimensional tensor space; weighting each dimension in the multi-dimensional tensor space through a space-time dynamic coding mechanism to form a dynamic tensor representation reflecting a real-time state and a future prediction state; constructing a user intention-resource state coupling tensor; calculating a dynamic matching potential index; and establishing an intelligent matching result. According to the method and the device, the technical problems of insufficient matching accuracy and low efficiency caused by insufficient fusion of multi-source data in charging stop resource matching and lack of dynamic adaptation and space-time scheduling consideration in the prior art are solved, and the technical effect of improving the matching accuracy and efficiency of the charging stop resource and the user demand is achieved.

The invention relates to a method and a device for constructing multi-modal and multi-dimensional solid waste management data. The method comprises the following steps: acquiring a solid waste scene image and a remote sensing monitoring image of a solid waste management scene; respectively labeling the two types of images according to a preset labeling strategy, and converting the two types of images into a structured data set through data cleaning, data enhancement and unified tensor representation; based on the data set, driving a general large model through a solid waste field expert Prompt template, automatically generating multiple groups of questions and candidate answers, and forming a solid waste visual question and answer corpus after verification; and finally, evaluating a corpus by using a plurality of evaluation difficulties, and iteratively optimizing the large model and the labeling strategy according to a result. By creating a systematized, multi-modal and multi-task data construction scheme and evaluation benchmark in the field of solid waste disposal management for the first time, the problems of shortage of targeted and universal training data, lack of model evaluation mechanisms and the like in the field of solid waste management are solved, the intelligent level and efficiency in the field of solid waste management are greatly improved, and iterative innovative design is achieved.

The invention relates to the technical field of natural language processing text classification, in particular to a multi-label text classification method based on semantic representation enhancement and dynamic weighted depolarization contrast learning and application of the multi-label text classification method. Preprocessing data in the training data set to obtain input tensor representation; enhancing text semantic representation by fusing multi-layer hidden semantic representation of a depth model; secondly, an improved label graph convolutional network is constructed, regularization, layer normalization, residual connection and label perception attention pooling are introduced into a graph neural network, fine-grained representation of the relation between labels is achieved, and label-text interaction is enhanced; and finally, introducing depolarization weighted contrast loss to construct dynamic weighted depolarization contrast learning, endowing a negative sample with a higher weight, and reducing false negative sample interference, thereby overcoming label semantic overlapping, and aiming at solving the problem of how to enhance the characterization capability of the model in a multi-label semantic overlapping and label incomplete scene.

The invention relates to the technical field of intelligent manufacturing workshop logistics scheduling, in particular to a multi-agent path planning method based on social value orientation and DRL fusion, and the method comprises the steps: S1, building multi-channel tensor representation based on local observation, carrying out the spatial coding of dynamic information, and building an interpretable thinking feature space; s2, receiving neighbor agent dynamic information based on a heterogeneous graph attention network communication mechanism of SVO, separating and aggregating the information through a multi-head SVO perception heterogeneous graph attention network, and extracting information which is most critical to self decision; and S3, based on an SVO double-layer decision-making mechanism of a neighbor agent group, combining SVO and PPO algorithms, and dynamically adjusting an SVO strategy by adopting a local environment and a neighbor agent group to realize dynamic change of a self-adaptive environment of a lower-layer action strategy. According to the method, path conflicts and deadlocks are effectively eliminated, the throughput and success rate of the system are improved, an efficient collaborative solution is provided, and the workshop operation efficiency and the intelligent level are improved.

The invention discloses a traffic missing data complementing method based on C-Tucker fourth-order space-time tensor representation, which comprises the following steps: constructing a fourth-order space-time tensor model of four dimensions of time, day, period and space based on space-time relevance of traffic flow data; performing Tucker decomposition on the observation tensor to calculate a core tensor; n-order modal expansion of the observation tensor and the core tensor is calculated, singular value decomposition (SVD) is adopted to extract a feature vector set, and a factor moment Nt and a core matrix are formed based on feature vectors to construct a fusion matrix; and reconstructing the observation tensor through the core tensor and the fusion matrix to realize recovery of traffic data. According to the method, under the condition that discrete data and continuous missing data coexist, efficient and robust data completion is achieved, and the accuracy of data recovery is effectively improved.

The present description concerns a method of detecting a useful signal, the method comprising the acquisition of a raw signal, by a sensor; the delivery of the raw signal, to a processing device, the signal being represented by a tensor of order d greater than or equal to 3; the computing of an invariance value associated with the tensor, the invariance value being computed based on at least one trace invariant for tensors of order d; the comparison of the invariance value associated with the tensor with a first reference value; based on the comparison, the provision, by the processing device, of an estimate of the signal-to-noise ratio of the raw signal; and if the estimated signal-to-noise ratio is different from 0, the provision of the tensor to a circuit configured to process the raw signal.

