7results about How to "Improve capture sensitivity" patented technology

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 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.

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 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.