249 results about "Hankel matrix" patented technology

A hydroelectric generating set vibration signal noise reduction method based on mode autocorrelation analysis of SVD and VMD comprises the steps of constructing a Hankel matrix of a set vibration signal and performing singular value decomposition (SVD), selecting an effective singular value based on a mean value filtering strategy for reconstructing the signal, and realizing pre-filtering; performing decomposition through variational mode decomposition (VMD) for obtaining a series of mode functions, calculating an autocorrelation function of each mode component, selecting effective mode components according to an energy set of the autocorrelation function, and obtaining a signal after noise reduction through adding all effective mode components. According to the hydroelectric generating set vibration signal noise reduction method provided by the invention, a noise reduction experiment is carried out through simulation analysis and actual measurement of a vibration signal; and a result represents a fact that the method has relatively high noise reduction performance and can effectively improve hydroelectric generating set vibration signal analysis precision.

A method includes generating a correlation vector of baseband-signal vectors based upon signals received from a plurality of sensor devices, generating a generalized Hankel matrix R representing a covariance matrix to which a spatial averaging is applied based upon the correlation vector, generating a kernel matrix Ω1, which is a projection matrix onto noise subspace, by performing linear operation using submatrices R1 and R2 of the generalized Hankel matrix R, generating a kernel matrix Ω2, which is orthogonal with the kernel matrix Ω1, and estimating a direction of arrival of a signal based upon the kernel matrices Ω1 and Ω2.

Methods and apparatuses for processing seismic data carrying information about a subsurface structure are provided. One method includes splitting the seismic data in groups, each group including subsets of seismic data for which a distance from a respective shot point to a receiver line in a patch of active detectors corresponding to the shot point is within a predetermined range for the group, and extracting signal data for each block Hankel matrix based on an SVD analysis thereof. Another method includes extracting signal data by subtracting from seismic data an estimated noise model obtained by rank reduction of a Hankel matrix followed by dip filtering, enhanced based on a least square matching filter with the seismic data.

The invention discloses a seismic signal restoration algorithm based on tensor nuclear norm regularization. The method comprises the following steps: S1, constructing an objective function based on Hankel tensor nuclear norm regularization; and S2, solving the objective function by using an ADMM algorithm. According to characteristics of correlation, redundancy and low rank of the pre-stack seismic signal, the invention provides a Hankel tensor nuclear norm regularization method to reconstruct missing seismic data. With a HTR-SVD decomposition method, a Hankel matrix construction mode is applied to a tensor nuclear norm, so that the correlation of a tensor data block is increased, the low-rank performance of the tensor data block is improved, and a problem that a pre-stack seismic signal can not be restored because of lack of a tangent plane is solved; and thus work area data selected randomly can be processed by the method, so that the the robustness and adaptability of processing themissing seismic data are improved. While filling of missing data is carried out, noises in signals are suppressed, so that the signal to noise ratio of the signals is increased.

The invention discloses a quick reconstruction method for under-sampling magnetic resonance spectra, and relates to magnetic resonance spectra. The method comprises the steps of firstly, performing under-sampling on time domain signals of magnetic resonance spectra, and filling a Hankel matrix with sampling data; then reconstructing complete time domain signals by using the low rank characteristic of the matrix, and performing Fourier transformation to obtain high-quality spectra, wherein in the process, the minimization of a matrix nuclear norm item is approached by using a matrix factorization method and minimizing a matrix Frobenius norm item, so that singular value decomposition used when the nuclear norm item is solved is avoided, and the effect of accelerating reconstruction is achieved. The method is excellent in effect and easy to operate, and can be applied to under-sampling magnetic resonance spectrum reconstruction of one, two and higher dimensions.

The invention discloses a low-frequency oscillation dominant mode distinguishing method based on improved SVD noise reduction and Prony. The method comprises the steps that according to an input signal and a basic inequality principle, Hankel matrices having maximum products of matrix line number and matrix column number are constructed in an SDV algorithm; according to the input signal, signal-to-noise ratio curves of the Hankel matrices are drawn and are analyzed to determine the best effective singular value order; according to the best effective singular value order, the identification orders in a Prony algorithm are selected to determine the best identification order; the SDV algorithm having the Hankel matrices and the best effective singular value order is utilized to process the input signal, and a denoised signal is obtained; the denoised signal is analyzed through the Prony algorithm having the best identification order, and a low-frequency oscillation dominant mode is identified. The low-frequency oscillation dominant mode distinguishing method based on improved SVD noise reduction and Prony has the advantages of being strong in noise suppression capability, high in identification precision and accuracy and the like.

