48 results about "Kronecker product" patented technology

The invention provides a multi-parameter fusion organic matter type comprehensive discrimination method and system. The method comprises a multi-modal feature fusion method based on heterogeneous discrete state and continuous distribution state data; constructing a mechanism by combining a composite discriminant function of tensor product and cross convolution operation; the invention discloses a dynamic threshold controlled iterative weight optimization system. According to the method, a discrete data set of a unified coordinate system is generated through a rigid-non-rigid mixed registration algorithm for density compensation of a Vorono i graph, a continuous distribution data set is generated by adopting anisotropic kernel function interpolation, and a dual-channel feature extraction architecture is constructed to realize parallel feature extraction of a pore topology and a concentration field. And establishing a composite discrimination function containing a Kronecker product and a three-dimensional cross convolution integral, and realizing iterative convergence of the multi-modal characteristic residual error through an alternating direction multiplier method and a dynamic quantile threshold.

The invention relates to the technical field of artificial intelligence, and discloses a large model construction method and device, equipment and a medium, and the method comprises the steps: carrying out the partitioning of a Transform weight matrix of a target model, so as to form a plurality of sub-matrixes; performing low-rank approximation on each sub-matrix through two-dimensional Kronecker decomposition to obtain a Kronecker product of the two small matrixes respectively corresponding to each sub-matrix; parameters of all the small matrixes are frozen, and additional parameters corresponding to all the small matrixes are obtained by using LoRA, so that a two-dimensional Kronecker-LoRA compression model is obtained; and taking the target model as a teacher model, and training the two-dimensional Kronecker-LoRA compression model by using the data set to obtain a large model. According to the method, the parameter scale and the resource demand of an existing large model can be reduced, an applicable large model is formed, meanwhile, the performance loss is effectively reduced, the balance between the high compression ratio and the high precision is achieved, dynamic reasoning and the image processing flow are deeply matched, and wide deployment on edge equipment is easy.

The application discloses a scanning radar two-dimensional super-resolution imaging method based on Bayesian sparse learning, first constructs a bearing-pitch two-dimensional scanning radar signal model, then, according to a maximum posteriori criterion under a Bayesian framework, establishes a sparse optimization target function about target scattering and environmental noise, finally, utilizes a conjugate gradient algorithm and Kronecker product properties to accelerate iterative estimation of target scattering and noise power, realizes adaptive sparse two-dimensional super-resolution imaging of the scanning radar. The method solves the problems of high complexity and poor noise robustness of prior art means, compared with prior art two-dimensional super-resolution methods, has lower calculation complexity, is more robust, has excellent noise adaptive capacity, and can realize two-dimensional scanning radar super-resolution imaging under a low signal-to-noise ratio condition.

The invention belongs to the technical field of power systems, and particularly relates to a new energy limited power prediction method based on a multi-observation hidden Markov chain. Comprising the following steps: preliminarily constructing a hidden Markov chain model, and determining observable variables; preliminarily constructing an observation matrix, and quantifying the influence of observable variables on external transmission power; constructing a Kronecker product of the state set; and obtaining an equivalent observation probability matrix in multiple observation chains through a Kronecker product by using the observation matrix, and proposing a time sequence model of the outgoing power of the new energy station. According to the time sequence production simulation method, the mapping relation between the hidden state chain and the observable chain is described through the hidden Markov model, the influence of various random factors is described, the influence of factors such as absorption space on external power transmission can be considered, and the reliability and the sharpness of the time sequence production simulation method are remarkably improved.

The application provides a cylindrical array sonar system and a target detection method based on high-resolution beam forming. The method comprises the following steps: converting a beam forming weight vector of a received sound wave signal into a Kronecker product, combining a joint probability density model to construct a negative log-likelihood function, and optimizing a vector in an azimuth dimension and an elevation dimension through a preset direction constraint condition to form a high-resolution beam; performing correlation analysis on echo signals reflected by a target at different azimuth angles and elevation angles and on transmitted sound wave signals at different time delays; constructing a three-dimensional matrix containing a distance, an azimuth angle and an elevation angle according to a correlation analysis result, performing normalization processing on the three-dimensional matrix to eliminate signal intensity differences, and outputting a target detection positioning result. Through the combination of Kronecker integral decomposition and three-dimensional matrix modeling, the application realizes high-resolution joint positioning detection of a target in an azimuth angle, an elevation angle and a distance dimension, effectively eliminates signal intensity differences, and improves positioning accuracy.

