10 results about "Attentional network" patented technology

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.

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.

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.

This application relates to the field of healthcare informatics technology, providing a drug recommendation method and related equipment based on drug representation and dynamic user modeling. User features are generated based on the acquired user's historical health records and current health status. Diagnostic features and procedural features are sequentially input into a GRU network and a Transformer network, respectively, to generate user representations through dynamic modeling. Drug features are input into a pre-constructed graph attention network to construct a heterogeneous graph between drug attributes and molecular motifs. Drug representations are generated by message propagation and stacking on this heterogeneous graph. User and drug representations are input into a pre-constructed feedforward neural network, outputting fused features between the drug and the user. The fused features are used to generate probabilities through an activation function, and recommendation information is generated based on the target drugs corresponding to these probabilities. This method can accurately match user health needs and provide personalized and effective drug recommendations.

This invention provides a crane collision avoidance early warning method based on a spatiotemporal graph attention network and a Transformer architecture, comprising the following steps: S1, real-time collection of raw monitoring data within the work area using sensing sensors to construct a set of historical trajectories of the target; S2, construction of a dynamic heterogeneous spatiotemporal topology graph and feature initialization of nodes; S3, modeling the spatial interaction relationships between different targets using a graph attention network, extracting spatial interaction features and assigning corresponding risk weights; S4, inputting the spatial enhanced feature vectors into a Transformer-based network in chronological order, performing temporal encoding, and outputting predicted trajectories for multiple future time steps; S5, combining the predicted trajectories with the crane's planned motion path to assess collision risk, and outputting corresponding early warning signals or control commands when the risk exceeds a preset threshold. This method achieves early perception and dynamic prevention of potential collision risks during crane operation, improving operational safety and intelligence in complex working environments.

A vehicle light signal detection and recognition method, system, and computer program product includes defining one or more regions of an image of a car using a coarse attention module to generate one or more defined regions, the image including at least one of a brake light and a signal light generated by the car, the one or more regions including an illuminated portion, removing noise from the one or more defined regions using a fine attention module to generate one or more noise-free defined regions, and identifying the at least one of the brake light and the signal light from the one or more noise-free defined regions.

The invention relates to a training method, an audio-visual segmentation method, an electronic device and a storage medium, and the method comprises the steps: obtaining a training sample which comprises an audio signal, an image, a target semantic tag and a segmentation tag; extracting audio features and image features, and performing self-attention enhancement on the audio features based on the target semantic tag to apply target semantic consistency constraint to obtain enhanced audio features; performing cross attention reweighting on the unenhanced audio features based on the guidance of the enhanced audio features to obtain target pointing audio features; bidirectional interactive fusion is executed based on the image features and the target pointing audio features, and a sparse self-attention network is adopted in the image feature fusion process to suppress unmatched image region response; generating a segmentation prediction result based on the fused image features and audio features, and training to obtain an audiovisual segmentation model; and inputting a to-be-processed video into the trained audio-visual segmentation model, and outputting a segmentation result. The image segmentation accuracy of the sounding target can be improved.

This invention discloses a financial transaction risk assessment method based on clustering sampling and meta-ensemble. The method includes the following steps: constructing diverse training subsets through a sampling mechanism based on supervised fuzzy clustering, balancing the number of risky transactions and normal transactions within the subsets, and ensuring that the union of the subsets covers the original complete financial transaction dataset as much as possible; then training base classifiers; extracting meta-features by calculating indicators based on classification difficulty and model diversity, while considering classification difficulties caused by class overlap and the diversity of base classifiers to make ensemble selection judgments; constructing a meta-aggregator based on self-attention networks and convolutional neural networks, enabling it to consider the relative performance of multiple base classifiers simultaneously, rather than simply assigning weights to individual base classifiers. This invention demonstrates better performance in improving the model's ability to identify risky transactions while minimizing its impact on the model's predictive ability for normal transactions.

This invention discloses a smart traffic data management and control method and system based on edge computing, comprising the following steps: S1, collecting multi-dimensional traffic perception data at intersections and edge node resource status parameters; S2, segmenting the foreground of the video stream and mapping the radar point cloud to a unified coordinate system to generate a standardized edge input dataset; S3, generating a dynamic management and control weight set using a multi-view spatiotemporal interactive attention network; S4, extracting traffic situation features based on the weight set and generating queue instructions; S5, mapping the features to a local microscopic digital twin and generating a handover data packet; S6, sending the handover data packet and introducing a bilinear pooling mechanism using an improved squeezing and excitation residual network to trigger precise evaporation of non-critical data; S7, reading the status and encapsulating the output management and control data packet. This invention achieves efficient diversion and precise evaporation of traffic data, alleviates the storage pressure on edge nodes, and improves real-time transmission performance.

Embodiments of the present application provide a kind of pathological image classification method and related device based on double domain coordination graph attention network.Therein, the method comprises: obtaining pathological whole section image;The pathological whole section image is preprocessed, and instance feature matrix and instance coordinate matrix are obtained;Graph construction is carried out to instance feature matrix and instance coordinate matrix, and target graph structure is obtained;According to target graph structure, instance is encoded based on graph using self-attention mechanism, and graph feature matrix is obtained;Frequency domain transformation and enhancement are carried out to instance feature matrix, and frequency domain enhanced feature is obtained;Space frequency domain cross attention fusion is carried out to graph feature matrix and frequency domain enhanced feature, and instance level feature is obtained;Feature aggregation is carried out to instance level feature, and global feature vector is obtained;Global feature vector is input into classifier, and the classification prediction result of corresponding pathological whole section image is obtained.Based on this, the accuracy and robustness of complex pathological image classification can be improved by embodiments of the present application.