100 results about "Matrix decomposition" patented technology

The invention relates to a multi-target heuristic load flow equation matrix optimal block calculation method and device, and the method comprises the steps: building a Jacobian matrix optimal block model with the minimum load balance and sub-matrix calculation total cost as the target; and taking an objective function of the Jacobian matrix optimal block model as a fitness function of a multi-objective genetic annealing algorithm, and performing Jacobian matrix optimal block calculation to obtain an optimal block division structure of the Jacobian matrix. According to the method, resource consumption during matrix decomposition can be reduced, and the power flow equation matrix decomposition efficiency is improved.

The invention discloses a multi-point non-stationary non-Gaussian random process efficient simulation method based on a spectral representation method, and belongs to the technical field of random process simulation. Aiming at the problem of low efficiency of existing potential Gaussian random process power spectrum matrix solving and matrix decomposition, the method comprises the following steps: determining a power spectrum matrix and non-Gaussian features of a target process, establishing a transfer equation by using a Mehler formula, calculating time and frequency interpolation points based on the amplitude of the power spectrum matrix, and performing matrix decomposition and orthogonal decoupling at the interpolation points. And generating a potential Gaussian process sample through interpolation and fast Fourier transform, and finally converting to obtain a target process sample. According to the method, time-frequency domain resource allocation is optimized through a self-adaptive interpolation strategy, so that interpolation points are concentrated in an area with high energy, redundancy decomposition is avoided, and the calculation efficiency is remarkably improved on the premise of ensuring the simulation precision.

The invention discloses an overlapping community detection method, system and device and a medium, and particularly relates to the technical field of community detection, and the technical key points are as follows: extracting node information data from a pre-constructed adjacent matrix of an overlapping community, and inputting the node information data into a point mutual information function to calculate and obtain point mutual information; obtaining a superpoint mutual information matrix by combining the point mutual information with the hypergraph structure; constructing a target function by using the super-point mutual information matrix and a pre-constructed three-factor illegal matrix decomposition optimization model based on graph regularization; decomposing the super-point mutual information matrix in the objective function to obtain an indication matrix, and solving the indication matrix by using an alternating iteration method to obtain an optimal solution of the indication matrix; and detecting the overlapping community based on the optimal solution of the indication matrix to obtain an overlapping community detection result.

The invention discloses a multi-signal-source scene-oriented generative radio map collaborative estimation method, which comprises the following steps of: S1, sampling received signal strength of a plurality of users on a plurality of frequencies to construct a matrix form, and decomposing by adopting an NMF matrix decomposition method to obtain an NMF matrix; each decomposed received signal strength component corresponds to an independent signal source; s2, constructing and training a radio map inference model of a single signal source; and S3, for each received signal strength component obtained by decomposition in the step S1, estimating correspondence by using a radio map reasoning model of a single signal source. According to the method, the signal intensity components corresponding to multiple signal sources are decomposed by adopting the matrix decomposition theory, and the neural network is trained by utilizing the learning algorithm based on the GAN, so that the accuracy and generalization ability of the system are improved. The method can effectively work under the condition that geographic data is inaccurate or matrix decomposition results have errors.

The invention relates to a power network structure weakness detection method based on multi-diagonal-block matrix decomposition, and belongs to the technical field of super-large-scale integrated circuits. According to the method, an original large-scale sparse matrix is converted into a band edge diagonal block structure and divided into a plurality of sub-matrixes capable of being solved independently by constructing a layering and blocking strategy for eliminating tree drive, and redundancy of full-matrix calculation is avoided. Meanwhile, a local approximate inverse algorithm of column norm truncation is designed, and target elements are calculated on the premise that a preset error threshold value is met. According to the method, the parallel computing architecture of the multi-core processor is fully utilized, the computing complexity is reduced by 2-3 orders of magnitude, the computing efficiency is effectively improved, the memory occupation is remarkably reduced, and a high-precision and high-efficiency solution is provided for detecting the weakness of the power network structure of the super-large-scale integrated circuit.

