24 results about "Cholesky decomposition" patented technology

Disclosed in the present invention are a method and apparatus for synthesizing spatially cross-correlated multi-point ground motions fitting a response spectrum. The method comprises: using an influence matrix method to obtain ground motion acceleration time histories fitting a target reaction spectrum with high precision, and calculating a Fourier amplitude spectrum and a phase spectrum thereof; and considering a calculation relationship between a power spectral density function and the Fourier amplitude spectrum, taking the power spectral density function of the obtained time histories as an initial value, using a random vibration theory and Cholesky decomposition to obtain spatially cross-correlated multi-point ground motion time histories, further taking into account a phase spectrum of the ground motion time histories fitting the target reaction spectrum with high precision, and performing correction and iteration to obtain spatially cross-correlated multi-point ground motions fitting a response spectrum. The present invention overcomes the shortcomings in current multi-point ground motion time history synthesis techniques, such as the difficulty in ensuring good fitting to a target response spectrum and the non-stationarity of synthesized ground motions, helping to improve the reliability of dynamic response analysis results for large-scale engineering structures under the action of multi-point ground motions.

A sensor unit is coupled to a machine and configured to detect anomalous behavior of the machine. The sensor unit includes a low power microcontroller that learns to recognize a plurality of operations of the machine. The sensor unit generates mean vector and inverse of a Cholesky decomposition matrix for each operation. During a detection mode the sensor unit computes a Mahalanobis distance for each feature vector, mean vector and first matrix. The sensor unit detects anomalous behavior or classifies the operation of the machine based on the Mahalanobis distances.

The invention relates to the technical field of data center optimization, and particularly discloses a data center two-stage stochastic optimization method and system considering wind and light uncertainty, and the method comprises the steps: carrying out the modeling of a data center energy supply system, generating a wind and light output sample based on Monte Carlo simulation, enabling the generated random sample to meet wind power and photovoltaic output complementation through Corisky decomposition; introducing a first-order autoregression model, generating random scenes in combination with multivariate normal distribution, and obtaining a wind and light output curve in each scene; a two-stage stochastic optimization model containing computing power scheduling and multi-energy coordination is constructed by taking the minimum expected operation cost of a system as a target, the model is solved, and an optimal time sequence operation strategy of a computing power task is decided. According to the two-stage random optimization, the adjustment cost caused by prediction errors is covered with low risk premium, and effective support is provided for reliable operation of the data center under new energy output fluctuation and computing power load time sequence mismatch.

An electronic device includes a communication circuit including 2N reception antennas, and a communication processor including a covariance matrix generation circuit that generates a covariance matrix for the 2N reception antennas based on a measurement of a received signal, and a whitening filter matrix generation circuit that calculates a whitening filter for N reception antennas based on Cholesky decomposition. The covariance matrix includes a first sub-matrix, a second sub-matrix, a third sub-matrix, and a fourth sub-matrix, and the whitening filter matrix generation circuit calculates a whitening filter matrix for the 2N reception antennas based on characteristics of a first whitening filter matrix and the Cholesky decomposition for the first sub-matrix corresponding to a diagonal sub-matrix of the covariance matrix.

This application discloses an accelerated Cholesky decomposition calculation method based on a dataflow architecture. The method includes: a data preparation step, a 2×2 matrix block calculation step, and a 1×1 matrix block calculation step. The dataflow architecture divides the processing unit (PE) array into multiple PE groups. The irregular matrix to be calculated is divided into multiple regular matrix blocks according to a predetermined partitioning rule. If the N of the N×N matrix to be calculated is greater than a predetermined threshold, a 2×2 block-based Cholesky splitting algorithm is used to iteratively calculate the matrix blocks in each PE group. Data transfer between PE groups is achieved through the data dependency relationship between different PE groups on the Cholesky decomposition calculation, and the size of the matrix to be calculated is dynamically adjusted and reduced. If the N of the N×N matrix to be calculated is less than or equal to a predetermined threshold, a 1×1 block-based Cholesky splitting algorithm is used to iteratively calculate until the calculation is completed and the calculation result is output, thus completing the accelerated Cholesky decomposition calculation based on the dataflow architecture.

