86 results about "Attentional network" patented technology

The present application relates to the technical field of load prediction, and provides an electrical load prediction method and device based on spatial-temporal correlation. The method comprises: performing spatial-temporal modeling on the basis of historical electrical load data and historical environment data to obtain a multi-layer spatial-temporal model, wherein each layer model is a directed graph and represents spatial causality in the fixed time, and a directed edge between the layer models represents temporal causality; on the basis of an interpretable spatial-temporal attention transformer, performing feature extraction and priori knowledge fusion on the multi-layer spatial-temporal model to obtain spatial-temporal characteristics of an electrical load, wherein the interpretable spatial-temporal attention transformer comprises a spatial causal attention network, a temporal attention network, and a spatial dependence comparison module; and on the basis of the spatial-temporal characteristics of the electrical load, and the real-time electrical load data and the real-time environment data of a target electrical node, predicting by using a trained STEF-DHNet model to obtain the predicted electrical load data of the target electrical node. The present application can improve the prediction accuracy and the model applicability.

The invention discloses an enterprise big data security early warning method based on anomaly detection, and the method comprises the following steps: S1, collecting original data, and carrying out the format unification and structure standardization processing; s2, preprocessing is carried out, and feature vectors are constructed; s3, a behavior entity relation graph is constructed, a graph attention network is adopted for training, and structural features in a normal behavior mode are learned; s4, calculating the deviation degree between the current behavior and the normal behavior; s5, reconstructing the feature vector, measuring the deviation degree between the current behavior and the standard behavior distribution by using a mahalanobis distance, and calculating the posterior anomaly probability of the behavior through a Bayesian updating mechanism; and S6, evaluating the risk level of the current behavior according to the posterior anomaly probability, and generating a corresponding early warning event. According to the method, the graph attention network and the Bayesian self-coding technology are fused, enterprise behavior anomaly detection and grading early warning are achieved, and the method has the advantages of being high in recognition precision, high in self-adaption and timely in response.

The invention belongs to the field of electroencephalogram signal processing, and provides a time-frequency space electroencephalogram emotion recognition method based on three-dimensional space position embedding, which comprises the following steps of: firstly, constructing a three-dimensional electrode space position matrix based on an international 10-20 system standard, determining a space adjacency relation between electrodes, and calculating a phase locking value to obtain a functional connection matrix; then, deep feature fusion of an electrode spatial position matrix and a functional connection matrix is realized by adopting a hierarchical cross Transform architecture, the spatial position matrix represents spatial distribution features of a cerebral cortex region, and the functional connection matrix quantifies phase synchronization features of cross-brain region neural oscillation and simulates a brain spatial topological structure; and finally, extracting time, frequency and spatial features of the electroencephalogram signals through combination of a graph attention network and bidirectional long-short-term memory with an attention mechanism for emotion recognition. The method can effectively extract space structure information highly related to the emotional state, and significantly improves the accuracy of emotion recognition.

The invention belongs to the technical field of nuclear power, and particularly relates to an abnormal time series data detection method and device based on a multi-view space-time interactive attention network. According to the method and the device, complex spatial-temporal correlation dependency in the multi-source time series data of the industrial control system can be fully mined, so that time series data anomaly detection can be more comprehensively modeled. The method focuses on carrying out joint characterization learning on multi-view features such as global level and local level of time series data; constructing a time domain and space domain multi-head self-attention mechanism to perform space-time content interactive aggregation to obtain a content perception representation, and performing space-time association learning by using a plurality of stacked graph attention network layers to obtain an association perception representation; and finally, in a double attention intensified comparison reconstruction module, respectively adopting global and local multi-head cross attention mechanisms to further obtain a reconstruction time sequence, thereby effectively realizing multi-source time sequence data anomaly detection in the industrial control system.

