5results about How to "Guaranteed sparsity" patented technology

This invention belongs to the field of sensors and signal processing, specifically relating to a wavelet kernel-scale sensitivity-guided denoising method for abrasive particle induced voltage signals. The method includes: acquiring abrasive particle induced voltage signals and performing harmonic cancellation to obtain a preprocessed signal; constructing a wavelet kernel function and calculating a kernel-scale guided spectrum using it and the preprocessed signal; constructing a sparse joint denoising model based on the kernel-scale guided spectrum; processing the sparse joint denoising model to obtain a convex optimization objective function; solving the convex optimization objective function using an adaptive step-size gradient descent method and an adaptive iterative shrinking threshold method to obtain a weight vector characterizing the distribution of abrasive particle characteristic signals; binarizing the weight vector characterizing the distribution of abrasive particle characteristic signals to obtain a feature indicator vector; performing a Hadamard product between the feature indicator vector and the preprocessed signal, followed by low-pass filtering to obtain a denoised signal. This invention can adaptively and non-destructively enhance and denoise abrasive particle characteristic signals under strong interference environments.

The application provides an OTFS fractional Doppler multi-target detection method based on group sparsity, and the implementation steps are as follows: initializing parameters; constructing and solving a group sparse signal recovery problem in two stages of coarse estimation and fine estimation; and obtaining a multi-target detection result through the two-stage solving result. The application constructs and solves a group sparse signal recovery problem in two stages of coarse estimation and fine estimation, calculates the detection result of the multi-target through the real number domain channel gain of the two-stage solving, utilizes the complex number characteristics of the real number domain channel gain, maintains the group sparsity and non-sparsity within the group of the time delay-Doppler domain channel gain, and avoids the defect that the amplitude of the non-target position is increased due to interference and noise. In the group sparse signal recovery, the channel gain of other channels is updated through iteration, and the signal components corresponding to other channel gains are subtracted, so that the interference between targets is gradually eliminated, and the detection precision is improved.

The application discloses a kind of AC / DC hybrid distribution network power flow calculation method and device considering incomplete LU decomposition preconditioning, and relates to the field of smart grid.The method comprises the following steps: establishing active / reactive control equation based on VSC steady-state equation;Based on Kirchhoff's current law, establish AC / DC node power imbalance equation;Taylor expansion is carried out on the above equation, and high-order terms are ignored, to obtain linearized unified power flow equation set and matrix form Jacobian matrix;Incomplete LU decomposition preconditioning is carried out on Jacobian matrix, and stable double-conjugate gradient method is used to solve, to obtain power flow solution.The application establishes unified linearized power flow model, combines incomplete LU decomposition preconditioning and stable double-conjugate gradient method, effectively reduces the sensitivity to initial value, improves the robustness and convergence speed of complex AC / DC hybrid system power flow calculation.

The invention discloses a graph neural network enhanced combined personalized federal recommendation method, and belongs to the crossing field of federal learning and recommendation systems. The problem that the personalized recommendation effect is limited due to the fact that hidden information among users cannot be fully utilized due to privacy protection in an existing recommendation method is solved. The method comprises the following steps: constructing a user relation graph on a server by using locally updated project embedding without accessing a user interaction record; personalized items are embedded in different devices for local fine tuning, and global item embedding is obtained through calculation between user specific item embedding; the sparse global view is encouraged to save the communication cost of federated learning, two regularizers are used to ensure complementation of the two views, and finally, the weight is dynamically adjusted to realize combination personalization; a differential privacy technology is adopted, and privacy protection based on data locality is realized according to a data locality principle. According to the invention, on the premise of ensuring privacy security, the personalized performance and communication efficiency of the recommendation system are improved.

The invention belongs to the technical field of natural language processing and computer vision crossing, and discloses a multi-modal topic modeling method based on semantic consistency driving, which comprises the following steps: step 1, constructing a text-image multi-modal corpus and preprocessing; step 2, obtaining vector representation; step 3, creating a theme inference network; and step 4, training the topic inference network by taking the Dirichlet prior loss of the comparative learning loss, the multi-modal topic alignment loss and the energy loss on the multi-modal topic distribution as a joint optimization target, and completing cross-modal topic alignment and joint topic modeling. According to the method, the shared combined topic space is constructed, the topic distribution centrality of the same semantic samples is improved, the topic distribution overlapping degree of different semantic samples is reduced, it is ensured that the topic distribution of the text and the topic distribution of the image keep the corresponding relation statistically, and combined modeling and semantic association of the text and the image in the unified topic space are achieved.