The invention discloses a high-order extended Kalman filtering method for a nonlinear system. The method comprises the following steps: S1, establishing a nonlinear system composed of a linear state model equation and a nonlinear measurement model equation; s2, establishing a calculation algorithm for solving the statistical characteristics of the Kronecker product type composite random variables; s3, analyzing the high-order characteristics of the state estimation error of the nonlinear system by using the algorithm established in the step S2; s4, combining the known measurement information with the high-order features of the errors, designing and optimizing extended Kalman filtering, and gradually estimating the next state in the nonlinear system; and S5, circulating the process of the step S4, estimating the state of the nonlinear system step by step until the state estimation of the set step number is completed, and obtaining the state estimation value of each step. According to the method, the estimation model in the filtering process is adaptively adjusted based on the high-order statistical characteristics of the system state estimation error and the known measurement information, so that the stability and the precision of the filter are improved.

The invention provides a cylindrical array sonar system and a target detection method based on high-resolution beam forming. The method comprises the steps of converting a beam forming weighted vector of a received sound wave signal into a Kronecker product, constructing a negative logarithm likelihood function in combination with a joint probability density model, and optimizing vectors of an azimuth dimension and a pitching dimension through a preset direction constraint condition so as to form a high-resolution beam; correlation analysis is carried out on echo signals reflected by the target at all azimuth angles and pitch angles and transmitted sound wave signals under different time delays; and according to a correlation analysis result, constructing a three-dimensional matrix including the distance, the azimuth angle and the pitch angle, carrying out normalization processing on the three-dimensional matrix so as to eliminate a signal intensity difference, and outputting a target detection positioning result. According to the invention, through combination of the Kronecker integral solution and three-dimensional matrix modeling, high-resolution joint positioning detection of the target in the azimuth angle, the pitch angle and the distance dimension is realized, the signal intensity difference is effectively eliminated, and the positioning precision is improved.

A UE that includes one or more non-transitory computer-readable media storing one or more computer-executable instructions for a PUSCH transmission and at least one processor coupled to the non-transitory computer-readable media is provided. The processor is configured to execute the one or more instructions to cause the UE to determine that an OCC is applied to the PUSCH transmission based on an indication received from a BS; determine the spreading factor of the OCC; determine the number of RBs allocated to the PUSCH transmission; determine the number of a first set of modulation symbols before applying the OCC to each OFDM symbol as a function of the number of allocated RBs and the spreading factor, and determine a second set of modulation symbols after applying the OCC to each OFDM symbol by performing a Kronecker product of the first set of modulation symbols and a sequence of the OCC.

The application relates to the technical field of time series prediction, in particular to a data center cooling system return water temperature prediction method and device, wherein the method comprises the following steps: collecting initial operation data of a cooling system in a data center; pre-processing the initial operation data to obtain final operation data meeting certain conditions; inputting the final operation data into a pre-trained time series prediction model to obtain a cooling system return water temperature prediction result, wherein the time series prediction model has a Kronecker product layer, a kernel matrix layer and a multilayer perception layer. Thus, the problems that, in the related art, model parameters are large, blackboxing is caused by expressing nonlinear characteristics through a complex activation function, it is difficult to directly express cross-variable relationships, the effectiveness and interpretability of the model in actual application are limited, and the understanding and trust of a user for a model prediction result are affected are solved.

The application discloses a compression matrix splitting design method, system, device and storage medium, and belongs to the field of image compression. First parameter matrix and second parameter matrix are generated by acquiring picture data; a process matrix is generated according to the picture data, the first parameter matrix and the second parameter matrix; the process matrix is processed by a preset deep frequency domain neural network to generate correction data; a convergence value is acquired, and one of the following steps is executed according to the correction data and the convergence value: if the correction data is greater than or equal to the convergence value, the updated first parameter matrix and the updated second parameter matrix are generated by deep frequency domain neural network processing, and the data acquisition step is jumped to; or, if the correction data is less than the convergence value, the current first parameter matrix and the second parameter matrix are calculated by Kronecker product operation to generate a compression matrix. A compression matrix design scheme which can be designed for different compression data is effectively provided.

