8 results about "Sparse approximation" patented technology

The invention discloses a mass spectrum data high-throughput alignment and parallel qualitative method based on deep characterization learning, and relates to the field of chromatography-mass spectrometry data processing and calculation mass spectrography. The method comprises the following steps: performing streaming, micro-batch and single traversal analysis on original mass spectrum data files of a plurality of samples to be detected; extracting metadata and main data by taking the fragmented spectrogram as a unit, inputting the metadata and the main data into a pre-trained depth representation learning model to generate a high-dimensional embedded vector, and incrementally writing the metadata and the main data into a column type storage database; constructing a sparse approximate nearest neighbor graph; executing a clustering algorithm on the graph to generate a consensus spectrogram set, and generating a consensus feature vector for each clustering cluster and a single example cluster; determining the nature of the consensus feature vector; and constructing a sample * feature matrix based on the cluster affiliation relationship and the qualitative result. According to the method, the technical problems of I / O bottleneck, memory overflow, combinatorial explosion, computing power waste and the like in large-scale mass spectrum data analysis are effectively solved.

The application relates to a real-valued super-resolution direction estimation method of a high-order acoustic field sensor array, which uses a received signal covariance matrix to approximately calculate an estimated value of noise power, avoids the iterative operation process of noise power in the prior art, improves the calculation efficiency, and reduces the floating-point operation amount. By constructing an augmented matrix, the array receiving data matrix and the array manifold matrix with a multi-dimensional structure of an element are made into Hermitian matrices, so that the unitary transformation processing of the related parameters of the high-order acoustic field sensor array is realized. On the basis of obtaining the array receiving data matrix and the array manifold matrix in the real number domain, the array receiving data matrix and the array manifold matrix are applied to a sparse approximate minimum variance method with a variable exponential factor, and the completely real-valued sparse approximate minimum variance direction estimation with a variable exponential factor is realized.

This invention relates to the field of detection, and in particular to an online method and system for detecting impurities in calcium carbonate ore. Specifically, it includes: acquiring a continuous multispectral image sequence of calcium carbonate ore on a conveyor belt, and performing radiometric correction and spectral normalization preprocessing; identifying dominant spectral clusters of pure calcium carbonate ore by statistically analyzing the pixel spectra of the preprocessed images, and selecting a subset of high-confidence pixel spectra as the initial background training set; training a background dictionary, applying a set of sparse regularization constraints during training, and using the correlation between spectral bands to constrain the atomic energies of the dictionary to continuous or strongly correlated band groups; for each pixel of the detected image, using a sparse approximation algorithm combined with the background dictionary, calculating a sparse reconstruction coefficient vector and a reconstruction residual vector, and obtaining the reconstruction residual and coefficient activation uncertainty measure, respectively; if the reconstruction residual exceeds a first threshold, or the uncertainty measure exceeds a second threshold, it is determined to be an impurity point, and the location and quantity are summarized to generate an online impurity distribution map.

The invention discloses a power customer segmentation and behavior discovery method based on a Mahalanobis distance related Chinese restaurant process, and belongs to the technical field of power customer data analysis, and the method comprises the following steps: S10, data preprocessing: collecting multi-dimensional power consumption data of power customers; s20, the Mahalanobis distance is calculated; s30, sparse approximate optimization is carried out; s40, a Chinese restaurant process related to the Mahalanobis distance; s50, integrating a hierarchical Dirichlet process, and automatically learning optimal clustering configuration in a hierarchical structure; s60, performing abnormal behavior detection, and setting a threshold value to perform abnormal customer marking; and S70, outputting and explaining a result, outputting a customer segmentation result and the feature description of each group, and generating an explainable behavior mode report. According to the method, the potential segmentation structure of the power customer can be automatically found under the condition that the clustering number is not specified in advance, meanwhile, high-dimensional data, noise interference and a complex dependency relationship are effectively processed, and accurate customer behavior pattern recognition is achieved.

This application discloses a method, apparatus, device, and medium for simulating aircraft flow fields based on CFD block sparse approximate inverse parallel solution, relating to the field of computational fluid dynamics. The method includes: determining the dependencies between lower triangular blocks in the target inverse matrix of the BSR matrix corresponding to the CFD equations of the aircraft flow field to construct a dependency index array; filling the target blocks in the target parallel tasks corresponding to the current column in shared memory, and performing multiplication, accumulation, and outer product compensation operations on the obtained tasks to be executed using tensor kernels and CUDA kernels; updating the approximate inverse results in the current column based on the obtained single-block execution results, determining the new target parallel task corresponding to the next column, and repeating this process until block operations in all columns of the target inverse matrix are completed; determining the block sparse approximate inverse results based on the obtained approximate inverse results of all columns, and then determining the flow field simulation results to quickly solve ultra-large-scale sparse linear equation systems.

The application provides an angle domain hybrid beamforming method suitable for a cell-free millimeter wave MIMO system. Firstly, the method uses angle domain hybrid beamforming instead of full digital beamforming to reduce hardware power consumption; secondly, a user-centered service scheme is adopted in the system, and the transmission power and the power consumption of the backhaul line are reduced through reasonable user association. The method respectively uses variables to represent whether the user is associated with the access point, the beam selection of different access points and the digital beamforming vector, then models the system user association and beamforming design problem as an energy efficiency maximization problem under the user service quality constraint and the power consumption constraint; through sparse approximation, Dinkelbach method, equivalent transformation and first-order Taylor expansion, the initial non-convex problem is converted into a convex problem which can be solved iteratively to obtain the optimal energy efficiency of the system. Compared with the early centralized MIMO system and the full digital beamforming method, the application improves the system energy efficiency while ensuring the user service quality.

This application discloses an image super-resolution reconstruction method, device, and storage medium, relating to the field of image processing technology. The method includes: performing sparse approximation operations on original image patches and corresponding redundant atom dictionaries based on an initial Mamba model to obtain sparse feature representation data of the original image patches; determining a first image patch reconstructed based on the sparse feature representation data, and constructing a loss function based on the mean square error between the first image patch and the corresponding real high-resolution image patch; performing backpropagation on the initial Mamba model according to the loss function to obtain a target Mamba model; performing super-resolution reconstruction on each original image patch according to the optimal atom kernel of the target Mamba model to generate corresponding target image patches; and stitching the target image patches together to obtain a target image. This improves the reconstruction efficiency of super-resolution reconstruction of low-resolution images.

The invention particularly relates to a real-valued super-resolution azimuth estimation method for a high-order sound field sensor array, which approximately calculates an estimated value of noise power by using a received signal covariance matrix, avoids the process of iterative operation of the noise power in the existing method, improves the calculation efficiency and reduces the floating point calculation amount. By constructing an augmented matrix, an array receiving data matrix and an array manifold matrix of which array elements have a multi-dimensional structure become a Hermitian matrix, so that unitary transformation processing of related parameters of a high-order sound field sensor array is realized. On the basis of obtaining an array receiving signal data matrix and an array manifold matrix of a real number field, the array receiving signal data matrix and the array manifold matrix are applied to a sparse approximate minimum variance method of a variable exponential factor, and completely real-valued variable exponential factor sparse approximate minimum variance orientation estimation is realized.