18 results about "Nonnegative matrix" patented technology

The invention discloses a GNSS interference detection method based on NMF-SSA, and relates to a GNSS interference detection method. The invention aims to solve the problems of low detection probability, poor precision and poor adaptability to different types of interference in the existing satellite navigation signal interference detection method. The method comprises the following steps: 1, acquiring a satellite navigation signal from a radio frequency front end, and carrying out frequency mixing processing on the satellite navigation signal to obtain a down-conversion complex sampling sequence; performing amplitude normalization on the complex sampling sequence to obtain a target frequency band; performing short-time Fourier transform on the target frequency band to obtain a complex spectrum; obtaining a non-negative matrix based on the complex spectrum; processing the non-negative matrix to obtain a normalized matrix and meta-information of the normalized matrix; 2, constructing an activation aggregation sequence; 3, outputting binary judgment based on the reconstruction energy and the threshold value of the signal; the satellite navigation signal has an interference signal, and the satellite navigation signal has no interference signal. The method is applied to the field of satellite navigation signal interference detection.

The invention provides a transformer partial discharge mode identification method and system based on NMF and JS-SVM algorithms, and belongs to the technical field of power system fault diagnosis, and the method comprises the steps: obtaining a transformer partial discharge signal, and carrying out the feature extraction; carrying out dimensionality reduction processing on the extracted features by adopting a non-negative matrix to obtain dimensionality-reduced features; penalty parameters and kernel parameters of the classifier are determined through a JS algorithm; and inputting the dimensionality-reduced features into a classifier to identify different discharge types.

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.

To provide an information processing system configured to obtain a highly accurate analysis result even for significant overlap of spectra originating from components.SOLUTION: An acquisition step A6 acquires target measurement data indicating a result of measurement using an X ray for a sample to be analyzed, and at least one piece of reference data. A designation receiving step A7 receives a designation of the reference data. A setting step A9 sets an objective function for executing non-negative matrix factorization, including a linear combination of at least one variable basis and at least one fixed basis, based on the designation. Each fixed basis corresponds to the designated reference data. The components of the variable basis are set to be variable parameters in the non-negative matrix factorization. The components of the fixed basis are set to be fixed parameters in the non-negative matrix factorization.SELECTED DRAWING: Figure 4

This invention relates to the field of image encryption and discloses an optical image asymmetric encryption method based on nonnegative matrix factorization. The encryption method includes the following steps: S1, generating a preliminary encryption result through a first optical transformation based on the original image and a first random phase mask; S2, generating an encrypted result through a second optical transformation based on the preliminary encryption result and a second random phase mask; S3, performing nonnegative matrix factorization on the encrypted result to generate an image matrix and a coefficient matrix; S4, using the coefficient matrix as ciphertext and the image matrix as the private key to complete image encryption. This invention significantly improves the security of optical encryption. It combines nonnegative matrix factorization with double random phase coding to construct an asymmetric encryption mechanism that effectively resists known plaintext, chosen plaintext, and special attacks, while also being robust to cropping and noise interference. The framework is highly scalable and applicable to image and audio data encryption.

Various embodiments for systems and methods of tracking disaster footprints using social streaming media using nonnegative matrix factorization are disclosed herein. The system extracts a summarization output from historical data and compares the summarization output with incoming data to identify differing or similar topics within the data. The summarization output is projected to adjust a time-dependency of the summarization output to enable a more direct comparison. The system additionally uses the summarization output to encode topic data within historical data to reduce computational overhead.

The present application provides a facial image clustering method, apparatus, device and storage medium. It relates to the field of image processing technology. The method includes: inputting the required clustering data, which is a high-order graph regular non-negative matrix; calculating and constructing a high-order similarity matrix; constructing a high-order graph constraint; defining and initializing the basis matrices and representation matrices of each layer of the high-order graph regular non-negative matrix decomposition; obtaining the update formulas of the basis matrices and representation matrices of each layer of the high-order graph regular non-negative matrix decomposition; updating the basis matrices and representation matrices of each layer of the high-order graph regular non-negative matrix decomposition; obtaining a low-dimensional representation matrix; and outputting the clustering results. The present application is aimed at facial image data with complex similarities, and can capture more levels of similarity information, thereby improving clustering performance.