The invention discloses a cascade hydropower scene generation method and device, equipment, a medium and a product. The method comprises the steps of performing standardization and quantile transformation on actually measured data of a plurality of cycles before a plurality of hydropower station prediction periods of a target drainage basin; performing iterative random sampling through multivariate normal condition distribution and Cholesky decomposition by adopting a preset TVP-VAR model according to the converted measured data to generate a flow scene of each period in a prediction period; wherein the preset TVP-VAR model is formed by splicing a lag coefficient matrix and a disturbance covariance matrix of each period according to a time sequence; and quantile inverse transformation and inverse standardization are carried out on the flow scene of each period of the prediction period to obtain cascade hydropower flow scenes of the plurality of hydropower station prediction periods of the target drainage basin. The calculation efficiency under the high-dimensional condition and the model compatibility under the small sample condition can be considered, the consistency of the distribution of the generated scene and the real flow distribution of the hydropower station is improved, and the scene generation precision is improved.

The invention discloses a power grid prediction state estimation method and system based on square root unscented particle filtering, and the method comprises the steps: obtaining the historical operation data of a power grid, generating an initial particle set, and obtaining a state variable estimation value of the power grid and the weight of the state variable estimation value through employing a square root unscented particle filtering method based on a prediction state estimation model; in iteration, calculating a weighted covariance matrix by using a state variable estimation value of the power grid and a corresponding normalized weight, performing Cholesky decomposition on the weighted covariance matrix to obtain a square root of the weighted covariance matrix, and taking the square root as a square root estimation value of each particle after resampling; the number of effective particles is used as a normalized weight initial value of each particle after resampling; the state variable estimation value of the power grid and the weighted mean value of the weight of the state variable estimation value serve as the prediction state estimation result of the power system, and the accuracy and robustness of state estimation of the power system with new energy access are improved.

The invention relates to the technical field of automatic driving, in particular to a self-adaptive trajectory tracking control method for an automatic driving vehicle, and the method comprises the following steps: building a vehicle dynamics trajectory tracking model; based on the vehicle dynamics trajectory tracking model, a high-order strong tracking volume Kalman filtering algorithm is adopted to estimate the lateral force of the tire, and in the high-order strong tracking volume Kalman filtering algorithm, singular value decomposition is adopted to replace Cholesky decomposition, and a fading factor is introduced to dynamically adjust the filtering covariance; according to the tire lateral force obtained through estimation and the tire lateral force obtained through calculation based on a linear tire model, a cornering stiffness correction factor is calculated; and correcting a tire cornering stiffness parameter in a model prediction controller in real time by using the obtained cornering stiffness correction factor, and constructing a self-adaptive model prediction controller. According to the invention, the path tracking performance of the autonomous vehicle under the uncertain working condition can be improved.

The invention provides a fan three-dimensional flow field reconstruction method based on multi-point observation. The fan three-dimensional flow field reconstruction method comprises the following steps: S1, multi-point observation data acquisition: acquiring a wind speed time sequence of N actual measurement points; s2, trend and turbulence separation and frequency domain transformation: obtaining a spatially distributed average wind speed field and a disturbance time sequence, and obtaining actually measured frequency domain disturbance distribution; s3, generating a standard background turbulent flow field: based on a Kaimal turbulent flow spectrum model, combining a coherence function, and generating a standard turbulent flow field conforming to statistical characteristics and frequency domain distribution of the standard turbulent flow field through random phase superposition and inverse Fourier transform; s4, observation constraint superposition and spatial coherence reconstruction: establishing a joint vector matrix; constructing a complex covariance matrix based on the coherence function; carrying out Cholesky decomposition, and extracting an actual measurement-simulation structure coupling relationship; standardization processing of actual measurement constraints; constructing simulation disturbance of a belt actual measurement structure; performing inverse Fourier transform to generate a time domain turbulence field; and S5, superimposing a trend field and outputting a final flow field.

