28 results about "Matrix form" patented technology

THE WAY IN WHICH THE ESSENTIAL ASPECT OF THE STRATEGE "FROM NOTHING - SOMETHING" IS IMPLEMENTED IN INFORMATION TECHNOLOGIES. The initial essence "Nothing" (0, Zero) of the method is the abstract essence. The mathematical model of the Projected Object Entity (PEO) is embodied in material, software, and mixed entity objects from various disciplines—the "something" of the method. The methodology of comparative cause-and-effect analysis is applied in the creation of the PEO, and the methodology of essential synthesis is used in its design: subject-specific and essential interdisciplinary properties of similarity, universal mathematical models in the form of orgraphs and their matrices, graphs with algorithms of nodes and their matrices, as well as essential identities, equations, and differential equations that reflect the balance of actions and counteractions.And since the professional and interdisciplinary properties of similarity for individual objects of different disciplines continue to apply, the transfer of the parental properties, qualities and the collective image of "nothing" and its "something" takes place "according to the image and likeness" of the essence.

The invention relates to a matrix type differential passivity index determination method and device, equipment and a storage medium. The method comprises the following steps: acquiring a linear system model corresponding to target equipment in a power system, wherein the power system is a multiple-input multiple-output system; determining a corresponding differential passivity equivalent system model according to the linear system model; based on the linear matrix inequality, a matrix type passivity index of the differential passivity equivalent system model is solved, the matrix type passivity index is used as a matrix type differential passivity index of the linear system model, the matrix type differential passivity index is in a matrix form, and the matrix type differential passivity index is used as a differential passivity index of the linear system model; matrix elements in the ith row and the jth column in the matrix represent coupling passivity characteristics between an input channel i and an output channel j in the power system. By adopting the method, the passivity characteristics in the MIMO dynamic system can be represented more accurately and comprehensively.

This invention presents a method for establishing large deformation viscoelastic constitutive relations based on Prony technology. It addresses the problems of existing viscoelastic constitutive models lacking universality, and the complexity and programming difficulties associated with their mathematical relationships. The invention includes: decomposing the stress tensor into the sum of the deviatoric stress tensor and the mean stress; decomposing the strain tensor into the sum of the deviatoric strain tensor and the mean strain; and providing a convolutional expression for the deviatoric stress tensor and the mean stress. The increment of the deviatoric stress tensor is expressed as a function of the deviatoric strain increment, and the increment of the mean stress is expressed as a function of the mean strain increment. The tensor form of the incremental constitutive relation is written in matrix form. This method has been programmed, and the numerical results obtained from the program agree well with the experimental results in specimen-level experiments on solid rocket motor propellant grains.

The invention discloses a BP16 matrix multiplication and addition operation circuit based on an RISC-V framework, and relates to the technical field of processor microstructures and artificial intelligence calculation acceleration. The circuit comprises an input module, a matrix multiplication and addition operation module, an accumulation operation module and an output module, wherein the input module is used for inputting a first operand in a BP16 format, a second operand in the BP16 format and a third operand in a preset format; the matrix multiplication and addition operation module periodically performs row and column multiplication operation on a plurality of first floating point elements of a preset row number in the first operand and a second floating point element in the second operand in a matrix form to generate a fourth operand; the accumulation operation module performs accumulation calculation on the fourth operand and the third operand to generate a fifth operand; and the output module outputs the target operand based on the fifth operand. The throughput rate and the energy efficiency ratio of AI acceleration calculation under the RISC-V architecture can be remarkably improved.

The application discloses a kind of based on time delay recursive discrete grey model's weld seam tracking error real-time prediction method, the method includes the following steps: step S1: obtaining real-time weld seam tracking error data;Step S2: according to new information priority principle, using new information priority accumulation operation to time series data smoothing weld seam tracking error data;Step S3: using time delay grey differential equation and discrete equation establishes the matrix form of TRDGM;Step S4: using recursive least square method to derive the recursive equation of TRDGM structure parameter;Step S5: construct the nonlinear optimization objective function of TRDGM;Step S6: using heuristic optimization algorithm to optimize the hyperparameter of TRDGM;Step S7: select prediction accuracy index and computational efficiency index, evaluate the prediction performance of TRDGM.The TRDGM proposed in the above can quickly and accurately predict weld seam tracking error under small sample condition, help to predict in advance the risk of weld seam tracking failure.

The application discloses a kind of meta-heuristic test design optimization methods based on matrix transformation, belong to computer-aided test design and statistical modeling technical field, this method uses a two-stage optimization framework: first, in global exploration stage, with minimizing global uniformity index as target to carry out unconstrained optimization, to obtain well space coverage sample set;Subsequently in local development stage, with minimizing adjacent distance variance as target, optimization is carried out under the constraint of maintaining the good global coverage obtained, to fine adjustment sample point local spacing distribution, improve uniformity;The application encodes test design scheme into matrix form individual, is updated iteratively by meta-heuristic algorithm, and adopts global normalization strategy to ensure that sample is always located in design space;The application can systematically consider the global coverage of sample and local uniformity, significantly improve the sample distribution quality in high-dimensional space, generate comprehensive performance excellent test design scheme.