The invention relates to the technical field of semi-nonnegative matrix factorization and anomaly detection, in particular to a data anomaly detection method based on semi-nonnegative matrix factorization, which comprises the following steps: S1, dividing a normal data set X into N sub-data sets Xi (1 < = i < = N), training the data anomaly detection network W based on semi-non-negative matrix factorization by using all the sub-data sets Xi to obtain a data anomaly detection model M; s2, the normal data set X comprises n1 pieces of data xj, j is greater than or equal to 1 and less than or equal to n1, and each piece of data represents normal data; s3, the sub-data set Xi comprises n2 pieces of data xk, wherein k is greater than or equal to 1 and less than or equal to n2; and S4, inputting to-be-detected data y into the data anomaly detection model M to obtain an anomaly detection result R. According to the invention, a better data anomaly detection effect can be realized.

Provided are a processing device, a system, a method, and a program capable of improving the accuracy of decomposition by correcting at least a portion of a substrate distribution map by performing non-negative matrix factorization on a measurement distribution map of X-ray powder diffraction. A processing device (400) for performing non-negative matrix factorization on a measurement distribution diagram of X-ray powder diffraction is provided with: a measurement distribution diagram acquisition unit (410) for acquiring a plurality of measurement distribution diagrams; a decomposition unit (420) that performs non-negative matrix factorization on the measurement distribution diagram and calculates a base distribution diagram; an index calculation unit (430) that acquires the base distribution map and calculates an index based on the unevenness of the base distribution map; a basis distribution diagram classification unit (440) that classifies the basis distribution diagrams into a plurality of groups on the basis of the indicators; and a basis distribution map correction unit (450) that performs correction on at least one of the plurality of groups on the basis of the index and calculates a corrected basis distribution map.

According to one aspect of the present invention, an information processing system is provided. The information processing system includes at least one processor. The processor is configured to execute the program so as to perform the following steps. In the acquisition step, target measurement data indicating the result of measurement using X-rays on a sample to be analyzed and at least one reference data are acquired. In the designation acceptance step, designation of the reference data is accepted. In the setting step, an objective function for performing non-negative matrix factorization including a linear combination of at least one variable base and at least one fixed base is set based on the designation. In this case, the fixed substrates each correspond to the specified reference data. Components of the variable substrate are set to parameters that are variable when non-negative matrix factorization is performed. The components of the fixed substrate are set to parameters that are fixed when non-negative matrix factorization is performed.

The application provides a student cognitive modeling method based on a self-encoder type non-negative matrix collaborative factor decomposition, and comprises the following steps: obtaining historical answering data of students, and extracting question-knowledge point association information of test question texts; according to the answering data of the students and the question-knowledge point association information, building an encoding module to construct low-dimensional hidden space representations of the students, the questions and the knowledge points, and building a decoding module to represent the mastery degrees of the students on the knowledge points; fusing the encoding module and the decoding module to complete the building of a student cognitive model; adopting a block coordinate descent projection gradient method with constraints and combining a Lipschitz step length solving strategy to perform model training; completing a learning achievement prediction task according to the low-dimensional hidden space representations of the questions and the students, and completing a knowledge level diagnosis task according to the mastery degrees of the students on the knowledge points obtained through training. The application can realize the accurate prediction of learning achievements and the accurate diagnosis of knowledge levels of students simultaneously.

The invention relates to the technical field of anomaly detection of multi-auditory-angle data, in particular to an acoustic sensing array data anomaly detection method based on multi-auditory-angle self-adaptive sparse semi-nonnegative matrix factorization, which comprises the following steps: S1, assuming that a piece of normal array data acquired by an acoustic sensing array consisting of nv acoustic sensors represents vth auditory-angle data, 1 < = v < = nv, the dimension is 1 * m, and training an anomaly detection network W based on multi-auditory-angle sparse semi-nonnegative matrix factorization and Gaussian distribution estimation by using batch normal array data to obtain an anomaly detection model M; s2, the to-be-detected array data y is input into the anomaly detection model M, an anomaly detection result R is obtained, the to-be-detected array data collected by the sensing array is represented, the dimension of the to-be-detected array data is 1 * m1, the to-be-detected data collected by the vth sensor is represented, and v is larger than or equal to 1 and smaller than or equal to nv. According to the method, the problems of multi-auditory-angle information fusion and information redundancy in multi-auditory-angle data processing of a current acoustic detection method can be effectively solved.

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.