The invention relates to a modal parameter recognizing method based on multi-input multi-output signal noise reduction. The method is characterized by comprising the steps of step 1, performing block-Hankel matrix conversion on noise-containing multi-point pulse responding signals to obtain a first block-Hankel matrix; step 2, processing the first matrix by utilizing structural low rank approximating according to the rank of the first matrix to obtain a first non-block-Hankel matrix after the low rank approximating; step 3, replacing the elements of each block in a second matrix with math average values of the block elements on a back-diagonal to obtain a second block-Hankel matrix; step 4, repeating the steps 2 and 3 until convergence, thus obtaining the noise-reduced multi-point pulse responding signal; and step 5, performing modal parameter recognizing. The modal parameter recognizing method based on multi-input multi-output signal noise reduction can save machine time and improve recognizing accuracy.

The invention discloses a signal noise reducing method for modal parameter identification. The signal noise reducing method for modal parameter identification comprises the steps that 1, a Hankel matrix is built through a pulse response signal of a noise-containing structure; 2, a rank of the Hankel matrix is resolved, an order determination index is resolved according to the rank of the Hankel matrix, and a model order is determined through the order determination index; 3, the Hankel matrix is processed through the order determination index and structure low rank approximation to obtain a rebuilt matrix processed through low rank approximation; 4, the step 2 and the step 3 are repeatedly performed until the convergent standard is met, and therefore a noise reducing signal is obtained; 5, modal parameter identification is performed through the noise reducing signal. The signal noise reducing method for modal parameter identification has the advantages that the fact that a Frobenius norm of a difference between the Hankel matrix before being processed through noise reducing and the Hankel matrix after being processed through noise reducing approaches to be the minimum can be achieved by setting the mode of the convergent standard and structure low rank approximation, that is, the improvement of the precision of the noise reducing signal can be achieved.

An accurate waveform measuring method for lightning strike-induced malfunctions of an electrical transmission line, comprising: using a self-integrating, flexible, nonmagnetic cored Rogowski coil (9) to sample and measure the electric current on an electricity transmission line, and designing an correction system for correcting measured values. Different from the traditional correction method of increasing inductance and some current correction methods of using hardware circuitry, the present method utilizes a certain software algorithm to enable the transfer function of the correction system. When acquiring the transfer function of the correction system, first a pulse current generator is used to simulate a square wave output; the Rogowski coil (9) and a shunt resistance are sampled and measured; the input and output of the correction system are acquired on the basis of the correction system backward-restoring the measuring system; the least square method is used to calculate the impulse response of the correction system; a Hankel matrix is used to calculate the order of the transfer function; finally, the least square method is used to identify and solve the coefficient of the transfer function.

The invention discloses a method for estimating the angle of MIMO (Multiple-Input Multiple-Output) radar with array element faults based on block Hankel matrix completion. According to the method, firstly, a matrix with a 2-fold Hankel structure is constructed by using column vectors of a virtual array covariance matrix, a block Hankel matrix with a 4-fold Hankel structure is constructed by takinga matrix formed by column vectors as a sub-matrix for enabling each row and each column of the constructed block Hankel matrix to have sampling elements and meet the low-rank property, and missing data in the block Hankel matrix is completed by utilizing matrix completion; then, the corresponding redundant elements are averaged in the recovered block Hankel matrix; and finally, inverse transformation is carried out on the block Hankel matrix without data missing to obtain a complete virtual array covariance matrix, and the DOD (Direction of departure) and DOA (Direction of arrival) of a target are estimated by adopting an array covariance matrix-based algorithm (such as an ESPRIT algorithm). According to the method, a large amount of missing data in the covariance matrix of the MIMO radarvirtual array with the array element faults can be effectively recovered, and the angle estimation performance when the array element faults are transmitted or received is improved.

