96 results about "Robust principal component analysis" patented technology

The invention relates to the technical field of precision manufacturing nondestructive testing and machine vision image processing, in particular to a titanium alloy ring piece surface microcrack defect detection system based on machine vision, which comprises an image acquisition module for acquiring a to-be-processed image data set; the manifold calibration module is used for acquiring a main direction field of background textures and converting the to-be-processed image data set into a standard space image with aligned texture flow; the sparse decomposition module is used for acquiring a shear wave coefficient and decomposing the shear wave coefficient into a low-rank component matrix and a sparse component matrix; the reconstruction judgment module is used for generating a microcrack defect distribution diagram, obtaining a residual image and generating a final defect distribution diagram; the self-adaptive feedback module is used for adjusting the sparsity constraint weight in the robust principal component analysis algorithm; according to the method, the problem of signal aliasing caused by frequency overlapping of cracks and background textures is effectively solved, and the detection sensitivity under the strong texture background is remarkably improved.

The invention provides an electroencephalogram signal emotion recognition method and system, and relates to the technical field of artificial intelligence, and the method comprises the steps: constructing an electroencephalogram data matrix; performing robust principal component analysis on the electroencephalogram data matrix, and decomposing to obtain noise reduction data; constructing a graph neural network model; training the graph neural network model to obtain a trained graph neural network model; and taking the noise reduction data as the input of the graph neural network model, and outputting an emotion classification recognition result by the graph neural network model. According to the electroencephalogram signal emotion recognition method and system, the robustness of non-Gaussian and non-stationary noise is guaranteed, the electroencephalogram signal processing efficiency is improved, and the emotion recognition accuracy and generalization ability are improved.

The invention discloses a spaceborne synthetic aperture radar scattering wave mutual interference suppression method and system based on matched filtering, a storage medium and electronic equipment, and the method comprises the following steps: estimating related parameters of mutual interference according to the prior information of an orbit and a time-frequency relation, and carrying out the range pulse compression of an interference signal in echo data; detecting whether the range pulse contains interference or not by adopting a maximum characteristic value sequence detection method; calculating a relative signal to interference ratio containing interference pulses; reconstructing and extracting interference signals under the conditions of high signal to interference plus noise ratio and low signal to interference plus noise ratio respectively by using a feature subspace projection method and a robust principal component analysis method; and filtering the echo data, and carrying out mutual interference inverse distance compression processing on the data so as to recover normal echoes without interference. According to the invention, the compression gain of mutual interference can be effectively utilized, mutual interference signals in synthetic aperture radar echoes can be extracted more effectively, and high-precision interference suppression is completed while useful signals are better protected.

The invention relates to the technical field of nondestructive testing, in particular to an automatic defect detection and segmentation technology based on eddy current pulse thermal imaging, which comprises a thermogram sequence preprocessing step, a defect signal feature extraction step and an image segmentation post-processing step, in the thermogram sequence preprocessing step, an excitation peak frame image is dynamically selected through an image entropy difference, a static background is inhibited by combining image difference operation, and a geometric coil mask is generated by utilizing edge detection; and a defect signal feature extraction step: carrying out defect matrix reconstruction on the robust principal component analysis model based on the difference image by adopting an augmented Lagrange multiplier method. And an image segmentation post-processing step: designing a dual-threshold segmentation method based on reconstruction matrix local space consistency, and screening thresholds by combining an adaptive threshold of image gray level distribution and an area screening threshold of a defect candidate region. Automatic detection and segmentation of defects in eddy current pulse thermal imaging are achieved, and the purposes of defect identification and quantitative detection are achieved.

The invention relates to the technical field of image analysis, in particular to a metal composite material surface defect intelligent detection method based on machine vision, which comprises the following steps: acquiring an original image and converting the original image into an observation matrix; decomposing the matrix into a low-rank matrix and a sparse matrix through a robust principal component analysis algorithm; utilizing a nuclear norm constraint low-rank matrix to establish a background statistical model, and utilizing a norm constraint sparse matrix; positioning a defect area in the sparse matrix and extracting a gray level co-occurrence matrix feature vector; performing connected domain analysis on the sparse matrix, calculating geometrical morphology parameters and evaluating a stress concentration coefficient; and establishing a self-adaptive decision model to carry out fusion judgment so as to judge the physical damage. According to the method, through low-rank sparse matrix decoupling and multi-dimensional statistical geometric feature fusion analysis, accurate stripping of weak defects and quantitative evaluation of physical damage attributes under a complex background are realized.