In accordance with an example aspect of the present disclosure, data is encoded or decoded using a polar code, where a generation matrix of the polar code includes rows of a polar transformation matrix formula (I), where is an N * N-bit inversion matrix, where N = 2n is a non-negative integer, and where formula (II) is an n-fold Kronecker product of formula (III), where N = 2n is a non-negative integer. The index set of the rows forming the polarization transformation matrix of the generating matrix is set A.

The application discloses a bidirectional discriminative feature alignment hierarchical knowledge fusion method, comprising the following steps: inputting samples into a teacher model to obtain a teacher soft prediction result set, and inputting unlabeled image data into an initial student model to obtain a student model prediction result; extracting the last layer feature and inputting the last layer feature into a common feature extractor to obtain a common feature; through a discriminative centroid clustering strategy, different class centers are far away from each other, and each teacher common feature is close to the same class center; the fuzziness of the teacher soft prediction result is measured by entropy impurity, reliable source domain features and target domain features are constructed, pseudo labels are respectively subjected to Kronecker product with the common features of the source domain and the common features of the target domain for discriminative mapping, and feature alignment is performed through a maximum average difference method; a total loss function is constructed, and the initial student model is trained through the total loss function to obtain a comprehensive student model capable of accurate classification.

The invention provides a memristor-based fixed-point matrix FFT (Fast Fourier Transform) calculation method, which belongs to the technical field of signal processing and image processing, and comprises the following steps of: firstly, constructing a non-linear voltage controlled memristor model, simulating a state conversion behavior in logic operation through a dynamic regulation mechanism, and realizing the function of a basic logic gate; according to the method, an FFT algorithm based on matrix block optimization is designed, FFT calculation is decomposed into small-scale matrix operation units, calculation efficiency is optimized in combination with a Kronecker product and an arrangement matrix, FFT operation with higher point number is expanded in a recursion mode, meanwhile, a full adder and a multiplier are designed based on a memristor, efficient fixed-point number operation is achieved, and finally, the FFT calculation efficiency is improved. The method is applied to image FFT processing, and a processing result is obtained. The scheme of the invention has higher calculation efficiency and lower energy consumption while ensuring the calculation precision, can be widely applied to the fields of image processing, signal analysis, scientific calculation and the like, and has important practical value and application prospect.

This invention relates to the field of traffic safety technology, specifically to an efficient simulation method for quantum systems based on parallel order reduction. The method includes the following steps: receiving the physical parameters of the quantum system and simulation requirements, whereby the physical parameters include electron mass, potential well size, and initial wave packet parameters; and the simulation requirements include simulation physical time and accuracy requirements. Based on the physical parameters, a time-dependent Schrödinger equation describing the dynamic behavior of the quantum system is established, and the Schrödinger equation is rearranged into matrix form using the Kronecker product. This invention utilizes Arnoldi to reduce the order of the matrix-form Schrödinger equation. By reducing the order, the large matrix in the original space is projected into a relatively small subspace, improving the efficiency of the simulation solution. The reduced-order Schrödinger equation is solved using a time-parallel algorithm. This approach overcomes the time step limitation imposed by the CFL condition, ensuring that a stable solution can be obtained with fewer time steps.

The invention provides a robust super-directional beam forming method and system based on a Kronecker product. Inherent inter-frame correlation between adjacent frames of a voice signal in a short-time Fourier transform domain is introduced into robust super-directional beam forming: firstly, a signal model observed by a microphone array is expressed as a Kronecker product form containing a steering vector and an inter-frame correlation vector, and the steering vector is determined by a geometric structure of the array; the inter-frame correlation vector can be obtained by adopting three methods of time-invariant long-time estimation, time-varying recursive adaptive estimation or data-driven deep learning estimation. And then designing a super-directional beam former into a Kronecker product of a time domain filter and a spatial domain filter, optimizing the time domain filter to realize maximization of white noise gain, and optimizing the spatial domain filter to realize maximization of directional gain. Compared with a traditional scheme, the noise suppression robustness is remarkably improved through combination of time-space domain optimization, and better noise reduction performance is shown in a complex acoustic environment.

