34 results about "Decomposition theory" patented technology

The invention belongs to the field of on-line monitoring technology of insulative condition of SF6 (sulfur hexafluoride) gas insulated electric equipment, and in particular relates to a gas insulated electric equipment partial discharge multi-source joint detection method. The detection method mainly comprises the steps of preparing a test, cleaning a stainless steel cylinder, filling with SF6 gas, performing an industrial frequency AC SF6 gas PD test, pulse current detection, ultrahigh frequency sensor detection, fluorescent optical fiber sensor detection, discharge gas collection and component detection and the like. The detection method provided by the invention is simple in operation, liable to promote, and provides references for multi-dimensional information joint detection method. The detection method is widely applied to SF6 gas insulated electric equipment partial discharge multi-sensor joint detection under the condition of power frequency, and provides a simple test scheme for multi-dimensional information extraction of PD (Potential Difference) decomposition characteristics and a decomposition theory.

The invention relates to a direct current gas insulation electrical equipment partial discharge decomposition simulation experiment apparatus and belongs to the technical field of direct current gas insulation electrical equipment insulation state on-line monitoring. The direct current gas insulation electrical equipment partial discharge decomposition simulation experiment apparatus comprises a direct current power supply system, a gas discharge enclosed air chamber, a detection system and a typical insulation defect physical model, wherein the direct current power supply system comprises a voltage regulation console, a test transformer, a protection resistor, a capacitive voltage divider and a high voltage rectification silicon stack lamp; the detection system comprises a pulse current detection system and a gas chromatography-mass spectrometry detection system; and the typical insulation defect physical model comprises a metal protrusion insulation defect model, a free metal particle insulation defect model, an insulator metal pollution insulation defect model, an insulator air gap insulation defect model and the like. The invention can effectively simulate a typical insulation PD fault and the decomposition process of SF6 under the action of a typical insulation defect PD in direct current gas insulation electrical equipment, thereby providing a solid basis for a system to research direct current PD characteristics, SF6 direct current PD decomposition characteristics and decomposition theories. The invention can be widely used for simulating direct current gas insulation electrical equipment partial discharge.

The invention provides a wind power gearbox fault diagnosis method based on the self-adaptive resonance sparse decomposition theory, relates to the wind power gearbox fault diagnosis method based on the self-adaptive resonance sparse decomposition theory, and aims at solving the problems that the early composite fault signals of the existing wind power gearbox have strong background noise and fault characteristic information is weak. The concrete process of the method comprises the steps that step one: the vibration data of the test gearbox are acquired by using a BBM noise vibration detection system so that fault vibration signals of planetary carrier bearing outer ring peeling and planetary gear local peeling are obtained; step two: the quality factor and the proportional coefficient of resonance sparse decomposition are simultaneously optimized so that X* is obtained; step three: X* is substituted in a self-adaptive resonance sparse decomposition method so that the high and low resonance components of the fault vibration signals are obtained; and step four: envelope analysis is performed on the high resonance component of the fault vibration signals so that fault information and the non-fault vibration signals are identified. The wind power gearbox fault diagnosis method based on the self-adaptive resonance sparse decomposition theory is suitable for the field of fault diagnosis.

The invention discloses an acoustic vector array wideband direction finding method based on a sparse decomposition theory, and relates to a direction finding technique and implementation method for target broadband continuous spectrum noise in the acoustic vector sensor array detection field. The sparsification idea is introduced to an acoustic vector array direction finding system; a combined sparse constraint is established according to frequency point components in broadband continuous spectrum noise signal bandwidth; and finally, a unified airspace sparse decomposition form is obtained, thereby realizing direction finding of the broadband continuous spectrum noise. According to the method, a relatively-sharp spectrum peak and a low-amplitude noise floor can be formed; when the number of array snapshots is small, the method is stable in performance and can distinguish coherent signal sources; and when the target DOA (direction of arrival) approaches to the array axial direction, the phenomenon of noise floor fluctuations occurring under the condition of low signal-to-noise ratio of a conventional beam forming (CBF) method and a minimum variance distortionless response (MVDR) method does not exist.