The invention discloses a pulse thermal imaging sequence key frame extraction method based on robust principal component analysis, and the method comprises the steps: firstly calculating a difference image between adjacent images of an original thermal image sequence, solving the L1 norm of a difference result, and determining a starting frame of pulse excitation according to the maximum value of the L1 norm; decomposing the thermal image sequence by using robust principal component analysis, and extracting a key frame according to the sparsity of a sparse matrix; and finally, extracting a sample region in the thermal image by using a local maximum entropy threshold segmentation method to obtain a thermal image key frame sequence containing the sample region. According to the method, manual key frame screening and ROI clipping in the prior art can be perfectly replaced, the space-time distribution characteristics of the thermal imaging sequence are fully utilized, a complete and efficient pulse thermal imaging sequence key frame automatic extraction algorithm framework is developed, and the defect recognition efficiency and the automation level are improved in practical application.

The invention discloses a small target detection method fusing a region average gray difference and an infrared image block, and belongs to the technical field of image processing, target detection and image segmentation. The method comprises the following steps: obtaining an original image matrix containing a single-frame infrared small target; constructing an original image matrix into an infrared block image matrix by using a local region construction method; performing robust principal component analysis on the infrared block image matrix to obtain a background matrix and a to-be-detected target matrix; obtaining all connected domains in the target matrix to be detected, namely suspected target areas; a rectangular local area is constructed for each suspected target area, and the center of each rectangular local area coincides with the center of the corresponding suspected target area; and calculating and comparing the average gray difference between each suspected target area of the target matrix to be measured and the corresponding rectangular local area to obtain a real target. According to the technical scheme of the invention, the regional average gray difference and the infrared image block model are fused, so that the detection rate of the target is effectively improved.

The invention discloses a dual robust principal component analysis image denoising method based on a graph, and belongs to the technical field of image processing. The method comprises the following steps: flattening an image to be processed to construct a data matrix in a column vector form; constructing a graph structure by using a K nearest neighbor method, and generating a graph Laplacian matrix based on the graph structure; jointly considering an image reconstruction error, a sparse noise item, a linear mapping error item and a graph structure regular item, and constructing an optimization model; and carrying out variable alternating optimization by adopting an augmented Lagrange multiplier method and an alternating direction solution method, and obtaining an image denoising result according to the low-rank principal component. According to the method, image structure information and a double constraint mechanism are introduced into a robust principal component analysis framework, so that the detail retention capability and the structure consistency of the image are effectively enhanced, the robustness and the visual quality of image denoising are improved, and the method is suitable for application scenes such as image processing, video monitoring and target detection under complex backgrounds.

The invention discloses a power grid load interaction data anomaly detection method, device and equipment, a medium and a product. The method comprises the following steps: acquiring to-be-detected power grid load interaction data generated by at least two load main bodies; performing feature extraction on the to-be-detected power grid load interaction data to obtain target power grid load interaction data with power utilization mode features; performing initial classification on the target power grid load interaction data based on a preset robust principal component analysis method to obtain an initial classification result; performing outlier removal on the first power utilization load mode data set and the second power utilization load mode data set according to a preset clustering algorithm to obtain corresponding anomaly detection results; according to the embodiment of the invention, through the technical scheme, the abnormal data detection of the power grid load information interaction can be realized, and the detection efficiency and accuracy are improved, so that the safety interaction protection capability among the power grid load information is realized, and the improvement of the safety and reliability of a power system is facilitated.

The invention provides a wind power converter DC support capacitor health state real-time detection method, and belongs to the technical field of wind power converter DC support capacitor health state detection. Comprising the following steps: collecting a ripple voltage signal and a temperature signal of a DC bus capacitor in real time; variational mode decomposition is carried out on the ripple voltage signals, and time domain features, frequency domain features and nonlinear features of multi-mode components are extracted; meanwhile, mean value features and dynamic change rate features are extracted from the temperature signals; on the basis of robust principal component analysis, dimension reduction and feature enhancement are carried out on the extracted features, and a state feature matrix is generated; inputting the state feature matrix into a pre-trained intelligent learning model, and outputting a health state classification result of the DC support capacitor; according to the method, the health state of the capacitor is accurately reflected in real time, degradation and faults are found in time, and the method can be widely applied to real-time monitoring of the wind power converter.

