38 results about "Matrix representation" patented technology

The invention relates to the technical field of textile production scheduling and resource allocation planning, in particular to a textile equipment scheduling management and optimization system of a textile factory, which comprises a data perception and integration module, a scheduling optimization decision module, a plan execution and equipment control module and a closed-loop feedback and self-learning module. The data sensing module collects and fuses order data, equipment operation state data and production environment data in real time, and a unified real-time data view is generated through cleaning and alignment processing; the scheduling module runs a mixed integer programming dynamic model, and minimizes the comprehensive cost and synchronously optimizes the equipment utilization rate and energy consumption in combination with rolling horizon optimization under the condition of meeting the process constraints of order delivery time limit and process dependency matrix representation; the plan execution module analyzes the scheduling instruction into an equipment executable instruction, drives equipment operation and collects execution deviation; and the closed loop module triggers rescheduling when the deviation exceeds the limit or the order is plugged. The scheduling accuracy and adaptability are improved, the cost is reduced, and efficient and stable production is guaranteed.

The invention discloses a base station positioning method and system, and relates to the technical field of indoor positioning, and the method comprises the steps: obtaining target scene space structure information and signal propagation shielding characteristics which meet preset space constraints, building a scene shielding model, determining a candidate deployment region, and eliminating a high-attenuation shielding region. Acquiring a base station position Pi and a distance Lij between a label and the base station in the candidate area, and constructing a label position solving equation containing vector and matrix representation; and determining a base station layout avoidance scheme, deploying base stations, re-collecting data, correcting distance data, substituting the corrected distance data into the equation to obtain a label accurate position by combining the condition that the matrix determinant is close to zero and a scene shielding model, and obtaining a label positioning result. The system comprises a scene modeling module, a candidate area determination module, a data acquisition and equation construction module, a base station layout optimization module, a distance data correction module and a label position solving module, and solves the problems of unreasonable layout and large distance measurement error of the existing method.

The invention discloses a seismic source positioning method considering the influence of an abnormal value and a dynamic wave velocity, and the method comprises the steps: arranging sensors to detect sound waves emitted by a seismic source, and observing the time when the sound waves reach each sensor; subtracting the control equation established based on the reference sensor from the control equation established based on the measurement sensor, and carrying out linearization and matrix representation on a nonlinear equation line obtained by subtraction; the matrix is solved, an analytic solution enabling the sum of squares of the residual errors to be minimum is found, the obtained analytic solution is substituted into an overdetermined matrix equation to obtain a fitting model, and weight estimation is conducted according to the relation between the residual errors and weights; iteration is carried out between weight estimation and overdetermined matrix solving, after optimal weight estimation is obtained, overdetermined matrix solving is completed according to the optimal weight, and a final seismic source positioning result is obtained based on an analytical solution. According to the method, the technical problem that the influence of abnormal values and wave velocity measurement errors on the seismic source positioning precision in a complex engineering environment is large is solved, and the fault tolerance and the real-time performance of seismic source positioning are improved.

The invention discloses a community discovery method of a graph neural network based on multi-view information fusion, and belongs to the technical field of data mining. The method comprises the following steps: preprocessing data, performing PCA dimension reduction on an attribute matrix, and normalizing an adjacent matrix; inputting the correlation matrix combination into an automatic encoder and a graph attention automatic encoder to obtain a new matrix representation and fusing the new matrix representation into a final node representation matrix; then back-propagating the optimization model by using various loss functions; and finally, community division is realized by using a k-means clustering algorithm. According to the method, the structure and attribute information of the original data are fully utilized, multi-view information is effectively fused, the accuracy of community discovery is improved, and the method has great significance in the fields of research on citation networks, recommendation systems and the like.

