20 results about "Non-negative matrix factorization" patented technology

This application discloses a deconvolution method for gas chromatography-mass spectrometry (GC-MS) data, relating to the field of GC-MS data analysis technology. This method utilizes a non-negative matrix factorization function to initially decompose the time-mass-charge ratio two-dimensional intensity matrix, obtaining an initial elution curve matrix and an initial mass spectrometry matrix. An exponentially modified Gaussian peak shape modeling function is then used to fit the initial peak shape parameter set. For each component: based on the initial peak shape parameter set, an exponentially modified Gaussian peak shape modeling function is used to generate the original elution curve matrix. Non-negative least squares optimization and non-linear least squares optimization methods are used to iteratively optimize the mass spectrometry matrix, peak shape parameter set, and elution curve matrix. Finally, a comprehensive scoring function is used to select the optimal component set, and the optimal peak shape parameter set, mass spectrometry matrix, and elution curve matrix for the optimal component set are sorted and output. This application enables precise separation of co-eluted components, improving the accuracy and reliability of complex mixture analysis.

The application proposes an unsupervised explanation method based on adaptive semantic features, and the constructed explainable model mainly includes three core modules: feature-level explainability module, adaptive feature expression module and feature importance calculation module. Firstly, the deep feature mapping of the deep neural network is regarded as the abstract expression of the high-level concept semantics learned by it, then the non-negative matrix decomposition technology is used to unsupervisedly extract key semantic information from it, and feature-level explanation or structured attribution is carried out; through the dimension scaling scheme, adaptive number of feature extraction is realized; in the feature importance calculation, the Shapley value algorithm is used for calculation. In addition, a saliency visual explanation is generated to highlight the key area of the model decision. Experiments show that the method has higher explanation accuracy, and in the environment of different data sets and explained models, the explanation accuracy is better than that of the existing method, and has the characteristics of accurate explanation, robustness and universality.

The invention relates to the technical field of immunohistochemical staining image quantitative analysis, in particular to an immunohistochemical staining image quantitative analysis method and system, and the method comprises the following steps: S1, obtaining a digital image of an immunohistochemical staining section; and S2, carrying out color deconvolution processing on the digital image, and decomposing the digital image into an exclusive channel image corresponding to at least one target coloring agent and a background channel image. According to the method, color deconvolution is realized through a non-negative matrix factorization algorithm, target coloring agent signals and background and other dyeing signals in the composite dyeing image are accurately split, and the problem that dyeing components of an existing system are not thoroughly separated is solved; through combination of a Mask R-CNN deep learning network and multi-mode adaptive parameters, positive and negative cells in different dyeing modes can be accurately identified, and multiple scenes such as nuclear dyeing, membrane dyeing and slurry dyeing can be adapted.

The invention relates to the field of machine learning and pattern recognition, and particularly discloses a variance-covariance subspace distance-based multi-kernel subspace clustering method, which comprises the following steps of: S1, acquiring a high-dimensional data matrix, calculating a variance-covariance matrix and second-order statistical information thereof, defining a preliminary VCSD kernel value and obtaining a VCSD kernel matrix through exponential transformation and normalization, constructing a multi-core pool of r base cores; s2, constructing an optimized objective function based on non-negative matrix factorization, and solving to obtain a local affinity feature map of a block diagonal structure under constraint conditions; s3, capturing high-order feature association through a matrix method, and converting tensor optimization into matrix operation; s4, solving the target function by adopting an alternating optimization strategy, reducing the calculation complexity, and generating a target affinity graph; and S5, clustering the target affinity spectrum, and outputting a result. According to the technical scheme provided by the invention, the problems of missing statistical characteristics, complex calculation and nuclear noise interference of the existing method are solved, and the clustering precision and efficiency are effectively improved.

The application belongs to the field of satellite data processing, and particularly relates to a Swarm satellite electron density data daily global drift disturbance automatic identification and removal method, which comprises normalization processing; short-time Fourier transform is performed on the normalized data to obtain a time-frequency matrix, the time-frequency matrix is classified into strong disturbance orbit data and other orbit data by using a two-dimensional convolutional neural network, and a component containing daily global drift disturbance is identified; for the decomposition result of the strong disturbance orbit, the identified disturbance component is removed; for the disturbance component of the other orbit, a medium-strong disturbance interval is identified and located, and a disturbance latitude range is obtained according to a mapping relationship between an image coordinate and a latitude; for the identified medium-strong disturbance orbit, pre-normalization data thereof are obtained and subjected to non-negative matrix decomposition, a corresponding part in the disturbance component is cut off according to the disturbance latitude range, and the remaining part and the remaining components are added to serve as effective signals and reserved, so that automatic cleaning of massive satellite data is realized.