The application relates to the technical field of human-computer interaction, in particular to a method and device for predicting an embodied intelligent human-computer interaction system based on motion recognition, wherein the method comprises the following steps: collecting three-dimensional coordinates of a plurality of skeleton key points of a human body and time frames corresponding to the coordinates of each skeleton joint to generate initial skeleton data; preprocessing the skeleton data to obtain final skeleton data meeting a condition; performing skeleton coding on the final skeleton data to extract at least one skeleton feature; and training a prediction model based on the at least one skeleton feature, wherein the prediction model comprises a Kronecker product layer, a Kernel Matrix layer and a multilayer perception machine layer to identify a future motion trajectory. Thus, the problem that, in the prior art, due to a large model parameter quantity, strong data dependency and difficulty in intuitively displaying the relationship between variables, the effectiveness and interpretability of the model in actual application are limited, and the trust of a user in an identification result is affected, is solved.

The application relates to a design method of a robust vector array differential beamformer, belonging to the field of sound signal processing, which comprises the following steps: setting system working parameters; transmitting a detection signal; judging whether a target signal is detected; receiving and preprocessing array signals; reconstructing full-array steering vectors by using Kronecker product operation; selecting an ideal differential beam pattern form, setting target direction distortionless response constraints and non-target direction response constraints; decoupling the system total weight vector into the Kronecker product form of spatial weight and channel weight by using the Kronecker product property, taking the white noise gain maximization as an objective criterion, and solving the optimal channel weight; constructing a weight approximation model based on the Maclaurin series expansion, solving the optimal spatial weight by using the least square method, synthesizing the system total weight vector, and realizing the design of a differential beamformer of any order. The application can significantly improve the robustness of the differential beamformer and effectively suppress spatial white noise.

The application relates to the technical field of artificial intelligence, and discloses a large model construction method, device, equipment and medium, the method comprising the following steps: performing block processing on a Transformer weight matrix of a target model to form a plurality of sub-matrices; performing low-rank approximation on each sub-matrix through two-dimensional Kronecker decomposition to obtain the Kronecker product of two small matrices corresponding to each sub-matrix; freezing the parameters of all the small matrices, using LoRA to obtain additional parameters corresponding to all the small matrices to obtain a two-dimensional Kronecker-LoRA compression model; taking the target model as a teacher model, and using a data set to train the two-dimensional Kronecker-LoRA compression model to obtain a large model. The method can reduce the parameter size and resource demand of an existing large model, form a suitable large model, effectively reduce performance loss, achieve a balance between high compression rate and high precision, adapt the dynamic reasoning and image processing process depth, and facilitate wide deployment on edge devices.

The invention discloses an access synchronization sequence construction method, a detection method and a base station access method, and the access synchronization sequence construction method comprises the steps: constructing a first initial sequence set based on a plurality of first initial sequences satisfying low correlation, constructing a second initial sequence set based on a plurality of second initial sequences meeting low correlation; the low correlation meeting comprises that the sequence itself meets the low autocorrelation and the sequences meet the low cross correlation; and performing Kronecker product operation on any two initial sequences from the first initial sequence set and the second initial sequence set to obtain a plurality of target sequences, and constructing a set of access synchronization sequences distributed to a user terminal based on the target sequences. The requirement that large-scale users access the network at the same time can be met, and high detection performance and system reliability are guaranteed.

The present invention discloses a method for estimating a time-varying underwater acoustic channel. The method comprises the following steps: (1) using the Fourier transform method to perform time offset compensation on real underwater acoustic data; (2) performing Kronecker product structural decomposition on the real underwater acoustic channel and selecting structural parameters using the Bayesian Information Criterion; and (3) using the sparse enhanced conjugate gradient method based on known structural parameters to online estimate the time-varying channel impulse response and output a multi-pulse underwater acoustic channel estimation result. The method has the advantages of utilizing the potential structured sparsity characteristics of the real underwater acoustic channel to reduce the channel impulse response parameter estimation dimension, effectively suppressing false estimation under low signal-to-noise ratio conditions, and utilizing the adaptive recursive concept to provide good online continuous estimation capability for multi-pulse time-varying underwater acoustic channels and strong noise interference resistance.

