13results about How to "Improve capture sensitivity" patented technology

The invention relates to a high-orbit Beidou signal capturing method and device, and the method comprises the steps: converting a Beidou satellite signal into a digital intermediate frequency signal, and determining the number of sampling points of a 1-millisecond signal; fourier transform compensation data and inverse Fourier transform compensation data are determined; mixing the 1 millisecond digital intermediate frequency signal with a local carrier to strip the carrier; fourier transform compensation is carried out on the carrier stripping signal, and then Fourier transform is carried out; fourier transform compensation is carried out on the 1-millisecond local ranging code sequence, and then Fourier transform is carried out; carrying out conjugate multiplication on the two Fourier transform results; inverse Fourier transform is carried out on a conjugate multiplication result, and then inverse Fourier transform compensation is carried out; delaying an inverse Fourier transform compensation result by a code phase and then performing differential coherent operation; and performing peak value judgment by using the accumulated sum of the differential coherence results, and determining that the signal is successfully captured when the peak value is greater than or equal to a threshold value. According to the method, storage resources and computing resources are saved, the influence of noise is small, and the signal capturing sensitivity is high.

The invention relates to the field of satellite navigation and low-orbit satellite service application, and provides a low-orbit satellite navigation enhanced signal capturing method based on high and middle orbit assistance. According to the method, the signal features and enhancement application modes of the navigation enhancement signals are fully analyzed for meeting the requirement for engineering implementation of receiving and processing of the low-orbit satellite navigation enhancement signals, and dynamic estimation and correction of the low-orbit satellite navigation enhancement signals are achieved in a high-and-medium-orbit constellation space-time information assisting mode. Therefore, the dynamic pressure, the time pressure and the logic resource pressure of signal capture are reduced. High-sensitivity and high-resolution capture processing of low-orbit high-dynamic signals is realized by matching with a two-dimensional correlation capture method of frequency domain zero-fill segmentation matched filtering, carrier Doppler and pseudo code phases of the signals are obtained through peak judgment, and finally, capture processing work of high-dynamic low-orbit satellite navigation enhanced signals is realized. The method is of great significance to development of a low-orbit satellite navigation enhanced receiver and construction of a high, medium and low-orbit collaborative navigation constellation in China.

The invention discloses a visibility time sequence prediction method based on a DeepAR model and a weather prediction field, mainly relates to the technical field of visibility time sequence prediction, and is used for solving the problems that in an existing scheme, weather prediction field feature fusion is insufficient, only a single value is output, and weather prediction field features cannot be spliced by an existing DeepAR model. Comprising the steps of generating a comprehensive feature vector according to a feature result, historical visibility time sequence observation data and a preset additional covariable, inputting the comprehensive feature vector into a hidden layer of an initial DeepAR model, and completing construction of a future meteorological field extraction module in the DeepAR model; a DeepAR model with a future meteorological field extraction module is trained and optimized based on historical visibility time sequence observation data, and a trained DeepAR model is obtained; and performing visibility time sequence prediction by using the trained DeepAR model.

The invention provides a rare cell capture reagent combination and a capture method thereof. The reagent combination comprises a substrate, a capture antibody, a confining liquid, an incubation buffer solution and magnetic beads, the substrate is a compound as shown in a formula (I) and a derivative thereof; the capture antibody comprises but is not limited to one or more of an anti-CK antibody, an anti-EpCAM antibody, an anti-PD-L1 antibody, an anti-HER2 antibody, an anti-TROP2 antibody, an anti-c-MET antibody, an anti-Claudin18.2 antibody, an anti-EGFR antibody, an anti-MUC1 antibody, an anti-HER3 antibody, an anti-CD31 antibody, an anti-CD44 antibody and the like; the capture antibody is an oxidoreductase labeled antibody; and the magnetic beads are avidin labeled magnetic beads. The reagent combination and the capturing method can be widely used for capturing and separating rare cells with low expression of specific antigens. Compared with a traditional secondary antibody capturing method, the method disclosed by the invention is higher in capturing efficiency and higher in capturing sensitivity, and meanwhile, the specificity is not influenced.

