16 results about "Singular matrix" patented technology

The invention relates to a matrix decomposition device and method based on a memristor cross array, which are suitable for efficient hardware implementation of singular value decomposition (SVD), the memristor cross array receives a conductance value mapped by a Grubrum matrix constructed by a to-be-decomposed matrix and a bit line input voltage converted by a bit line column vector, and outputs a corresponding current; converting the corresponding current into a voltage vector; performing normalization processing on the voltage vector to obtain a normalized vector; judging whether convergence occurs or not; performing normalization processing on the steady-state voltage vector to obtain a final output vector; and according to the final output vector, main characteristic values are extracted based on a first normalization circuit, and main singular values and corresponding singular matrixes are calculated. Compared with the prior art, the high parallel computing characteristic of the memristor cross array is utilized, efficient operation and hardware acceleration of matrix decomposition are achieved, the method has the advantages of being low in power consumption, high in speed and good in expansibility, and the method is suitable for application scenes such as artificial intelligence, signal processing and large-scale matrix operation.

The application belongs to the technical field of scientific and technological information processing, and provides a method and system for determining a cause-effect relationship based on a scientific and technological information analysis knowledge graph. The method comprises the following steps: determining a first verb, a second verb and a scientific and technological information text segment to be processed, performing vector conversion to obtain a first verb embedding vector, a second verb embedding vector and a first sentence embedding vector, performing vector merging and attention operation to obtain a first transformation matrix, performing first transformation processing on the first transformation matrix to obtain a second sentence embedding vector; after performing second transformation processing on the first sentence embedding vector and the second sentence embedding vector, performing SVD decomposition processing, inputting the obtained left singular matrix into a verb cause-effect relationship prediction model, and outputting a cause-effect relationship analysis result of the first verb and the second verb. The application can focus on learning relevant key information of the first verb and the second verb, reduce information redundancy, and improve the classification accuracy of the cause-effect relationship.

The compression device (101) is configured to include: a matrix acquisition unit (1) that sequentially acquires a partial feature matrix for each of a plurality of partial images into which an image is segmented; and a first global matrix calculation unit (3) that performs singular value decomposition on the first partial feature matrix acquired by the matrix acquisition unit (1), i.e., the first partial feature matrix, and calculates a first global singular value matrix containing the singular values ​​of the first partial feature matrix as the first global singular value matrix, and a first global right singular matrix containing the right singular vectors corresponding to the singular values ​​of the first partial feature matrix as the first global right singular matrix. In addition, the compression device (101) includes: an object matrix calculation unit (4) that calculates a matrix that is the object matrix of the bth singular value decomposition, which includes the bth partial feature matrix obtained by the matrix acquisition unit (1) (b is an integer of 2 or more), the (b-1)th global singular value matrix, and the (b-1)th global right singular matrix; and a second global matrix calculation unit (5) that performs singular value decomposition on the object matrix of the bth object matrix calculated by the object matrix calculation unit (4), calculates the singular value matrix of the object matrix of the bth object matrix that includes the singular values ​​of the object matrix of the bth object matrix as the global singular value matrix of the bth object matrix, and calculates the right singular matrix of the object matrix of the bth object matrix that includes the right singular vector corresponding to the singular value of the object matrix of the bth object matrix as the global right singular matrix of the bth object matrix.

The application relates to the technical field of data processing, and discloses a multi-dimensional data intelligent management method for regional safety management software, which comprises the following steps: obtaining a left singular matrix, a right singular matrix, a diagonal line matrix and eigenvectors of the left singular matrix and the right singular matrix of a two-dimensional driving data matrix; obtaining a first kind of reservation degree, which is divided into time periods; obtaining an adjusted voting value and marking important time periods; obtaining an influence weight value, determining a first eigenvector, obtaining a reconstructed two-dimensional driving data matrix of the first eigenvector, obtaining a reconstructed influence weight value according to the reconstructed two-dimensional driving data matrix, and then obtaining a feature weight of the first eigenvector; and determining data that needs to be reserved and stored according to the feature weight, so as to realize multi-dimensional data intelligent management for the regional safety management software. The application aims to solve the problem of low multi-dimensional data intelligent management efficiency caused by excessive redundant data.

According to an aspect, there is provided an apparatus configured to perform the following. The apparatus obtains an approximate effective channel matrix for a radio channel between the apparatus, acting as a transmitter, and a receiver. The apparatus calculates an eigenvalue decomposition, EVD, of a matrix product of the approximate effective channel matrix and a conjugate transpose of the approximate effective channel matrix and determines, based on the EVD, a left singular matrix of a singular value decomposition, SVD, of the approximate effective channel matrix and a diagonal matrix of singular values of the SVD of the approximate effective channel matrix. The apparatus calculates eigenvectors of the approximate effective channel matrix based on the approximate effective channel matrix and the left singular matrix of the SVD.