The invention discloses a method for generating a cutting track of a convex riser on the surface of a casting based on machine vision, and the method comprises the steps: carrying out the self-adaptive anisotropic downsampling of an original point cloud, so as to reduce the data volume while keeping key geometric features; performing robust principal component analysis plane fitting on a point cloud downsampling result to obtain a reference plane parameter of the casting body under a preset coordinate system; differential geometry-based riser region segmentation is carried out on the point cloud downsampling result according to the reference plane parameters, and convex riser region point clouds are obtained; according to the point cloud of the convex riser region, constructing a triangular mesh curved surface about the surface of the convex riser; performing plane cutting on the triangular mesh curved surface to obtain a cutting contour line; based on a beam projection method, an initial cutting path is obtained according to the cutting contour line; and constructing a cutting path optimization energy functional so as to optimize the initial cutting path to generate an optimal cutting track of the convex riser on the surface of the casting. The problems that a traditional manual cutting mode is low in cutting efficiency, poor in precision, poor in consistency and large in potential safety hazard are solved.

Systems and methods are provided to adaptively suppress reverberation clutter signals in ultrasound imaging. A robust principal component analysis (RPCA) may be used to separate a static or low dimension background signal from sparse, moving or high dimension objects in the presence of outliers. A tissue signal may be transformed to the wavelet domain to fulfill the sparsity conditions of RPCA. The use of the RPCA combined with wavelet kernels may be used to suppress reverberation clutter signals to achieve robust ultrasound attenuation coefficient estimation.

The invention discloses a fluctuation signal coupling noise suppression method based on a neural network, which is a coupling noise suppression method in an auto-encoder fluctuation signal based on adjacent position constraint and first arrival pre-sample guidance, and is applied to the field of fluctuation signal data processing. In order to solve the problem that effective signals are prone to being greatly damaged when the coupled noise is suppressed in the prior art, the invention designs a method for representing the characteristics of waveform similarity of adjacent spatial positions of the coupled noise and fluctuation signals based on the weight of a noise sample before first arrival, and under a robust principal component analysis framework, on the basis of a model and data combined driving strategy. Effective signals and strong coupling noise are separated in an unsupervised mode, and damage to the effective signals is reduced.

The invention relates to a THz-SAR moving target high-resolution imaging method, device and equipment, and the method comprises the steps: constructing a target echo model, representing the model as a distance image sequence in a matrix form based on a compressed sensing theory, constructing a distance image sequence model, constructing an azimuth phase error matrix through employing a maximized image contrast criterion, and carrying out the high-resolution imaging of a THz-SAR moving target. Constructing a target function according to the target range profile matrix, the background range profile matrix and a to-be-solved moving target image, introducing an imaging model and an auxiliary variable to restrain the target function, and converting the to-be-optimized target function into a plurality of sub-problems for iterative solution by adopting an alternating direction multiplier method; and expanding the moving target into a moving target self-focusing imaging network based on robust principal component analysis, preprocessing a target echo signal obtained by actual measurement, and inputting the preprocessed target echo signal into the network to obtain an SAR high-resolution imaging result of the moving target. By adopting the method, high-precision SAR imaging can be carried out on the moving target under the condition of high error sensitivity.

The invention discloses a face recognition method based on adaptive sine angle robust principal component analysis, and belongs to the technical field of face recognition, and the method comprises the steps: obtaining a face image, and carrying out the preprocessing of the face image; constructing a robust principal component analysis model based on an adaptive sine angle, and training the robust principal component analysis model by using the training set; solving an optimal projection matrix by using a non-greedy iterative algorithm; mapping the original face features to a discrimination space by using the optimal projection matrix; and performing face recognition by using a nearest neighbor classifier, and performing robustness evaluation on a recognition result. According to the method, the adaptive sine angle is added, abnormal values can be effectively suppressed in the face of noise data, the influence of the abnormal values on the projection direction is reduced, and the method plays a key role in improving robustness.