The invention discloses a magnetic resonance spectrum reconstruction method based on a block Hankel matrix. The method comprises the following steps: firstly acquiring a magnetic resonance signal under-sampling template, constructing a block Hankel matrix of a two-dimensional matrix, and establishing a low-rank reconstruction model based on the block Hankel matrix of a magnetic resonance free attenuation signal; and then solving the reconstruction model through an iteration algorithm and acquiring a reconstructed free attenuation signal; and finally performing the Fourier transform to obtain a complete magnetic resonance spectrum. The reconstruction method disclosed by the invention is small in required sampling data amount, high in precision, and capable of reconstructing a complete magnetic resonance spectrum from less under-sampling data; the sampling speed of the method provided by the invention is faster than that of the existing method under the condition of obtaining the reconstructed spectrum data with the same quality.

The invention discloses a PRCE-based low-frequency oscillation mode on-line identification method and belongs to the power system low-frequency oscillation field. The method includes the following steps that: the angular velocity measurement signals of a plurality of generators after the disturbance of a section of a power system are read; a Hankel matrix is constructed through free oscillation signals, and a column vector is solved by using the least squares method; and a matrix polynomial is constructed so as to solve an eigenvalue matrix and a right eigenvector matrix, and therefore, the oscillation frequency, damping ratio and oscillation mode of the system can be obtained. The method of the invention, based on multi-channel signals, can identify oscillation modes and can obtain more stable identification results. Compared with a TLS-ESPRIT method and an SSI (stochastic subspace identification) method, the method of the invention is advantageous in more stable identification results, higher identification accuracy and higher identification efficiency.

The invention discloses an image reconstruction method for multi-channel magnetic resonance imaging, and relates to magnetic resonance imaging. The method comprises the following steps: firstly, establishing an image reconstruction model combining a low-rank Hankel matrix and data consistency, then establishing an improved reconstruction model for avoiding singular value decomposition, then reconstructing Fourier space data of magnetic resonance through an iterative algorithm, and finally converting the Fourier space data into a final magnetic resonance image. By combining the low-rank Hankelmatrix with the data consistency, the correlation between coils of parallel magnetic resonance is utilized, and the influence of inaccurate sensitivity images is reduced, so that magnetic resonance images with better artifact inhibition and more reserved edges can be reconstructed, the artifacts can be effectively inhibited, and more edge characteristics can be reserved.

The invention belongs to the technical field of damage identification and relates to a structure damage identification experimental method based on PVDF (polyvinylidene fluoride) piezoelectric-film sensors and strain modes. The damage identification experimental method comprises the following steps: collecting corresponding signals of all piezoelectric-film sensors after applying ambient excitation, and carrying out pre-treatment on the signals; obtaining an approximate impulse response matrix of reference points and answering points by using a natural excitation technique; constructing a generalized Hankel matrix by using the obtained impulse response matrix, and obtaining a system matrix with minimum order by using a singular value decomposition method; carrying out eigenvalue decomposition on the obtained system matrix so as to obtain structure modal parameters and modal shapes; respectively obtaining damaged or non-damaged structure modal parameters and determining damage positionsand carrying out damage identification. The method provided by the invention has the advantages that the error control is relatively good, the operability is strong, the sensitivity in damage identification can be improved, and modal analyzing and computing efficiency is improved.

The invention discloses a method for suppressing high-power-frequency noise in parallel seismic source seismic prospecting data based on principal component analysis. Parallel seismic source seismic prospecting refers that a plurality of seismic sources are arranged at different shot points in a prospecting area, data is acquired by adopting a nearly simultaneous excitation technology, and the seismic prospecting production efficiency and the seismic data quality can be improved. Original seismic record acquired by parallel seismic source excitation in a human activity area are usually mixed with high-power-frequency noise, a Hankel matrix is built according to a phase space theory for an interference channel, and principal component decomposition and useful signal recovery are performed on the Hankel matrix. It is proved that the method can realize effective high-power-frequency noise suppression for the seismic prospecting data obtained by parallel seismic source acquisition. Compared with a traditional high-power-frequency noise suppression method, the method disclosed by the invention is fast in data processing, does not need to accurately estimate the frequency of the high-power-frequency noise, is free of phase deviation and good in signal protection, can effectively improve the seismic data quality of parallel seismic source prospecting and reduces the parallel seismic source seismic prospecting data processing cost.

