16 results about "Nonnegative matrix factorisation" patented technology

This invention relates to the field of resource scheduling, and proposes a task scheduling optimization method and system based on dynamic task profile modeling. The method includes the following steps: real-time collection of task metadata of the target task and computing node operation data of the intelligent computing center, and standardization processing; extraction of discriminative features from the standardized task metadata; input of the discriminative features into the dynamic task profile modeling model to generate a dynamic task profile; wherein the dynamic task profile modeling model is configured with a spatiotemporally coupled tensor model for multi-dimensional representation of the target task, and an online non-negative matrix factorization algorithm for dynamically updating the feature matrix of the discriminative features; based on the standardized computing node operation data and the dynamic task profile, a Pareto optimal solution set is generated based on a multi-objective optimization algorithm, and the task scheduling optimization scheme is output.

The application discloses a coal water-structure state recognition method based on terahertz spectroscopy, and relates to the technical field of material property characterization. The method prepares a characteristic state coal sample containing raw coal, collects a terahertz frequency band transmission spectrum and constructs a difference spectrum; three typical mechanism spectra of water-induced polarization absorption, structure scattering enhancement and cavity polarization nonlinearity are extracted through non-negative matrix decomposition to construct a frequency spectrum-mechanism-structure mapping model; the spectrum of a coal sample to be measured is collected and a difference spectrum is constructed, and after solving an activation weight vector, the difference spectrum is matched with characteristic samples to inverse the water-structure state thereof. The method can realize nondestructive and accurate recognition of the water-structure state of coal, adapt to disaster precursor warning such as water inrush in coal mines, and expand the engineering practical value of terahertz technology.

This invention discloses a method for detecting the aging state of asphalt pavement by integrating hyperspectral imagery and deep learning, relating to the field of deep learning technology. The method includes: acquiring and preprocessing hyperspectral images of the target road section to obtain surface reflectance images; dynamically identifying and masking vehicles and their shadows in the images using a spectral angle matching method to extract effective pixel spectral datasets; performing hybrid pixel decomposition on the effective pixel spectral datasets using a non-negative matrix factorization algorithm to obtain asphalt endmember spectra and aggregate endmember abundance maps; inputting the asphalt endmember spectra into a pre-trained oxidation degree assessment model to output asphalt oxidation degree values; overlaying the aggregate endmember abundance map with near-infrared band images and inputting it into a pre-trained exposure rate segmentation network to output a binary mask of aggregate exposure areas and calculate the aggregate exposure rate. This method solves the technical problem that existing technologies cannot quickly and non-destructively detect the aging state of asphalt pavement.

This invention relates to the field of biomedical image processing technology, and discloses a method and system for quantitative analysis of hypothalamic immunofluorescence images. The method includes: performing spectral unmixing on multispectral fluorescence microscopy images based on a nonnegative matrix factorization algorithm to obtain a clean signal distribution map; using Gaussian Laplace filtering and watershed transform to achieve cell detection and segmentation; performing affine and B-spline registration between slice images and standard brain atlases to generate regions of interest masks for neural nuclei; using a local background adaptive correction strategy to perform fluorescence quantification and positive determination; and calculating Pearson correlation coefficient and Manders overlap coefficient to achieve colocalization analysis. The system includes a spectral unmixing module, a cell detection and segmentation module, an atlas registration and region recognition module, a fluorescence intensity quantification module, and a colocalization analysis and statistical output module.

This invention discloses a water pollution data monitoring method and system based on multi-objective analysis, belonging to the field of water pollution source tracing technology. The method includes: collecting a GIS map of the target watershed and time series data on rainfall, flow, and water quality; hydrologically segmenting the flow series according to the opening and closing status of dams to obtain surface runoff and baseflow components; constructing a two-dimensional water quality observation matrix, setting pulse and delay constraints in conjunction with rainfall and runoff components, and using a non-negative matrix factorization algorithm with ratio constraints for source analysis to obtain a source feature matrix and a source contribution time coefficient matrix; identifying pollution source emission attributes based on the ammonia nitrogen to total phosphorus ratio and the correlation between the source contribution coefficient and rainfall in the source feature matrix, and marking them on the GIS map. This invention integrates dam-controlled hydrological segmentation with matrix factorization based on physicochemical constraints to achieve refined source tracing, improving the accuracy and interpretability of pollution source identification.