The embodiment of the application provides a kind of based on agent model's oil reservoir remaining oil prediction method and system, the method includes: setting oil reservoir simulation data, combining oil reservoir simulation module, generate sample data set;Based on the data type of sample data set, corresponding data preprocessing is carried out, and data is converted into two-dimensional matrix form;Residual oil prediction model is constructed, and residual oil prediction model is the encoder-decoder network of fusion residual structure, and coordinate attention mechanism is introduced;Two-dimensional matrix form sample data set is input data, and residual oil prediction model is iteratively optimized, and iteration is until convergence, to obtain the trained residual oil prediction model;The complete input parameter of target oil reservoir is acquired, and the trained residual oil prediction model of input complete parameter is output target oil reservoir remaining oil distribution result.

The invention provides a multi-omics graph representation learning method and device based on a multi-head self-attention mechanism, and the method comprises the steps: carrying out the preprocessing of multi-omics original data, converting the data into a matrix form, and constructing or obtaining a multi-source priori knowledge network according to the existing priori knowledge; encoding the priori knowledge network by using a graph neural network to obtain a feature-level embedding representation; inputting the multi-omics feature matrix into a multi-head self-attention mechanism, capturing potential association inside each group of omics data and among different omics data, and performing dimension reduction processing on original high-dimensional data to obtain low-dimensional sample-level embedding representation; splicing and fusing feature-level embedding and sample-level embedding to obtain unified representation of comprehensive data features and prior knowledge; and finally, inputting the fused representation into a task specific module, performing supervised optimization by combining a cross entropy loss function of a real label, and learning model parameters. In the application stage, a classification result can be output only by inputting the to-be-analyzed multi-omics data into the trained model.

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 embodiment of the invention discloses an FP8 matrix multiplication and addition operation circuit based on an RISC-V framework, and relates to the technical field of processor microstructures and artificial intelligence calculation acceleration. The circuit comprises an input module used for receiving a first operand in an FP8 format, a second operand and a third operand in a preset format; the matrix multiplication and addition operation module is used for periodically carrying out row and column multiplication operation on a plurality of first floating point elements of a preset row number in the first operand and a second floating point element in the second operand in a matrix form; the accumulation operation module is used for performing accumulation calculation on the intermediate result and a third operand; the output module is used for outputting a target operand, the problem that in the prior art, FP8 matrix multiplication and addition lacks special hardware support is solved, the computing power density and the energy efficiency ratio in an AI scene are remarkably improved, high fusion with an RISC-V vector architecture can be achieved, and the method is suitable for a CPU, an AI accelerator and a heterogeneous computing platform.

The application provides a multi-omics graph representation learning method and device based on a multi-head self-attention mechanism, pre-processes multi-omics original data, converts the multi-omics original data into a matrix form, constructs or acquires a multi-source priori knowledge network according to existing priori knowledge, encodes the priori knowledge network by using a graph neural network to obtain a feature-level embedding representation, inputs the multi-omics feature matrix into the multi-head self-attention mechanism to capture potential correlations between different multi-omics data and between different multi-omics data, and performs dimension reduction processing on the original high-dimensional data to obtain a low-dimensional sample-level embedding representation, splices and fuses the feature-level embedding and the sample-level embedding to obtain a unified representation of comprehensive data features and priori knowledge, and finally inputs the fused representation into a task-specific module, performs supervised optimization by combining a cross-entropy loss function of a real label, and learns model parameters. In the application stage, only the multi-omics data to be analyzed needs to be input into the trained model, and a classification result can be output.

The application belongs to the field of model-based system engineering application and practice, and relates to a method for quantitatively analyzing complexity of an architecture model of an engineering complex system. A plurality of alternative system architecture models are designed in advance, and then an architecture model to be evaluated is selected. The system architecture model is abstracted according to graph theory to obtain a graph representation of the architecture model, and the complexity of each vertex in the graph is determined (1). Then, the graph representation of the architecture model is converted into a matrix form of the architecture model, and the complexity of each connection relationship in the matrix is determined (2). Then, the topological complexity of the matrix is determined (3). Then, the complexity of the architecture model is calculated according to (1), (2) and (3), and the complexity values of all the alternative architecture models are calculated until the complexity values of different architecture models are compared to evaluate, compare and optimize the architecture model of the system or product. The selected architecture model has the lowest complexity and the lowest implementation difficulty, and the calculation method is convenient for engineering implementation.