This invention discloses a method and system for smoothing quantization of large models based on learnable rotation matrix Kronecker integral solutions, relating to the field of artificial intelligence model compression technology. The method includes: constructing a search space containing Kronecker integral solutions of different numbers of matrices, with the tensor-matrix i-th modal product as the computational implementation method for each solution; adaptively selecting the optimal rotation matrix hybrid decomposition scheme for the attention modules and feedforward networks of each decoder layer of the large model through a preset scoring function and layer-by-layer greedy heuristic search; training the complete parameters of the decomposition matrix, learnable weights, activation value truncation threshold, and diagonal scaling matrix parameters of the scheme; fusing the extra parameter matrix on the weight side of the linear layer, retaining the extra parameter matrix on the activation value side, and quantizing the model based on the trained quantization parameters to generate a deployable model. This effectively solves the problems of accuracy loss and computational overhead under low-bit quantization of large models, significantly improving computational and storage efficiency while maintaining high accuracy.

The application discloses a privacy protection method, device and system based on cross-relinearization, and belongs to the technical field of privacy protection. The method comprises the following steps: negotiating homomorphic encryption parameters by a calculation executor and a data holder; acquiring, by the calculation executor, ciphertext ct1, ciphertext ct2, a cross-relinearization key and a rank reduction key sent by the data holder; calculating the Kronecker product between the ciphertext ct1 and the ciphertext ct2 by the calculation executor; performing, by the calculation executor, cross-relinearization operation, rank reduction operation and homomorphic addition operation on the Kronecker product in combination with the cross-relinearization key and the rank reduction key; and transmitting, by the calculation executor, the re-scaling result ct'' to the data holder after performing re-scaling operation on the homomorphic addition result ct', so that the data holder acquires the Hadamard product of the complex vector and the complex vector based on the re-scaling result ct''. The application not only cannot make the calculation executor acquire the Hadamard product between the complex vectors, but also improves the calculation efficiency of the Hadamard product.

The invention discloses a Bayesian neural network prior construction method based on feature tag data, and relates to the technical field of machine learning, and the method comprises the steps: carrying out the standardized preprocessing of input feature tag data, calculating a covariance matrix between features, and adding a regularization term to guarantee the numerical stability; constructing structured prior distribution of a first layer of weight of the network by using the covariance matrix, and fusing feature correlation information into a covariance structure of weight parameters through a Kronecker product; and on the basis, a complete Bayesian neural network probability model is established, posterior distribution approximation is performed by adopting a variational inference method, and finally a prediction result is obtained through sampling. According to the method, the adaptive prior distribution is constructed by using the covariance structure in the feature tag data, so that the complex relevance between the features can be effectively captured, and the structural information is fused into the regularization constraint of the model parameters.

The invention discloses a deep neural network-based dynamic load prediction method for lifting compartments of an overhead passenger ropeway. The method comprises the following steps of 1, collecting and preprocessing operation data of a plurality of lifting compartments in the overhead passenger ropeway; step 2, inputting the operation data into a Times Net network; 3, calculating a numerical difference value between adjacent time points of the initial load predicted value sequence, marking local anchor points, and constructing a cross-lift-compartment anchor point diagram; 4, calculating the disturbance intensity of the anchor point pair based on the two-dimensional lift car position index matrix, and generating a disturbance embedded tensor; 5, performing Kronecker product operation on the embedded representation tensor and the disturbance embedded tensor, and inputting the embedded representation tensor and the disturbance embedded tensor into a prediction head structure; and 6, calculating a numerical error and a trend error between the load predicted value sequence and the real load sequence, and updating the offset value. According to the method, through the TimesNet network and disturbance reconstruction, the prediction precision and robustness of the lift car load are improved.