The invention discloses a generation method and device for a mixed precision quantization operator, and relates to the field of deep learning, and the method comprises the steps: converting a quantization program inputted by a user into an initial Qtile calculation graph based on Qtile tensor representation; performing equivalent transformation on the initial Qtile calculation graph to generate a plurality of candidate Qtile calculation graphs which are equivalent to the initial Qtile calculation graph; according to the reuse frequency of operator input data in each candidate Qtile calculation graph, determining an optimal data layout position and an optimal data loading strategy of each candidate Qtile calculation graph; on the basis of the optimal data layout, the optimal data loading strategy and a code generation technology based on a template, codes of all the candidate Qtile computational diagrams are generated, and the codes of the candidate Qtile computational diagrams are achieved through codes of mixed precision quantization operators. According to the invention, an optimized high-performance mixing precision quantization operator can be output.

The invention relates to the field of English customs consultation, and discloses an English customs consultation-oriented multi-agent collaborative workflow construction method, which comprises the following steps of constructing a four-dimensional dynamic hypergraph; converting the four-dimensional dynamic hypergraph into tensor representation, performing tensor decomposition, and identifying key agent role nodes under a high weight dimension; constructing an alliance value function and carrying out optimization solution; respectively representing Chinese and English input by adopting an encoder, generating a semantic representation vector, and obtaining a structured semantic fusion result; and carrying out incremental updating on system weight parameters to realize model synchronization of dynamic policy contents. According to the invention, a multi-agent cooperation mechanism and a dynamic hypergraph construction technology are adopted, and real-time information integration in English customs affair consultation is realized. According to the technology, the accuracy of policy interpretation is remarkably improved, and compared with an existing information access information processing technology based on a static database, the problem of information isolated island is solved, and customer requirements are responded in time.

A system and method and for retrieving one or more one or more multimodal assets includes receiving a search query for searching for one or more multimodal assets from among a plurality of candidate multimodal assets, encoding the search query into one or more query embedding representations via a trained query representation machine-learning (ML) model, comparing, via a matching unit, the one or more query embedding representations to a plurality of multimodal tensor representations, each of the plurality of multimodal tensor representations being a representation of one of the plurality of candidate multimodal assets, and identifying, based on the comparison, at least one of the plurality of the candidate multimodal assets as a search result for the search query, and providing the at least one of the plurality of the candidate multimodal assets for display as the search result.

The invention discloses a multi-modal denoising method for a hyperspectral image, which is characterized by comprising the following steps of: S1, respectively performing low-rank tensor representation on the hyperspectral image and a registered multispectral image by utilizing Tucker decomposition, and extracting core tensors of the hyperspectral image and the registered multispectral image; s2, establishing a correlation model between the hyperspectral core tensor and the multispectral core tensor through model-driven linear mapping or data-driven multi-layer perceptron network; s3, iteratively solving a core tensor, a factor matrix and correlation model parameters by adopting an alternating direction multiplier method; and S4, reconstructing a denoised hyperspectral image by using the optimized hyperspectral core tensor and factor matrix. Compared with the prior art, the method has the advantages that the spectral details of the hyperspectral image and the high-signal-to-noise-ratio spatial information of the multispectral image are fully mined and utilized, the restoration precision in the mixed noise scene is effectively improved through the double-Tucker decomposition framework and the core tensor association strategy, and the high-quality hyperspectral image is obtained.

The invention provides a flood storage and detention area multi-scheduling scheme comparison algorithm based on an AI large model, and relates to the technical field of water conservancy, and the algorithm comprises the steps: receiving and analyzing input data and a scheduling instruction, then automatically generating a plurality of candidate scheduling schemes, carrying out the efficient simulation deduction of the candidate schemes, and carrying out the multi-dimensional comprehensive evaluation and comparative analysis. And finally, generating an interpretable comparison result and carrying out interaction. According to the method, human semantic instructions are accurately converted into structured preference tags and JSON task description through semantic analysis, task formalization and context enhancement by using a large language model, and then the semantic tags are dynamically converted into specific weight coefficients of a reinforcement learning proxy award function, so that the learning efficiency is improved. Therefore, a high-level intention is directly mapped into a computable optimization target, a mixed generation mechanism combining a rule template and a reinforcement learning agent is driven, a diversified and physically feasible scheduling scheme represented by a three-dimensional tensor is output, and the problem of dependence on fixed experience is solved.

Apparatuses, systems, methods, and computer program products are disclosed for hardware geometric regularization. A method includes receiving training data comprising labeled data points. A method includes selecting a class of self-adjoint differential operator equations. A method includes selecting a set of orthogonal polynomials as a spectral basis. A method includes iteratively optimizing parameters of the differential operator equations based on the training data using a gradient-based optimizer to minimize an objective function. A method includes solving the optimized differential operator equation using a spectral method with the selected orthogonal polynomials to generate a reproducing kernel represented by a kernel tensor. A method includes outputting a machine learning model comprising the reproducing kernel combined with support vectors derived from the training data, the model configured to infer labels for unseen data points by estimating similarity measures using the reproducing kernel.

