19 results about "Higher-order singular value decomposition" patented technology

The invention relates to the technical field of electrical equipment fault diagnosis, in particular to an intelligent fault diagnosis method and system for electrical equipment, and the method comprises the steps: constructing a multi-dimensional tensor model, uniformly fusing the equipment state information, electrical distance weighted connection and phase dynamic coupling relation, and extracting an abnormal propagation mode through high-order singular value decomposition; designing a space-time-frequency coupling interference stripping mechanism, and combining structure guide disturbance deconstruction, multi-scale dictionary learning and sparse low-rank decomposition to accurately separate transmissible and non-transmissible interferences; reconstructing a fault trajectory based on a generative adversarial mechanism, coupling a graph structure dynamic encoder, a topology consistency discriminator and a time controllable generator, and restoring a real propagation path; and finally, tensor semantic compression, a three-view graph neural network and fault label back projection interpretation are integrated through a multi-source semantic fusion mechanism. According to the method, cross-space-time and cross-structure fault diagnosis and traceability are realized, and the accuracy and interpretability are improved.

The invention relates to the technical field of power equipment state monitoring, and particularly discloses a power cable electrical performance detection method and system, and the method comprises the steps: synchronously collecting a broadband electromagnetic signal, a mechanical vibration signal and a temperature signal at a cable monitoring point; calculating a wavelet coherence coefficient between the signals through continuous wavelet transform, and constructing a multi-modal coupling tensor fusing amplitude and cross-modal time-frequency correlation characteristics; performing time slicing and high-order singular value decomposition on the tensor to obtain a time-varying core tensor sequence, mapping the time-varying core tensor sequence into a high-dimensional manifold curve, and generating a system state fingerprint by calculating local curvature distribution and topology invariants of the curve; inputting the fingerprints into a pre-trained defect prediction model, and directly outputting defect inoculation probability and evolution stage judgment; according to the method, the limitation that early weak defect detection is not sensitive in a traditional method is broken through, and early warning and accurate diagnosis of cable insulation latent defects are achieved.

The invention discloses a fuzzy logic self-adaptive adjusting system of box-type temperature control equipment, and relates to the field of fuzzy logic self-adaptive adjustment, and the fuzzy logic self-adaptive adjusting system comprises a feature extraction module, a mapping module, an activation module, a control module and an adjusting module. The method comprises the steps of obtaining a temperature field, energy consumption and airflow velocity, establishing a thermodynamic vortex field model and performing feature extraction analysis, obtaining a curvature phase energy third-order feature tensor, performing high-order singular value decomposition, constructing a five-dimensional manifold space, and obtaining a final geometric invariant curvature membership degree and a learnable parameter tensor integral solution through a reaction diffusion equation and minimum projection. The method comprises the following steps: constructing a high-dimensional rule network, obtaining a rotation invariant rule activation tensor through quantum entropy screening and nonlinear activation, constructing a fractional order model, obtaining a final control signal through potential energy well resonance and coherence optimization analysis, constructing a parameter update vector through a key coefficient, and adjusting the final control signal in real time.

The invention discloses a vector hydrophone array orientation estimation method for performing tensor decomposition by using a propagation operator, relates to the technical field of vector hydrophone array orientation estimation, and discloses a vector hydrophone array orientation estimation method for performing tensor decomposition by using a propagation operator. The method comprises the following steps: firstly, constructing a three-dimensional array manifold tensor composed of an array direction matrix and a vector hydrophone output matrix; respectively expanding received signal tensors according to three modes, solving a propagation operator based on a column block covariance matrix, constructing a normalized signal subspace, establishing a spatial spectrum function with a noise subspace, and obtaining a pitch angle and an azimuth angle of a sound source through spectrum peak search; according to the method, high-order singular value decomposition is avoided, the operand is greatly reduced, meanwhile, high resolution and low sidelobe direction finding performance are kept, and the method is suitable for a real-time underwater acoustic direction finding system of a ship-borne platform, a buoy platform and an unmanned platform.