The invention relates to the technical field of data processing, in particular to a dynamic weight matrix decomposition energy consumption prediction method and system, and the method comprises the steps: standardizing internal energy consumption data, and generating a standard data matrix and a sparse mask matrix; based on the mask matrix, processing the standard data matrix by adopting a dynamic regularization matrix decomposition technology, and determining a sparse internal factor matrix and a reconstructed data matrix; and smoothing the noise points by using a local weighted regression algorithm to generate a smooth data matrix. Standardizing the external influence factor data to form low-dimensional external factor embedding; and calculating dynamic influence weights of the external factors by adopting an attention mechanism algorithm, and fusing to generate weighted external factor representation. And when newly added energy consumption data is obtained, a final factor matrix is obtained through an incremental learning optimization algorithm. And outputting a future energy consumption prediction result based on the final factor matrix and the smooth data matrix, thereby effectively improving the accuracy, robustness and efficiency of energy consumption prediction in a complex scene.

The invention discloses a vehicle spatial position uncertainty modeling method based on a factor graph, and relates to the technical field of automatic driving. Establishing a factor graph model, and constructing state variable nodes and factor nodes; an objective function of the factor graph optimization problem is converted into a nonlinear least square problem, and optimal state estimation is obtained through iterative solution by adopting a Gauss-Newton method; on the basis of an objective function under a Gaussian Newton method framework, a posterior covariance matrix is obtained through calculation of a Hesse matrix and inversion; performing incremental optimization on the factor graph, and updating a global Jacobian matrix through matrix decomposition and Givens transformation; and carrying out confidence ellipse geometric quantization, and representing the positioning uncertainty in a geometric form. Based on a factor graph and a confidence ellipse theory, through multi-sensor space-time constraint fusion and geometric interpretable representation, a vehicle positioning uncertainty modeling framework is constructed, and the safety and robustness of an automatic driving system downstream decision planning algorithm are improved.

The invention discloses a multi-mode private illegal content retrieval method and system based on semantic granularity perception, and belongs to the technical field of image-text multi-mode content retrieval. In order to solve the technical problem that private and illegal contents are difficult to effectively retrieve through explicit text description, the method comprises the following steps of: constructing an image identifier consisting of clustering, matrix decomposition, quantization and unique coding, and realizing generative learning of an image and the image identifier by utilizing a multi-modal large model; and constructing a soft label based on the similarity between the image and the text representation, and outputting a corresponding image identifier through text-driven generative retrieval learning. According to the method, the illegal image content with semantic matching can be retrieved from the private text description, and the method is suitable for scenes such as illegal content examination, risk identification and content filtering.

The invention relates to the technical field of data feature extraction, in particular to a high-dimensional data feature extraction and dimension reduction processing system and method based on matrix factorization, and the system comprises a data acquisition module which is used for reading an original high-dimensional matrix; the preprocessing module is used for receiving the original high-dimensional matrix and outputting a preprocessed matrix; the noise pattern recognition module is used for receiving the preprocessed matrix and outputting a noise category label vector and a confidence coefficient vector; the weight matrix generation module is used for receiving the noise category label vector and the confidence coefficient vector and outputting a diagonal weight matrix; the matrix decomposition module is used for receiving the preprocessed matrix and the diagonal weight matrix and outputting a low-dimensional feature matrix and a basis matrix; and the post-processing module is used for receiving the low-dimensional feature matrix and outputting a normalized feature matrix. According to the method, the discrimination and decomposition precision of low-dimensional features are remarkably improved through self-adaptive optimization of emphatically reserving effective samples and suppressing noise sample interference.

The present invention relates to the field of product evaluation, and more specifically, to a product quality evaluation method and system based on big data. The method comprises: collecting multi-dimensional detection data, obtaining a quality evaluation value of a product, and calculating the degree of abnormality; calculating the degree of influence of the detection data of a target dimension on a target product; respectively calculating the correlation between the detection data of the target dimension and the detection data of each dimension, and using the global influence degree to correct the correlation coefficient correction value, constructing a correlation coefficient correction value matrix, and using matrix decomposition to obtain the weight of the detection data of the target dimension during quality evaluation; respectively multiplying the weight by the detection data of the corresponding dimension to obtain new multi-dimensional detection data to complete product quality evaluation. Through the technical solution of the present invention, the accuracy and efficiency of product quality evaluation can be improved.

The present application relates to a kind of multi-objective heuristic power flow equation matrix optimal block computing method and device, wherein, method includes: with load balance and sub-matrix total cost minimum as target, construct Jacobian matrix optimal block model;With the objective function of the Jacobian matrix optimal block model as the fitness function of multi-objective genetic annealing algorithm, and carry out Jacobian matrix optimal block calculation, obtain the optimal block division structure of Jacobian matrix.The present application can reduce the resource consumption when matrix decomposition, accelerate the efficiency of power flow equation matrix decomposition.