The application provides a decoding method, an encoding method, a device, a chip and a computing device. The method comprises the following steps: a decoding device acquires a first matrix, and then decodes a first code word vector according to the first matrix to obtain a decoding result. The first matrix is a 0-1 matrix representing elements in a second matrix on a polynomial ring. The second matrix is a check matrix on a finite field 2q, and the number of rows is r and the number of columns is n. A first polynomial corresponding to the polynomial ring is a q-th irreducible polynomial on a binary field. The number of 1s in the first matrix is less than the number of 1s in the 0-1 matrix representation of the second matrix. The first code word vector comprises k information code elements and r check code elements, and n=k+r. Each code element comprises q data units, and each data unit comprises at least one bit. The scheme can effectively reduce the hardware implementation complexity and cost of RS encoding and decoding.

The invention relates to the technical field of kinetic analysis of mechanical structures, in particular to a structural modal parameter identification method based on joint low-rank tensor decomposition, which comprises the following steps: performing tensor modeling on expression X = phi Q of structural response under modal coordinates, performing segmentation operation on collected vibration response, and expressing the vibration response in a high-order tensor form; then, low-rank representation is carried out on each column of modal shape vectors and low-rank representation is carried out on each row of modal responses by utilizing the low-rank performance of the modal response vectors and the modal shape vectors; further, the combined low-rank tensor of the high-order tensor is decomposed and solved, and a matrix representation phi reflecting a modal shape and a matrix representation Q of modal response are obtained; and finally, extracting modal frequency and damping ratio information from the obtained Q. According to the structural modal parameter identification method, the dynamic parameters such as the structural modal frequency and the modal shape are effectively identified by using the low-rank characteristics of the modal response vector and the modal shape vector.

Certain examples of the present disclosure relate to an apparatus (10, 120) comprising: means (11, 15) for sending, to one or more User Equipment, UE (110), demodulation reference signal, DMRS, matrix information (401) indicative of a row-orthogonal DMRS matrix (P); means (11, 15) for sending, to a UE (UEi) of the one or more UE, DMRS submatrix information (402) indicative of a submatrix (Pi) of the row-orthogonal DMRS matrix that has been assigned to the UE; means (11, 15) for receiving, from the UE, said at least one DMRS determined by the UE based, at least in part, on the DMRS matrix information and the DMRS submatrix information.

This invention relates to the field of drawing recognition technology, and discloses a method, apparatus, device, and readable storage medium for beam span recognition in vector graphics. The method includes: acquiring a target vector graphic and its corresponding matrix representation; identifying key beam span points from the matrix representation; determining the beam span type based on these key points; and mapping the positions of the key beam span points to the target vector graphic to obtain beam span elements corresponding to the beam span type in the target vector graphic. By implementing this invention, the recognition of beam span elements is not limited by the drawing method, ensuring the accuracy of beam span element recognition. This enables accurate identification of the positional information of beam elements, further guaranteeing accurate identification and attribute matching of beam elements.

The invention provides a security detection method and device for network traffic data, and the method comprises the steps: extracting a plurality of preset network features of each traffic data packet in a target time period, and obtaining a plurality of pieces of evolution data; dividing the plurality of evolution data of the target time period into to-be-detected data of a plurality of windows by using a sliding window; processing the to-be-detected data of each window to obtain a matrix representation of the to-be-detected data; determining an approximation ratio test value distribution based on the matrix representation of the data to be detected; determining a threshold value based on the approximate ratio test value distribution and a preset false alarm probability; whether the to-be-detected data is larger than a threshold value or not is judged, and if yes, it is determined that the to-be-detected data is abnormal data; and processing the abnormal data by utilizing hash function calculation and bit operation, and judging whether the abnormal data is attack data or not according to a processing result. According to the invention, the detection efficiency and detection precision of the network traffic data are improved.

The invention provides a CNN model parameter accelerated extraction method and system in a black box hard tag environment, and belongs to the technical field of artificial intelligence. Comprising the following steps: reconstructing a convolution kernel of a convolutional neural network into a matrix representation with a BTTB sparse structure based on structure information of the convolutional neural network; a double-point set is searched and determined through query and decision boundary geometrical shape analysis; and executing a clustering algorithm taking a convolution kernel as a center in the calculation unit to generate a double-point cluster, reconstructing a critical hyperplane by combining subspaces corresponding to each double point in the cluster, and extracting a normal vector of the critical hyperplane as unsigned estimation so as to determine the weight of a convolution layer. According to the method, through BTTB matrix reconstruction, clustering with the convolution kernel as the center and multi-stage optimization of covariance matrix acceleration, the memory overhead in the calculation process can be remarkably reduced, and the CNN model parameter extraction efficiency of a GPU processor is effectively improved.