The invention relates to the technical field of anomaly detection, in particular to an anomaly detection method based on semi-nonnegative matrix factorization and Gaussian estimation, and the method comprises the steps: S10, dividing a normal data set X into N sub-data sets Xi (1 < = i < = N), inputting the N sub-data sets Xi into a joint anomaly detection network W for training, and outputting an anomaly detection model M; s20, the joint anomaly detection network W comprises a semi-nonnegative matrix factorization network and a Gaussian density estimation network; s30, the semi-nonnegative matrix factorization network extracts a feature data set Ci and decomposes the feature data set Ci into a basis matrix F and a coefficient matrix G; s40, reconstructing the Ci, and outputting a reconstructed feature data set Ci '; s50, calculating a mean value parameter and a covariance parameter by the Gaussian density estimation network; s60, outputting a logarithm probability density value p; s70, based on S10-S60, finding out n quantiles in p, and taking the n quantiles as a threshold value delta; and S80, inputting to-be-detected data y into the anomaly detection model M, and outputting an anomaly detection result R. According to the invention, Gaussian density estimation can be carried out more accurately, and the anomaly detection effect is improved.

This invention relates to a method for extracting fault characteristic parameters of a reciprocating piston pump under information-based conditions, comprising the following steps: 1. Acquiring the cylinder vibration signal Y of the reciprocating piston pump; 2. Obtaining the reconstructed signal X based on the LMD algorithm and the comprehensive evaluation index CEI; 3. Constructing a time-frequency matrix S based on time-frequency analysis. M×N 4. Determine S M×N 5. Obtain the optimal decomposition dimension k in nonnegative matrix decomposition; 6. Obtain the basis matrix W that can characterize different features of the reciprocating piston pump. M×k And coefficient matrix H k×N 6. Construct the fault basis matrix V based on the optimal decomposition dimension k and cosine similarity theory. M×k 7. Fault characteristic parameters of reciprocating piston pumps are obtained based on time-frequency analysis theory. This invention can overcome the influence of piston impact characteristics and accurately extract fault characteristics of reciprocating piston pumps under operation, providing reliable technical support for subsequent fault diagnosis of reciprocating piston pumps.

The application provides a brain function connection map construction method based on non-negative matrix three-factor orthogonal decomposition (ONMTF). The method first establishes a group level function similarity matrix between two brain region voxels of each subject in a resting state functional magnetic resonance imaging data set according to a function connection measurement between the two brain regions, wherein the rows and columns of the matrix represent the voxels of the two brain regions. Then, the group level function similarity matrix X is decomposed into the product of three non-negative matrices, namely ASY, through ONMTF. According to the two posterior probability label matrices A and Y obtained through the decomposition, a refined function connection network between the two brain regions is obtained. The function of the unknown subregion network extracted by the method can be inferred through the function of the subregion network which has been studied thoroughly. Moreover, the brain function connection network extracted by the method has better clustering quality, namely better function consistency.

Provided is an acoustic signal analysis system and the like that can accurately separate a sound into its individual components even if the components of the constituent units overlap in time. [Solution] An approximation calculation unit 31 performs nonnegative matrix factorization on a spectrogram matrix obtained by short-time Fourier transform of an acoustic signal of a speech sound to estimate a basis matrix indicating a spectral pattern and an activation matrix indicating time changes in signal strength. A normalization unit 32 normalizes the basis matrix using the Euclidean norm of the basis matrix and scales the activation matrix using the Euclidean norm. A separation unit 33 separates the acoustic signal into basis components based on the normalized basis matrix and the scaled activation matrix.

This invention provides a community detection method and system based on symmetric nonnegative matrix three-factor decomposition with modularity constraints, belonging to the field of network graph data analysis technology. This invention constructs a unified joint optimization framework that simultaneously integrates adjacency matrix reconstruction error and modularity maximization objective within the same objective function. A consistency regularization mechanism is designed to achieve coupling and coordination between different community representation matrices, thereby enhancing the expressive power of global modular features while preserving local network structural information. By constructing multiplicative update rules that satisfy nonnegativity constraints and combining Lagrange function derivation and KKT optimality condition verification, the algorithm is guaranteed to have theoretical stability and optimality, thus improving the accuracy, stability, and interpretability of community detection results. Furthermore, while ensuring computational feasibility, the applicability and engineering generalization of the model are enhanced.