The invention discloses a multi-cell coordinated distributed robust beamforming method. A central problem is converted into a form of multiple subproblems and a main problem through a dual decomposition theory, and the subproblems are converted into semi-definite programming (SDP) problems through the semi-definite relaxation (SDR) and S-procedure technology in a complex field and then solved. The main problem is solved through a sub-gradient algorithm, the lagrangian dual problems of respective subproblmes are solved to obtain the lagrangian multipliers and are exchanged through a return link at each base station, and the base station calculates the sub-gradient of the main problem at a disturbance item between cells according to the obtained multiplier information of other base stations, so that the main problem can be rapidly solved through the sub-gradient algorithm. The distributed algorithm is simulated and displayed through an actual cellular system, the performance close to the central algorithm can be achieved through a little iteration, and the algorithm is high in convergence rate when the channel error is low.

The present invention proposes a magnetotelluric meshless numerical simulation method for a random conductive medium model. The method is for heterogeneity of underground space, and according to the method, a shape function is constructed based on discrete nodes, and dependence on a mesh no longer exists. The method provided by the present invention comprises: using the spectral decomposition theory and the hybrid autocorrelation function theory of the stochastic modeling process to construct a random conductive medium model of underground space; establishing a function of the magnetotelluric corresponding boundary value problem; establishing a shape function of a meshless method by using a least squares method; using the Lagrange multipliers method to load essential boundary conditions; resolving linear equations by using the biconjugate gradient stabilized (BICGSTAB) algorithm of incomplete LU decomposition preprocessing, to obtain a field value of each node in the region; and using the field values to calculate the apparent resistivity and phase of each point, thereby completing the whole numerical simulation calculation process. The section calculated according to values of the random conductive medium model better complies with the actual situation, and is more advantageous for the interpretation and processing work of the magnetotelluric data.

The invention discloses a method for quickly estimating underwater target shell radial vibration low-frequency sound radiation signals based on mode decomposition, and belongs to the technical field of underwater target sound radiation. The method includes the following steps that firstly, based on uncertainty of a researched object, the range of researched frequency bands is determined; secondly, based on a mode decomposition theory and different model structures, a simulation sound pressure equation is determined; thirdly, based on a mode truncation theory, an appropriate lower order axial mode order is calculated by adopting a higher circumferential mode order; fourthly, an appropriate lower order circumferential mode order is calculated by adopting a higher axial mode order; eventually the radiation signals through simulating calculation are acquired through calculation, and then the underwater target sound radiation condition is analyzed. According to the technical scheme, the underwater target sound radiation signals generated by radial vibration are accurately simulated through the few mode orders, and therefore calculation complexity is low and accuracy is high.

The invention discloses a physiological data preclinical processing method and system. The physiological data preclinical processing method comprises preprocessing physiological data based on the time series; carrying out association rule analysis according to the calculated abrupt change score and by means of a multidimensional abrupt change detection model and an integrated learning algorithm fused with multiple classifiers, and obtaining a disease associated network according to the result of association rule analysis; selecting a disease associated network characteristic from the disease associated network by means of an improved clustering algorithm, obtaining a disease diagnosis result according to the disease associated network characteristic and historical data, wherein the improved clustering algorithm is based on a nonnegative matrix decomposition theory and a self-learning mechanism, and extracting a corresponding connected subgraph from large graph data of the disease associated network as the disease associated network characteristic through adjusting the subgraph density. The physiological data preclinical processing method and system are advantaged in that the method and system are wide in applicability, and high in efficiency and precision, and is flexible and convenient, and can be widely applied to the field of data processing.

The invention discloses a method for extracting road surface disease features based on the sparse decomposition theory and relates to the road surface disease detection technique, which solves the problems that the contour signal analysis technique based on structured light has insufficient feature extraction and unsatisfactory actual application effect and the like. The method comprises the following steps of: firstly, establishing different disease feature atom dictionary according to different disease features, taking position and scale as parameters which change in a different ranges, and normalizing the atoms, thus obtaining a complete disease feature atom dictionary; secondly, according to the signal spreading theory, selecting K atom pair signals from the complete disease feature atom dictionary to carry out K approximations, and then selecting an atom combination with a most sparse decomposition coefficient from the K atom combinations according to the sparse decomposition theory. The selection of the coefficient Ck of the disease feature should satisfy a sparse constraint condition which is as follows:* C 0 s. t f =*Ck Phi k; the disease feature can be expressed as: f (t) = fk + Sigma = Sigma Ck Phi k (t)+ Sigma, wherein k = 0, 1, 2, and other integers; and Sigma is an approximate error. The method for extracting road surface disease features is used for detecting road surface disease features, such as crackles, tracing ruts, pits or earth bulges, and the like.