The application discloses a kind of non-stationary wind field high-efficiency simulation method based on dynamic interpolation setting point strategy, including determining simulation point number and its space position;According to the time-varying fluctuation power spectrum model of typhoon, the evolution power spectrum density matrix is calculated;Determine the time range and frequency range of simulation, and it is evenly dispersed into P and Q nodes on time axis and frequency axis respectively;In these discrete nodes, the interpolation node coordinates are selected by dynamic interpolation setting point strategy;By Cholesky decomposition to the evolution power spectrum at interpolation node, the spectral decomposition matrix at the place is obtained, then combined with interpolation method, the spectral decomposition matrix at non-interpolation node is obtained;According to the formula of harmonic superposition method, generate fluctuating wind speed sample.The application is based on classical harmonic superposition method, constructs a kind of dynamic interpolation setting point strategy, can accurately, conveniently select the interpolation node of frequency dimension, effectively reduces the amount of calculation, significantly improves simulation efficiency.

A kind of data service recommendation result diversification method based on determinant point process, comprising the following steps: first, construct service recommendation score auxiliary matrix and service function similarity auxiliary matrix;Second, construct the service information kernel matrix required by determinant point process calculation, process determinant calculation result, optimize calculation process;Third, using cholesky decomposition, quickly judge in the process of greedy iteration, generate diversified reordering service recommendation result.The present application grasps the diversification demand under the service recommendation scene, as a kind of post-processing method, can carry out diversification processing on the basis of existing service recommendation model;While considering the functional correlation and diversity between services;Using the optimized solving method based on decomposition factor and row vector, the diversification process is optimized, and the calculation consumption of each iteration is reduced;For the existing service recommendation algorithm with recommendation accuracy as index, the recommendation supplement of diversity demand is carried out.

The invention discloses a natural gas pipeline time-varying reliability prediction method considering corrosion correlation, and relates to the field of natural gas pipeline safety assessment, and the method comprises the steps: collecting finite continuous ILI data of a natural gas pipeline, building a logarithmic normal probability model of pipeline corrosion defect geometric parameters, and calculating the corrosion defect geometric parameters of the pipeline; acquiring distribution parameters and correlation coefficients of the geometric parameters; based on the distribution parameters and the correlation coefficients, constructing a covariance matrix, and performing Cholesky decomposition to generate an initial corrosion random sample of the pipeline; based on limited continuous ILI data, constructing a gamma process growth model, and performing defect evolution prediction on the initial corrosion random sample to obtain a prediction result; on the basis of the prediction result, the bursting pressure of the natural gas pipeline at the prediction time point is calculated, a limit state equation is constructed, and the time-varying failure probability of the pipeline is calculated through a PHI2 method; through the four steps, the accuracy and scientificity of pipeline corrosion safety risk decision making under the condition of limited detection data are improved.

The invention provides a dynamic optimization method for strip matrix Cholesky decomposition. The dynamic optimization method comprises the following steps: firstly, generating a partitioning strategy according to hardware characteristics and matrix bandwidth, carrying out iterative computation along a main diagonal, decomposing a current diagonal block and updating in-band related sub-blocks in each iteration, and processing out-of-band data through a working area; secondly, fusing dot product, matrix-vector multiplication and vector scaling operations in diagonal block decomposition into a single kernel function, and dynamically allocating threads according to a current column calculation load; and finally, organizing the decomposition process into a fixed assembly line based on the strategy and the fusion kernel, and scheduling the corresponding kernel to execute along a diagonal sequence according to the block type until the decomposition is completed. The invention further provides a dynamic optimization system for the strip matrix Cholesky decomposition. Therefore, by means of the method, the kernel starting overhead is effectively reduced, the data locality and parallel efficiency are improved, and the calculation performance of strip matrix Cholesky decomposition on a heterogeneous platform is remarkably improved.