The invention discloses a fully expressive sparse matrix representation with finite element data. Systems and techniques for compressing a dense matrix into a fully expressive sparse matrix representation with finite element data are disclosed. The techniques include generating a sparse matrix with corresponding metadata based on the dense matrix. Generating a sparse matrix with respective metadata includes: identifying a first number (M) of elements to be compressed, a second number (N) of elements to be retained, and a third number (B) of bits used by each metadata value; determining a metadata value of each of the N elements of the dense matrix; packing the first metadata value having more than B bits into a second metadata value having B bits; and generating a sparse matrix containing the N elements of the dense matrix. The techniques include storing a sparse matrix and corresponding metadata, wherein the corresponding metadata includes a second metadata value.

The invention discloses arithmetical operations using expressive sparse matrix representations with finite element data, and specifically discloses systems and techniques for performing matrix multiplication operations on expressive sparse matrix representations with finite element data. The techniques include receiving a sparse matrix, metadata corresponding to the sparse matrix, and a matrix operand. The sparse matrix contains a first number (N) of elements to be retained in a dense matrix comprising at least a second number (M) of elements. The metadata corresponding to the sparse matrix is based on a third number (P) of positions and a format determined during compression of the dense matrix. The techniques include selecting, by one or more selection circuits, a subset of elements of a matrix operand based on metadata corresponding to a sparse matrix, and performing one or more matrix multiplication operations on the sparse matrix and the subset of elements of the matrix operand.

In order to carry out an improved image correction, it is provided that a number of NM multiplication influence factors and a number of NC convolution influence factors are taken into account in the image recording (1), said multiplication influence factors describing multiplication factors between image points (p) in the interference-reduced image (U) and the same image points (p) in the interference-affected image (F), each multiplicative influence factor is described in a vector-matrix representation as a single multiplicative matrix (Mi) in the form of a diagonal matrix having matrix elements for each pixel (p) on a diagonal line, and the multiplicative matrix M is determined as a matrix product of the single multiplicative matrixes (Mi) from a number NM of the single multiplicative matrixes (Mi), and wherein the convolution influence factors describe the influence of a plurality of image points (p) in the interference reduction image (U) on an image point (p) in the interfered image (F), and each convolution influence factor is described in the form of a single convolution matrix (Ci), each row of individual convolution matrices (Ci) describes how an image point (p) in the interfered image (F) is affected by a respective image point (p) in the interference-reducing image (U), and a convolution matrix C is determined as a matrix product of the individual convolution matrices (Ci) from a number of NC individual convolution matrices (Ci), and wherein the matrix product of the multiplication matrix M and the convolution matrix C is used as a correction operator (A) for image correction, i.e. A = MC.

Disclosed are systems and techniques for decompressing an expressive sparse matrix representation with limited metadata. The techniques include receiving a sparse matrix and metadata corresponding to the sparse matrix. The sparse matrix is a compressed representation of a dense matrix. The sparse matrix contains a first number (N) of elements to retain from the dense matrix which comprises at least a second number (M) of elements. The metadata corresponding to the sparse matrix is based on a third number (P) of positions and a format determined during compression of the dense matrix. The techniques include generating an uncompressed matrix based on the sparse matrix and the metadata corresponding to the sparse matrix.

The invention discloses a cross-format receipt semantic content extraction method based on layout semantic nodes, and belongs to the technical field of computer application, and the method comprises the following steps: S1, constructing a keyword dictionary; s2, defining a semantic content extraction template, and representing the semantic content extraction template by using a local relation direction matrix; the documents are recognized through OCR, and a global relation direction matrix is constructed according to the keyword dictionary; s3, global-local matrix matching is carried out to position a target area; and S4, semantic extraction position determination, text block screening and structured output. According to the cross-format receipt semantic content extraction method based on the layout semantic nodes, fault tolerance is improved, low-cost and fast deployment is achieved without large-scale data labeling, auditing performance is guaranteed through explicit modeling, and the mismatching rate is reduced; the whole text processing process is covered through a parameterization strategy, complex scenes such as cross-column and broken-line are efficiently dealt with, and the blank of weak adaptation capability of complex layout is filled up.