The invention belongs to the field of wireless communication, and discloses a pre-coding method for a multi-user MIMO-OTFS system. The method comprises the following steps: establishing an optimization problem according to a pre-coding matrix, a complete channel matrix, a system noise variance, a user weight and a total transmitting power constraint in a multi-user MIMO-OTFS communication model; a complete channel matrix in the optimization problem is approximated to be an LoS path approximation channel matrix, a matrix SVD is obtained, precoding matrix design is carried out based on a right singular matrix in an SVD decomposition result, and a directional component and a power component are included; substituting the designed precoding matrix into an optimization problem, constructing a sub-optimization problem about the power component and the complex gain of each sub-channel, optimizing the transmitting power of each sub-channel, and outputting the optimal power component; and constructing a final precoding matrix according to the direction component and the optimal power component. According to the method, efficient utilization of system resources can be realized, and the calculation complexity can be greatly reduced while the WSR performance is ensured.

The embodiments of the present specification disclose a kind of based on differential privacy's data to be shared processing method, device, equipment and medium. Scheme can include: obtaining original data matrix;The original data matrix contains data to be shared;The data to be shared contains privacy information;The singular value decomposition is carried out to the original data matrix, and left singular matrix, diagonal matrix and right singular matrix are obtained;Based on the preset perturbation value, the diagonal matrix is perturbed, and the perturbed diagonal matrix is obtained;Based on the left singular matrix, the perturbed diagonal matrix and the right singular matrix, perturbed data matrix is obtained;Using the preset Gaussian random matrix, the perturbed data matrix is randomly transformed, and covariance matrix is obtained;The covariance matrix is used to publish the data to be shared.

The application discloses a kind of amplitude and phase high-precision estimation method and device suitable for fast dynamic change signal, the method of the application includes the discrete signal sequence of continuous signal sampling, constructs Hankel matrix and decomposes left singular matrix U, diagonal matrix D, right singular matrix V;The estimated value of the number of frequency components p is estimated by D, combined with extracting the subspace of right singular matrix V and using least square method to obtain diagonal matrix Q;The damping coefficient of frequency component is calculated by Q and constructs system matrix A;The dynamic phasor coefficient vector S is calculated by A and discrete signal sequence using least square method;The amplitude and phase of continuous signal are calculated by S and damping coefficient.The application aims at solving the problem that the main lobe overlap of dense harmonic / interharmonic with small frequency interval in power system causes spectral leakage interference, extremely serious even can submerge part of main spectrum line, and then lead to the accuracy of spectral leakage affecting the amplitude and phase estimation of dynamic change signal.

The present invention relates to an apparatus, a computer-readable storage medium, a method, and a system for a cryptosystem based on singular matrices. In particular, an encryption scheme of the cryptosystem comprises a generation of singular matrices representing the whole or a part of an information block to obtain a ciphertext and a private key to be sent to an intended receiver. In similar manner, a decryption scheme of the cryptosystem comprises an inversion of singular matrices representing the whole or a part of said ciphertext to obtain the information block from a sender. Certain embodiments comprising of an at least one processor, an at least one memory, an at least one storage medium, an at least one communication implement, and an at least one display implement is used to implement the present invention.

A method for beamforming is disclosed. The method performed by a beamformee may include receiving, from a beamformer, a sounding packet, computing estimated channel matrices based on the sounding packet, computing singular value decomposition (SVD) based on the estimated channel matrices, computing normalized right singular matrices based on SVD, computing precoding matrices based on a decompression scheme and the normalized right singular matrices, computing diagonal unitary matrices based on a product of the channel matrices and the precoding matrices, computing, during a beamforming phase, modified beamformed channel matrices based on the diagonal unitary matrices, and receiving, from the beamformer, a beamformed data transmission based on the modified beamformed channel matrices.

The application discloses a kind of underwater target positioning methods based on underwater acoustic particle velocity polarization processing.The method includes: arranging vector hydrophone array and carrying out underwater acoustic signal acquisition, and write the vector received signal of vector hydrophone array into tensor form;The tensor received signal of vector hydrophone array is carried out high-order singular value decomposition and the noise subspace of each dimension information matrix is solved by truncation to n module expansion left singular matrix;Array manifold matrix of vector hydrophone array is constructed and is written into polarization array manifold tensor;Azimuth-polarization two-dimensional spectrum search is carried out using array manifold tensor and noise subspace, and the azimuth and polarization parameters of the detected target are obtained from two-dimensional spectrum peak value.The underwater target positioning method provided by the application can accurately estimate the polarization parameters and the angle of arrival of incident signal, which helps to complete the underwater acoustic signal information based on frequency, wave number, mode and other characteristics by means of polarization parameters, and significantly improves the underwater target detection capability.