The invention discloses a driver cognitive load electroencephalogram signal classification method based on a dynamic graph attention network. The method comprises the following steps: constructing a dynamic graph attention neural network; electroencephalogram data of a driver in the driving process are obtained in real time and divided according to time windows, data of each time window is preprocessed and then input into the trained dynamic graph attention neural network, and a classification result of the electroencephalogram data corresponding to the time window is obtained; wherein the dynamic graph attention neural network comprises an electroencephalogram signal acquisition unit, a feature extraction unit, a graph attention unit and a classification unit; wherein the electroencephalogram signal acquisition unit is used for acquiring electroencephalogram data in real time and preprocessing the electroencephalogram data through a construction map structure; a feature extraction unit constructs a feature matrix of nodes in the graph structure; the graph attention unit comprises a plurality of graph attention layers based on a multi-head graph attention mechanism, and updates the feature matrix based on the adjacent matrix; and the classification unit performs classification based on the final feature matrix and outputs a classification result.

The invention discloses a multi-modal data drawing logical relationship analysis method, electronic equipment and a medium, and the method comprises the steps: generating a node set based on drawing image data and text data; generating a cross-modal hyperedge set based on the spatial proximity relationship, the visual feature similarity and the semantic correlation between the node sets; generating a hypergraph embedding input representation based on the node set and the cross-modal hyperedge set; the hypergraph is embedded into the input representation input improved hypergraph self-attention network model, and a hyperedge logic relation type and a corresponding hyperedge confidence coefficient are generated; generating a graph structure result based on the hyperedge logic relationship type and the node set, wherein the graph structure result meets the structure legality requirement; and performing hyper-parameter automatic adjustment and convergence control on the atlas structure result based on hyper-edge confidence, and generating an optimal atlas analysis model and a structured output result. According to the method, the reliability and the quality of analysis of component nodes, logic edge relationships and semantic structures in the drawing are improved.

The invention discloses a reconfigurable intelligent surface (RIS)-assisted multiple-input-multiple-output (MIMO) implicit channel estimation method based on a graph attention network, which is used for efficient downlink transmission in a multi-user scene. Firstly, a graph attention network is designed, user nodes and RIS nodes are modeled in a unified mode, received pilot signals serve as initial features, spatial feature expression is enhanced in combination with user three-dimensional position information, and therefore interference between users and a spatial correlation structure are accurately represented; secondly, end-to-end feature aggregation is achieved based on a message passing mechanism, a base station beam forming matrix and an RIS reflection coefficient are directly predicted under the condition that explicit channel estimation is not needed, and the total transmitting power constraint and the unit mode constraint are met through normalization processing so as to complete joint optimization; according to the method, the users and the speed of the system can be remarkably improved under limited pilot frequency overhead, and the method has excellent generalization performance and robustness in a multi-user complex propagation environment.

The invention provides a multi-modal sentiment analysis method based on graph-attention collaborative optimization cross-modal recombination, and relates to the technical field of multi-modal sentiment analysis. The method comprises the following steps: firstly, designing a modal self-adaptive multi-modal graph construction module, constructing a local sparse graph based on KNN-RBF for a language modal, and adopting a low-rank representation method combined with nuclear norm regularization for an audio and video modal; secondly, the processed modal features are transmitted into a graph attention network to realize high-order feature aggregation; then, a language-guided hierarchical cross-modal interaction mechanism is constructed, and multi-granularity semantics are accumulated in combination with an advanced multi-modal feature container module; and finally, designing an advanced feature recombination strategy based on dynamic matching, and realizing feature alignment by taking a language feature container as an anchor point. According to the method, graph learning and sequence learning are unified in a collaborative framework through a graph-attention collaborative optimization cross-modal recombination model, so that the problems of cross-modal attention noise interference, modal imbalance, insufficient cross-modal feature alignment efficiency and the like can be effectively solved.

The invention relates to the technical field of image processing, in particular to a multi-modal image fusion method based on a deep coding and decoding axis interactive attention network, which comprises the following steps of: S1, acquiring data of an infrared image and a visible light image, normalizing the infrared image and the visible light image, and then inputting a model for feature extraction; the feature extraction method comprises an encoder, a fusion strategy and a decoder. S2, in a feature extraction stage of an encoder, multiple times of extraction is performed on two paths of infrared and visible light images through a convolutional neural network and transformer, so that local information of two modal images is captured, and encoding representation is obtained; s3, performing multi-time layered fusion on the features coded by the encoder by a fusion strategy; s4, reconstructing and decoding the fused image by a decoder; the feature representation capability is higher, the utilization rate of original information is higher, image detail mining is more sufficient, and the fusion effect is better.