The invention relates to a model-driven deep learning multicolor fluorescence blind source unmixing method, which comprises the following steps of: establishing an optimization problem of regularization constraint containing image denoising based on a linear spectrum mixing model, and constructing a non-negative matrix factorization blind source unmixing framework; non-negative matrix factorization is realized based on gradient descent and an iterative shrinkage threshold algorithm, the gradient descent and the iterative shrinkage threshold algorithm are expanded into a multi-layer recurrent neural network, and a transformation function is replaced by the multi-layer convolutional neural network; designing a spectrum extraction algorithm to obtain prior information of a fluorescence emission spectrum, and initializing parameters of the multilayer recurrent neural network according to the prior information; and constructing a multicolor fluorescence imaging simulation data set, and carrying out end-to-end training and testing on the multilayer recurrent neural network. Compared with a method for demixing the multicolor fluorescence image directly through a traditional non-negative matrix factorization algorithm, the method has the advantages that a better demixing effect is obtained under the condition that a small number of network layers is used, artifacts are fewer, and structural information is more accurate.

The invention relates to a community detection method and device and a storage medium, and the method comprises the steps: determining the harmonic centrality of each user in a communication user network, constructing community division according to the harmonic centrality and a preset community number estimation range, and obtaining a plurality of candidate communities; calculating the affiliation probability and the community modularity of nodes in each candidate community so as to determine the optimal community number; selecting a target community center node according to the optimal community number and the harmonic centrality of each user; constructing a node similarity matrix according to the target community center node, and constructing an optimization target function according to the node similarity matrix; and performing iterative solution on the target function by adopting a non-negative matrix factorization iterative optimization algorithm, and determining a community division result according to a solution result. The problem that the number of communities needs to be preset in a traditional method is effectively solved, and the method has good adaptivity and stability.

The application discloses a kind of electroencephalogram emotion signal recognition method based on graph regular non-negative matrix decomposition, comprising the following steps: step one, using nearest neighbor method for electroencephalogram emotion signal constructs an adjacent matrix;Step two, establish a graph regular non-negative matrix decomposition model, and the non-negative constraint of matrix after decomposition is carried out;Step three, introduce a projection matrix, further to matrix implement three decomposition;Step four, the model is optimized;Step five, using the new representation PX obtained after the multiplication of trained model parameter P and sample X replaces the representation matrix V of original model, joins corresponding label matrix and trains a classifier in linear SVM classifier;Step six, the class of test sample is predicted by inputting into classifier.The application can effectively identify electroencephalogram emotion signal, compared with other classic electroencephalogram emotion recognition method, the application effectively improves recognition rate.

The application discloses a neural anatomical heterogeneity analysis method integrating standard model and non-negative matrix factorization, and belongs to the field of biomedical signal processing. The application comprises: preprocessing structural magnetic resonance imaging data of patients and corresponding healthy control groups to extract brain morphological indexes; establishing a brain morphological standard model of the patient through a transfer pre-training model to estimate a deviation score matrix of the patient's cortical thickness and surface area, and extracting a non-positive part to obtain a brain morphological atrophy deviation matrix; performing non-negative matrix factorization on the atrophy deviation matrix to generate a preliminary disease factor matrix and a weight composition matrix; and rearranging and sampling the deviation matrix, calculating an average half-decomposition stability coefficient and a reconstruction error to determine the disease factor and the weight composition under the optimal component number. The method of the application integrates spatial pattern information at the group level while retaining individual heterogeneity information, and provides a new technical means for precise diagnosis of neuropsychiatric diseases.

This invention belongs to the field of computer data clustering technology and discloses a multi-view clustering method, apparatus, electronic device, and storage medium. The method includes: acquiring multi-view data to be clustered, wherein the multi-view data is one of images, videos, audio, and text; determining the Laplacian matrix, depth decomposition matrix, and global consensus embedding matrix for each view based on the original data matrix of the multi-view data; performing hypergraph regularized deep non-negative matrix decomposition on each layer of the original data matrix through the depth decomposition matrix to obtain the decomposition result; performing high-level semantic alignment and consensus representation fusion on the decomposition result through the global consensus embedding matrix to obtain the final layer representation of the view; determining the clustering indicator matrix of the final layer representation according to the objective function; and processing the clustering indicator matrix using the k-means clustering algorithm to obtain the multi-view clustering result. Through the technical solution of this invention, the accuracy of multi-view clustering is achieved.

