12results about How to "Increase salience" patented technology

The invention relates to the technical field of molecular breeding, and discloses a molecular marker related to the LDL-C level of porcine serum, a detection reagent and a breeding method. The invention also discloses a primer group for identifying the molecular marker, and the primer group is used for amplifying the following SNP sites: SNP01, which are obtained on the basis of a pig reference genome Sscrofa11.1 and are positioned on 112bp, 008bp and 261bp of a third chromosome, and the genotype is A / G; sNP02 is located at 112bp, 008bp and 354 bp of a third chromosome, and the genotype of SNP02 is A / G; sNP03 is located at 112bp, 008bp and 583bp of the third chromosome, and the genotype of SNP03 is A / G. The primer group for identifying the molecular marker can quickly and effectively identify the LDL-C level of pig serum, and is used for assisting in breeding pig strains with low cholesterol or specific lipid metabolism characteristics.

The application discloses a multi-modal radio frequency authentication method based on a multi-scale signal representation, which comprises the following steps: firstly, pre-processing the original IQ signal of a target device received to obtain an instantaneous envelope signal; carrying out multi-scale decomposition and denoising processing on the instantaneous envelope signal to obtain a denoised envelope signal; constructing a multi-modal data set according to the original IQ signal and the denoised envelope signal; carrying out feature extraction and fusion on the multi-modal data set through a pre-trained target feature extraction network to obtain a fused feature representation; and carrying out classification on the fused feature representation through a pre-trained target classification network to output a classification result corresponding to the target device. The amplitude, frequency and time-frequency energy distribution information can be complementarily fused by constructing the multi-modal data set; the multi-modal features are extracted and fused through the target feature extraction network, so that the information loss is effectively avoided; and the target classification network is used for rapid classification, thereby reducing the calculation cost and ensuring the accuracy of identification.

The application discloses a smart community police management system and method based on Internet of Things security, and belongs to the technical field of smart communities, and solves the problem that existing system data processing cannot utilize edge computing nodes for low-delay real-time analysis, and cannot meet timely early warning requirements, the method comprising preprocessing of multi-source heterogeneous community monitoring information, analysis and processing of the community monitoring information based on edge computing, and generation of a risk propagation path in combination with front-end analysis results and heterogeneity characteristics; in the application, analysis and processing of the community monitoring information based on edge computing avoids the delay of centralized processing, can quickly trigger early warning in the early stage of a risk event, thereby gaining golden disposal time for risk event disposal, and a risk analysis model realizes a leap from single-node anomaly detection to composite risk quantification through multi-dimensional feature fusion and dynamic space-time reasoning, and realizes multi-dimensional feature fusion and dynamic reasoning capability of the risk event.

In order to solve the problems of long design time, low efficiency, high calculation cost and low prediction accuracy caused by information loss in the prior art, a resonance super surface design method and device based on spectrum perception improvement are provided.The method comprises the following steps: designing a GLSAT forward prediction network based on spectrum perception improvement;training and optimizing the GLSAT forward prediction network; designing a DNN reverse design network; cascading the DNN reverse design network and the trained GLSAT forward prediction network to obtain a cascaded reverse design network; inputting the ideal spectrum pretreated by GSSG into the cascaded reverse design network, training and optimizing the DNN; and completing the design of the resonance super surface by using the optimized DNN reverse design network.The method has the characteristics of short time consumption, high efficiency and low calculation cost while improving the design prediction accuracy.

The present application relates to the field of construction engineering structure health monitoring, and particularly relates to a distributed optical fiber intelligent monitoring and early warning system and method for deep foundation pit construction, which comprises the following steps: obtaining an optical fiber strain sequence and differentially extracting a gradient sequence; performing multi-scale space-frequency decomposition to obtain a space-frequency energy matrix; extracting a slowly varying component to construct a background model, calculating a logarithmic domain difference to generate a residual saliency map; statistically quantifying multi-scale gradient direction consistency to quantify edge confidence, and adaptively adjusting a smoothing scale to output refined features; combining feature purity and spatial dispersion to generate a comprehensive atlas through weighted fusion; and constructing an energy function considering spatial adjacent consistency and performing global optimization to solve the problem. The present application effectively improves the weak anomaly detection and early warning accuracy by segmenting physically coherent abnormal areas to trigger early warning.

The invention provides a wind-noise-adaptive low-speed small target rapid detection method and system, and belongs to the technical field of target detection, and the method comprises the steps: firstly collecting the acoustic data of an unmanned plane, calling a bird acoustic data set, and constructing a high signal-to-noise ratio data set, a training set, a test set and a verification set; carrying out synchronous compression wavelet transform on the acoustic signals of the high signal-to-noise ratio data set, separating high and low frequency information, respectively extracting waveform features and spectrogram features, carrying out feature fusion, and optimizing feature fusion weight in the fusion process to obtain self-adaptive wind noise fusion features; calling a YOLO network, optimizing the structure of the YOLO network based on the fusion features, and constructing a feature extraction and target detection network model; and after training, testing and verification are carried out through the training set, the test set and the verification set, the model is deployed to a mobile terminal or a ground station to carry out low-slow-small target detection. According to the invention, through bimodal feature fusion and network optimization, the detection precision and timeliness of the low-speed small target under dynamic wind noise are improved.