The invention discloses an expressway traffic flow missing data complementing method based on space-time multi-scale matrix factorization, which comprises the following steps: acquiring expressway multi-source heterogeneous traffic data, and constructing an observation matrix, wherein N represents the number of spatial positions, and T represents the number of time steps; decomposing the observation matrix into a product of a multi-scale space factor matrix and a time factor matrix by adopting Bayesian time sequence matrix decomposition; splitting the multi-scale space factor matrix into road section-level, region-level and network-level feature matrixes, and introducing dynamic evolution of a multi-order vector autoregression process modeling time factor matrix; performing model parameter learning through a Bayesian inference framework and a Gibbs sampling algorithm; and reconstructing an observation matrix by using W and X obtained by Bayesian inference, and outputting a complemented data matrix. According to the method, the problems of weak expression ability, high calculation complexity, limited missing value processing ability and the like of a traditional interpolation method are effectively solved, and the data completion precision is remarkably improved.

The invention relates to the technical field of data processing, and discloses a large matrix characteristic decomposition method, system, storage medium and device, the method carries out matrix decomposition based on House holder transformation and Givens transformation, and the problems of low efficiency and the like in the prior art are solved.

Implementations disclosed describe techniques used for compiling a quantum algorithm for execution on a plurality of quantum circuits, including accessing, by a processing device, the quantum algorithm, identifying a matrix associated with the quantum algorithm, determining a representation of the identified matrix as a matrix decomposition that includes a plurality of transformation matrices, wherein one or more of the plurality of transformation matrices perform multiple instances of two-dimensional rotations; and generating a circuit map that maps execution of the matrix decomposition on the plurality of quantum circuits.

The invention discloses a series fault arc identification method and system based on singular spectrum statistical characteristics, and the method comprises the steps: S1, collecting the current waveform of a series AC fault arc, and obtaining a current sampling sequence; s2, constructing a Hankel matrix Y based on the current sampling sequence, and then performing singular value decomposition on the Hankel matrix Y to obtain a singular spectrum; s3, statistical features are calculated based on singular spectrums, and feature vectors are formed; and S4, inputting the feature vector into a trained XGBoost classifier to obtain a category label of the series AC fault arc, and realizing real-time identification of the fault arc. According to the method, the singular spectrum of the current waveform is constructed through Hankel matrix decomposition, and the high-frequency harmonic and amplitude characteristics of the fault arc are efficiently captured through the singular spectrum, so that the recognition precision is improved; and the hyper-parameters of the classifier are optimized in combination with a differential evolution algorithm, so that the recognition efficiency of the series alternating-current fault arc is improved.

This invention discloses an efficient QR matrix decomposition method based on FPGA for implementing systolic matrices, belonging to the field of software-defined radio signal transmission technology for airborne networks. It is based on the design of a multi-channel digital front-end implemented on an FPGA chip. The method includes: designing a general handshake coordination mechanism, designing a timing flow for systolic matrix multiplication, performing fast QR matrix decomposition single-loop operations by calculating the Householder mirror transformation matrix, designing a top-level loop pipeline for QR matrix decomposition, and finally verifying the hardware scheme of the fast QR decomposition algorithm. This invention converts resource consumption into timing consumption by designing the overall pipeline logic and loop operations, enabling fast processing and accurate decomposition of digital information in a multi-channel digital front-end on an FPGA, effectively saving on-chip resources. In addition, this invention has excellent scalability, supporting 64 to 128-bit high-precision fixed-point number formats, and the data matrix order can reach up to 128.

The present invention provides a method for accelerating the calculation of a recommendation system based on an analog in-memory computing circuit, and belongs to the fields of semiconductor, analog computing, and integrated circuit technology. Based on the iterative calculation of the alternating least squares method, the method of the present invention designs a block matrix method, utilizes a variable resistor array to solve large-scale matrix decomposition problems, and uses analog computing to accelerate the large number of ridge regression calculations contained in the block matrix method, thereby realizing analog computing acceleration of the recommendation system. The method of the present invention can achieve high-speed and energy-efficient matrix decomposition of the recommendation system, providing a new solution for hardware acceleration of the recommendation system training process in the context of big data, and has broad application prospects.