The invention discloses a subspace identification based forecasting method for superheated steam output of a boiler of a firepower power station, which comprises the following steps: firstly, acquiring production data from plant-level DCS (Distributed Control System) configuration software at regular time; secondly, correcting bad points, filtering and denoising; thirdly, calculating input and output variables required by model identification; fourthly, constructing an Hankel matrix by utilizing continuous acquired and processed data and rolling to update; fifthly, approximating the boiler process to a linear state space model; sixthly, using a subspace theory to identify a matrix of the state space model [A, B, C and D]; seventhly, utilizing the latest input energy information as the obtained input variables of the state space model after the identification and calculating output energy of future moments; eighthly, calculating the mean value at the same time by utilizing a historical forecast value and a current forecast result and correcting by utilizing the past forecast deviation; and ninthly, rolling to update the data of identification of the model. The forecasting method is applicable to online forecast of superheated steam output energy in the boiler production process and provides guidance and reference for the optimization control of boiler combustion.

The invention discloses a channel estimation method of an OFDM (orthogonal frequency division multiplexing) system. The channel estimation method comprises the following steps of: generating a Hankel matrix of each sub-segment, performing SVD (singular value decomposition) on the Hankel matrix of each sub-segment, retaining one bit or multiple bits before diagonal entries in a singular value diagonal matrix corresponding to the Hankel matrix of each sub-segment, forming a new singular value diagonal matrix corresponding to the Hankel matrix of each sub-segment, further generating a new Hankel matrix of each sub-segment according to the new singular value diagonal matrix, extracting estimation channel information of each sub-segment according to the new Hankel matrix and finally combining the estimation channel information of each sub-segment to form the complete estimation channel information. According to the method disclosed by the invention, the accuracy in channel estimation can be improved.

The invention relates to a seismic irregular noise removing method based on robust singular spectrum analysis. The seismic irregular noise removing method is characterized by comprising the followingsteps that the single frequency seismic data are embedded in the Hankel matrix so as to construct the Hankel matrix of the single frequency seismic data; the objective function of the Hankel matrix ofthe single frequency seismic data is obtained based on the L1 and L2 mixed norm; the objective function is solved according to the preset regularization parameters in the objective function so as toobtain singular spectrum analysis of the Hankel matrix based on the L1 and L2 mixed norm; and alternating minimization and weighted least square minimization are performed on singular spectrum analysis in turn to solve the factor matrix of the Hankel matrix of the single frequency seismic data so as to obtain estimation of the Hankel matrix of the single frequency seismic data and complete noise reduction of the single frequency seismic data. The seismic irregular noise removing method based on robust singular spectrum analysis can be widely applied to the field of seismic data processing.

The invention relates to the monitoring technology field and more particularly, to a partial power discharge fault determining characteristics extracting method and a determining method. The invention provides a partial power discharge fault type determining characteristics extracting method, comprising: carrying out discrete sampling to the signals from a partial power discharge area; building a Hankel matrix on the discrete digital signals; performing singular value decomposition to the Hankel matrix to obtain the singular value matrixes; screening the singular values with the preserving of the larger singular values to obtain the screened singular value matrixes; based on the screened singular value matrixes, obtaining the improved Hankel matrix; performing phase-space reconstruction to the improved Hankel matrix to obtain the constructed phase-space matrix B; and calculating the energy moments of all the components of the phase-space matrix B to obtain the partial power discharge fault determining characteristic vector. With the method of the invention, it is possible to effectively avoid the problem that the diagnosing precision is influenced by the unequal number of the valid singular values generated from the singular value decomposition so as to improve other methods which neglect the mutual inherent relationship of the characteristic values in mode identification.

An index signal de-noising method relates to a signal de-noising method, and is easy to operate and exhibits excellent effects. The method includes the step of modeling index signals: filling the index signals in a Hankel matrix according to a set sequence, establishing a Hankel matrix low-rank reconstruction model, and solving the model to de-noise the signals. The method is fast in speed and high in precision, and parameters can be set on the basis of measured noise variances. During practical applications, the optimized model can be adopted to de-noise signals such as time-domain signals of nuclear magnetic resonance wave spectrums and signals that accord with the index characteristics, so the sampling time is reduced and the spectra resolution is increased. The index signal de-noising method, provided by the invention, exhibits excellent effects and is easy to operate.