The application particularly relates to a simulation method of a completely non-stationary wind field of a mountain bridge, which comprises the following steps: obtaining an evolution power spectrum function of a completely non-stationary wind field of a bridge to be simulated; determining representative interpolation nodes based on the evolution power spectrum function; the interpolation nodes comprise time domain interpolation nodes and frequency domain interpolation nodes; performing Cholesky decomposition on the interpolation nodes to obtain corresponding node Cholesky decomposition values; then further decomposing the node Cholesky decomposition values into a series of time and frequency function products by a non-negative matrix decomposition method; applying a Hermite interpolation method to establish global time and frequency interpolation functions; and generating a simulation high-efficiency calculation expression of the corresponding completely non-stationary wind field of the bridge based on the global time and frequency interpolation functions, so as to realize the simulation of the completely non-stationary wind field of the mountain bridge. The simulation method in the application can adapt to the completely non-stationary wind field, can take into account the time-varying frequency characteristics and spatial correlation of the wind speed field simulation, and can improve the practicability and efficiency of the bridge wind field simulation.

The application provides a mechanical and electrical equipment shock absorption and noise reduction installation method, and belongs to the technical field of mechanical and electrical equipment installation. A multi-dimensional vibration noise collection system is first constructed to collect comprehensive vibration noise characteristic data. Then, vibration noise characteristics are extracted through wavelet transform and non-negative matrix decomposition. Next, a small-scale model is constructed, and the influence of installation parameters is analyzed by using an orthogonal test method. Singular value decomposition is used to obtain a vibration noise variation matrix. A multi-objective optimization model is constructed based on this, and a particle swarm algorithm is used to solve optimal installation parameters. The rationality of the parameters is verified by using an elastic damping dynamics equation set. A matched shock absorber material and installation position are selected. Actual installation is performed according to the optimal parameters, and the effect is verified. Finally, a parameter correction model is established, a complete knowledge base is formed, and precise optimization of mechanical and electrical equipment shock absorption and noise reduction installation is realized.

The present application relates to a kind of based on hypergraph and multi-task cooperation small sample multi-pose face recognition method, belong to artificial intelligence face recognition technical field.The present application utilizes hypergraph and non-negative matrix decomposition to obtain image similar to frontal image, designs a kind of multi-pose face recognition framework based on hypergraph deflection.The framework is first separated to no pose deflection image.On this basis, a kind of feature coding method based on improved support vector description is proposed, the feature of no pose deflection image is extracted, and is optimized with the classifier based on dictionary learning, for feature extraction and feature classification.Feature coding method utilizes improved support vector data description and triangular coding, so that the feature extracted is more discriminative.At the same time, an effective feature extraction and feature classification optimization model is established, easy to obtain more close to global optimal solution, improves the recognition performance.

The invention relates to the technical field of signal processing, and discloses a micro-vibration analysis method and system based on FFT (Fast Fourier Transform) to 1 / 3 octave spectrum conversion, and the method comprises the steps: carrying out the time-frequency transformation of a micro-vibration signal, and carrying out the iterative separation through guided non-negative matrix factorization. The core of the method is a guided feedback mechanism: in iteration, a priori knowledge base is utilized to identify spectral lines and evaluate confidence, and then high-confidence spectral lines are locked to accelerate convergence, or decomposition parameters are adjusted to re-decompose a low-confidence part until iteration is completed. And finally, converting the separated basis matrix and the activation coefficient matrix into a 1 / 3 octave spectrum and a historical operation trend of each independent vibration source. According to the method, the accuracy and the efficiency of spectral line separation are remarkably improved through intelligent feedback, the spectral characteristics and the operation state of the independent vibration source can be accurately and stably analyzed from complex mixed signals, and a reliable basis is provided for equipment fault diagnosis and predictive maintenance.

The invention relates to the technical field of anomaly detection of multi-auditory-angle data, in particular to an acoustic sensor array data anomaly detection method based on multi-auditory-angle semi-nonnegative matrix factorization. The method comprises the following steps: performing joint training on multi-auditory-angle semi-nonnegative matrix factorization and Gaussian distribution estimation by using a batch of normal array data to obtain an anomaly detection model; and inputting the to-be-detected array data into the anomaly detection model to obtain a detection result representing whether the to-be-detected data is abnormal or not. The anomaly detection network designed by the invention can map each auditory angle data to the same low-dimensional shared feature through a multi-auditory angle semi-nonnegative matrix factorization model, realizes multi-auditory angle information fusion and reduces information redundancy, and then inputs the low-dimensional shared feature into a Gaussian distribution model for anomaly discrimination. 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.