The invention discloses a chaotic engineering system risk prediction method based on a graph attention network, and belongs to the chaotic engineering system risk prediction technology, and the method comprises the steps: storing topological structure information interacted between components in a chaotic engineering system; feature representations are generated for the classified data and the digital data through a self-attention mechanism, and intermediate vectors are obtained after connection; aggregating the topological structure information by using a graph convolutional network to obtain a final representation of an intermediate vector; reconstructing the final representation of the intermediate vector by using a decoder, and performing risk prediction on the chaotic engineering system by using a classifier; and predicting the system risk by using the trained classifier. According to the method, the internal topological relation of the system is fully utilized, and the limitation that complex interaction between components is ignored in a traditional method is overcome; weights are distributed to different features in a self-adaptive mode through a self-attention mechanism, complex correlation between data is captured, and the quality of feature representation is greatly improved.

The invention belongs to the technical field of audio signal processing, and discloses a music emotion recognition method and system based on parallel local attention and a converter, and the method comprises the steps: inputting an audio feature vector into a parallel local attention network and a converter network, and respectively extracting a local music feature and a global music feature; carrying out weighted fusion to obtain a fused feature vector; a music encoder is used to encode music features; using an emotion encoder to serialize the emotion labels into vectors, and outputting emotion features; using canonical correlation analysis to maximize the correlation between the music features and the emotion features; the similarity between music and emotion distribution is optimized through KL divergence loss; and by calculating the similarity, bidirectional retrieval of music recognition emotion and emotion recognition music is realized. The method can effectively improve the performance of the music emotion recognition task, has a wide application prospect, and particularly has an important application value in the fields of music recommendation, emotion calculation, human-computer interaction and the like.

A three-dimensional tooth model segmentation method of a double-branch geometric attention network based on centroid guidance is oriented to oral cavity three-dimensional scanning point cloud data and comprises the steps that firstly, normalization and normal vector estimation are conducted on the oral cavity three-dimensional point cloud data, a double-branch encoder for coordinate and normal decoupling is constructed, and a global topological structure and local geometric features are extracted respectively; secondly, a separable attention mechanism guided by the mass center is introduced into a coordinate branch so as to improve the distinguishing ability of adjacent teeth, and a graph convolution attention mechanism is introduced into a normal branch so as to strengthen the boundary expression of the teeth and gingiva; further, in the fusion stage, a mass center thermodynamic diagram and multi-scale feature aggregation are combined, and joint modeling of global and local structures is achieved; and finally, collaborative optimization of instance segmentation and centroid localization tasks is carried out through a dynamically weighted joint loss function. According to the method, the segmentation precision and robustness under complex cases are remarkably improved while the light weight is kept, and the automation and clinical practicability of oral diagnosis and treatment are enhanced.

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 application discloses a kind of attention mechanism trajectory generation and simulation methods for sparse data completion, belong to intelligent transportation and trajectory prediction field.The application fills in trajectory missing value using high-precision sensor and map information, combines graph attention network (GAT) and multi-modal fusion technology;Adopt the attention module based on distance (D-GAT) and based on view (V-GAT), capture the interaction between vehicles, improve the understanding of complex traffic scene;Through prediction supervision generator and multi-modal trajectory generator, combine LSTM and Gaussian mixture model (GMM) to generate multiple possible trajectories, and use Kalman filter for online adjustment, ensure the accuracy and real-time of trajectory.The application realizes the intelligent completion of sparse traffic data and the accurate generation of trajectory, provides reliable data support and decision basis for intelligent transportation system, helps the efficient operation and sustainable development of urban traffic planning and management.

