12 results about "Simple Features" patented technology

The invention discloses a multi-modal self-adaptive online shopping comment authenticity and score credibility evaluation system, which belongs to the technical field of online shopping comment analysis and comprises an enhanced text semantic understanding module, an image authenticity verification module, a user behavior deep modeling module, a self-adaptive weight distribution module and a two-dimensional prediction module. According to the multi-modal self-adaptive online shopping comment authenticity and score credibility evaluation system, the false comment recognition capability is greatly improved, the misjudgment rate is effectively reduced, comment authenticity and score two-dimensional evaluation is achieved, the multi-modal fusion effect is better than that of simple feature splicing, high-concurrency processing is supported, complex scenes can be covered, and the online shopping comment authenticity and score credibility evaluation method is suitable for large-scale popularization and application. And the engineering practicability is high.

The application relates to the field of alloy defect detection, and specifically discloses a praseodymium-neodymium alloy nondestructive detection method and system based on acoustic feature analysis, which comprehensively captures defect information contained in an original probe signal from two complementary physical perspectives of instantaneous dynamic characteristics and frequency band energy distribution by simultaneously adopting Hilbert-Huang transform and wavelet packet transform. Further, the scheme discards simple feature splicing, and instead utilizes canonical correlation analysis as an information decoupling tool to online decompose two groups of original feature vectors into a shared part describing defect commonality and unique information parts respectively representing the unique resolution capabilities of HHT and wavelet packet. Finally, the three decoupled components are structurally recombined to form a fusion feature vector which can effectively eliminate redundancy, amplify differences and has higher information density, thereby providing a clear structure and highly refined input for a subsequent classification model.

The application discloses a kind of multi-variable DC fault arc adaptive detection method based on similarity measure and transfer learning, to the system output current of different time period applies wavelet packet decomposition and coefficient reconstruction processing after extraction feature, and input to state identification model to carry out multi-period fault arc judgment, to current working condition through machine learning evaluation feature similarity measure value D and model output probability distribution P joint distribution mode, according to this, the extracted feature is distinguished into unidentifiable working condition type data and identifiable working condition type data, using field adaptive strategy to the simplest feature group of unidentifiable working condition type data carries out transfer learning, to update state identification model, to realize real-time accurate detection of DC fault arc under complex and variable working condition in this way.

The invention provides a foggy day smoke detection method. The foggy day smoke detection method comprises the following steps: step 1, collecting a plurality of modal characteristic patterns; step 2, mapping each modal feature pattern into a unified scale; step 3, performing spatial enhancement on each modal feature pattern through channel attention and spatial attention; step 4, fusing each modal feature pattern after space enhancement to obtain a fused feature pattern; and step 5, performing classification detection based on the fused feature map, and outputting a smoke detection result. According to the foggy day smoke detection method, various modal data are integrated, the robustness bottleneck of single-modal detection is broken through, and the foggy day smoke detection method is more suitable for detection in a dense fog scene; compared with simple feature splicing of an RGB-HSV-dark channel feature fusion technology, the method has the advantages that the obvious features of the smoke can be adaptively enhanced through dynamic cross-modal attention, fog interference is inhibited, the feature distinction degree of the fog and the smoke is improved, and a high-quality feature basis is provided for subsequent detection.

According to the natural language to structured query language model training method and device provided by the invention, the sample data set is obtained by performing synonym replacement on the initial sample data set and / or adjusting the initial database mode, so that targeted adversarial disturbance is introduced to the source of model training, and the training efficiency is improved. And the fixed literal correspondence between the natural language query keyword and the database mode is broken. Training is carried out based on the sample data set rich in disturbance features, so that a natural language to structured query language model is forced to jump out of simple feature matching logic, and a deep semantic alignment relationship between the natural language and a database architecture is learned; therefore, the robustness and generalization ability of the model when facing complex scenes such as synonymous rewriting in user query, structural change or disorder of a database mode and the like are remarkably improved, and high accuracy and usability of the generated structured query language are ensured.

The invention relates to the technical field of intelligent traffic management systems, and discloses an expressway event detection method and device based on long video semantic analysis, and the method comprises the following steps: S1, collecting and preprocessing long video data of an expressway scene, and generating an event-level vector, a segment-level vector and a key frame vector based on the video data; the semantic vector is used for subsequent event detection and discrimination; and S2, based on the obtained semantic vector samples, establishing corresponding probability density models for abnormal event samples and non-event samples in different environment domains, and constructing a calibration cache based on the non-event samples. The long video data of the expressway scene is collected and preprocessed, and then the event-level vector, the segment-level vector and the key frame vector are generated, so that the problems that the traditional video data processing method mostly depends on a simple feature extraction mode, and multi-level feature information in the video is not fully considered, so that the video data processing efficiency is greatly improved are solved. Therefore, the problem of low detection precision is solved.