The embodiment of the invention discloses a matrix multiplication and addition operation circuit based on an RISC-V framework, and belongs to the field of processor microstructures and artificial intelligence accelerated calculation. The circuit comprises an input module, a matrix multiplication and addition operation module, an accumulation operation module and an output module, wherein the input module is used for inputting operands and carrying out precision identification on floating point elements; the matrix multiplication and addition operation module is used for carrying out row and column multiplication operation on a first floating point element of a preset row number in the first operand and a second floating point element in the second operand in a matrix form on the basis of the identification information to obtain a fourth operand; the accumulation operation module accumulates the fourth operand and the third operand to obtain a fifth operand; and the output module outputs a target operand based on the fifth operand, thereby realizing compatibility of FP8 and BP16 matrix multiply-add operation in a single circuit, improving hardware reuse rate, reducing control complexity and power consumption, and being adaptive to an RISC-V vector expansion instruction set.

The invention discloses a security analysis method of an NTRU private key under any Hamming weight, which comprises the following steps of: obtaining different public keys generated by a same first private key and a different second private key, constructing a specific equation set in a matrix form based on a relationship between the public keys and the private keys, calculating a rank of the specific equation set, and calculating the security of the NTRU private key on the basis of the rank obtained by calculation. And solving a special solution meeting the specific equation set, solving a homogeneous solution space of the specific equation set based on the parity of the number of which the coefficient is 1 in each second private key, constructing a constraint condition for screening for the homogeneous solution space, screening out a unique real solution from the homogeneous solution space, recovering the first private key according to the unique real solution, and obtaining the second private key according to the unique real solution. And each second private key is recovered. According to the method, the private key can still be recovered under the condition that the Hamming weight of the first private key is unknown, the recovery of the private key is more efficient, the required time is shorter, and the method has wide applicability and universality.

The invention discloses a matrix parallel computing method for solving a minimum cut set of a fault tree, and relates to the field of fault tree analysis. The matrix parallel computing method for solving the minimum cut set of the fault tree aims at solving the problems that in the prior art, the computing process is serialized, the parallel efficiency is low, a data structure is not matched with a GPU architecture, and the minimum cut set of the large-scale fault tree cannot be efficiently solved. According to the method, structured coding is carried out on a fault tree, and gate events and input relations are constructed into a structure description matrix; vectorizing the basic event and representing a candidate cut set of the intermediate event in a binary matrix form; under a GPU parallel architecture, row merging is performed on an OR gate, broadcast type combination is performed on an AND gate, parallel cut set simplification is performed, and meanwhile, a blocking and cache multiplexing strategy is introduced to improve the calculation throughput rate and reduce memory occupation. And carrying out loop iteration until the top event matrix is stable, and outputting a minimum cut set. The method is suitable for safety and reliability analysis of a large complex engineering system.

The application discloses an AR model parameter estimation method and device based on forward and backward linear prediction, and the method comprises the following steps: obtaining an interference signal of an infrared spectrometer and converting the interference signal into a matrix form, so as to complete construction of an AR model; a forward and backward expansion matrix of the interference signal is constructed by using a forward and backward prediction total least square method, and a forward and backward linear prediction equation is formed based on the forward and backward expansion matrix; the forward and backward expansion matrix is converted into a homogeneous linear equation by considering the noise disturbance influence of an observation vector and a data matrix, and a total least square problem of the homogeneous linear equation is constructed; and a singular value decomposition method is used to solve the solution of the total least square problem, so as to obtain parameter coefficients of the AR model. The application has the advantages that the problem of spectrum line separation of a restored spectrum is solved, the influence of noise on parameter estimation is reduced, and the accuracy of the restored spectrum is improved.

This invention discloses a parallel computational method for simultaneous identification of structural dynamic loads and local damage, comprising the following steps: 1) Arranging accelerometers on the surface of a beam structure to collect structural responses, and storing the structural responses in a two-dimensional matrix form using a moving time window; 2) Establishing a finite element model using beam elements, and dividing the beam elements into neg groups to construct neg independently runnable identification subtasks; 3) For each subtask, using the element damage factor contained in that group as an optimization parameter, and combining matrix regularization to construct an optimization objective function; 4) Solving each subtask in parallel using the gradient descent method; 5) Determining the final identification result by combining the objective function value, the sparsity of the identified load, and the number of damaged elements. This invention is applicable to parallel computation, enabling simultaneous identification of structural dynamic loads and local damage, and is suitable for solving the overall sparsity problem of simultaneous identification of dynamic loads and local damage over long time spans.