The application relates to the technical field of state estimation methods of integrated navigation systems, and discloses an adaptive filtering method and device for integrated navigation, which comprises the following steps: obtaining sensor measurement data at a current time; obtaining a predicted state at the current time; constructing a differential feature sequence at a historical time; inputting the differential feature sequence into a hybrid neural network respectively to output a first feature vector and a second feature vector; performing physical constraint parameterization reconstruction based on Cholesky decomposition on the first feature vector and the second feature vector respectively to generate a predicted state covariance matrix and a measurement noise covariance matrix; obtaining Kalman gain at the current time by using a standard analytical formula of Kalman gain; and performing state updating to obtain a posterior state estimation at the current time, so that the technical bottleneck faced in an embedded application scene with a known measurement model, a fixed structure and a low dimension can be solved.

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 invention provides an electronic device and an operating method thereof. The electronic device includes a communication circuit and a communication processor, where the communication circuit includes 2N receiving antennas, and the communication processor includes a covariance matrix generation circuit and a whitening filter matrix generation circuit, the covariance matrix generation circuit generates a covariance matrix for the 2N receive antennas based on a measurement of a receive signal, and the whitening filter matrix generation circuit calculates a whitening filter for N receive antennas based on a Cholesky decomposition. The covariance matrix includes a first sub-matrix, a second sub-matrix, a third sub-matrix, and a fourth sub-matrix, and the whitening filter matrix generation circuit calculates a whitening filter matrix for the 2N reception antennas based on a first whitening filter matrix for the first sub-matrix and characteristics of a Cholesky decomposition, the first sub-matrix corresponds to a diagonal sub-matrix of the covariance matrix.

The invention relates to the technical field of communication measurement, in particular to a signal-to-noise ratio estimation method and device, electronic equipment, a storage medium and a program product, and the signal-to-noise ratio estimation method comprises the steps that dual-receiving-antenna terminal equipment receives a wireless signal sent by a base station; acquiring channel characteristics and noise characteristics based on the wireless signal; performing noise cancellation processing on the noise feature to obtain a first noise feature; acquiring signal transmission capability information of a channel between the dual-receiving antenna terminal equipment and the base station based on the channel feature and the first noise feature; and based on the signal transmission capability information and the unit matrix, obtaining first signal-to-noise ratio estimation corresponding to linear multiple-input multiple-output detection and second signal-to-noise ratio estimation corresponding to nonlinear multiple-input multiple-output detection. According to the method and the device, the Cholesky decomposition and matrix inversion operation required by a traditional scheme are avoided, the calculation complexity is greatly reduced, the implementation requirements of a hardware circuit on the area and the power consumption are relieved, and the link state after nonlinear MIMO detection can be accurately represented.

The present disclosure provides a terminal initialization method, device, electronic equipment and storage medium. The terminal initialization method comprises: obtaining initialization parameters, inertia constraints and visual constraints of a terminal; constructing an information matrix according to the initialization parameters, inertia constraints and visual constraints; performing Schur complement elimination on the information matrix to eliminate the feature points from the information matrix to obtain an eliminated information matrix; performing Cholesky decomposition on the eliminated information matrix to obtain an upper triangular matrix and a lower triangular matrix; inverting the image frame state quantity corresponding to the nearest preset time in the upper triangular matrix to obtain the square root of the covariance, and then determining the covariance; and determining the initialization state of the terminal according to the covariance and the initialization parameters. The method of the present disclosure can quickly solve the covariance and reduce the state quantity fluctuation amplitude.

The invention relates to an ARMA-Copula-based source load uncertainty scene generation method, belongs to the technical field of integrated energy system planning, and solves the problem of large scene generation deviation caused by difficulty in accurately describing renewable energy output and time sequence volatility and space cross correlation of multi-element loads in an existing method. According to the technical scheme, the method comprises the steps that edge distribution parameters at all moments are calculated through maximum likelihood estimation and mapped to a standard normal space through probability integral transformation, an autoregressive moving average model is constructed to strip time autocorrelation so as to extract white noise residual errors, and a space cross-correlation structure of the residual errors is reconstructed based on a Gaussian Copula function and Cholesky decomposition. An initial scene set is generated through forward recursion and inverse transformation of the model; and finally, dual-objective optimization reduction is carried out by adopting an improved non-dominated sorting genetic algorithm to screen an optimal typical scene. According to the method, the probability distribution and the space-time coupling characteristics of the source load data can be restored with high precision.