The invention discloses an electric power topological graph node classification method, system and device based on a federal asynchronous graph attention network, and a storage medium, and relates to the field of electric power system automation, and the method comprises the steps: collecting local electric power topological graph data of each regional power grid client, constructing a data set, and generating weighted embedding through label semantic embedding learning; based on this, a weighted tag semantic graph and an auto-encoder are constructed to obtain coding representation and loss, the two are combined to obtain a classification result through backbone network processing, and client model training is carried out. And finally, generating a global model at a server side through operations such as spectrum similarity and the like, wherein the global model is used for power topological graph node classification. According to the invention, the privacy of power grid data is guaranteed, and the generalization ability and robustness of the model are improved. The node type and state of the power topological graph are accurately identified, a decision basis is provided for troubleshooting and load distribution, the operation and maintenance efficiency of a power grid is effectively improved, and safe and stable operation of the power grid is guaranteed.

The application provides a text classification method and system based on a topic-aware hierarchical multi-attention network, comprising: obtaining text information to be classified; obtaining a classification result according to the obtained text information and a preset text classification model; wherein the text classification model is constructed in a hierarchical structure to form multiple attention mechanisms, and converts sentence-level and document-level inputs into sentence and document encoders respectively; the application takes self-attention as the main building block of the neural network, not only improves the modeling ability of the distance relationship, but also makes the training speed of the self-attention network faster due to the feedforward structure; a hierarchical neural structure is introduced, which converts sentence-level and document-level inputs into sentence and document encoders respectively, and realizes the most advanced classification accuracy.

This invention discloses a semantic segmentation method for ocean internal wave stripes in SAR images, relating to the field of semantic segmentation of remote sensing images. The method model consists of an encoder and a decoder. The encoder comprises four Transformer modules, each containing a self-attention layer, a feedforward neural network, and an overlap patch merging module. Within each module, the input image is processed N times through a multi-head self-attention mechanism, and then the merging module generates feature maps at four scales. The decoder consists of three modules: a serpentine convolution, an EVC module, and an expectation-maximization attention network. The advantages of this invention are: the model fully utilizes the multi-scale fusion module, improving performance and robustness; the use of serpentine convolution can better extract features of linear shapes; and the use of the expectation-maximization attention network improves model accuracy while reducing computational complexity.

The application provides a power load anomaly detection method and device, and belongs to the technical field of power grid safety. The method comprises the following steps: standardizing power load data and processing the power load data in blocks. Then, a multi-head self-attention network is used to calculate an attention matrix representation between blocks and within blocks, and the attention matrix representation is respectively up-sampled. Next, a divergence loss function of the two is calculated, and an anomaly score of each point is calculated according to the divergence loss function. Finally, whether the power load data is abnormal is determined through a preset hyperparameter threshold. Through the method, the distance between normal and abnormal user features is maximized, the distance between features of the same type of users is minimized, an effective power data representation is learned, different user power consumption data features are actively compared, and abnormal power consumption behaviors are effectively identified.

The invention provides a method and a system for constructing a knowledge representation learning model based on a periodic perception contrast graph attention network. The method comprises the steps of constructing a sub-graph sampler, constructing a graph attention network and performing time sequence perception contrast learning. According to the method, firstly, adjacent neighbor entities are screened and queried through a period time sensing sub-graph sampler in a period dynamic weighting mode, consumption of computing resources is reduced, and key time sequence context and period information are reserved; then, the graph attention network with the time perception function generates dynamic representation of time perception by using graph attention in the constructed query related sub-graph, and entity embedding representation fused with neighbor information is obtained; then, time sequence perception contrast learning improves robustness in time knowledge graph reasoning through dynamic and static representation contrast learning of an entity, and entity prediction is carried out through embedded representation obtained through learning. Finally, the entity representation is decoded using a decoder.

The present invention discloses an image segmentation method based on a multi-scale contourlet parallel reverse attention network, which relates to the technical field of image processing and includes: obtaining an image to be segmented; using a feature aggregation module in the multi-scale contourlet parallel reverse attention network to extract the spatial domain features, frequency domain features, and features after frequency domain enhancement of the image to be segmented, and aggregating the features after frequency domain enhancement through a parallel partial decoder in the multi-scale contourlet parallel reverse attention network to obtain a global mapping graph; according to the reverse attention module in the multi-scale contourlet parallel reverse attention network, combining the global mapping graph with the spatial domain features, features after frequency domain enhancement, and frequency domain features to layer by layer obtain reverse attention features, and processing the reverse attention features through an activation function to obtain a prediction graph. The present invention can increase the interpretability of the network model.