The invention provides an exponential signal denoising method achieved by means of prior information and relates to a denoising method for exponential signals.The prior exponential signals form a Hankel matrix according to a set sequence, the Hankel matrix is subjected to singular value decomposition, and a prior signal space and a prior singular value are obtained; then a Hankel matrix with the same size as the former Hankel matrix is established for the target exponential signals, and the matrix of the target signals is decomposed through the prior information space; after a weighted threshold is obtained from the prior singular value, a Hankel matrix of the denoised target signals is obtained from the singular value of the target exponential signals according to the weighted threshold; then the Hankel matrix of the target signals is solved, and finally the denoised signals are obtained.Through the prior information of the reference signals, the speed is high, the effect is good, and operation is easy.Denoising of a magnetic resonance spectrum can be achieved for signals with exponential features such as time domain signals of the magnetic resonance spectrum through the method, and the purposes of shortening the sampling time and increasing the signal to noise ratio of the spectrum are achieved.

The invention provides a wide band spectrum signal denoising method based on wavelet threshold difference. Wavelet decomposition is carried out on spectral signals with noise to obtain multilayer wavelet coefficients; soft threshold denoising is carried out on the multilayer wavelet coefficients to obtain the denoised multilayer wavelet coefficients; a Hankel matrix is constructed by wavelet coefficients of each layer after denoising; a singular value matrix is constructed according to the Hankel matrix, and singular values are obtained by orthogonal decomposition of the singular value matrix;difference calculation is carried out on the singular values to construct a singular value difference spectrum; singular value difference denoising treatment is carried out on the singular value difference spectrum; the denoised singular value matrix is calculated in a reconstructing mode to obtain the denoised Hankel matrix so as to reduce to denoised wavelet coefficients; and the denoised wavelet coefficients are subjected to inverse wavelets transform through a Mallat algorithm to obtain denoised spectral signals. The wide band spectrum signal denoising method has complete denoising effect, effective spectral characteristics can be retained, and measurement precision and accuracy of a wide-band spectrometer are improved.

The invention relates to a Beidou single frequency cycle slip detection method, which belongs to the field of Beidou navigation and positioning data processing. A pseudo range observation value is firstly used for subtracting a carrier phase observation value, the difference value is then differenced between epochs, and a cycle slip detection amount is obtained; then, a Hankel matrix is used for building an attractor track matrix for the cycle slip detection amount, singular value decomposition (SVD) is carried out, and a limited number of effective singular values capable of reflecting abrupt information can be obtained; and finally, SVD inverse operation is used for reconstructing a component signal for each effective singular value, and after the component signals are overlapped, the cycle slip is detected according to the position of the maximum value point in the overlapped signals. Amplitude changes only need to be features, whether cycle slip happens is judged only by a cycle slip detection amount amplitude through SVD processing, the problem that the traditional method is hard to detect small cycle slip can be solved, and small cycle slip with 1 to 5 cycles in the Beidou carrier phase observation value can be effectively detected.

The invention discloses a method for extracting the fault signal feature information of an aero-engine rotor system, and the method comprises the steps: collecting a rotor rub-impact vibration signal,and carrying out the FDM decomposition of the collected rotor rub-impact vibration signal to obtain complete rotor rub-impact vibration signal FIBFs components; constructing a Hankel matrix of the rotor rub-impact vibration signal after noise reduction, and drawing a singular value curve of the rotor rub-impact vibration signal after noise reduction; determining a singular value catastrophe point, carrying out the reconstruction of an order number, and carrying out the envelope spectrum analysis, thereby achieving the effective extraction of fault information. According to the invention, themethod achieves the reasonable extraction of the fault feature frequency of a rotor system based on the FDM decomposition and singular value differential spectrum analysis, and achieves the efficientfiltering of the noise signal component in the rub-impact vibration signal, thereby achieving the extraction of the weak fault feature information from the low-frequency strong noise background, and achieving the effective extraction of the fault information.