The invention discloses an early-stage arc risk prediction method based on an MSC-Informer algorithm, and the method comprises the steps: fusing the electrical quantity data collected by the edge end of a power system at high frequency and the multi-modal data collected by the cloud end at low frequency, and constructing a time-modal-feature three-order tensor; performing high-order singular value decomposition on the third-order tensor, decoupling an interaction relationship among time, modality and feature dimensions, and extracting multi-modal fusion features; the multi-modal fusion features are input to an MSC-Informer prediction model, the MSC-Informer prediction model comprises an MSC multi-scale convolution module and an Informer network with a probability sparse attention mechanism, and the MSC multi-scale convolution module extracts local mutation features in the electric signals by paralleling convolution kernels of different scales; the Informer network captures a long-range dependency relationship in the electric signal; and calculating a comprehensive risk score through weighted fusion on the basis of multiple arc early risk state probabilities output by the MSC-Informer prediction model, and dividing arc early risk grades according to a preset threshold.

The invention discloses a reverberation suppression method and device for shallow sea distributed scene feature learning. The method comprises the following steps: obtaining a target platform tensor for each target platform; constructing a corresponding deep expansion network for each target platform tensor; for the deep expansion network of the current target platform tensor, after parameter initialization is carried out by using a spectrum method, obtaining the hyper-parameter of the current deep expansion network based on the high-order singular value decomposition training of self-supervised learning; performing low-rank sparse decomposition on the deep expansion network of the current target platform tensor and the deep expansion network of the adjacent target platform tensor by using the current deep expansion network hyper-parameter; a current deep expansion network hyper-parameter is used as an initial hyper-parameter, a hyper-parameter of a deep expansion network of a non-adjacent target platform tensor is obtained based on high-order singular value decomposition training of self-supervised learning, and then low-rank sparse decomposition is performed. The method can be applied to shallow sea distributed scenes and has high-precision reverberation suppression.

This invention provides a method for synchronously extracting multi-channel composite fault features of bearings in the running gear of electric locomotives. The method mainly includes: Step S1, preprocessing the multi-channel vibration signals of composite faults in the bearings of electric locomotives using a tensor synchronous denoising method based on higher-order singular value decomposition; Step S2, adaptively filtering and decomposing the preprocessed signals using a multi-layer K-value MVMD algorithm; and Step S3, calculating the fault peak factor of the envelope spectrum of each channel and plotting a tower-shaped EC diagram. Based on the tower-shaped EC diagram, the optimal analysis result of the multi-channel vibration signal is selected and output, synchronously extracting the composite fault features of the bearings. This method overcomes the problems of severe noise interference and ineffective detection of composite faults encountered by traditional methods when processing multi-channel signals of composite faults in electric locomotive bearings. It achieves synchronous and visualized output of composite faults, providing a strong basis for the extraction and identification of weak and composite fault features in complex dynamic signals of the running gear.

The application relates to the technical field of radars, and provides a multi-target parameter dimension reduction estimation method and device for a frequency control array radar, which comprises the following steps: determining a receiving signal matrix of the frequency control array radar based on a transmitting steering vector, a receiving steering vector of each target and a transmitting signal of the frequency control array radar at each snapshot moment; solving the receiving signal matrix by using a high-order singular value decomposition method to obtain a noise subspace matrix of the receiving signal matrix; constructing a multi-target parameter dimension reduction estimation model based on the noise subspace matrix and the transmitting steering vector and the receiving steering vector of each target; solving the multi-target parameter dimension reduction estimation model by using a polynomial root-finding method to estimate an angle estimation value of each target; and estimating a distance estimation value of each target corresponding to the angle estimation value of each target based on the angle estimation value of each target. The application not only reduces the calculation complexity under the condition of few snapshots, but also improves the precision of parameter estimation.

The invention relates to the technical field of vehicle data processing, and provides a safety belt and air bag cooperative control method and system based on multi-mode signals. Vehicle data and user data are collected, and processing results obtained through standardization processing are combined to generate a multi-modal signal tensor; decomposing the signal tensor through high-order singular value decomposition to obtain a collision mode component representing collision dynamic characteristics and a user characteristic component representing user biomechanical characteristics; performing feature extraction on the two components through a long-short-term memory network to obtain a time-space dependent damage risk probability; and modeling the safety belt controller and the airbag controller based on the injury risk probability to generate a safety function, and solving the safety function to generate a cooperative control strategy. According to the scheme, the safety belt and the air bag can achieve the synergistic effect according to different conditions, and the reliability of the safety device in vehicle safety control and the safety of passengers in collision accidents are effectively improved.

