9results about How to "Strong application value" patented technology

An ultra-wideband end-fire antenna with a common aperture embedded in a metal carrier includes a metal cavity with a coaxial cable compartment on one side. A radiator module is connected to the bottom of the metal cavity via a radiator fixing slot. The radiator module includes a common aperture antenna substrate. A low-frequency radiator is printed on the side of the common aperture antenna substrate closer to the coaxial cable compartment, and a high-frequency radiator is printed on the side farther from the coaxial cable compartment. This invention utilizes a dual-band antenna in a common aperture design to enhance the channel capacity and anti-fading capability of the communication system. At the same time, by achieving the antenna's carrier conformal characteristics, high isolation, and miniaturization, it enriches the antenna's application scenarios and improves the overall performance of the communication system.

This invention discloses a method for controllable preparation of nanoparticles using a multi-channel microreactor. The method utilizes a microchannel reactor to segment droplets and prepare quantitatively synthesized nanoparticles with controllable particle size. The invention employs capillary action to draw the reaction solution into the microchannel, segmenting one or more droplets (each droplet into 10-100 portions). By changing the width of the microchannel, controllable preparation of nanoparticles within a small range (±3 nm) can be achieved; by changing the length of the microchannel, controllable preparation of nanoparticles within a large range (±10-30 nm) can be achieved. Furthermore, the reactor structure of this invention is simple, enabling highly precise and quantitative synthesis of various nanoparticles. Compared to large-scale reactors, the miniaturization of the synthesis reaction in this invention effectively reduces energy waste and safety hazards, making it highly valuable for applications in safe chemical engineering, green chemical engineering, high-efficiency chemical engineering, microchemical engineering, and precise reaction monitoring.

The application discloses a verifiable privacy protection convolutional neural network system, which uses a given trained convolutional neural network on private data, characterized in that a private verifiable scheme or a public verifiable scheme is adopted. The application provides a verifiable privacy protection convolutional neural network system, which realizes data privacy and verifiability at the same time. The system provides two schemes to meet the needs of different application scenarios, both of which realize data privacy and verifiability. The difference lies in that one is a private verifiable scheme with faster verification speed but needing a private key in the verification process, and the other is a public verifiable scheme with slower verification speed but not needing a private key in the verification process. The system disclosed by the application has strong application value.

The application discloses a kind of defect sample generation method, device, automatic optical detector and storage medium.The defect sample generation method includes: the normal sample output after being detected with AOI detection equipment as research object, and using component positioning method to extract solder joint image, by analyzing the characteristics of defect sample solder joint area, the qualitative characteristics of defect are converted into the quantitative characteristics convenient for modeling analysis, and the complete scheme of defect sample generation is provided using graphic modeling and image processing technology.The generation of a variety of defect samples is realized, and it is convenient to batch generate defect samples, and has strong innovation and application value.

The application discloses a kind of based on multi-modal data fusion's injury condition early warning method and device, method includes: obtaining continuous physiological waveform data and relevant structured data;After waveform data is preprocessed, it is converted to two-dimensional image representation by time series topology enhancement algorithm;Structured data is standardized to extract structured features;Through heterogeneous feature correlation weight mechanism, two kinds of features are semantically aligned and complementary fusion;Based on multi-window risk aggregation algorithm, output injury condition early warning result.The application solves the problems of difficulty in waveform and structured data fusion, insufficient use of time series features, and poor early warning stability in the prior art by enhanced image representation of waveform data, deep multi-modal fusion, and multi-scale risk aggregation, improving the accuracy, generalization ability, and engineering practicality of injury condition early warning. It can be widely applied in clinical first aid, intensive care and trauma treatment scenarios.

The application discloses a sparse unmixing method based on hypergraph denoising and superpixel weighting, and comprises the following steps: step 1, hypergraph smoothing filtering and denoising of a hyperspectral image; step 2, constructing a spatial weighting matrix based on superpixels; and step 3, weighted sparse unmixing of the hyperspectral image. The method combines hypergraph learning and hypergraph smoothing filtering and denoising to obtain a denoised image, and can effectively preserve the spatial structure of the original image; the denoised image is segmented by using the SLIC superpixel segmentation method, the superpixel structure of the image can be more accurately extracted, and the method has better robustness to Gaussian noises with various signal-to-noise ratios. Therefore, the application has the characteristics of strong practical application value and good reliability, and is suitable for promotion and use.

The application discloses a lithium battery state of health estimation method and system based on physical information and Mamba, which comprises the following steps: firstly, the battery voltage and current data of each cycle of the lithium ion battery in the charging stage are acquired, and pretreatment is performed to form a pretreatment data set; secondly, two features of constant voltage charging time and constant voltage charging capacity are extracted based on the pretreatment data set, two features of the maximum incremental capacity and the incremental capacity variance are extracted based on the ICA curve, and a feature vector is constructed; then, the feature vector is input into a PINN-Mamba neural network to obtain a SOH prediction value; finally, the PINN-Mamba neural network is optimized and trained by using a loss function, and is tested and verified. The application solves the problem of uneven distribution of features of the battery in different states, and effectively improves the prediction accuracy of the SOH.

The application provides a three-dimensional atmospheric tomography method and system for low-altitude airborne Beidou observation, comprising: obtaining dynamic airborne Beidou observation data, processing the dynamic airborne Beidou observation data by using a preset GNSS processing software to obtain an airborne information set; projecting Beidou zenith troposphere delay information onto a slant path by using signal elevation angle and azimuth angle information to obtain Beidou slant path troposphere delay information; determining the boundary space position, horizontal resolution and vertical resolution of each layer of tomography according to the aircraft position information and the Beidou slant path troposphere delay information through a dynamic adaptive tomographic boundary optimization algorithm; constructing a Beidou atmospheric tomography observation equation according to the intercept information of the Beidou signal in the tomographic voxel block; solving the tomographic equation set by using meteorological background field data through an adaptive algebraic reconstruction algorithm; and performing smoothing processing on the tomographic horizontal grid result by using a Gaussian filtering method to obtain the final three-dimensional atmospheric tomographic result.

The application discloses a wheat Tilletia indica-specific biomarker combination, a screening method and application thereof, and the biomarker combination comprises 20-hydroxyarachidonic acid, 1-octadecyl lysophosphatidic acid, linolenic acid, 13-hydroxydocosahexaenoic acid, 4-methoxy estrone, hypoxanthine, 3-hydroxymyristic acid, beta-ecdysterone, 17-phenyltrinorprostaglandin F2 alpha cyclopropylmethylamide, 11,12-dihydroxyeicosatrienoic acid and oxidized octadecadienoic acid. When the biomarker combination is used for identifying Tilletia indica, the time-consuming long and the accuracy easily interfered by the spore development stage limitations of traditional morphological identification are overcome; in view of the molecular level identification difficulty caused by the high genome homology of Tilletia indica and Tilletia controversa, the application digs the feature difference at the level of metabolomics, and constructs a specific chemical fingerprint, so that the technical bottleneck that the close relatives are difficult to accurately distinguish is effectively solved.