The application relates to an airborne dual-frequency high-resolution SAR image fusion method, device, equipment and medium. The method comprises the following steps: performing multi-scale transformation on a first source image and a second source image which have been accurately registered to obtain low-frequency sub-band coefficients and high-frequency sub-band coefficients in each direction corresponding to the first source image and the second source image; obtaining an observation matrix in the form of additive noise according to the low-frequency sub-band coefficients corresponding to the first source image and the second source image, decomposing the observation matrix by using a non-negative matrix decomposition algorithm to obtain a feature matrix, performing non-negative matrix decomposition on the feature matrix to obtain low-frequency fusion coefficients; combining the high-frequency sub-band coefficients in each direction by using an improved Laplace energy sum to obtain high-frequency fusion coefficients; and performing inverse transformation on the low-frequency fusion coefficients and the high-frequency fusion coefficients to obtain a fusion image. The method can obtain a high-frequency and low-frequency SAR fusion image with higher resolution, more comprehensive information and smaller distortion.

The invention relates to a method for extracting time-frequency characteristics of GIS ultrahigh frequency partial discharge signals, which comprises the following steps: S1, carrying out background noise estimation on the partial discharge signals after noise reduction, calculating three standard deviations, namely 3 sigma threshold values, as pulse detection thresholds by adopting an absolute median difference method, and then extracting parameters; s2, performing frequency domain analysis on the partial discharge signal after noise reduction, and obtaining time-frequency characteristic representation of the partial discharge signal through S transformation; s3, decomposing the ST time-frequency matrix by adopting a non-negative matrix decomposition method to obtain a series of frequency domain base vectors and corresponding time domain base vectors; and S4, extracting parameters such as information entropy from each frequency domain base vector and each time domain base vector. The method can effectively extract the time-frequency characteristics of the GIS ultrahigh frequency partial discharge signal, has high accuracy and reliability, has a wide application prospect in the field of fault diagnosis and state evaluation of GIS equipment, and can provide powerful guarantee for safe and stable operation of a power system.

The invention discloses a multi-component collaborative vibration monitoring and fault positioning system for a transmission chain of a wind turbine generator, and relates to the technical field of state monitoring and fault diagnosis of wind power generation equipment. An improved order analysis method based on second-order fitting and absolute value nearest neighbor matching is adopted to obtain a stable order spectrum resisting rotation speed fluctuation; after an observation feature matrix is constructed, an SCAD penalty function is introduced to establish a sparse non-negative matrix factorization model, efficient and stable solution is carried out by adopting a near-end alternating linearization minimization algorithm, and accurate decoupling of fault features of multiple components such as a main bearing, a gearbox and a generator is realized; and finally, accurate fault positioning and identification are realized through cosine similarity matching. According to the method, the problems of coupling, aliasing and feature extraction of fault signals of multiple parts under the variable rotating speed are effectively solved, and the diagnosis accuracy, the coverage range and the positioning precision are remarkably improved.

The invention belongs to the technical field of computer data clustering, and discloses a multi-view clustering method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining multi-view data to be clustered, the multi-view data being one of an image, a video, an audio and a text; determining a Laplacian matrix, a depth decomposition matrix and a global consensus embedding matrix of each view according to the original data matrix of the multi-view data; performing hypergraph regularization depth non-negative matrix factorization on each layer of the original data matrix through the depth factorization matrix to obtain a factorization result; performing high-level semantic alignment and consensus expression fusion on a decomposition result through a global consensus embedding matrix to obtain a final layer expression of the view; determining a clustering indication matrix represented by a final layer according to the objective function; the clustering indication matrix is processed through a k-means clustering algorithm, a multi-view clustering result is obtained, and the accuracy of multi-view clustering is achieved through the technical scheme.

The application discloses a gas chromatography-mass spectrometry data deconvolution method, and relates to the technical field of gas chromatography-mass spectrometry data analysis. The method preliminarily decomposes a time-mass-to-charge ratio two-dimensional intensity matrix by using a non-negative matrix decomposition function, obtains an initial elution curve matrix and an initial mass spectrum matrix, and adopts an exponential modified Gaussian peak shape modeling function to fit an initial peak shape parameter set. For each component number: according to the initial peak shape parameter set, the exponential modified Gaussian peak shape modeling function is used to generate an original elution curve matrix; the non-negative least square optimization and the nonlinear least square optimization method are used to cyclically optimize the mass spectrum matrix, the peak shape parameter set and the elution curve matrix; and thus the optimal component number is selected by using a comprehensive score function, and the optimal peak shape parameter set, the mass spectrum matrix and the elution curve matrix under the optimal component number are sequentially output. The application can realize accurate separation of co-flow-out components and improve the accuracy and reliability of complex mixture analysis.