Parameters of a probabilistic model comprising Gaussian processes are learned. A system (100) is disclosed for learning a set of parameters of a probabilistic model having layers of multiple Gaussian processes (e.g., deep GPs) from a training data set. The set of parameters includes at least induced locations for the multiple Gaussian processes, and parameters of a probability distribution approximating outputs of the multiple Gaussian processes at the multiple induced locations. The probability distribution comprises a multivariate normal probability distribution having a covariance matrix defined by a Kronecker product of a first matrix indicating similarities between the multiple Gaussian processes and a second matrix indicating similarities between the multiple induced locations. A system (200) is also disclosed for determining one or more samples of an output of the probabilistic model for a given input using the set of parameters, e.g., to determine a mean and / or uncertainty estimate for the probabilistic model.

The present disclosure relates to generating polar codes and also relates to encoding and decoding data using polar codes. A method of generating a polar code includes obtaining a first matrix that is the m-th Kronecker product of a 2×2 binary lower triangular matrix, where m = log2(M / 2), M < N, and N is the length of the polar code to be generated. A second matrix can be obtained, where the inverse matrix of the second matrix is a lower triangular band matrix. A transformation matrix for the polar code can be generated by calculating the Kronecker product of the second matrix and the first matrix. An information set I that identifies reliable bit channels for the polar code can be determined. A polar codeword of length N can be obtained using the polar code, and the polar code is decoded by iteratively applying a sliding decoding window of length M to the polar codeword, where M < N.

This invention relates to a visual pose estimation method, system, laser device, and electronic device, comprising: acquiring two different features of a target object; acquiring a connected coordinate system of a transmitting unit and a receiving unit, wherein the transmitting unit emits laser beams to the receiving unit and is located on different features; acquiring an image of laser spots formed by multiple laser beams, and transforming the quantized representation of the laser spots in the image coordinate system into a representation equation in the connected coordinate system through homography transformation; establishing a relative pose calculation model between the connected coordinate systems, proposing an optimization objective, and transforming the optimization objective function into a relational expression of a column vector consisting of a corresponding matrix and the unknown quantity through the Kronecker product; transforming a nonlinear system of equations into a linear system of equations, and calculating the solution of the linear system of equations; calculating the gradient matrix of the objective function with respect to the desired spinor using mathematical tools such as Lie groups and spinors, and obtaining the optimal solution through Newton-Raphson iteration using the solution of the linear system of equations as the initial value.

An information transmission method and a communication device generate a preamble sequence or a physical uplink control channel (PUCCH) sequence using a product of K vectors (such as a Kronecker product), K being an integer greater than or equal to 2, and transmit the preamble according to the preamble sequence, or transmit uplink control information according to the PUCCH sequence. According to the embodiment of the invention, the number of generated preamble sequences or PUCCH sequences can be increased, so that the communication requirement of the system is met, and the communication efficiency is improved. For example, for a preamble sequence, in a random access process, the number of users accessed by using the preamble sequence can be increased. For the PUCCH sequence, the capability and reliability of transmitting the uplink control information by using the PUCCH sequence can be improved.

This application provides a communication method and apparatus that can reduce the computational complexity of detecting information carrying a first bit, improve detection efficiency, and enhance communication performance. The method includes: determining a first signal based on a first sequence corresponding to the first bit information, wherein the first sequence is constructed based on the Kronecker product of a second sequence and a third sequence, the second sequence carrying the first bit of the first bit information and the third sequence carrying the second bit of the first bit information; and transmitting the first signal.

The application discloses a kind of intelligent reflecting surface auxiliary cascading surface array channel nested tensor decomposition estimation methods, first constructs cascading channel model, initialization each end guiding matrix and channel matrix in turn;Zero intelligent reflecting surface phase shift is first placed, respectively constructs the tensor model of transceiver end parallel factor decomposition, combined with three linear alternating least square method and least square method, completes two-dimensional angle estimation of transceiver end;Again control phase shift, through two linear alternating least square method, Khatri-Rao decomposition, complete intelligent reflecting surface two-way angle estimation, finally rely on Kronecker product and Khatri-Rao product model solves beam gain.The application relies on nested parallel factor decomposition, realizes four angles automatic pairing, adapts single, multi-beam scene, reduces high-dimensional calculation amount, suppresses off-grid energy leakage, without signal autocorrelation matrix and complex angle pairing, adapts super large scale surface array, improves channel estimation applicability, supports sensing integration system design.