8results about How to "Improve separability" patented technology

This invention discloses a smart contract vulnerability detection model and device. The model includes a code structure feature extraction module, a thought chain text feature extraction module, a semantic space adaptation module, a multi-head cross-attention module, and a vulnerability classification module. Based on code structure information modeling, this invention further introduces vulnerability reasoning thought chain text features and achieves effective alignment between code structure semantics and vulnerability reasoning semantics through the synergistic effect of semantic space adaptation and multi-head cross-attention. Compared to detection methods that rely solely on overall semantic modeling of a single code modality, this invention no longer depends solely on the overall statistical features of the source code for vulnerability identification. Instead, guided by vulnerability reasoning semantics, it further focuses on core code segments strongly related to the vulnerability triggering logic, reducing the interference of irrelevant business code and redundant code on the feature extraction process, and improving the accuracy and robustness of vulnerability detection.

The present application relates to the technical field of fabric detection, and particularly relates to a fabric flaw intelligent detection method and system based on machine vision, which comprises the following steps: performing gray-scale processing, geometric correction and denoising, illumination compensation and texture enhancement processing on the fabric image collected under controlled illumination, extracting edges and tracking contour point sequences; calculating outer angles, directionality and signed curvatures based on local point sets, combining concave-convex discrimination, key point reservation, thinning and anti-aliasing smoothing, and reconstructing continuous defect boundary trajectories; and then matching with a defect feature interval library to output defect types and boundary positioning information. Through the present application, the problems of missing detection / mis-detection and unstable positioning of defect boundaries under complex texture and illumination fluctuation conditions in the prior art are solved.

This invention belongs to the field of wireless communication and relates to a machine learning-based method for wireless communication channel estimation. The method includes: acquiring pilot signals, antenna array geometric parameters, and multipath propagation parameters from the transceiver end of a millimeter-wave massive MIMO system to construct a three-dimensional propagation structure model; determining candidate regions for the channel sparse support set and generating a prior constraint matrix for the channel sparse support; inputting the prior constraint matrix and pilot observation data into a sparse reconstruction algorithm for channel sparse feature extraction and dimensionality reduction; performing super-oscillatory feature enhancement on the dimensionality-reduced channel sparse features to form an enhanced channel feature vector; inputting the enhanced channel feature vector and the prior support vector matrix into a complex domain deep expansion network to output the channel estimation result; and outputting the final beamforming weights. Its beneficial effects are improved sparse support set matching accuracy and channel estimation precision with low pilot overhead, and enhanced system throughput and interference suppression capabilities.

The application discloses an AI image discrimination method based on frequency domain and noise domain consistency, and belongs to the technical field of image processing, computer vision and artificial intelligence security. The method comprises the following steps: acquiring an image to be discriminated and performing pretreatment, converting the image into a brightness channel and acquiring frequency spectrum information; inputting the image into a dual-domain physical guidance feature extraction module to extract directional spectrum features and multi-scale noise flow features; inputting the directional spectrum features and the multi-scale noise flow features into a cross-domain physical consistency module to generate a local consistency distance map and a global consistency score through shared embedding mapping and feature distance calculation, and obtaining consistency perception features; inputting the directional spectrum features, the multi-scale noise flow features and the consistency perception features into a dual-flow fusion module for cross-flow interactive fusion and global feature aggregation to obtain discrimination feature representation; and inputting the discrimination feature representation into a classification head to output a discrimination result of whether the image to be discriminated is a real image or an AI generated image. The application realizes effective discrimination of AI generated images by jointly modeling the physical consistency relationship between frequency domain features and noise domain features, and improves the robustness and generalization ability of the model under the conditions of cross-generator, cross-dataset and image compression, blurring and other post-processing conditions while ensuring detection accuracy.

This invention discloses a method and system for snow and ice identification based on remote sensing images. The method includes: acquiring remote sensing images of the target area and performing sensor-specific preprocessing and topographic radiometric correction to obtain a standardized surface reflectance image; extracting improved spectral index features, scale-adaptive texture features, and topographic occlusion compensation features based on multi-level feature coupling rules to construct a multi-dimensional feature set; generating a snow and ice probability map through a feature pyramid network based on an attention mechanism; generating an initial snow and ice mask using a dynamic window adaptive threshold segmentation strategy, combined with elevation zonation constraints and multi-temporal change trajectories; and finally, removing transient coverage and noise through spatiotemporal consistency joint optimization to generate an accurate snow and ice coverage map. This invention effectively solves the problem of spectral confusion between snow and ice, clouds, and bare rock, overcomes the influence of topographic shadows and transient interference, and significantly improves the accuracy and reliability of snow and ice identification in complex environments.

A multi-class motor imagery brain-computer interface decoding method combining EEGNet and FBCSP includes: 1) segmenting the continuous EEG signals of the subject into single-test signals and using filter banks for sub-band filtering; 2) using the training data of all subjects to perform transfer learning training on EEGNet, and classifying the test data to obtain classification scores for all tasks; 3) based on the OVO strategy, training the test subjects' training data on FBCSP to obtain spatial filters for each sub-band and each pair of tasks, concatenating the training and test feature signals of all sub-bands for each pair of tasks after spatial filtering, using the former to train an SVM to classify the latter to obtain classification scores for each pair of tasks, and summing all scores for each task; 4) standardizing the score vectors obtained from EEGNet and FBCSP and summing them, with the maximum value corresponding to the task as the test signal label. This invention can improve the classification accuracy of BCI and promote its practical application.

The application discloses a discharge detection device and method for a general high-low voltage switch cabinet for mines, and belongs to the technical field of switch cabinet detection. The device comprises a modularized sensor assembly, a signal acquisition unit, an edge computing node unit and an upper monitoring platform, and adopts a UHF sensor, an ultrasonic sensor and a transient ground voltage sensor for cooperative detection. The method comprises multi-source signal synchronous acquisition, adaptive denoising based on a sparrow search algorithm optimization, double-domain feature extraction, discharge type identification based on an attention-enhanced convolutional neural network, and multi-sensor information fusion and hierarchical early warning based on evidence theory. The application compensates for the blind area of a single detection method through cooperative work of multiple types of sensors, improves the denoising effect through adaptive parameter optimization, enhances the recognition accuracy through double-domain features and an attention mechanism, and improves the diagnosis reliability through multi-sensor fusion, so that accurate detection and intelligent diagnosis of local discharge of a mine switch cabinet are realized.

The application provides a marine target detection method and system, electronic equipment and a storage medium. The method comprises the following steps: acquiring radar echo detection data; extracting feature points of the radar echo detection data to form a feature vector; matching the feature vector with a decision region in a feature space to detect whether a target exists in the radar echo detection data, and the decision region in the feature space is constructed based on a feature combination strategy generated according to sample features. Since the decision region is constructed according to the feature combination strategy, the matching of the feature points in the feature sequence with the decision region in the feature space can improve the overall separability of the to-be-detected radar echo data in the feature space, thereby improving the marine small target detection performance.