The invention relates to the technical field of data processing, in particular to a multidimensional data analysis method based on machine learning, which comprises the steps of data preprocessing, feature extraction and fusion, model construction and optimization, and data analysis and decision making. During preprocessing, an improved isolated forest algorithm is used for removing abnormal values, a Bayesian network is used for supplementing missing values, minimum-maximum scaling and logarithm transformation are combined with normalization data, in feature extraction fusion, a high-order singular value decomposition tensor is combined with an attention mechanism for weighting fusion components, and during model construction optimization, a DDQN architecture is improved, and the probability that the model is optimized is lowered. Parameters are updated in combination with empirical regression and a strategy gradient algorithm, finally processed data are input, and decision suggestions are generated by using a multi-objective decision and a Pareto frontier analysis method; the objective of the invention is to solve the problems of insufficient processing precision of abnormal values and missing values during preprocessing of multi-dimensional data, incapability of dynamically capturing key information by feature extraction and fusion, weak model generalization ability and difficulty in processing multi-target conflicts.

This invention discloses a monitoring and evaluation system and method for conjugate soil slope restoration based on multi-source sensing, belonging to the field of civil engineering monitoring and safety assessment technology. The system includes a heterogeneous sensing layer, an edge fusion layer, a cloud evolution layer, and an autonomous decision-making layer. The method includes: acquiring multi-dimensional state information of the conjugate system through a biomimetic multi-source sensor network; constructing a four-dimensional time-series tensor model and using high-order singular value decomposition with confidence constraints for data fusion and restoration; dynamically evaluating the system's health based on a physical information element learning network; and predicting failure paths and generating graded early warnings using a micro-damage propagation graph network. This invention treats the restoration body and the original soil as a conjugate system, realizing a shift from phenomenon monitoring to mechanism inversion and from static evaluation to dynamic prediction, solving the problems of isolated data, static evaluation, and delayed early warning in traditional monitoring.

The application discloses a four-dimensional clutter suppression method and system for ultrasonic micro blood flow imaging, and belongs to the field of ultrasonic imaging. The method is applied to a four-dimensional data set after multi-frame multi-angle plane wave transmission and beam synthesis based on time accumulation, utilizes the angular coherence principle, that is, main lobe components have high coherence in angular space while side lobe components are opposite, and adopts high-order singular value decomposition for clutter suppression. The four-dimensional clutter filtering method based on angles expands the data dimension (increases the feature space), increases the information quantity, is beneficial to blood flow separation, can effectively suppress clutter with fewer frames, can effectively improve the micro blood flow imaging quality in small data sets, requires smaller multi-angle data quantity, has lower calculation cost, and solves the problems in the prior art.

The application relates to the technical field of power equipment state monitoring, and specifically discloses a power cable electric performance detection method and system, wherein wideband electromagnetic signals, mechanical vibration signals and temperature signals at cable monitoring points are synchronously collected; wavelet coherence coefficients among the signals are calculated through continuous wavelet transformation, and a multi-modal coupling tensor fusing amplitude and cross-modal time-frequency correlation characteristics is constructed; time slicing and high-order singular value decomposition are performed on the tensor to obtain a time-varying core tensor sequence, which is mapped into a high-dimensional manifold curve, a system state fingerprint is generated by calculating the local curvature distribution and topological invariant of the curve; and the fingerprint is input into a pre-trained defect prediction model to directly output the incubation probability and evolution stage of defects; the application breaks through the limitation of traditional methods that are not sensitive to early weak defects, and realizes early warning and accurate diagnosis of cable insulation latent defects.

The invention belongs to the technical field of urban infrastructure data processing and intelligent water affair, and particularly relates to a high-order singular value decomposition data cleaning method for an urban underground drainage pipe network, a program, equipment and a storage medium. The method comprises the following steps: firstly, integrating and preprocessing multi-source data according to time-space characteristics of original multi-source data of the urban underground drainage network, and detecting abnormal data by adopting a dynamic weighted KNN algorithm after eliminating invalid values; then, constructing a four-dimensional tensor comprising a time dimension, a space dimension, a dynamic monitoring data dimension and a meteorological data dimension, compressing data through high-order singular value decomposition with time sequence constraint by adopting a high-order singular value decomposition method, and retaining key features; determining a truncation threshold value of the core tensor, and restoring the truncated factor matrix into a dynamic monitoring data set to realize data cleaning; and finally, an SWMM hydraulic model is introduced to verify a cleaning result, closed-loop optimization is formed, and it is ensured that data conforms to the pipe network hydraulic law.

