9 results about "Robust principal component analysis" patented technology

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.

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.

This invention belongs to the field of image processing and computer vision technology, specifically disclosing an infrared small target detection method based on deep unfolded networks and learnable sparse transforms. This invention maps the single-iteration steps used in traditional robust principal component analysis (PCA) for updating the background, target, multipliers, and reconstructing the image to an end-to-end trainable model composed of multiple cascaded stages in a deep neural network. The model proposes a multi-channel attention-supervised transmission enhancement module, which drives attention selection by introducing multi-channel feature fusion and actively blocks the cross-stage propagation of errors by truncating low-response channels and retaining high-response channels. A learnable sparse transform target extraction module is introduced to achieve accurate target separation under extremely low signal-to-noise ratio (SNR) conditions. A dynamically attention-guided feature enhancement module is constructed to capture spatially changing background patterns, improving the fidelity of image reconstruction. This invention achieves high-precision, low-false-alarm-rate target detection under extremely low SNR conditions.

The application relates to the technical field of SCGD-OES online detection, and specifically provides a multi-element online quantitative method, system, equipment and medium of a high-salt matrix solution, which comprises the following steps: synchronously collecting multi-modal data; constructing a state vector according to the multi-modal data; calculating a stability index through robust principal component analysis and Mahalanobis distance; combining with working condition parameters to distinguish the working condition and the stability state; and adaptively selecting a quantitative analysis mode; under the working condition constraint, inputting spectral line characteristics into a segmented weighted least square regression model which is fused with monotonicity constraint, multi-spectral line consistency and self-absorption / quenching penalty term, compensating for the matrix effect and calculating the concentration; and finally, fusing the stability index, spectral line consistency deviation and model residual error to calculate the comprehensive confidence, and outputting the concentration and the confidence. The application improves the accuracy, robustness and result reliability of multi-element online analysis under high-salt, dynamic and nonlinear complex working conditions.

This invention relates to the field of image processing technology, and more specifically, to an online detection method for surface defects of hexagonal screws based on machine vision. The method includes: acquiring an image of the hexagonal slot of the hexagonal screw, and dividing the image into multiple image blocks along a preset grid; for each image block, constructing an equilateral distance normal gradient deviation index and an equilateral distance six-fold symmetry intensity deviation index; the equilateral distance normal gradient deviation index is used to characterize the degree to which the local gradient vector deviates from the normal edge normal characteristics. This invention, by constructing an in-band joint anomaly response index, spatially adaptively adjusts the regularization parameters of the Robust Principal Component Analysis (RPCA) algorithm, achieving effective suppression of normal hexagonal step textures and high-sensitivity extraction of real minute defects, perfectly balancing the anti-interference capability and extremely high accuracy of industrial inspection.

This application provides an active sonar streaming processing method based on physical sensing and GPU acceleration, comprising: acquiring temporal beam data of the current frame in the current beam direction; constructing a physical sensing weighted matrix; constructing a physical sensing weighted robust principal component analysis (PCA) optimization model based on the PCA weighted matrix; inputting the temporal beam data into the PCA weighted robust PCA optimization model and solving it using a GPU-accelerated randomized singular value decomposition (RSD) method to obtain a sparse target matrix; and determining the azimuth history data result of the current frame based on the sparse target matrix. The method proposed in this application utilizes the low-rank sparse separation characteristics of weighted robust principal component analysis, combined with the physical sensing weighted matrix, to accurately identify and remove reverberant backgrounds with strong time-frequency correlations, effectively suppress random noise interference, and significantly recover submerged target signals under low signal-to-mixing ratio conditions.

The application 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 observation matrix into a low-rank matrix and a sparse matrix through a robust principal component analysis algorithm; establishing a background statistical model by using a kernel norm to constrain the low-rank matrix and using a one norm to constrain the 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 geometric morphological parameters and evaluating a stress concentration coefficient; and establishing an adaptive decision model to perform fusion discrimination to determine physical damage. Through low-rank sparse matrix decoupling and multi-dimensional statistical geometric feature fusion analysis, the application realizes accurate stripping of weak defects and quantitative evaluation of physical damage attributes under a complex background.

This invention discloses a machine vision-based method for generating cutting trajectories for convex risers on casting surfaces. The method includes: adaptive anisotropic downsampling of the original point cloud to reduce data volume while preserving key geometric features; robust principal component analysis (PCA) plane fitting of the downsampled point cloud results to obtain reference plane parameters of the casting body in a preset coordinate system; differential geometry-based riser region segmentation of the downsampled point cloud results based on the reference plane parameters to obtain a point cloud of the convex riser region; constructing a triangular mesh surface about the convex riser surface based on the point cloud of the convex riser region; performing planar cutting on the triangular mesh surface to obtain a cutting contour line; obtaining an initial cutting path based on the cutting contour line using a beam projection method; and constructing a cutting path optimization energy functional to optimize the initial cutting path and generate the optimal cutting trajectory for the convex riser on the casting surface. This invention solves the problems of low cutting efficiency, poor accuracy, poor consistency, and significant safety hazards associated with traditional manual cutting methods.