The present invention provides a Bayesian tensor completion algorithm based on multi-measurements. The multi-measurement data is represented by multiple tensors, and it is assumed that each measurement value of each tensor element of the tensor follows a Gaussian distribution. Then, CP decomposition is performed on the tensor to obtain the corresponding factor matrices, and it is assumed that the parameters of the factor matrices follow a conjugate prior distribution. Furthermore, the Gibbs sampling method is used to sample the posterior conditional distributions of the respective parameters, and the estimated value of the tensor is output. The missing values in the multi-measurement data are interpolated based on the estimated value of the tensor, thereby realizing data completion. In summary, the completion method of the present invention is aimed at measurement data with low measurement accuracy, high cost, and repeated measurements in some regions. The Gibbs sampling method combined with CP decomposition is used to realize data completion. Compared with the completion methods in the prior art, since the method of the present invention can utilize the information of all measurement data, it can provide a more accurate estimated value, thereby realizing more accurate data completion.

The invention relates to a dynamic three-dimensional head reconstruction method based on compact tensor representation and an electronic device, a compact tensor representation method is used to encode texture attributes of a 3D Gaussian model, static three planes are used to store appearance information of neutral expressions, and lightweight one-dimensional feature lines are used to represent dynamic texture details, so that the dynamic three-dimensional head reconstruction method based on compact tensor representation is realized. Therefore, the storage cost is obviously reduced, and real-time rendering is realized. The invention further provides an adaptive truncation opacity punishment and category balance sampling strategy to improve the generalization ability of the model among different expressions. Compared with the prior art, the method has the advantages of high dynamic texture capture precision, low storage requirement and high rendering efficiency, can realize 300FPS rendering speed and single person 10M storage while ensuring high-quality dynamic detail reconstruction, and is suitable for wider application scenes.

In some implementations, a device may obtain a training corpus, from a set of pre-processed log data, associated with an alphanumeric format. The device may encode the training corpus to obtain encoded data using a set of tokens. The device may calculate a sequence length based on a statistical parameter associated with the training corpus. The device may generate a set of input sequences and a set of target sequences based on the encoded data, where each input sequence and each target sequence has a length equal to the sequence length. The device may generate a training data set based on combining the set of input sequences and the set of target sequences. The device may train a deep neural network (DNN) using the training data set and based on one or more hyperparameters to obtain a set of embedding tensors associated with an embedding layer of the DNN.

The invention provides a visual SLAM (Simultaneous Localization and Mapping) algorithm based on multi-scale neural tensor representation, which belongs to the technical field of indoor scene real-time reconstruction, and can efficiently capture the global structure and fine details of a scene by utilizing a tensor decomposition technology, and reduce the memory usage amount and the calculation overhead at the same time. In addition, the invention further introduces an efficient pixel-based color query method, and each pixel only needs neural network forward regression once in the method, so that the real-time performance of the neural radiation field-based SLAM (Simultaneous Localization and Mapping) is further improved. Experiments on a synthetic data set and a real data set show that the method is superior to the most advanced method in the aspects of reconstruction quality and camera tracking precision, and the efficiency in the aspects of running time and memory use is very high.

Systems, methods, and computer program products are provided for streaming video over a network. In various embodiments, a source video including at least a source frame is read. The source frame is encoded into a corresponding tensor representation by a machine learning model. The corresponding tensor representation is decomposed into a plurality of sub-tensors. Each of the plurality of sub-tensors is encoded into a corresponding packet and transmitted via a network from a source node to a receiver node.

The invention discloses a human body signal filtering processing method and device. The method comprises the following steps: acquiring a clean human body signal of a user and a corresponding label by using multi-channel human body signal sensor equipment; preprocessing the human body signal, wherein the preprocessing comprises standardization processing and tensor representation; designing a conditional denoising diffusion model to gradually add noise to the human body signal, then performing multi-scale feature extraction and domain classification on the human body signal with noise, and training an artificial intelligence model of human body signal filtering; and finally, evaluating the human body signal filtering effect of the user, and further applying the human body signal filtering effect to downstream tasks. The human body signal filtering processing device comprises a human body signal acquisition module, a preprocessing module, a training diffusion model module, a multi-scale feature extraction module, a condition fusion module, a cross-subject generalization module and a human body signal filtering effect evaluation module. By using the human body signal filtering processing method and device provided by the invention, the denoising problem of the existing human body signal filtering method is solved.

The invention discloses an equipment data processing method and system based on semantic annotation, equipment and a medium, and relates to the technical field of deep learning, and the method comprises the steps: collecting multi-source heterogeneous equipment data, constructing a unified data set through analysis and annotation, fusing multi-source features, constructing equipment unified representation and an initial knowledge graph, and receiving an event stream in real time to generate new knowledge. The graph is dynamically updated through conflict resolution, and real-time updating and dynamic evolution of the knowledge graph are achieved. According to the method, accurate construction and intelligent evolution of the equipment knowledge graph are realized by constructing a flow of multi-source data semantic fusion, multi-dimensional feature tensor representation and dynamic conflict resolution, and the real-time performance, the accuracy and the high credibility of a knowledge system are ensured.