The invention discloses an online education method, which comprises the following steps of: (a) constructing a dynamic course planning model based on a cognitive state tensor # imgabs0 # (b) of a learner, and updating a knowledge point weight through a stochastic differential equation; and (c) teaching resource allocation is optimized by adopting a non-Euclidean space projection algorithm, wherein the accuracy of multi-dimensional cognitive modeling is remarkably improved, and the system can accurately model the cognitive state and knowledge association of the learner through a high-order singular value decomposition (HOSVD) and hypergraph convolutional network technology. Experimental data show that the error rate of knowledge graph construction is reduced to 8%, and the error is reduced by 42.6% compared with that of a traditional matrix decomposition method. The modeling precision of the hypergraph network for the complex knowledge dependency relationship reaches 89.3% (F1-score), and is improved by 23% compared with a common graph neural network.

The application discloses a kind of underwater target positioning methods based on underwater acoustic particle velocity polarization processing.The method includes: arranging vector hydrophone array and carrying out underwater acoustic signal acquisition, and write the vector received signal of vector hydrophone array into tensor form;The tensor received signal of vector hydrophone array is carried out high-order singular value decomposition and the noise subspace of each dimension information matrix is solved by truncation to n module expansion left singular matrix;Array manifold matrix of vector hydrophone array is constructed and is written into polarization array manifold tensor;Azimuth-polarization two-dimensional spectrum search is carried out using array manifold tensor and noise subspace, and the azimuth and polarization parameters of the detected target are obtained from two-dimensional spectrum peak value.The underwater target positioning method provided by the application can accurately estimate the polarization parameters and the angle of arrival of incident signal, which helps to complete the underwater acoustic signal information based on frequency, wave number, mode and other characteristics by means of polarization parameters, and significantly improves the underwater target detection capability.

The application relates to the technical field of electrical equipment fault diagnosis, in particular to an intelligent fault diagnosis method and system for electrical equipment, which comprises the following steps: unifiedly fusing equipment state information, electrical distance weighted connection and phase dynamic coupling relationship by constructing a multi-dimensional tensor model, and extracting an abnormal propagation mode by using high-order singular value decomposition; designing a space-time-frequency coupling interference stripping mechanism, combining structure-guided disturbance deconstruction, multi-scale dictionary learning and sparse low-rank decomposition to accurately separate propagating and non-propagating interference; reconstructing a fault trajectory based on a generative adversarial mechanism, coupling a graph structure dynamic encoder, a topological consistency discriminator and a time-controllable generator, and restoring a real propagation path; and finally integrating tensor semantic compression, a three-view graph neural network and fault label back-projection interpretation through a multi-source semantic fusion mechanism. The application realizes cross-time-space and cross-structure fault diagnosis and tracing, and improves accuracy and interpretability.

This invention relates to a hyperspectral image denoising method based on tensor-based higher-order singular value decomposition (HDVDe), belonging to the field of hyperspectral image processing technology. To overcome the problems in existing technologies, this invention provides a hyperspectral image denoising method based on HDVDe, comprising: dividing the hyperspectral image tensor into blocks and merging similar blocks into clusters; establishing a hyperspectral image denoising optimization model based on the nonlocal self-similarity and global correlation in the spectral domain of the hyperspectral image; solving the hyperspectral image denoising optimization model using the ADM algorithm; and restoring a noise-free hyperspectral image by combining noise-free image blocks. This invention effectively improves the problem of imbalanced tensor expansion matrices by separating the nonlocal self-similarity domain information, spectral domain information, and spatial domain information of the hyperspectral image to apply low-rank constraints in the spectral domain, thus greatly utilizing the global and local correlations in the spectral domain of the hyperspectral image.

This invention discloses an underwater sound source matching field localization method based on higher-order singular value decomposition of tensor signals, comprising the following steps: Step 1, reconstructing the spatiotemporal frequency multidimensional data recorded by the vertical receiving array into tensor data; Step 2, calculating the tensor signal subspace based on higher-order singular value decomposition and singular matrix truncation. Since singular value decomposition is performed on the matrix expanded in each dimension of the tensor, noise can be further suppressed, resulting in a more accurate signal subspace and improving the accuracy of target localization; Step 3, specifying the receiving array configuration and inputting marine environmental information such as hydrological conditions, seabed topography, and seabed sediment. This invention, while accurately estimating the target location, has stronger background suppression capabilities and outperforms conventional matching field processing.