The present application relates to the technical field of computer vision, and relates to a 3D Gaussian sputtering stylization method, system, device and storage medium; wherein the 3D Gaussian sputtering stylization method comprises the following steps: obtaining a style image and a three-dimensional Gaussian scene to be stylized; performing non-negative matrix decomposition on the style image to extract an initial color basis matrix and an initial coefficient matrix of the style image; constructing a color orthogonal decoding model according to the initial color basis matrix and the initial coefficient matrix, and recalculating color parameters of each Gaussian primitive of the three-dimensional Gaussian scene; performing style transfer optimization on the Gaussian primitive, updating trainable parameters until an iteration termination condition is reached; and determining a stylized three-dimensional Gaussian scene according to updated geometric attribute parameters and color attribute parameters. The present application can solve the problems of color incoordination and dirty color during 3D Gaussian sputtering stylization.

This invention relates to the field of intelligent identification technology for swine diseases, and discloses a method for identifying and making decisions about swine diseases by integrating body temperature and image features. The method separates the independent basis components of each disease in a mixed infection by performing non-negative matrix decomposition on the body temperature time spectrum. It then uses the Hungarian algorithm to establish a cross-time point correspondence between the basis components, and uses an optimal transmission algorithm to associate lesion instances with the body temperature basis components to generate a feature sequence of disease components. Finally, it uses a time-series perceptual graph neural network to perform independent inference on the symptom evolution time-series graph, outputting the disease type identification confidence and disease stage inference confidence for each pathogen. Based on a multi-pathogen joint decision rule base, it generates a coordinated treatment plan.

This invention discloses a method for identifying the physiological state of adult product users based on multi-sensor data acquisition, relating to the fields of intelligent health monitoring and biosignal processing technology. This invention uses non-negative matrix factorization (NMF) technology to decompose multi-channel non-steady-state electromyographic signals collected from the pelvic floor and core muscle groups into multiple muscle coercive elements and their temporal activation coefficients. Each coercive element represents a fixed pattern of muscle cooperating under neural drive, while the activation coefficient reflects the change of this pattern over time. Sexual arousal, as a specific neurophysiological process, induces the activation of coercive elements with specific spatiotemporal patterns. By tracking the activation and evolution of specific coercive patterns, the neural control fingerprint reflecting sexual arousal is extracted from the original signal contaminated by motion noise. This effectively distinguishes between ordinary muscle contractions caused by device use and physical activity and autonomous neuromuscular activities related to sexual responses, overcoming the defects of susceptibility to interference and insufficient specificity.

This invention discloses a differentiated load profile generation method based on load response characteristic clustering, relating to the field of power system operation and dispatching technology. It addresses the problem of inaccurate load profile generation in existing methods. The method includes the following steps: acquiring historical load data and extracting electricity consumption patterns from the historical load data to obtain electricity consumption pattern curves; acquiring response characteristic parameters; decomposing the daily load curves of each node using a non-negative matrix factorization method based on the electricity consumption pattern curves to obtain the load composition coefficients of each node; constructing multi-dimensional feature vectors and clustering the nodes based on these feature vectors to divide them into multiple node groups; and for each node group, constructing a load profile optimization model and generating a load profile curve adapted to that node group by solving the model. This invention obtains accurate load profiles by constructing a complete process scheme for load characteristic analysis, user clustering, and profile generation.

The application discloses a crawler behavior detection method and device, equipment and a storage medium, and relates to the technical field of data leakage prevention. The method comprises the following steps: obtaining original data of user access behavior; processing the original data according to a preset data processing rule to obtain standard data, and performing vectorization processing on the standard data based on a preset vectorization rule to obtain a behavior feature vector; obtaining a corresponding normalized similarity matrix based on the behavior feature vector, and performing sparse non-negative matrix factorization to obtain an initial crawler behavior screening result; and using a preset BiLSTM+Attention network to predict the initial crawler behavior screening result to obtain a crawler behavior prediction result. In this way, the application can obtain a behavior feature vector corresponding to data based on a preset vectorization rule, and can extract deep access behavior information. The problem of large access traffic and no data label can be solved by sparse non-negative matrix factorization, and then secondary prediction is performed through the preset BiLSTM+Attention network, so that the result accuracy can be improved.

This invention discloses an image specular highlight removal method based on weakly supervised learning, comprising the following steps: First, the highlight region of the image is decomposed using a sparse non-negative matrix, and a highlight-free image is cropped from the non-highlight region as training data; then, the training data is input into three joint training modules connected end-to-end to perform highlight generation, highlight removal, and image reconstruction tasks respectively, and a recurrent generative adversarial network (RGAN) architecture is used to train the network and generate the final highlight-free image. This image specular highlight removal method based on weakly supervised learning completes highlight removal by jointly training the highlight generation, highlight removal, and reconstruction modules, using a RGAN architecture. During training, the loss function of subsequent modules is fed back to the preceding module, enabling training to be completed using only the highlight image and achieving good highlight removal results. Compared with traditional algorithms and existing weakly supervised learning methods, it has the advantages of simple operation and excellent highlight removal effect.