The invention discloses a matrix inversion method, a matrix inversion circuit, a signal processing method, a signal processing device, an integrated circuit, an electromagnetic wave sensor and terminal equipment, which can realize El Mitt matrix inversion. The El Matrix inversion circuit is used for performing inversion on an El Matrix A. The El Matrix inversion circuit comprises a Cholesky decomposition circuit used for calculating a lower triangular matrix L to enable LLH to be equal to A, and LH is conjugate transpose of L; and the triangular matrix inversion circuit is used for calculating the inverse L-1 of the lower triangular matrix L, and the product of the conjugate transpose of the L-1 and the L-1 is the inverse of the El matrix A.

The application discloses an optical Ising machine based on Cholesky decomposition. The application encodes the interaction matrix of the Ising model into an optical modulation system after Cholesky decomposition, inputs an optical signal carrying the spin group state information of the Ising model, and outputs an optical intensity signal encoded with the Hamiltonian information of the Ising model, so that the high-speed calculation of an arbitrary Ising model Hamiltonian is realized. The application has the advantages of large scale, wide application range, high speed and the like.

The invention provides a weight binary quantization method and system for a large language model, and the method comprises the steps: carrying out the reordering operation of a calibration data matrix and a weight matrix, obtaining a reordered weight matrix and a reordered calibration data matrix, carrying out the Cholesky decomposition of the reordered calibration data matrix, and obtaining a target calibration data matrix; splicing the quantization results of the weight blocks to obtain the quantization results of the weight blocks; based on a target error matrix, error compensation is carried out on the reordering weight matrix, a reordering weight matrix after error compensation is obtained, and the target error matrix is obtained after discarding operation is carried out on a target error value in the propagation error matrix; the target error value is determined according to index information of the weight blocks; and obtaining a target quantization weight matrix according to the reordering weight matrix after error compensation. While the model is compressed to the maximum extent, the output precision of the model is improved.

The application discloses a kind of time-varying reliability prediction methods of natural gas pipeline considering corrosion correlation, it is related to natural gas pipeline safety evaluation field, the method includes: collecting the finite continuous ILI data of natural gas pipeline, constructs the lognormal probability model of pipeline corrosion defect geometric parameter, obtains the distribution parameter and correlation coefficient of the geometric parameter;Based on the distribution parameter and the correlation coefficient, covariance matrix is constructed, and Cholesky decomposition is carried out, to generate the initial corrosion random sample of pipeline;Based on finite continuous ILI data, construct a gamma process growth model, defect evolution prediction is carried out to the initial corrosion random sample, and the prediction result is obtained;Based on the prediction result, the burst pressure of natural gas pipeline at the prediction time point is calculated, the limit state equation is constructed, and the time-varying failure probability of pipeline is calculated using PHI2 method;The application improves the accuracy and scientificity of pipeline corrosion safety risk decision under the condition of limited detection data through the above four steps.

The invention discloses a probability load flow calculation method based on importance-Hammersi sampling and characteristic decomposition, and belongs to the technical field of power system analysis and optimization. Through importance sampling, a probability density function is constructed in a compressed domain of an expected range so as to reduce the burden of probability load flow calculation; a uniform sample is obtained by using a low-difference sequence of a Hammersi sequence, so that the sampling efficiency is improved; and decomposing positive definite and non-positive definite correlation matrixes through an eigenvalue decomposition method to obtain a probabilistic load flow calculation result. An importance sampling method with variable kernel density estimation is adopted to construct a probability density function with small variance, so that the sampling efficiency is improved, and a better probabilistic load flow result is obtained under the condition that the sampling frequency is reduced; using a Hammersi sequence to obtain a more uniform sample sequence under the condition of small sample size; compared with a traditional Cholesky decomposition method, the characteristic decomposition method provided by the invention has wider applicability due to the fact that positive definite and non-positive definite correlation matrixes can be decomposed.