The invention provides a method for resolving a sea level stormy wave spectrum by means of full-polarized SAR echo data. The method is based on decomposition of a sea level scattering mechanism of polarized SAR measured data. The method comprises the steps of performing polarization decomposition on the full-polarized SAR echo data; according to a coherent polarization decomposition theory and a sea level scattering theory, by means of a Bragg scattering theory model, separating simple Bragg scattering contribution from the full-polarized SAR echo data; and using the echo component which is concentrated by Bragg scattering into calculation of an image cross spectrum, thereby analyzing a directional spectrum of sea level stormy wave frequency band which corresponds with Bragg scattering except for a stormy wave cut-off frequency. The method for resolving the sea level stormy wave spectrum by means of the full-polarized SAR echo data is characterized by comprising the technical processes of (1), a polarization decomposition method of full-polarized SAR sea level echo data; and (2), a spectrum characteristic analysis method of Bragg scattering contribution.

The invention discloses a high-precision longitudinal and transverse wave impedance inversion method. The method comprises the following steps of firstly, further optimizing a longitudinal and transverse wave impedance reflection coefficient approximation formula on the basis of a Fatti approximation formula; secondly, building an L1-2 minimum inversion objective function of synchronous constraintof a longitudinal and transverse wave impedance low-frequency model according to a reflection coefficient odd-even decomposition theory and a Lagrangian multiplier method; and finally, solving the function by combining a difference of convex algorithm (DCA) and an alternating direction method of multipliers (ADMM) to obtain longitudinal and transverse wave impedance.

The invention provides a joint optimization method and system for planning and operation of an energy storage system in a photovoltaic-containing power distribution network. The method comprises the steps of building a three-layer robust optimization model for planning the energy storage system of the power distribution network, wherein the three-layer robust optimization model includes a planningmain problem model, a safety verification sub-problem model and an economic operation optimal sub-problem model; and solving through three-layer iteration to obtain the optimal solution in consideration of the worst scenes of different conditions under voltage out-of-limit safety analysis and economic optimal operation through three-layer decomposition of the robust optimization model. The sourceload uncertainty is fully considered, and the optimal configuration scheme in the worst scene is sought on the basis of ensuring that the voltage out-of-limit problem does not occur to the power distribution network. However, the problem is complicated due to too many dependent variable dimensions, so that the original problem is decomposed into a planning main problem, a safety verification sub-problem and an economic operation optimal sub-problem according to a decomposition theory, and iterative solution is carried out conveniently, quickly and effectively.

The invention discloses a polarization SAR model decomposition evaluation method based on electromagnetic scattering simulation. The method comprises the steps of (S1) setting surface electromagneticgeometric parameters, aircraft flight parameters and radar satellite position parameters and calculating a surface real beta value of a target scene by using a classical forward model, (S2) processingthe polarization SAR simulation data of the target scene by using a polarization SAR model decomposition method to be evaluated and carrying out inversion to obtain an inversion beta value, and (S3)calculating a mean square root error of the inversion beta value and the surface real beta value, and evaluating the polarization SAR model decomposition method to be evaluated with a principle that the smaller the mean square root error is, the better the effect of the decomposition method is. According to the scheme of the invention, the electromagnetic scattering simulation theory and the polarization SAR model decomposition theory are organically combined, a decomposition algorithm is evaluated from the perspective of electromagnetic wave simulation and model decomposition, the method is fair and just, and a reference can be provided for the selection of an excellent model decomposition method for different application scenes.

The invention provides a power distribution network single-phase grounding fault section positioning method based on a generalized group knapsack. Traditional fault positioning algorithms based on a power system transient signal all adopt a fixed basis function to decompose the signal and have the problems of insufficient decomposition accuracy and redundant decomposition results. The invention proposes to use a sparse decomposition theory to extract an attenuation DC component in the power system transient signal, and an area where the single-phase grounding fault is located is judged according to the existence of the attenuation DC component. In order to overcome the problem of huge space-time complexity of a sparse decomposition theory classical MP algorithm, a generalized group knapsack model is constructed based on dynamic programming, and a linear combination of selected atoms approximates an original transient signal with O(VN) time complexity. According to the method, the basisfunction can be adaptively selected according to the characteristics of the transient signal to obtain a simplest form of signal decomposition, and a new idea is provided for the single-phase groundfault positioning method of the power distribution network.