This invention discloses a method for imputing missing values ​​in scRNA-seq data based on nonnegative matrix factorization, belonging to the technical field of deep learning and data imputation methods. This invention solves the problem of low accuracy in imputing missing values ​​in single-cell RNA sequencing data using existing methods. The main technical solution of this invention is as follows: Step 1: Filter cells based on gene data to obtain RNA sequencing data of the remaining cells after filtering; normalize, screen genes, and perform logarithmic transformation on the RNA sequencing data of the remaining cells after filtering to generate matrix X; Step 2: Decompose matrix X to obtain a feature matrix and a coefficient matrix; Step 3: Construct an input matrix for an autoencoder based on the feature matrix, use the input matrix as the input of the autoencoder, and output the imputed feature matrix through the autoencoder; Step 4: Perform decomposition, logarithmic reduction, and inverse normalization on the imputed feature matrix in sequence to obtain the imputed result. This invention can be applied to RNA sequencing data imputation.

This invention discloses an integrated method for identifying and functionally annotating metaprograms in tumor single-cell transcriptomes, belonging to the fields of bioinformatics, tumor biology, and single-cell transcriptome sequencing. This invention aims to address the problems of unstable metaprogram identification, significant technical artifact interference, and fragmented functional annotation in existing technologies. Based on multi-rank nonnegative matrix factorization, it systematically extracts co-expression modules at different scales from the tumor single-cell expression matrix. Robustness screening ensures the consistency and reliability of the obtained metaprograms under multiple factorizations and cross-sample conditions. Furthermore, it performs systematic functional analysis of the metaprograms to reveal the multi-dimensional functional states of tumor cells. This method improves the stability and biological interpretability of tumor metaprogram identification, providing a new technical means for tumor heterogeneity research and the discovery of precision therapeutic targets.

This invention discloses a method for extracting weak components from spectrally aliased radio electromagnetic signals, comprising the following steps: performing a short-time Fourier transform on the spectrally aliased radio electromagnetic signal received by the monitoring node to obtain its frequency domain characteristics; then performing diffusion noise reduction processing on the spectrally aliased radio electromagnetic signal; next, using the CM-NMF algorithm to extract the signal components layer by layer, through multi-layer iterative non-negative matrix decomposition, extracting the high-energy signal components in each layer, thus highlighting the weak components and improving the performance of weak component extraction; finally, outputting each component of the spectrally aliased radio electromagnetic signal, realizing the extraction of weak components from the spectrally aliased radio electromagnetic signal. This invention has stronger weak signal separation capabilities and can work stably in dynamic noise and complex interference environments, improving the accuracy of signal separation.

The application relates to a multi-view sample clustering and feature selection method based on non-negative matrix decomposition, comprising the following steps: acquiring unlabeled multi-view query samples, which comprise original feature data of multiple views; inputting the unlabeled multi-view query samples into a trained multi-view unsupervised feature selection model to perform the following steps on each view: performing descending order sorting on the original feature data of each view to obtain an original feature sequence arranged in descending order of feature importance; selecting part of the features from the sorted feature sequence to form a feature subset of the view; and outputting the feature subset of the view or a low-dimensional embedding representation obtained after dimension reduction by a projection matrix; wherein the multi-view unsupervised feature selection model is obtained by decoupling common and private representations and introducing k-nearest neighbor tolerance redundancy penalty through alternating iterative optimization training based on a non-negative matrix decomposition framework. The method can obtain better downstream clustering or classification performance in an unsupervised scene.

The application discloses a CSI action recognition method and system based on deep non-negative matrix decomposition, and the method comprises the following steps: collecting CSI signal data and performing subcarrier amplitude calculation and processing on the CSI signal data to obtain the subcarrier amplitude of the CSI; performing feature extraction processing on the subcarrier amplitude of the CSI by a deep non-negative matrix decomposition method to obtain a low-dimensional feature coefficient matrix; introducing position coding, performing classification processing on the low-dimensional feature coefficient matrix by a self-attention mechanism classifier to obtain a CSI signal data classification result. The application combines the deep non-negative matrix decomposition method and the self-attention mechanism classifier to effectively capture the global features of the CSI signal data and improve the classification accuracy. The application can be widely applied to the technical field of human action recognition.