The application discloses a kind of recommendation algorithm based on label neighborhood model and label matrix decomposition model, it is related to user recommendation technical field, including the construction UTagUser-CF algorithm and TagMF algorithm, according to the UTagUser-CF algorithm and TagMF algorithm constructed to construct UTagUser-TagMF algorithm;The recommendation algorithm based on label neighborhood model and label matrix decomposition model, by using TagMF algorithm, so that the algorithm has lower recommendation error, better recommendation effect, using label information model helps matrix decomposition model to increase implicit feature information, to further solve the cold start problem and data sparsity problem, to solve the accuracy problem, using UTagUser-TagMF algorithm when facing the cold start problem, so that the algorithm has better recommendation performance, can effectively solve the cold start problem and data sparsity problem.

This invention relates to a direction-finding method based on matrix decomposition, belonging to the field of array signal processing technology. The invention first calculates the covariance matrix of the received data, then performs matrix decomposition and reconstruction based on the covariance matrix, and finally constructs a solution function by combining the spatial relationship between the reconstructed matrix and the array steering vector. The direction of arrival of the signal source is obtained from the mapping relationship between the peak value of the solution function and the signal incident angle. This invention utilizes the orthogonality between noise vectors to reduce the impact of noise on the direction of arrival (DOA), which can further improve the stability of the direction-finding algorithm.

The invention discloses an ellipsometry depolarization correction method and system, and the method comprises the steps: measuring a to-be-measured sample through a Mueller matrix ellipsometer, and obtaining a measurement Mueller matrix Me of the to-be-measured sample; decomposing the measurement Mueller matrix Me to determine a depolarization matrix M3 representing a depolarization effect; and correcting the measurement Mueller matrix Me by using the depolarization matrix M3 to obtain a corrected Mueller matrix M0. According to the method, the depolarization effect can be effectively separated and corrected through the accurate matrix decomposition and correction process, and interference of the depolarization effect on the measurement result is avoided. The innovative nonlinear fitting method combines the corrected Mueller matrix and depolarization matrix, and constructs a joint evaluation function for global optimization, so that the extraction of optical parameters is more accurate and physically self-consistent. The iterative optimization step further improves the measurement precision, and compared with the prior art, the measurement problem of a complex sample can be better solved, and a more reliable solution is provided for the field of optical measurement.

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.

The invention relates to the technical field of carbon emission calculation, and discloses an electric power carbon emission factor calculation method, system, equipment, medium and product based on QR matrix factorization. According to the method, a node power balance equation and a carbon emission conservation equation are determined by obtaining carbon emission information and tidal current power information of all generators in a power grid; the method comprises the following steps: constructing a carbon emission factor linear equation, solving the carbon emission factor linear equation based on QR matrix decomposition to obtain a carbon emission factor value of each node, so that efficient solving is realized by utilizing sparse matrix characteristics and QR decomposition, rapid response of multi-node carbon emission factor solving is realized, and the method does not depend on initial solution setting; the slow convergence or failure risk of an iteration method is avoided, the numerical stability is excellent, and the accuracy and reliability of carbon emission factor calculation are improved.

The application discloses a kind of based on gradient descent method's uniform array ultra-wideband radar positioning imaging method, it is related to radar positioning technical field, the signal matrix model after matching filtering is constructed in the application, matrix decomposition is carried out by gradient descent method, joint direction matrix and joint electromagnetic guide vector matrix are obtained, then, two groups of two-dimensional angle estimation values with high resolution and no ambiguity are respectively obtained from the two matrices, accurate angle parameters are obtained by weighted fusion, and target three-dimensional coordinates are calculated accordingly, finally, radar image is generated by back projection algorithm, so that the number of array elements and hardware complexity are significantly reduced, the inherent angle ambiguity problem of array is overcome, the multi-target angle resolution and parameter estimation stability are improved, and the calculation efficiency and engineering realizability are also considered.

The invention relates to the technical field of data processing, and discloses a matrix decomposition method and system, a storage medium and a device, the method performs matrix decomposition based on Givens transformation, and the problems of low efficiency and the like in the prior art are solved.

The application discloses a data prediction method based on real-time adaptive signal decomposition, which comprises adaptive signal dynamic adjustment, original data matrix decomposition, linear equation set construction, linear equation set solution, prediction result obtaining and sub-matrix decomposition method combined model. The application dynamically adjusts signal parameters of input signal change through a parameter adjustment model, and the purpose of adjusting parameters is to make the signal decomposition algorithm better adapt to signal change. The adaptive signal decomposition can adjust decomposition parameters according to dynamic change of the signal under the real-time requirement of signal processing, and realizes accurate decomposition of the signal. The linear independent sub-matrix is used to reduce the dimension of original data and reduce data volume. By selecting a full-rank sub-matrix, the prediction accuracy of the model in different subspaces is ensured, and by selecting the full-rank sub-matrix, noise in original information is effectively removed, and the prediction performance of the model is improved.