The invention discloses an indoor fall-down behavior detection method with visual privacy protection advantages, and the method comprises the steps: firstly compressing a video frame through multilayer compressed sensing, and achieving a visual shielding effect; secondly, separating a target area from the shielded video by using a low-rank sparse decomposition theory, and extracting motion trail features of the target through a dense trail algorithm on the basis; and finally, converting the tumble detection problem into a signal sparse identification problem. While the video data volume and the video resolution are reduced, the fall detection recognition rate is still kept at a high level, and the method has the dual advantages of high privacy protection and high fall recognition rate, andhas a high practical application value.

The invention relates to a harmonic detection method combining extreme symmetry mode decomposition and Hilbert transform, which is aimed at the problem that the existing power system harmonic signal detection methods are not high in precision, and the problem that well-studied empirical mode decomposition has modal aliasing in harmonic detection, can provide a specific harmonic detection step based on a combination of the extreme symmetry mode decomposition ESMD and Hilbert transform (HT) according to the extreme symmetry mode decomposition theory and algorithm. Firstly, a signal is subjectedto extreme symmetry mode decomposition to obtain a series of intrinsic mode functions of different feature scales. Then, Hilbert transform is performed on the IMF component to obtain the instantaneousamplitude and instantaneous frequency information of each harmonic. The invention has strong self-adaptive decomposition ability in harmonic detection, high detection precision and good real-time performance, and can effectively avoid the modal aliasing phenomenon of the EMD method in harmonic detection without adding noise.

The invention relates to a method for mapping mineral by taking use of the statistics characteristics of data without prior information for a high spectrum data, which comprises the following procedures: reading in the high spectrum data, centralizing the data, sphericalizing the data, setting up an analysis model of independent components, rating the independent components, extracting end members of mineral, min. noise separating and transforming, mixing, modulating, matching and filtering, mapping mineral. The method allows to extract end members of eroded mineral based on the analysis model of independent component by taking use the hi-order statistics feature of data themselves without unknowing any prior information, and to map eroded mineral by a way based on mixing, modulating, matching and filtering. The method avoids in the mineral identification the influence of uncertainness of spectrum feature of mineral caused by spectrum mixing, illumination, and mineralizing condition, combines the restricting condition of end-member content to be positive and 1 in the linear mixing decomposition theory, has effectively improves the detection limit for mineral and mapping mineral accuracy.

The invention relates to the medical imaging technology, in particular to a diffusion tensor imaging method for processing nerve fiber reconstruction in a nerve complex distribution area, device and magnetic resonance equipment. A multivariate high-order tensor model is adopted to describe the diffusion motion in a voxel, and in combination of a high-order tensor decomposition theory, the directions of all fibers in the voxel are solved. The method disclosed in the invention is particularly suitable for reconstructing nerve fibers in the nerve complex distribution area and can effectively process nerve fiber bundle intersection, bifurcation and other conditions. The method, the device and the equipment have important application value in neuroscience, medical imaging and the like.

The invention relates to a voltage flicker parameter detection method based on extreme point symmetry mode decomposition. Based on the extreme symmetry mode decomposition theory and algorithm, a voltage flicker parameter detection step based on ESMD extreme point symmetry mode decomposition is given. Firstly, a voltage flicker signal is decomposed into single-frequency amplitude-modulated waves bythe ESMD method, and then the instantaneous amplitude and instantaneous frequency information of each single-frequency amplitude-modulated wave is obtained by the direct interpolation DI method. Theover-fitting and under-fitting of an envelope of the EMD method is overcome, and the modal aliasing phenomenon of the EMD method can be effectively avoided in the detection without adding noise; the defect that the detection result of the EEMD method strongly depends on the selected noise is overcome. The voltage flicker parameter detection method has strong self-decomposing ability in the detection of voltage flicker parameters, high detection precision, good real-time performance, less false mode, small end-end distortion of an amplitude-frequency curve, and small fluctuation.