The invention discloses an intelligent nursing method and system for infants and disabled old people. The method comprises the steps that vital sign data, behavior state data and an environment data set of a user are collected in real time; carrying out normalization processing on the collected data, carrying out feature extraction by adopting RPCA robust principal component analysis to obtain an intelligent nursing data set, and dividing the intelligent nursing data set into a training set, a verification set and a test set; constructing a TimeInf-LSTM time sequence anomaly detection model based on the intelligent nursing data set, and outputting a user demand analysis result; generating a personalized nursing instruction according to the user demand analysis result; a PLO aurora optimization algorithm improved based on lens imaging reverse learning and SPM chaotic mapping is adopted to optimize hyper-parameters of the TimeInf-LSTM model; according to the invention, multi-dimensional dynamic monitoring and accurate analysis of the user state are realized, and the accuracy, reliability and long-term applicability of nursing demand identification are significantly improved.

The invention discloses a method for detecting a moving target under a dynamic background based on non-convex space-time low-rank robust principal component analysis. The method comprises the following steps: acquiring video information of the moving target; converting the video information of the moving target into a three-dimensional observation matrix; decomposing the observation video matrix O into a low-rank static background matrix B, a sparse moving target matrix S and a dynamic background matrix E according to the background of the moving target and the characteristics of the moving target; constraining the decomposed matrix, and constructing a moving target detection model under the dynamic background based on non-convex space-time low-rank robust principal component analysis; converting a moving target detection model of the non-convex space-time low-rank robust principal component analysis under the dynamic background into an augmented Lagrange function, and solving a moving target detection problem of the model under the dynamic background by using an alternating multiplier method; obtaining an extracted moving target according to a sub-problem solving result, and performing binarization masking on the extracted moving target to realize detection of the moving target; according to the invention, the moving target can be completely detected in a dynamic background.

According to the multi-frame reverberation suppression method based on combination of the alternating direction multiplier method and the low-complexity high-order nonlinear cumulant, underwater reverberation in inter-frame low-rank slow-change components in reverberation is suppressed through the alternating direction multiplier method; water surface reverberation, volume reverberation and noise in inter-frame sparse fast-change components are suppressed by using low-complexity high-order nonlinear cumulants, and continuous moving target bright spots are enhanced, so that an angle-distance sonar two-dimensional image with lower reverberation intensity and clear continuous target bright spots is obtained. Compared with the reverberation suppression performance of an existing low-complexity high-order nonlinear cumulant and multi-frame accumulation after processing of an existing robust principal component analysis method, the multi-frame reverberation suppression method combining the alternative direction multiplier method and the low-complexity high-order nonlinear cumulant has a better reverberation background suppression effect.

The invention discloses a photovoltaic direct current series arc fault identification method and system, and relates to the technical field of arc fault identification, and the method comprises the steps: obtaining a bus current signal of a photovoltaic power generation system; decomposing the bus current signal by using a variational mode decomposition technology to obtain K mode components; performing feature extraction on each modal component from M dimensions, and combining sub-features extracted from each modal component to obtain M first feature vectors; selecting N first feature vectors of which the relevance ranks top N from the M first feature vectors; performing feature fusion processing on the N first feature vectors by using a robust principal component analysis technology to obtain fused features; and inputting the fusion features into a fault identification model to obtain a fault type corresponding to the bus current signal, the fault type comprising a branch arc fault or a bus arc fault. The method can determine whether the fault is a bus arc fault or a branch arc fault.

The invention relates to the technical field of underwater target detection, and discloses a reverberation suppression method and device based on tensor robust principal component analysis. The method comprises the following steps: arranging frame signals received by a sonar receiving array according to a time sequence to obtain a distance-azimuth echo sequence; performing histogram matching processing on the distance-azimuth echo sequence, aligning the intensity distribution of each frame to a reference frame, and obtaining a distance-azimuth echo sequence after matching processing; after the distance-azimuth echo sequence after matching processing is preprocessed, low-rank component estimation initialization processing is executed; and after the initialization processing is completed, executing an adaptive threshold iterative optimization process, and iteratively solving an iterative formula by using a scale gradient descent method to obtain a low-rank component and a target component. By applying the method, the problems of high target false alarm rate and insufficient detection precision caused by reverberation interference in a complex environment can be solved.