This application relates to a method, apparatus, and device for screening the risk of reading and attention deficit disorder in children. The method includes acquiring multi-channel raw brain blood oxygenation signals under task-induced conditions using a specific layout fNIRS array integrated into a wearable headband, based on a rapid naming cognitive paradigm. Based on the raw brain blood oxygenation signals, a fusion feature vector representing the reading and attention networks is generated by calculating temporal waveform features and frontotemporal functional connectivity strength. The multi-dimensional fusion feature vector is then processed and analyzed using a Transformer classification model to generate classification results indicating the risk level of reading disorders and comorbid ADHD. This application achieves portable and rapid brain function signal acquisition by integrating a targeted fNIRS array with a standardized cognitive paradigm. By fusing temporal dynamics and brain network connectivity features, a multi-dimensional neural representation is constructed. Finally, a lightweight Transformer model is used to output the risk level of reading disorders and comorbid ADHD end-to-end, achieving high-precision automated assisted screening.

The application provides a visual question answering method and system based on a multi-level visual feature enhancement network, which can enhance the relationship between local objects and local objects and the relationship between regional objects and global concepts, thereby jointly learning the visual semantic relationship of multiple spatial contexts. A separation visual feature module based on a graph attention network is used to capture pixel-level visual features and object-level regional features; a joint visual feature representation based on a graph attention network is used to jointly represent the pixel-level features and the object-level features, simultaneously learn the semantic relationship between different levels, better associate with the question text, and thus provide more rich visual feature representation. The application solves the problem that the traditional visual feature representation loses the context relationship between the regional features and the global features, so that the global semantic cannot be fully utilized, and the visual features are lost.

The present invention discloses a method for predicting risks of a chaos engineering system based on a graph attention network, belonging to the technology of predicting risks of a chaos engineering system. The method includes: storing topological structure information of interactions between components in the chaos engineering system; respectively using a self-attention mechanism to generate feature representations for categorical data and numerical data, and obtaining an intermediate vector after connection; using a graph convolutional network to aggregate the topological structure information to obtain a final representation of the intermediate vector; using a decoder to reconstruct the final representation of the intermediate vector, and using a classifier to predict the risks of the chaos engineering system; using the trained classifier to predict the system risks. The present invention makes full use of the inherent topological relationship of the system, overcomes the limitation of traditional methods that ignore the complex interactions between components; uses the self-attention mechanism to adaptively assign weights to different features, captures the complex correlations between data, and greatly improves the quality of feature representations.

The present invention discloses a time-series-aware knowledge graph reasoning method based on an attention network, which belongs to the field of artificial intelligence knowledge reasoning technology, including inputting a historical event time-series-aware knowledge graph and an attribute time-series-aware knowledge graph; using a graph convolutional network to establish the connection between a target entity and a high-order neighbor entity in the historical event time-series-aware knowledge graph; introducing an attention mechanism to distinguish the impact of different relationship types on the target entity; using a layer of graph convolutional network without self-loops to obtain the embedding of each entity in the attribute time-series-aware knowledge graph about the attribute; introducing a time gate mechanism to filter the historical information embedded in the entity time series; using a gated cyclic unit to update the relationship embedding matrix under different timestamps; and realizing entity prediction and relationship prediction. The present invention adopts the above method to improve the reasoning accuracy and efficiency, improve the processing ability of complex time-series knowledge graphs, and realize accurate prediction of future events and their relationships.

The application discloses a texture feature guided texture preserving low dose CT image denoising method, and belongs to the field of medical image processing.The application specifically discloses a multi-scale deep residual attention network model with texture feature guidance, which is applied to low dose CT imaging.The main network model comprises four sub-models, one is a multi-scale initial denoising network model for denoising low dose CT, and the other network is used for extracting texture details after the initial denoising network, and the two network parts work cooperatively.The extracted texture details and the initial low dose CT are fused through a multi-scale image and texture feature fusion network model, and then enter a multi-scale main denoising network for further denoising of the low dose CT, which is beneficial to the main denoising network to learn more unobvious details.The low dose CT image denoising method disclosed by the application efficiently removes the noise and stripe artifacts in the low dose CT image, and meanwhile, the structural information and texture feature detail information in the image are preserved.