The application discloses a kind of multi-contract interactive transaction network modeling method and system based on hypergraph, belong to information security technical field, the application first abstracts four kinds of heterogeneous nodes of user, contract, token and application program, generates feature vector using the layered encoder of fusion static attribute and dynamic timing;Second, based on atomic operation, price influence and other semantic rules, combined with space-time clustering algorithm dynamically generates hyperedge, and the weight is calculated by fusing the multi-factor function of time attenuation, fund size and attack mode;Finally, sliding window is used to construct timing sub-hypergraph, and the global network is updated and matrix representation is generated by union operation.The application accurately describes cross-contract collaborative behavior such as lightning loan by hyperedge, and improves the real-time and accuracy of transaction modeling by space-time feature fusion, providing high-dimensional and accurate data basis for attack detection and vulnerability positioning in the field of decentralized finance.

A neural network approximator for approximating an equilibrium solution of a multi-agent system based on a revenue matrix representation of the system, and a method of training the approximator are provided. A method of training an approximator based on revenue matrix representations includes: generating and providing a training set comprising a plurality of revenue matrix representations of different multi-agent systems; a neural network approximator determines an approximation of an equilibrium solution for one of the plurality of revenue matrix representations and a loss function; the determining step is repeated for each representation of the plurality of revenue matrix representations while minimizing the loss function. The one or more determined approximate solutions may then be input as one or more initial points for a downstream algorithm, where the downstream algorithm is used to determine a precise equilibrium solution.

The invention provides a dynamic identification method and device based on landslide evolution, and relates to the technical field of landslide identification, and the method comprises the steps: constructing a space-time similarity matrix based on a direction consistency item and a spatial proximity item of monitoring point data, carrying out the eigenvalue decomposition of the space-time similarity matrix, and constructing matrix representation total energy; analyzing according to a space-time similarity matrix to obtain a main energy concentration index, and calculating a standard deviation of the energy concentration by combining with a sliding time window to serve as volatility measurement; according to the matrix representation total energy, performing development stage judgment on the main energy concentration index, the volatility measurement and the fluctuation constraint condition to obtain a slip band development stage result; and training the deep feed-forward neural network based on a back propagation algorithm, and inputting the slope body parameters, the rock-soil body parameters and the slip zone development stage result into the trained neural network to carry out slope safety coefficient prediction so as to obtain a landslide evolution result. According to the invention, the problem of insufficient identification precision in the slip band forming stage is solved.

Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design of physical structures include: obtaining a B-Rep model including parametric surfaces and associated coedges; generating feature matrices from the coedges associated with the parametric surfaces; using the feature matrices in a convolution layer of a convolutional neural network of a machine learning algorithm to recognize a collection of faces or edges in the B-Rep model, including concatenating results of transforming the feature matrices using a topological walk matrix that represents topological information for the parametric surfaces based on adjacency relationships among the coedges (e.g., including ordering information); and preparing, based on the at least one collection of faces or edges recognized by the machine learning algorithm, (i) a display of the B-Rep model to facilitate user changes or (ii) a design of the B-Rep model to facilitate manufacturing a physical structure using the B-Rep model.

The invention relates to an M3C double-fundamental-frequency coupling characterization method, device, equipment and medium, and the method comprises the steps: building a basic dynamic equation of bridge arm electrical parameters based on an M3C average value model; based on the basic dynamic equation, performing frequency domain decomposition and matrix representation on the electrical parameters under power frequency and low frequency conditions to generate a two-dimensional frequency domain matrix; performing linear equivalent modeling on the frequency domain operation relation of the two-dimensional frequency domain matrix to obtain a structured matrix; dimensionality reduction processing is carried out on the structured matrix, and steady-state frequency representation of the bridge arm electrical parameters is obtained; and based on the steady-state frequency representation, deducing to obtain a steady-state frequency domain harmonic model of the bridge arm loop. The method has the effect of improving the accuracy of analyzing the steady-state frequency characteristics of the electrical parameters of the bridge arm by the M3C.

