6 results about "Variational regularization" patented technology

The invention discloses a social recommendation method and system based on denoising and preference balance, and belongs to the technical field of graph neural networks and social recommendation. The method comprises the following steps: constructing a social graph; denoising the social graph to obtain a denoised social graph; taking the normalized user item interaction matrix and the de-noised social graph as input, performing double-branch variational coding learning probabilistic user embedding, and performing variational regularization, gating de-noising and attention fusion to obtain user fusion representation; based on user fusion representation, joint reconstruction of an interaction graph and a social graph is carried out, and a training target is obtained through a preference balance optimization target. According to the method, through adaptive graph structure optimization and a long-tail preference constraint mechanism, accurate modeling of a real social relation and balanced expression of diversified preferences are realized, and finally, the reliability, fairness and recommendation precision of the model in a noise sparse social environment are improved.

This invention relates to the field of detection and imaging technology, specifically to a muon quasi-scattering imaging device and method based on sparse representation and dictionary learning. The device includes a modular muon detector array deployed on one side, an FPGA high-speed data acquisition system, and a computational control unit, and can also integrate a momentum spectrometer module. The method includes offline dictionary preparation and online sparse reconstruction steps. First, a complete dictionary is trained using GEANT4 simulation and the K-SVD algorithm. Then, data is collected to construct an enhanced objective function containing block sparseness and anisotropic total variational regularization. The reconstructed image is obtained by optimizing the solution using the ADMM algorithm. This invention overcomes the limitations of two-sided detection, adapts to single-sided detection scenarios in geotechnical engineering, significantly improves imaging efficiency and quality under low-throughput data, has strong algorithm robustness and a scalable framework, and can accurately identify anomalies such as underground cavities and isolated boulders.

The application discloses a kind of turbulent flame geometry three-dimensional tomography method and system based on multi-probe CGI, comprising: N single-pixel detector is arranged around flame to form multi-probe detection array, dynamic space illumination modulator is used to generate structured light field to illuminate flame, each detector synchronously acquires barrel detection signal;Through compressed sensing reconstruction algorithm combined with convex optimization solving method, flame two-dimensional projection image under each view angle is recovered;Using adaptive histogram equalization, combined with filtering method, the calculation ghost imaging noise is inhibited, and two-dimensional projection image preprocessing is realized;Based on multi-view geometric projection relationship, construct tomographic projection matrix, use algebraic reconstruction technique combined with full variation regularization iterative algorithm to solve three-dimensional voxel distribution, and the flame front geometry three-dimensional structure is reconstructed.The application uses single-pixel detector to replace traditional area array camera, with the advantages of low cost, strong ability to resist harsh environment, high time resolution, suitable for high space-time resolution three-dimensional measurement.

The invention discloses an industrial operation data filling and repairing method and device based on dynamic graph learning, equipment and a medium, and relates to the technical field of data processing, and the method comprises the steps: constructing a non-convex low-rank regularization term through introducing a truncated SCAD penalty function, so as to protect main signal energy; a graph attention neural network is utilized to construct dynamic space correlation evolved along with time among dynamic graph total variation regularization item capture variables, an adaptive graph wavelet contraction mechanism is combined to distinguish mutation signals and random noise in a multi-scale frequency domain, and finally an alternating direction multiplier method is adopted for iterative solution. According to the method, the non-convex low-rank regularization item of the data is accurately reserved, dynamic association of the industrial data is adapted, effective abrupt change and noise are distinguished, and the precision and adaptability of data filling and repairing are improved.

The application discloses a hyperspectral anomaly detection method and system based on tensor multi-subspace learning, and the method comprises the following steps: acquiring hyperspectral image tensor data of a detection area; performing waveband-by-waveband normalization processing; decomposing into an anomaly tensor, a noise tensor and a structural background component; designing a non-convex tensor correlation total variation regularization; constructing an iterative sparse weight tensor; adopting a structured-norm; constructing a robust dictionary tensor; establishing an anomaly detection model; adopting an effective iterative updating algorithm based on an alternating direction multiplier method to perform optimization, acquiring an optimal anomaly tensor; and performing detection on the obtained optimal anomaly tensor to generate an anomaly detection result image. The application realizes effective separation among the background, the anomaly and the noise by respectively performing regularization constraint optimization on the background component, the anomaly component and the noise component and constructing a robust background dictionary.

A method is described for performing full waveform inversion with partial variation regularization on seismic data to generate the multi-dimensional map of physical properties of the earth's subsurface. The method must be executed by a computer system.