The application discloses a project recommendation method and device, a computer readable storage medium and an electronic device. It relates to the field of recommendation algorithm, and the method comprises the following steps: obtaining the scores of each user in N users to at least part of M projects, and constructing a score matrix according to the scores; randomly generating an initial user feature matrix, an initial hidden vector matrix and an initial project feature matrix; updating the initial user feature matrix, the initial hidden vector matrix and the initial project feature matrix based on a matrix decomposition method until the iteration of the matrix decomposition method meets a preset iteration condition; determining the predicted scores of each user to all the M projects according to the user feature matrix, the hidden vector matrix and the project feature matrix; determining the to-be-recommended projects corresponding to the N users respectively according to the predicted scores; and recommending the to-be-recommended projects to the N users. The application solves the technical problem of low recommendation accuracy when recommending projects to users based on matrix decomposition in the related art.

The invention discloses an AUV (Autonomous Underwater Vehicle) velocity field reduction method based on intrinsic orthogonal decomposition and neural network fusion, which comprises the following steps: constructing a velocity field snapshot matrix of an AUV, each velocity field snapshot being a multi-dimensional velocity field obtained by training sample simulation; preprocessing the speed field snapshot matrix, and then performing matrix decomposition by using a POD method to obtain a standard orthogonal basis matrix and a diagonal matrix; setting a truncation coefficient, determining the number of singular values needing to be reserved from the diagonal matrix by using the truncation coefficient, performing dimensionality reduction on the standard orthogonal basis matrix by taking the number as a dimensionality reduction order, and determining an order reduction coefficient matrix based on the standard orthogonal basis matrix after dimensionality reduction; constructing a working condition matrix, wherein the working condition matrix comprises working conditions of different dimensions; and fitting a mapping relation between the working condition matrix and the reduced-order coefficient transpose matrix by using a neural network so as to predict the velocity field under the to-be-predicted working condition.

The present application relates to the field of image technology, especially to a miRNA and disease prediction method based on cross-modal and graph convolution, comprising: obtaining a filled correlation matrix by using a cross-modal data filling algorithm; constructing miRNA integrated similarity and disease integrated similarity; constructing a heterogeneous network graph G and a feature matrix H(0) as inputs of a graph convolution neural network by using the miRNA integrated similarity, the filled correlation matrix and the disease integrated similarity; comparing the cosine similarity between three node embedding gates with a similarity threshold ST to determine whether to output an integrated convolution layer or a single convolution layer; obtaining a miRNA-disease score matrix by using a bilinear decoder; and training a CIGGNET model by using a weighted cross-entropy loss function. The present application solves the problems that the existing biological information prediction model cannot fill the correlation matrix and does not utilize unknown information of the correlation data when using a matrix decomposition method to process the correlation matrix.

This paper considers the challenges posed by DoS attacks in communication network systems and establishes a state-space model of intelligent communication network systems using a T-S fuzzy positive multi-agent system. Using T-S fuzzy rules to approximate linear systems, and employing Lyapunov functions and matrix decomposition techniques, a consistent control method based on a distributed PID controller is proposed. This method effectively prevents failures and other issues in intelligent communication network systems, regardless of DoS attacks. This modeling approach fully considers the positivity and nonlinearity inherent in actual communication network systems, and based on this, a fuzzy control protocol for intelligent communication networks under DoS attacks is designed.

The application discloses a reply text generation method and device, and relates to the technical field of model optimization, which comprises the following steps: performing quantized tensor column decomposition on a feature training matrix in a model training process to calculate a weight matrix and a bias; in a pre-filling stage of model inference, performing matrix multiplication and addition operation on the feature matrix after the matrix tensor decomposition operation by using the weight matrix and the bias in the form of tensor decomposition after the model training; in a decoding stage, performing tensor decomposition on a one-dimensional feature vector to perform matrix multiplication and addition with the weight matrix and the bias in the form of tensor decomposition, so as to generate a target reply text based on the corresponding calculation result, thereby solving the technical problem that the selection requirement of the rank in the matrix decomposition calculation process is high in the related art, the complexity is difficult to effectively reduce, or the error between the calculation result obtained by using the decomposition form and the original result is large, and the technical effect of greatly reducing the storage and calculation complexity and effectively improving the inference performance is achieved.