The invention discloses a method for identifying modal parameters of an ocean platform structure. The method comprises the following steps of 1, carrying out directional processing on input environmental excitation response data of the ocean platform structure; 2, constructing a Hankel matrix by utilizing the structural environment excitation response data, carrying out orthogonal triangular decomposition on the Hankel matrix to obtain a projection matrix, then carrying out singular value decomposition on the projection matrix, and solving a vibration system state space equation by utilizing aleast square method and the like; and finally, extracting structural modal information from the system matrix A and the output matrix C in the state-space equation of the vibration system; 3, performing clustering analysis based on density division on the modal result set by utilizing a density-based clustering algorithm; and 4, obtaining real modal parameters, and analyzing a result of the realmodal parameters. According to the method, the real mode of the ocean platform structure can be automatically extracted from the environmental response signal. Therefore, most of noise points are eliminated. Moreover, the health condition of the platform can be evaluated according to a modal parameter identification result.

The present invention discloses a seismic data noise reduction method. For the seismic data noise reduction problem, the method comprises the steps of: S1, employing a robust Huber norm to replace anerror item of a target function to obtain the target function based on the Huber norm; S2, introducing a schatter p<-> norm into the target function obtained in the steps to obtain the target functionbased on the schatter p<-> norm; and S3, performing solution of the target function obtained in the step S2 to obtain robust estimation of a Hankel matrix. The seismic data noise reduction method caneffectively remove regular noise while suppressing random noise.

The invention provides an on-orbit modal identification system and method for satellite solar cells. The on-orbit modal identification system for satellite solar cells comprises a pulse excitation module, a signal acquisition module, a satellite on-orbit vibration monitoring and modal identification module, a data transmission module and a data processing module, wherein the signal acquisition module includes a plurality of sensors which are arranged on a satellite solar cell array, and is used for acquisition of pulse response signals at each measuring point on the satellite solar cell array; the data transmission module is used for transmitting the pulse response signals at each measuring point to the ground; the satellite on-orbit vibration monitoring and modal identification module is used for receiving and monitoring the pulse response signals at each measuring point; and the data processing module is used for constructing a characteristic system state equation according to the pulse response signals at each measuring point, and acquiring solar cell array on-orbit modal parameters by constructing a Hankel matrix. The on-orbit modal identification method for satellite solar cells is high in reliability, and can solve the problem that modal frequency measurement on a large array plane solar cell array ground is not accurate.

The invention relates to a low frequency oscillation identification method for a power system based on an O3KID algorithm. The method embeds an observer in the stochastic model of the power system, and uses the basic equation of the O3KID algorithm and the least squares method to estimate the Markov parameters and residuals of the observer, and transforms the stochastic system identification of the power system into an identification problem of the deterministic system. The introduced device is equivalent to the Kalman filter. The Hankel matrixes are respectively constructed by using the output of the observer and the residual time series. The orthogonal projection and singular value decomposition methods of the deterministic system are used to effectively identify the reduced-order modelof the power system, and accurately extract the frequency, damping ratio and vibration mode parameter information of the dominant mode of the low-frequency oscillation. The method provided by the invention is suitable for the low-frequency oscillation mode analysis of the power system for the WAMS synchronous measurement environment excitation signal and the transient ring-down signal, and the IEEE-39 node system simulation and the US Eastern Power Grid WAMS measured data analysis verify the effectiveness of the method.

The invention discloses a rapid super-complex magnetic resonance spectrum reconstruction method, relates to magnetic resonance spectrums, and provides a method for reconstructing complete magnetic resonance spectrums from under-sampled super-complex magnetic resonance spectrums. The method comprises the following steps of: firstly filling an obtained under-sampled super-complex magnetic resonance spectrum into a blocked Hankel matrix; converting the super-complex blocked Hankel matrix into a super-complex companion matrix; constructing a reconstruction model, a nuclear norm of which corresponding to the super-complex companion matrix is minimized; and finally solving the reconstruction model by adoption of a rapid algorithm, so as to obtain a complete magnetic resonance spectrum.

The invention aims at providing a method for identifying a voltage sag source based on multi-resolution singular value decomposition. Firstly, a second-order Hankel matrix is constructed for voltage signals, MRSVD analysis is conducted, a group of component signals of different frequencies are obtained, then an energy moment of the component signals is obtained, the main features of the signals are effectively extracted, a recognition feature vector is formed, and an LS-SVM recognition model is input for voltage sag source recognition.