The invention discloses a seawater intrusion real-time prediction method based on physical mechanism guidance feature selection, and the method comprises the steps: building a quantitative and fixed-source model through combining an evolution law of an underground water system, obtaining the contribution of a seawater intrusion source, taking the contribution as a weight of a physical mechanism, and calculating the contribution value of each feature to each sample through employing an SHAP method based on an XGBoost model, and carrying out weighted summation on the sample contribution values by taking the physical mechanism weight as a weighting coefficient so as to obtain a weighted overall feature importance ranking. And screening out a simple feature subset with mechanism reliability according to a ranking result, and training and tuning an XGBoost model based on the optimal feature subset. According to the method, the samples with high contribution degree of the seawater intrusion source are preferentially endowed with higher weights, so that feature misselection caused by neglecting of physical reliability of the samples in the prior art is effectively avoided, the consistency of model logic and a physical mechanism is ensured, and a technical scheme with a reliable mechanism is provided for low-cost and real-time early warning of seawater intrusion.

This invention relates to hyperspectral and LiDAR remote sensing image fusion and recognition technology. Considering the natural complementarity between hyperspectral images and LiDAR data in terms of spectral features and elevation information, and the problems of existing methods introducing redundant interference through simple feature stitching, this invention provides a collaborative recognition method for hyperspectral and LiDAR images based on Mamba attention fusion. This invention constructs a dual-branch convolutional feature extractor to extract shallow spatial-spectral features of both modalities; introduces a spatial Mamba module for multi-scale global-local long-range dependency feature mining; and designs a dual-attention interactive guided fusion module to achieve deep interaction and fine fusion of cross-modal complementary information through a parallel mechanism of channel attention and spatial attention. This invention effectively suppresses redundant information interference, significantly improves the accuracy and robustness of ground feature fusion classification, and provides high-quality ground feature classification support for remote sensing applications such as urban planning.

The invention relates to the technical field of rolling bearing diagnosis, and discloses a rolling bearing fault diagnosis method based on an SDP image, and the method comprises the following steps: S1, a multi-source data collaborative collection stage; s2, an SDP image generation and enhancement stage; s3, a dual-path feature extraction and model training stage; s4, a real-time diagnosis and closed-loop optimization stage; s5, a fault traceability and report generation stage; the objective of the invention is to solve the problems that an existing fault diagnosis method mostly depends on single-type operation data, complex features of bearing faults are difficult to comprehensively capture, and missed judgment of slight faults or composite faults is easily caused by data dimension limitation; meanwhile, a traditional method mostly adopts a single feature extraction mode, either only simple features of a shallow layer can be excavated, or the stability of basic features is ignored due to excessive dependence on deep features, the identification requirements of different fault types cannot be considered, and the adaptability to operation condition changes is poor. And fault type misjudgment or inaccurate severity evaluation is easy to occur.

The invention discloses a multi-modal image fusion-based coronary plaque stability evaluation system and method, and particularly relates to the technical field of modal image fusion analysis, and the method comprises the steps: extracting depth feature vectors from registered coronary CT, intravascular ultrasound and optical coherence tomography images; constructing a heterogeneous graph by taking the depth feature vectors as nodes, aggregating neighbor node information through a graph convolutional network, and generating enhanced feature representation fused with cross-modal context semantics; a cross-modal attention mechanism is applied to the enhanced features to optimize feature weights, and refined feature vectors are obtained; and finally, outputting a stability classification result by using a classifier, and generating a feature heat map for visual interpretation. According to the method, the multi-modal features are fused by adopting the graph structure, and the decision basis visualization is provided, so that the problems of insufficient fusion, low evaluation accuracy and unexplainable result caused by simple feature splicing in the existing method are solved.

The application discloses a toy package plastic layer online quality detection method based on multi-modal visual perception, relates to the field of toy quality visual detection, constructs each modal feature map into a dynamic graph structure, realizes multi-modal feature collaborative evolution through graph neural network iterative message passing, outputs a comprehensive quality index and a defect classification result, and performs sorting control according to the comprehensive quality index. Through the collaborative work of the multi-modal visual perception system, the collaborative evolution of the multi-modal features is realized in combination with the physical mechanism constraint, and the precision and comprehensiveness of the toy package plastic layer quality detection are improved. Compared with the prior art, the application can effectively identify thickness defects, material defects, appearance defects and material structure coupling abnormalities, and solves the problems of incomplete single modal detection and poor simple feature splicing and fusion effect.

This invention discloses a generative model-driven distributed remote sensing image encoding and decoding method. The encoding end obtains a compact latent representation containing key semantic information of the source view image through simple feature transformation. The decoding end extracts common features between the side information view image and the source view image using the side information view image, and then fully extracts the rich multi-scale semantic feature representation of the side information view image spatially using a multi-scale spatial feature mining module. This feature representation contains most of the spatial semantic information of the source view image, effectively reducing the number of bits required for compressed transmission of the source view image at the encoding end. Furthermore, this multi-scale semantic feature representation can serve as a semantic prior, assisting the diffusion model in generating more robust reconstructed feature representations, ensuring that the inverse transform module recovers a higher-fidelity reconstructed image, achieving low-bitrate, high-fidelity remote sensing image compression, suitable for distributed remote sensing image compression tasks in satellite constellation scenarios.