Some embodiments disclose tetraphenylpyridine derivatives for use in electrochromic materials, including compounds represented by the following general formula or salts thereof: Wherein, X is any one of F, Cl, and Br, and Y is ethyl or n-hexyl; electrochromic materials are prepared using compounds represented by the general formula; the obtained electrochromic materials include the compound represented by the general formula, ferrocene, poly(vinylidene fluoride-hexafluoropropylene), and an ionic liquid, with a molar ratio of 4:1:15:60; the ionic liquid is 1-butyl-3-methylimidazolium bis(trifluoromethanesulfonyl)imine or 1-ethyl-3-methylimidazolium tetrafluoroborate. The introduction of heavy atoms F, Cl, and Br into the tetraphenylpyridine structure effectively increases the light absorption capacity and enhances the significance of the color change. The thin-layer structure of this tetraphenylpyridine derivative exhibits excellent light transmittance and provides structural stability and durability of the electrochromic material during the electrochromic process.

The invention discloses a thermal imaging-based constructional engineering quality nondestructive testing system and method, and belongs to the technical field of constructional engineering quality detection.The system comprises a multispectral thermogram fusion module, a physical constraint deep learning module, a temperature time sequence analysis module and a defect depth estimation module, the system enhances defect features through multi-band infrared thermal image adaptive fusion, carries out defect identification based on a thermal conduction physical equation constraint deep learning network, evaluates the material aging degree through temperature time sequence analysis, and estimates the defect depth based on a thermal diffusion theory. All the modules form a closed-loop cooperation mechanism through confidence feedback and depth prior feedback, continuous optimization of detection performance is achieved, the system can detect hidden defects 15 cm below the surface of a building structure, the detection accuracy reaches 91.3%, and an efficient tool is provided for building engineering quality management.

The invention discloses a wavelet noise reduction and gradient feature extraction method for buried pipeline welding seam positioning, and relates to the technical field of nondestructive testing. The method comprises: acquiring an original magnetic field signal of a pipeline area through a magnetic field acquisition device; wavelet transformation is carried out on the signal, wavelet reconstruction is carried out after a noise coefficient is suppressed through threshold processing, and a denoised signal is obtained; finally, gradient feature extraction is conducted, specifically, the gradient of the denoised signal is calculated and standardized, extreme points in the standardized gradient are recognized by setting a threshold value to serve as welding seam feature points, and therefore accurate positioning of the welding seam position is achieved. According to the method, non-periodic noise is effectively filtered through wavelet transformation, gradient analysis is combined to strengthen and extract the magnetic field abrupt change features of the welding seam, the problem that a traditional frequency domain method is poor in adaptability in buried pipeline welding seam positioning is solved, and the method has the advantages of being good in filtering effect, accurate in feature extraction and high in robustness.

The invention discloses a video monitoring scene intelligent analysis and event classification system based on semantic segmentation. The system comprises a data preprocessing module, a preliminary classification module, a weight generation module and a correction and detection module. The data preprocessing module is used for acquiring and preprocessing original video data of a video monitoring scene, and extracting scene semantic features of the preprocessed video data through a semantic segmentation model; and the preliminary classification module is used for inputting the scene semantic features into a classifier for performing preliminary event classification to obtain an initial classification result. The invention relates to the technical field of video monitoring and intelligent analysis. According to the video monitoring scene intelligent analysis and event classification system based on semantic segmentation, the coupling degree between modules of the system is low, the semantic category number, the classifier structure and the updating strategy can be freely configured according to different monitoring tasks, and the system is suitable for various application scenes such as traffic flow analysis, personnel gathering monitoring and industrial operation anomaly detection.

This invention relates to a method for improving the crystal quality of large-size thick-film silicon epitaxial wafers. By comprehensively designing parameters such as the radial temperature gradient difference of the silicon substrate placement area, the silicon epitaxial layer growth temperature, the evacuation time before silicon epitaxial layer growth, the silicon epitaxial layer growth rate, the flatness of the graphite substrate, and the rotation speed of the graphite substrate during silicon epitaxial layer growth, the crystal quality of large-size thick-film silicon epitaxial wafers is improved with simple process and batch reproducibility. It avoids problems such as edge chipping, cracking, and fragmentation of silicon epitaxial wafers. It can be applied to the industrial mass production of large-size thick-film silicon epitaxial wafers with diameters of 150~200mm and silicon epitaxial layer thicknesses greater than 100μm.

This disclosure provides a semantic segmentation method, system, and medium for camouflaged objects based on two-stage edge guidance. The method utilizes SAM2 and Hiera backbone networks to extract multi-scale features and generates an edge prior map based on these features. Then, a multi-granularity edge feature perception module provides a feature basis for two-stage edge guidance through its edge representation capabilities at different scales and directions, resulting in enhanced multi-scale features. Based on the enhanced multi-scale features and the edge prior map, and combined with the edge confidence map of the upper-layer prediction results, the features are dynamically optimized layer by layer from low to high scale, generating intermediate feature results containing fine-grained edge information. Based on the dynamically optimized intermediate feature results, a cross-scale edge-assisted decoder is used to integrate and reconstruct the intermediate feature results. A layer-by-layer edge reconstruction feedback mechanism is used to achieve feature refinement and edge enhancement, ultimately outputting the semantic segmentation result of the camouflaged object.