The invention relates to a lattice-based algebraic erasure construction method, and belongs to the technical field of information security. According to the method, firstly, abstract algebraic erasure core operation is strictly summarized into one half-per-unit group right action, and the abstract algebraic erasure core operation is improved for the first time on the basis of a non-homogeneous short integer solution problem in lattice cryptography. In order to further improve the safety, a plait group matrix with a more complex algebraic structure is proposed to replace a trivial replacement group, so that a post-quantum cryptography scheme of which the safety depends on double difficulty assumption of a lattice problem and a plait group problem is constructed.

The invention relates to a code recommendation method and device, computer equipment and a storage medium. The method comprises the following steps: performing word segmentation processing on code context information to obtain a code snippet set containing at least one code snippet; and mapping the code snippet set to a language space to obtain an initial embedded matrix representation corresponding to each code snippet. And filtering the initial embedded matrix representation corresponding to each code snippet to obtain a target embedded matrix representation corresponding to each code snippet. And determining a code recommendation result according to the target embedded matrix representation corresponding to each code snippet. On the basis of the method, the code snippet set can be mapped to the language space to obtain the initial embedded matrix representation corresponding to each code snippet, and the initial embedded matrix representation corresponding to each code snippet is filtered to obtain the target embedded matrix representation corresponding to each code snippet; irrelevant information in the code context information can be effectively filtered, and the accuracy of a recommendation result is improved.

The invention discloses a rotating part circumferential matrix characterization method and system based on rotating speed super-resolution correction, and relates to the field of rotating machinery fault diagnosis, and the method comprises the steps: collecting an original vibration signal of a rotating part, carrying out the synchronous compression wavelet transformation, extracting a rotating frequency ridge line, and obtaining a preliminary estimation rotating frequency sequence; performing equal-angle resampling on the original vibration signal through the preliminary estimation frequency conversion sequence by using a keyless phase order tracking technology, so that the equal-time interval signal is converted into an equal-angle resampling signal; performing pulse ridge line extraction on a circumferential matrix formed after equal-angle resampling by using a sparse pulse ridge point dynamic planning tracking algorithm; correcting the sub-pixel-level super-resolution of the initial rotating speed to obtain a corrected rotating speed signal; and carrying out secondary angular domain resampling by using the corrected rotating speed signal to generate a circumferential characterization map capable of characterizing fault space-time distribution characteristics. According to the method, the rotating speed is finely corrected, and the interpretability and the identification degree of two-dimensional representation can be improved.

Systems and methods for decompressing a meaningful sparse matrix representation with limited metadata are disclosed. The methods include receiving a sparse matrix and metadata corresponding to the sparse matrix. The sparse matrix is ​​a compressed representation of a dense matrix. The sparse matrix contains a first number N of elements to be retained from the dense matrix, which comprises at least a second number M of elements. The metadata corresponding to the sparse matrix is ​​based on a third number P of positions and a format determined during the compression of the dense matrix. The methods include generating an uncompressed matrix based on the sparse matrix and the metadata corresponding to the sparse matrix.

Systems and methods for performing matrix multiplication operations with a meaningful sparse matrix representation containing limited metadata are disclosed. The methods include receiving a sparse matrix, metadata corresponding to the sparse matrix, and a matrix operand. The sparse matrix contains a first number N of elements, which are to be retained from a dense matrix comprising at least a second number M of elements. The metadata corresponding to the sparse matrix is ​​based on a third number P of positions and a format determined during compression of the dense matrix.The procedures involve selecting a subset of elements of the matrix operand by one or more selection circuits based on the metadata corresponding to the sparse matrix, and performing one or more matrix multiplication operations on the sparse matrix and the subset of elements of the matrix operand.