The invention provides a mining XLPE cable insulation cable electric branch growing stage identification method based on sparse decomposition, which comprises two steps of establishing an over-complete atom dictionary in electric tree growing stage in mining XLPE cable insulation and identifying electric tree growing stage in mining XLPE cable insulation. The method of the invention introduces thesparse decomposition theory into the identification of the electric tree growing stage of the mining XLPE cable insulation under the power frequency voltage, establishes an over-complete atom dictionary of the electric tree growing stage, and in the actual use process, the electric tree growing image data need not be collected, and the data, such as the skewness Sk + and the steepness Ku-, etc.,are used directly. A matching pursuit algorithm is used to identify the electrical tree growth stage of a to-be-tested sample, thereby reducing the difficulty of identification.

Provided is a cable partial discharge feature extraction method based on wavelet packet survival index singular entropy, belonging to the technical field of power cables and aiming to solve the problem of inaccurate extraction of partial discharge features in a conventional high-voltage cable partial discharge detection method by extracting the partial discharge features of high-voltage cables. The method includes that a 16-bit digital signal with the frequency range of 0-50MHz is obtained, and the survival index singular entropy is calculated, the singular value decomposition is carried out according to a matrix singular value decomposition theory, survival multivariate functions are calculated, and the wavelet packet survival index singular entropy is calculated. The wavelet packet energy index entropy is the singular entropy calculation of wavelet packet node coefficients or reconstructed signals, so that the problem that undefined and zero values in the information entropy are defined by logarithms can be effectively avoided, and the shortcoming of Shannon entropy is overcome.

The invention discloses a Mars entering flight state uncertainty quantification method. Random Mars entering dynamics is converted into an equivalent deterministic dynamics differential equation set through a polynomial chaos theory. The updating of the orthogonal basis in the polynomial chaos is realized by utilizing the spectral decomposition theory when the statistical characteristics of the Mars entering the flight state uncertainty change drastically, so that the uncertainty quantization accuracy is ensured under the condition that the polynomial order is not increased. And violent increase of the equation dimension under the condition of uncertainty of the initial state is inhibited by adopting a random space decomposition theory, so that the calculation efficiency is improved. The method is suitable for instantaneous state uncertainty quantification and state evolution uncertainty quantification of Mars entering flight.

The invention relates to an inverse mahalanobis distance measuring method based on weighting Moore-Penrose in the process of data mining, comprising the following steps: 1) calculating a covariance matrix S of a data totality X; 2) based on the spectrum decomposition theory of a real time symmetric matrix, expanding the covariance matrix S of a data totality X; 3) constituting a weight matrix M, and a weight matrix N by the following concrete process: 1. constituting an n n matrix M; and 2. constituting an n n matrix N; 4) calculating a weight Moore-Penrose inverse matrix of the covariance matrix S; and 5) calculating the mahalanobis distance between a data individual Xi and a data individual Xj. The invention provides an inverse mahalanobis distance measuring method based on weighting Moore-Penrose in the process of data mining, which is free from the influence of dimension (with invariance of linear conversion), maintains data mean value and variance information and ensures normal operation with higher performance no matter what relevance data are processed.

The invention discloses a Laplace wavelet basis sparse representation dictionary construction method based on waveform impact matching. The Laplace wavelet basis sparse representation dictionary construction method comprises the following steps: step 1, setting an initial value of a Laplace wavelet support interval; step 2, re-determining a support interval of the wavelet according to a 3 criterion of Laplace wavelet waveform energy; step 3, determining the Laplace wavelet with the optimal shock waveform matching degree with the bearing fault vibration signal; step 4, constructing a Laplace wavelet basis sparse representation dictionary after waveform impact matching through a point-by-point time shifting method; step 5, calculating a correlation coefficient between each atom in the Laplace wavelet basis sparse representation dictionary and a bearing fault vibration signal. According to the invention, the defect that a traditional Laplace wavelet correlation filtering method can only select appropriate wavelet correlation parameters through a large amount of priori knowledge and human experience is overcome. The method is not only suitable for constructing Laplace wavelet basis dictionaries, but also suitable for constructing Morlet wavelets and other wavelet-based dictionaries, and a new method is developed for constructing the wavelet-based dictionaries in the sparse decomposition theory.