The invention provides a data center energy-saving refrigeration method and device based on a power consumption-load-pressure prediction model, and the method comprises the steps: deploying a heterogeneous sensor network in the whole domain of a data center, collecting the original data of rack-level power consumption, server load and environmental parameters, and processing the original data to obtain a standardized time series data set; and synchronously constructing a power consumption prediction sub-model, a load prediction sub-model and a pressure prediction sub-model based on the time sequence data set, realizing feature crossing by sharing a hidden layer, and predicting joint probability distribution of power consumption, load and temperature of each rack in a future adjustment period. According to the method, a coupling prediction framework of a power consumption-load-pressure three-sub-model sharing hidden layer is constructed, so that deep crossing and closed-loop constraint of multi-physical field features are realized; and the thermodynamic similarity and the physical space connectivity of the rack are organically fused, so that precise division and physical feasibility of the refrigeration area are ensured, the internal temperature standard deviation of the area is effectively reduced, and the refrigeration resource matching degree is improved.

The present invention belongs to the field of satellite navigation technology, and discloses a Beidou weak signal capture method and system. First, the sampled data is stripped of carriers and code chips are merged, and then the overlapping folding method is used to realize the reuse of code-merged data, Doppler stripping and data folding, thereby reducing the consumption of hardware resources; and the data block is code-parallel coherent with the local pseudo code through FFT and IFFT to obtain the coherent values ​​of all code phases. Then, the coherent values ​​of different code phases are differentiated through the inter-code difference algorithm to avoid the influence of NH code jump and navigation data jump; then, the differential data is coherent after Doppler compensation to improve the signal-to-noise ratio of the signal, the data is incoherent, the capture sensitivity is improved, and finally, it is determined whether the satellite signal is captured through threshold judgment. The present invention finally captures Beidou D2 navigation data of ‑146dBm and D1 navigation data of ‑145dBm.

This invention discloses a method and system for detecting solder joints on PCB circuit boards based on image recognition, belonging to the field of image processing technology. The method first acquires and preprocesses 3D point clouds of PCB solder joints, and calculates the local geometric probability density. Non-uniform downsampling is performed using density inverse weighting, prioritizing the retention of sparse key features such as solder joint pins and removing redundant substrate data, outputting a set of physical feature points. This set is then input into a feature extraction network, and strong correlations in feature dimensions are eliminated through local and global forging mapping technology to construct isotropic hypersphere spatial feature vectors. Finally, a multi-coverage discrimination model containing defective hyperellipsoids and normal hyperspheres is constructed based on local density and spatial features. The solder joint quality is comprehensively judged using an integrated voting strategy. This invention effectively solves the problems of false detection and missed detection caused by sparse feature loss and sample imbalance, and significantly improves the detection accuracy of minute defects.

This invention relates to the field of medical assistance technology, specifically to a pneumonia medical assistance system based on an improved VMamba model. The system includes: dividing the lung image to be diagnosed into raster cells based on a raster cell partitioning algorithm; calculating the lesion representation coefficient of each raster cell within each management time period; calculating the lesion representation index of the lesion trend path within the management period based on the raster cells along the lesion trend path; determining whether the raster cells covered by the lesion trend path are lesion regions based on the lesion representation index; inputting the lung field feature map and semantic feature vector into the improved VMamba model after channel-level concatenation, and outputting the pneumonia classification diagnosis result; and linking the pneumonia classification diagnosis result with a pneumonia knowledge graph to generate a visual auxiliary diagnosis report, significantly improving the accuracy and stability of lung medical image processing and achieving full-process automation of intelligent image analysis.