The invention belongs to the technical field of bioinformatics, and more specifically relates to a spatial domain identification method based on a single cell large model and a graph attention network. The method comprises the following steps: S1, data preprocessing; s2, extracting hidden features by adopting an scGPT full-human model; and S3, screening out high-variation genes with top 3000 ranks from the preprocessed data, splicing the high-variation genes with hidden features, and finally inputting the spliced high-variation genes into a graph attention network to obtain a clustering result. According to the method, the missing part of ST data is acquired by using scGPT, the problem that high-quality single cell data is difficult to acquire is solved, and meanwhile, with the development of a single cell large model, GFGAT is also very convenient to use different single cell models. A finer graph attention network is used, and a part of the network is trained by adopting a cut adjacent matrix, so that more site information is reserved, and a more accurate spatial domain is identified.

The present invention relates to an image blind deblurring method based on local dual-domain mutual attention and global channel self-attention, belonging to the field of computer vision technology, and comprising the following steps: S1: constructing a window-based frequency-domain-spatial-domain mutual attention network WFMAN; S2: constructing a global spatial-domain channel self-attention network GSCAN; S3: constructing a frequency-domain filtering gating feature fusion module FFGF; S4: connecting WFMAN and GSCAN in series to form a basic module; constructing a novel U-shaped network by using a plurality of basic modules, FFGF modules and refinement layers; S5: training and testing the novel U-shaped network by using the image data sets before and after deblurring to obtain an image blind deblurring model; S6: using the image blind deblurring model to deblur the acquired images.

The invention relates to the technical field of digital operation and maintenance, in particular to an underground pipe network facility life prediction system and method based on multi-parameter time sequence analysis, and the method comprises the steps: generating a holographic state set through a multi-scale window and a tensor filling algorithm, building a pipe network topology through three-layer logic verification, and paying attention to network quantification risk conduction characteristics based on a space-time diagram. And performing state recursion and continuous integration by using a particle filter algorithm to predict the remaining life. According to the method, heterogeneous monitoring data is converted into a low-rank tensor model, and sparse graph matrix operation is combined, so that the calculation complexity and memory overhead in a high-dimensional spatial-temporal feature extraction process are effectively reduced; meanwhile, based on a numerical integration strategy of a Bayesian sequence Monte Carlo method, the particle degradation problem in nonlinear system state estimation is solved, and convergence and numerical stability of a residual life prediction result in a computer simulation environment are guaranteed.

To perform computer vision with reduced computational cost and improved accuracy.SOLUTION: A method by a computing system, comprising one or more computing devices, comprises: acquiring input data including an input tensor having one or more dimensions; providing the input data to a machine learning convolutional attention network; in response to providing the input data to the machine learning convolutional attention network, receiving, by the computing system, a machine learning prediction from the machine learning convolutional attention network. The convolutional attention network includes at least one attention block, the attention block includes a relative attention mechanism, and the relative attention mechanism includes the sum of a static convolution kernel and an adaptive attention matrix.SELECTED DRAWING: Figure 5

The application provides an Alzheimer's disease classification method and system based on a multi-modal supergraph attention network, and the method comprises the following steps: acquiring sMRI image data of the brain of a plurality of Alzheimer's disease patients and performing preprocessing; performing feature extraction on the preprocessed sMRI image data, and constructing a plurality of cross-modal supergraphs according to the image features and morphological features of the brain regions of the patients; establishing a supergraph attention neural network model, training the cross-modal supergraphs, finally acquiring sMRI image data of the brain of a patient to be diagnosed, constructing a corresponding supergraph, inputting the trained supergraph attention neural network model for classification, and obtaining an Alzheimer's disease classification result and the attention weight corresponding to each supergraph; the application can effectively improve the accuracy of the Alzheimer's disease classification task, and can also find out which brain regions and morphological supergraphs have a significant contribution degree in the model, which is helpful for accurate diagnosis by doctors.