8 results about "Matrix norm" patented technology

The present application provides a channel estimation method and a related apparatus, improving the accuracy of channel estimation. The method comprises: a terminal device receiving a first signal, the first signal being a signal received by the terminal device after a second signal sent by a network device passes through a wireless channel; receiving first information, the first information being used for indicating a first channel estimation parameter, and the first channel estimation parameter including at least one of the following: a matrix norm of a first expression and an iteration step size, wherein the first expression is determined by a precoding matrix, the first signal, and a kth channel estimation value, at least one of the matrix norm and the iteration step size is determined on the basis of the precoding matrix, k is a positive integer less than or equal to T, T is a positive integer, and T is the number of iterations of channel estimation; and on the basis of the first signal and the first channel estimation parameter, performing channel estimation T times to obtain a channel estimation result.

The application discloses a method for analyzing the interaction of multiple grid-connected inverters based on matrix norm theory, and aims at the interaction between grid-connected inverters, takes a power distribution system containing two grid-connected inverters as an analysis object, establishes an equivalent grid-connected model containing two inverters based on the Norton theorem, derives a transfer function matrix of a control system, and quantitatively analyzes the relationship between the interaction between inverters and the system frequency, equivalent grid-connected inductance and controller combination mode based on the spectral norm and condition number of the transfer function matrix, so as to provide a reference for the coordinated control of the inverters. A simulation model is built in Matlab / Simulink for analysis. The application provides a simple and effective method for analyzing the interaction of inverters, and provides a theoretical method for the coordinated control of the inverters.

A data processing device (10) that selects a feature quantity from data possessing a tree structure in feature quantities thereof, said data processing device (10) comprising: a Gram matrix norm calculation unit (11) that calculates a norm per each row of a Gram matrix of data imparted thereto; a parameter norm calculation unit (12) that acquires the norm of a parameter which is being optimized; a node upper bound calculation unit (13) that calculates an upper bound for the norm of the parameter, corresponding to a node in the tree structure; a node calculation pruning unit (14) that eliminates unnecessary calculation from among calculation corresponding to nodes in the tree structure and updates the parameter on the basis of the calculation result of the upper bound; a momentum calculation unit (15) that imparts a momentum to the parameter; and a convergence determination calculation unit (16) that determines convergence of the optimization on the basis of the parameter.

This invention discloses a bolt loosening location method and system based on environmental vibration and multi-scale PNN. The location method includes the following steps: S1: Establishing a refined finite element simulation model of the transmission tower and dividing it into multi-scale hierarchical substructures; S2: Simulating bolt loosening conditions in the finite element simulation model and extracting operational modal analysis results; S3: Calculating the frequency change ratio and modal compliance residual matrix norm, and constructing a multi-source damage-sensitive feature vector after weighted fusion; S4: Constructing and training a probabilistic neural network with an adaptive smoothing factor adjustment mechanism; S5: Acquiring on-site environmental vibration data and identifying modal parameters; S6: Extracting and calculating the measured data of the tower using the random subspace identification method, and inputting it into the trained probabilistic neural network to obtain the location results of the main bolt loosening area. This method achieves accurate bolt loosening location through scientific substructure division and multi-source fusion strategies, providing a reference for the safe operation and maintenance of tower bolts.

The application provides a radionuclide activity calculation method based on a Padé approximation considering multistage decay, which comprises the following steps: S1, constructing a complete decay matrix containing all nuclides; step S1 comprises the following steps: S 11 , receiving a decay chain data structure as input, constructing an n*n square matrix A; S 12 , initializing all elements of the entire matrix to zero values; S 13 , setting the element of the ith row and the ith column to the negative value of the decay constant of the ith nuclide, that is, A(i,i)=; S 14 , traversing all possible nuclide pairs (i,j); S2, calculating the matrix exponential by using a matrix norm driven adaptive scaling algorithm combined with a Padé approximation matrix; S3, performing corresponding radionuclide activity calculation according to the calculation mode. The application avoids truncation error by constructing a complete decay matrix and considering the contribution of all nuclides. The adaptive scaling algorithm combined with the Padé approximation matrix is used to solve the rigid system matrix calculation, which effectively avoids numerical overflow and rounding error, and guarantees the stability of the calculation process.

A data processing device (10) comprises a Gram matrix norm calculation unit (11), a lower bound calculation unit (12), a threshold value update unit (13), an upper bound calculation unit (14), and a candidate set update unit (15). The Gram matrix norm calculation unit (11) calculates a norm for each row of a Gram matrix of data including a plurality of parameters. The lower bound calculation unit (12) calculates a lower bound of an absolute value of each of the parameters on the basis of the norm. The threshold value update unit (13) updates a threshold value on the basis of the lower bound. The upper bound calculation unit (14) calculates an upper bound of the absolute value of each of the parameters on the basis of the norm. The candidate set update unit (15) updates a candidate set, which is at least a portion of the parameters, on the basis of the lower bound, the threshold value, and the upper bound.

The application relates to the technical field of artificial intelligence model optimization, and discloses a multi-granularity dynamic pruning method and system for a generative AI model. The system comprises a model state acquisition module, which acquires intermediate layer output in real time through a probe, constructs a feature information set of neuron activation distribution, weight matrix norm and connection topology, and outputs a sparsification risk value based on the feature information set, an initial sparseness parameter and a real-time computing resource state; a pruning planning collaborative analysis and screening module generates a multi-granularity pruning scheme when the risk value exceeds a threshold value, calculates a collaborative interference value of an accuracy recovery operation and screens an optimal scheme; and a pruning strategy execution feedback module updates model parameters and generates a log feedback to a management terminal. The system realizes adaptive structure optimization and resource scheduling of a generative model in an inference process.

A method for reducing computational complexity in determining DoA is implemented by a radar system receiving multiple reflection signals from a target object. The method includes: establishing a signal matrix based on the reflection signals; performing recursive computation for K times, wherein for a kth time, the recursive computation includes selecting a most relevant atom from the signal matrix, obtaining a kth row selection matrix and a kth column selection matrix for the most relevant atom, obtaining an optimal sparse matrix having a smallest matrix norm with the signal matrix, and obtaining a kth residual based on the optimal sparse matrix, and when k<K, performing the recursive computation for a next time; and after the Kth time of recursive computation, outputting a Kth row selection matrix and a Kth column selection matrix to obtain multiple DoAs of the target object.