The invention discloses an H2-Matrix moment vector multiplication acceleration method based on a GPU (Graphics Processing Unit). The H2-Matrix moment vector multiplication acceleration method comprises the following steps of S1, executing flattening conversion on a recursive tree structure and matrix representation of H2-Matrix and copying the recursive tree structure and the matrix representation to the GPU; s2, uploading the input vector to a GPU (Graphics Processing Unit); s3, constructing a heterogeneous flow scheduling mechanism, creating a low-priority flow (associated with dense block calculation) and a high-priority flow (associated with low-rank block calculation), and creating a dense block calculation task completion event; s4, constructing a calculation graph, executing low-rank block calculation, and outputting a low-rank component; s5, starting a dense block kernel function to output a dense component and recording a completion event; s6, after the high-priority stream waits for the completion event, a component accumulation kernel function is started to fuse the dense component and the low-rank component, a fusion result is transmitted back to the host end, and an H2-Matrix moment vector multiplication final result is obtained.According to the method, the problem that a GPU processes a recursive structure is solved, the resource utilization rate is increased through double-flow overlapping execution, and the kernel startup overhead is reduced by means of a computational graph.

The application discloses a layout semantic node-based cross-format document semantic content extraction method, and belongs to the technical field of computer application, which comprises the following steps: S1, keyword dictionary construction; S2, defining a semantic content extraction template, which is represented by a local relationship direction matrix; OCR recognizing a document, and constructing a global relationship direction matrix according to the keyword dictionary; S3, global-local matrix matching positioning a target area; S4, semantic extraction position determination, text block screening and structured output. The layout semantic node-based cross-format document semantic content extraction method has the advantages that fault tolerance is improved, low-cost fast deployment is realized without large-scale labeled data, auditability is guaranteed and misfit rate is reduced through explicit modeling, the whole text processing flow is covered through a parameterized strategy, complex scenes such as cross-column and broken-line are efficiently dealt with, and the blank of weak complex layout adaptation capability is filled.

The application relates to a quantum gate simulation and control parameter optimization method based on numerical calculation, which sequentially comprises the following three modules: a model Hamiltonian matrix representation module which can calculate the matrix representation of the model Hamiltonian according to specific problems; a system time evolution and fidelity calculation module which uses the Hamiltonian matrix to discretely solve the Schrodinger equation, gives the corresponding time evolution matrix, and calculates the gate operation fidelity by using the obtained time evolution matrix; and a control parameter optimization module which calls the previous two modules, adopts a local optimization algorithm or a global optimization algorithm to optimize the control parameters and the quantum gate operation time, and gives the optimal control parameters and the gate operation time. The application solves the problem that the gate fidelity of the current superconducting quantum computing system cannot meet the requirements of reliable operation of practical quantum algorithms, can simulate the detailed process of quantum gate operation through the numerical calculation method, and improves the quantum gate operation fidelity through the optimization of the control parameters.

Systems and methods for compressing a dense matrix into a meaningful sparse matrix representation with limited metadata are disclosed. The methods include generating a sparse matrix with corresponding metadata based on a dense matrix. Generating the sparse matrix with corresponding metadata involves identifying a first number M of elements to be compressed, a second number N of elements to be retained, a third number P of positions, and a format; determining a metadata value for each of the N elements of the dense matrix based on the identified P and the identified format, wherein the dense matrix has at least M elements; and generating the sparse matrix containing the N elements of the dense matrix.The methods involve storing the sparse matrix and the corresponding metadata, where the corresponding metadata includes the metadata value for each of the N elements of the dense matrix.

Disclosed are systems and techniques for compressing a dense matrix into an expressive sparse matrix representation with limited metadata. The techniques include generating a sparse matrix with corresponding metadata based on a dense matrix. Generating the sparse matrix with corresponding metadata includes identifying a first number (M) of elements to compress, a second number (N) of elements to retain, a third number (P) of positions, and a format; determining a metadata value for each of N elements of the dense matrix based on the identified P and the identified format, wherein the dense matrix includes at least M elements; and generating the sparse matrix containing the N elements of the dense matrix. The techniques include storing the sparse matrix and the corresponding metadata, wherein the corresponding metadata comprises the metadata value for each of the N elements of the dense matrix.