The invention relates to an unmanned platform small target detection method, device and equipment based on a rank sparse matrix. The method comprises the steps of obtaining a target detection image of an environment domain where the unmanned platform is located; and constructing an image detection model based on the rank sparse matrix. The image detection model adopts robust principal component analysis to separate a foreground and a background of a target detection image to obtain a foreground target image and a background image. And screening a candidate region from the target detection image, and marking the candidate region in the target detection image to obtain a region detection feature map. And inputting the foreground target image as prior information and the region detection feature map into an image detection model for feature extraction to obtain a small target feature map. And training an image detection model according to the small target feature map to obtain a trained image detection model, and performing small target detection through the trained image detection model. By adopting the method, the small target detection precision can be improved.

The invention relates to the technical field of industrial process soft measurement and quality control, and discloses a paper quality prediction method based on multivariate statistical latent variable fusion and space-time transformation, which comprises the following steps: acquiring space-time sequence data of a multi-source sensor in a papermaking process, constructing a space-time diagram structure reflecting a topological relation of equipment, and preprocessing. Then, multi-view latent variables are extracted through non-negative matrix factorization, independent component analysis and robust principal component analysis, attention fusion is conducted on the latent variables through an LV fusion module, and fusion latent variables are obtained; and inputting the fusion latent variable and original node data into a multi-scale convolution auto-encoder to obtain spatial feature embedding, and inputting the spatial feature embedding and the fusion latent variable into a space-time Transform module together to realize joint modeling of space correlation and time dependence. And finally, outputting a paper quality predicted value through a linear regression module. The method can achieve the accurate prediction of the paper quality under a high-dimensional and multi-noise working condition, and is suitable for online monitoring and modeling updating.

The invention discloses an aviation aluminum plate wave field image damage monitoring method based on non-convex total variation regularization RPCA. The method comprises the following steps: acquiring Lamb wave field image data of a to-be-detected aviation aluminum plate by using a sensor array; decomposing the mapped Lamb wave field image data by using a non-convex total variation regularization robust principal component analysis algorithm, and solving sub-problems by using an alternating direction multiplier method to obtain a low-rank matrix representing background wave field information and a sparse matrix representing damage abnormal values; performing post-processing including sparse response normalization processing, adaptive threshold segmentation, connected region filtering and morphological repair on the sparse matrix to extract a damaged region; according to the extracted damage area, the damage condition of the aviation aluminum plate to be detected is evaluated and positioned. According to the invention, the accuracy and real-time performance of damage identification can be improved.

The invention provides a rotating machine fault diagnosis method and system, and the method comprises the steps: collecting monitoring signals of a rotating machine in different fault states in a certain time period through a plurality of different types of sensors, and converting the monitoring signals into time-frequency images through a constant Q conversion method; performing graying, cutting and normalization processing in sequence to obtain a plurality of normalized pixel matrixes, and stacking each pixel matrix along the channel dimension of the tensor to obtain a plurality of third-order tensor samples in each fault state; then obtaining an intuitionistic fuzzy score of each three-order tensor sample by adopting an exponential membership function and an exponential non-membership function, extracting a low-rank feature tensor sample by utilizing a tensor robust principal component analysis method so as to construct a high-dimensional feature matrix, and finally establishing a nonlinear intuitionistic fuzzy twin support tensor machine model to obtain a solution vector; and a decision function is constructed to obtain a prediction label of each test sample in the test set, and fault diagnosis of the rotating machinery is realized.

The application relates to the technical field of underwater communication and signal processing, and provides an anti-interference method for underwater power carrier communication, which comprises the following steps: acquiring a power carrier time-frequency signal received by an SRM and synchronously collecting a control instruction stream, wherein the power carrier time-frequency signal is an OFDM signal; determining a state perception weight matrix based on a relay action time window, wherein the relay action time window is determined based on the control instruction stream; determining a joint optimization problem provided with a phase distortion correction operator based on the power carrier time-frequency signal, the state perception weight matrix and a frequency domain confidence matrix in combination with a double-weighted robust principal component analysis algorithm, wherein the frequency domain confidence matrix is determined based on the high-frequency attenuation characteristics of an underwater umbilical cable; and solving the joint optimization problem based on a linearized alternating direction multiplier method to determine a target power carrier signal. The application solves the problem that reliable communication cannot be realized under the double constraints of strong interference and severe phase jitter in the related art.