The invention provides a method for capturing a high-dynamic low-orbit satellite navigation enhanced signal, and relates to the field of satellite navigation and low-orbit satellite service application. According to the method, low-earth-orbit satellite navigation enhanced signal features and application modes are fully utilized, dynamic estimation of low-earth-orbit satellite navigation enhanced signals is realized in a high and medium-earth-orbit satellite spatio-temporal information assisting mode, and the capturing dynamic range of a receiver is directly corrected through dynamic estimation of the signals. The capturing and searching range of the low-orbit satellite navigation enhanced signal is greatly reduced, the capturing time is shortened, and the capturing probability is improved; meanwhile, high-sensitivity capture of signals is realized by matching with a capture method of frequency domain zero-fill segmentation matched filtering, and an engineering solution is provided for overcoming the disadvantages of short enhancement visual arc section of low-orbit satellite navigation, large overhead scale of capture resources, limited application scene and the like.

The invention relates to a computer system based on a specific calculation model, and discloses a single-chip microcomputer communication signal optimization method based on deep learning, and the method comprises the steps: obtaining a difference between a current sampling value and a historical sampling value of a communication interface, and mapping the difference into an integer index; according to the integer index and the binary state vector of the previous moment, performing cyclic shift on the state of the previous moment and performing XOR with the feature mask to update the state of the current moment; counting the number of logic 1 bits in the state, and if the number exceeds a preset threshold value, executing bitwise AND operation to perform sparsity regularization; and traversing a final state, accumulating a preset weight according to logic 1, and reconstructing a communication signal. According to the invention, by constructing a kinetic model in which bitwise evolution and Hamming density regularization are coordinated, high-dimensional nonlinear feature extraction and internal stability control are realized under the constraint of zero floating point computing power; the problems that a traditional model is poor in real-time performance on a single-chip microcomputer and poor in linear filtering anti-noise capacity are solved.

The invention discloses a classification method based on a photon pulse graph neural network and a linear calculation implementation method. The photon pulse graph neural network comprises D network layers and a decision output layer, the D network layers are connected in sequence, the output end of the last network layer is connected with the input end of the decision output layer, and the network structure of each network layer adopts a pulse neural network structure. The linear calculation implementation method comprises the following steps: selecting a weight matrix of M network layers of a trained photon pulse graph neural network; and loading the weight matrix of each network layer to an MZI network, loading an input signal required for linear calculation of each network layer to a light source, and injecting the light source loaded with the input signal into the corresponding MZI network to complete linear calculation of the input signal and output the input signal. According to the invention, the capture sensitivity of the network to the time sequence dependence characteristics can be obviously improved, and the method has higher processing speed, lower power consumption and stronger expandability.

This invention belongs to the field of parameter optimization technology, specifically relating to a method and system for optimizing parameters in high-pressure gas detection and testing. The method includes: simultaneously collecting pressure and temperature data from upstream and downstream nodes of a pipeline to construct thermodynamic-pressure coupling characteristics, and extracting a spatiotemporal turbulence calibration factor using physical delay alignment; subsequently, calculating pipeline turbulence dissipation indices based on this calibration factor, and generating dynamic pressure compensation weights based on an explosion-proof safety boundary mechanism; finally, adaptively weighting the pressure sequence and fusing it with temperature features, inputting the results into a deep neural network to output the final optimized parameters. This invention can effectively eliminate spurious interference from spatially propagated waves, capture the true fluid state, prevent the risk of overpressure rupture, and improve the safety and accuracy of automated high-pressure detection and control systems.

The present invention belongs to the field of satellite navigation technology, and discloses a method and system for capturing weak GPS signals based on code difference and code parallelism, including a digital down-conversion module, a code merging module, a data storage module, a Doppler stripping module, a data folding module, a step-by-step matching coherent module, a code difference module, a Doppler compensation module, a step-by-step coherent module and a step-by-step incoherent module, a peak judgment module, a Doppler search completion judgment module, a Doppler control module, and a result output module. The high-orbit GPS signal capture method based on code difference and code parallelism of the present invention provides a fast and efficient capture algorithm for a navigation receiver; the use of the code difference algorithm avoids the influence of navigation data jumps on the coherent integration time, and improves the capture sensitivity of the GPS signal by increasing the coherent integration time. The present invention adopts step-by-step coherent and step-by-step incoherent algorithms to achieve rapid capture of signals of different strengths and shorten the first positioning time of the navigation receiver.