The invention provides a data order reduction method and equipment for a digital twin model of power grid equipment. According to the implementation scheme, a sampling data matrix is constructed based on a temperature sampling data set of power grid equipment; carrying out main mode interception on the sampling data matrix and each sub-matrix to obtain a global basis and each local basis, carrying out combination and orthogonalization on the global basis matrix and the local basis matrix to obtain an orthogonalized mode basis matrix, projecting the sampling data matrix to the orthogonalized mode basis matrix, and carrying out singular value decomposition to obtain a singular value decomposition matrix; obtaining a first orthogonal matrix, a first singular matrix and a first transpose matrix; and performing main modal interception on the first orthogonal matrix based on the orthogonalization modal basis matrix to obtain a second orthogonal matrix, performing singular value reconstruction on the second orthogonal matrix, the first singular matrix and the first transpose matrix to obtain a reduced-order temperature data matrix, and performing prediction through a power grid equipment digital twin model to obtain a fault prediction result of the power grid equipment. According to the invention, the state prediction efficiency and accuracy of the power grid equipment can be improved.

This invention proposes an FTM-corrected non-line-of-sight ranging method based on matrix-bundled CSI multipath parameter estimation. First, Channel State Information (CSI) data from WiFi devices is collected, an augmented matrix is ​​constructed, and singular value decomposition is performed to obtain a left singular matrix and singular value vectors. Second, the average power of the CSI is calculated and transformed to the logarithmic domain to determine the maximum number of candidate paths. Based on the singular value vectors, a Minimum Description Length (MDL) criterion is constructed to adaptively estimate the number of effective paths. Then, the propagation delay of each path is obtained using the matrix-bundled algorithm, a Vandermonde matrix is ​​constructed, and the received power of each path is estimated using the least squares method. Finally, the power ratio of the direct path is calculated as a reliability factor. If it is below a threshold, the second strongest path is selected from the reflection paths with power greater than the direct path. If there is only one such path, it is selected. The average delay difference between the reflection path and the direct path is calculated using power weighting, and this delay difference is used to compensate for the FTM receiving timestamp to obtain the corrected ranging distance. This invention does not require a preset number of paths, is highly adaptive, and significantly improves the accuracy of FTM ranging in non-line-of-sight environments.

The invention provides a reconfigurable matrix inversion system and method supporting multiple matrix decomposition modes, and the system selects a decomposition process according to the attributes of a matrix through a top-layer control module, enables the system to process various non-singular matrixes, and enlarges the application range of hardware inversion. After the process is determined, by multiplexing a triangular matrix inversion module and a matrix multiplication module which are necessary in Cholesky and LU processes, the logic and area resource overhead caused by simply integrating two sets of independent hardware to support the two algorithms is avoided. Meanwhile, the data processing module is responsible for transposition, transformation and rearrangement operation, the calculation module is liberated from a complicated data formatting task, the calculation module can concentrate on core arithmetic operation, and the utilization efficiency of calculation resources and the overall performance of the system are improved. Therefore, according to the system, through multiplexing of hardware modules and reasonable division of tasks, effective balance between calculation performance and hardware cost is achieved; in the aspect of functions, two matrix inversion accelerators which are reconstructed into Cholesky inversion and LU inversion are supported, and high performance, universality and expandability of matrix inversion are guaranteed.

The invention discloses an ill-conditioned matrix SVD decomposition preprocessing method and device based on deep learning, and a medium, and belongs to the technical field of numerical algebra and deep learning. The method comprises the following steps: constructing an iterative deep neural network learning framework, extracting matrix features by using a convolutional layer, and constructing an orthogonal matrix through a House holder reflection decomposition method; training the network by using a mixed loss function, forcing the network to output an approximate diagonal matrix by punishing off-diagonal elements, and performing multiple rounds of iterative optimization by taking a learning result as the input of a new round of training; a precondition is constructed based on a matrix obtained through training, an original ill-conditioned linear equation set is preprocessed, and a preconditioned equation set is solved through an iterative algorithm. The method can be adapted to an ill-conditioned general matrix without a special structure, effectively solves the problems of insufficient approximation precision and poor generalization ability of a traditional method, and remarkably improves the numerical stability, convergence efficiency and calculation precision of high-dimensional ill-conditioned matrix solution.

This invention discloses an underwater sound source matching field localization method based on higher-order singular value decomposition of tensor signals, comprising the following steps: Step 1, reconstructing the spatiotemporal frequency multidimensional data recorded by the vertical receiving array into tensor data; Step 2, calculating the tensor signal subspace based on higher-order singular value decomposition and singular matrix truncation. Since singular value decomposition is performed on the matrix expanded in each dimension of the tensor, noise can be further suppressed, resulting in a more accurate signal subspace and improving the accuracy of target localization; Step 3, specifying the receiving array configuration and inputting marine environmental information such as hydrological conditions, seabed topography, and seabed sediment. This invention, while accurately estimating the target location, has stronger background suppression capabilities and outperforms conventional matching field processing.