This invention discloses a method, system, storage medium, and electronic device for suppressing mutual interference in spaceborne synthetic aperture radar (SAR) scattered waves based on matched filtering. The method includes the following steps: estimating relevant parameters of mutual interference based on prior orbit information and time-frequency relationships; performing range pulse compression on the interference signal in the echo data; detecting whether the range pulse contains interference using the maximum eigenvalue sequence detection method; calculating the relative signal-to-interference ratio (SIR) of the interference pulse; reconstructing and extracting the interference signal under high SIR and low SIR conditions using the eigenspace projection method and robust principal component analysis method, respectively; filtering it from the echo data; and then performing inverse range compression on the data to restore the interference-free normal echo. This invention effectively utilizes the compression gain of mutual interference, thereby more effectively extracting mutual interference signals from SAR echoes and achieving high-precision interference suppression while better protecting useful signals.

The present application relates to the technical field of marine remote sensing and water depth inversion, and discloses a water depth inversion method fusing multi-temporal information and geographic spatial perception mechanism, single-temporal remote sensing images of the same water area at different time points are acquired first to construct a multi-temporal remote sensing image sequence, then a robust principal component analysis method RPCA is used to fuse and process the multi-temporal remote sensing image sequence to obtain a fused image; then a plurality of core units are cascaded together to complete step-by-step feature extraction, and the last core unit is connected to a regression head to output continuous water depth values, thereby completing the construction of a water depth inversion network model; finally, the water depth inversion network model is trained by using the fused image combined with the geographic spatial position, and the trained water depth inversion network model is used to perform water depth inversion on the data-processed remote sensing image to be detected.

The application discloses an improved RPCA infrared small target detection method based on a local signal-to-clutter ratio. Firstly, a robust principal component analysis is performed on an infrared image containing a small target, and the image is decomposed into a background matrix and a target matrix. The background matrix captures the low rank of the image and describes the background information of the image, and the target matrix utilizes sparsity and effectively represents target information in the image. A local window is introduced in the target matrix, and a local signal-to-clutter ratio of the image in the window is calculated. The local signal-to-clutter ratio considers the difference between the target and the background, and is helpful to distinguish the real target from false alarms. By comparing the local signal-to-clutter ratio difference between the small target and the background in the infrared image, the real target can be accurately identified. The robust principal component analysis can effectively separate the target and the background in the image, thereby improving the detection rate of the small target.

The invention discloses an online clutter suppression method and system for ultrasonic tiny blood flow imaging, and the method comprises the steps: carrying out the ultrasonic collection based on ultra-high speed, obtaining a high-frame-frequency ultrasonic image, carrying out the modeling of a tissue signal and a blood flow signal through employing a robust principal component analysis IRLS-rPCA method based on iteration reweighted least squares, constructing an online IRLS-rPCA clutter filter, and carrying out the clutter suppression of the ultrasonic tiny blood flow imaging. The ultrasonic image is processed based on the online IRLS-rPCA clutter filter, clutter suppression is carried out, a blood flow signal in the current frame image is extracted and ultrasonic tiny blood flow imaging is carried out by utilizing the space-time correlation between the current frame image and the filtered image in an online calculation mode, and the ultrasonic tiny blood flow image is obtained. The method can better conform to an ultrasonic high-speed acquisition and frame-by-frame reconstruction mechanism, and is easier to realize ultra-fast micro blood flow imaging.

The invention discloses a small-size image recognition method, device and equipment, a medium and a product in the technical field of computer vision, and the method comprises the steps: obtaining a small-size training image, and carrying out the preprocessing of the small-size training image, and generating a training image matrix; splicing all the training image matrixes to generate an image augmentation matrix; constructing a low-rank and low-dimension combined 2DERPCA optimization model of enhanced robust principal component analysis, and inputting the image augmentation matrix into the 2DERPCA optimization model to calculate an optimal projection matrix; obtaining a to-be-recognized small-size image, and preprocessing the to-be-recognized small-size image to generate a to-be-recognized image matrix; performing feature extraction on the to-be-recognized image matrix and the training image matrix by using the optimal projection matrix to obtain corresponding low-dimensional features; inputting the low-dimensional features into a nearest neighbor classifier for identification; according to the method, feature extraction can be carried out on a picture containing noise, the influence caused by the noise is effectively suppressed, and the robustness of recognition is improved.