The application provides a machine tool multi-degree-of-freedom thermal error detection method and system. It belongs to the field of machine tool thermal error detection. The method comprises the following steps: installing a standard mandrel on a machine tool spindle; under the cold state of the machine tool, controlling the machine tool to move the standard mandrel to a plurality of measurement points arranged in a matrix form on a workbench in advance; synchronously collecting multi-dimensional geometric data of the standard mandrel through a plurality of sensor units integrated at the measurement points; obtaining complete multi-dimensional geometric data of all measurement points under the cold state; after the machine tool runs to a target thermal state, repeating the above steps to obtain complete multi-dimensional geometric data of all measurement points under the thermal state; and comparing the multi-dimensional geometric data of the same measurement point under the cold state and the thermal state to calculate the multi-degree-of-freedom thermal error value at the point. The method can improve the detection precision of machine tool thermal error.

The invention discloses a building structure load identification method and equipment. The method comprises the steps of determining an initial state quantity and an initial state quantity covariance of a building structure; based on the UT transformation principle of an unscented Kalman filter, generating a group of Sigma points according to the state quantity and covariance at the previous moment, and writing the Sigma points into a matrix form; calculating information of each column of Sigma points in the matrix on the basis of the Alembert principle, the structural quality and the matrix information to obtain a plurality of accelerated speed predicted values with the number equal to that of the columns of the matrix; calculating a weighted average value of all acceleration predicted values based on a weight value of UT (Unscented Kalman Filter); constructing a target function about the unknown load of the structure based on the acceleration measurement value measured by the sensor and the weighted average value; based on a Levenberg-Marquardt optimization method, calculating a corresponding load estimation value when the target function obtains a minimum value; updating the state quantity and the state quantity covariance of the unscented Kalman filter based on the load estimation value; and circularly executing the steps until all time steps are calculated.

Some embodiments of the present application provide a hand-eye calibration method and device, which simplifies the installation of the calibration block, uses a two-dimensional graphical target map on the manipulator, and uses a three-dimensional point set composed of the graph center to solve the pose, thereby reducing the processing and installation precision requirements of the calibration block, and the difficulty of solving the conversion relationship of the calibration block. The method uses a target map instead of a calibration block, reduces the error generated by the calibration block, and solves the target rigid body transformation relationship through a three-dimensional point set matching algorithm, which can effectively reduce the time consumption compared with the point cloud registration algorithm. In the linear solution of the hand-eye standard equation, the rotation matrix is solved by using the rotation axis method, which reduces the algorithm solving difficulty and effectively avoids the solving abnormality and failure problem caused by the matrix form represented by the trigonometric function or the rotation angle. Under the premise of ensuring the calibration accuracy, the calibration process is simplified and convenient to maintain, solving the problem that the current hand-eye calibration method is not accurate enough and not convenient to operate.

This invention discloses a security analysis method for NTRU private keys under arbitrary Hamming weights, comprising the following steps: obtaining different public keys generated from the same first private key but different second private keys; constructing a specific system of equations in matrix form based on the relationship between the public and private keys; calculating the rank of the specific system of equations; solving for a particular solution that satisfies the specific system of equations based on the calculated rank; solving for the homogeneous solution space of the specific system of equations based on the parity of the number of coefficients of 1 in each second private key; constructing constraints for filtering the homogeneous solution space; filtering out a unique true solution from the homogeneous solution space; recovering the first private key based on the unique true solution; and then recovering each second private key. This invention can still recover private keys even when the Hamming weight of the first private key is unknown, making private key recovery more efficient, requiring less time, and possessing wide applicability and versatility.

The application provides a global recursive reconstruction method of a space robot recursive Newton-Euler dynamics algorithm, and relates to the technical field of space robot dynamics modeling. The method obtains the global closed matrix form of the mechanical arm movement screw and its differential by forward recursive reconstruction of the recursive Newton-Euler dynamics algorithm; the secondary diagonal matrix of the recursive operator is constructed to simplify the expression of the differential of the global closed matrix form of the mechanical arm movement screw; based on the global closed matrix form of the movement screw and its differential, the global closed matrix form of the required torque of each joint and the required force screw of the base is obtained by backward recursive reconstruction; based on the similarity of the backward recursive reconstruction results at the mathematical structure level, the global closed matrix form dynamics modeling of the space robot is completed. The method constructs an explicit matrix mapping form of the floating base space robot dynamics model, and realizes the direct mapping between the system state quantity and the input quantity.

The application discloses a short text clustering method based on singular value decomposition and field pre-training, and steps are as follows: short texts are acquired as a data set, and the data set is preprocessed; a CBOW model is used to extract word vector features, including frequent words and corresponding documents; the frequent words and the documents are formed into a matrix form, the matrix is decomposed by using an SVD method, then a GCN representation is constructed, corresponding graph structure vectors are formed, a RoBERTa model is used to perform basic training on the documents, new semantic subtasks are added for training, NLP semantic vectors are generated, two vectors are combined, a D-D-Kmeans algorithm is used for clustering, and clustering results are obtained. The application can improve the clustering convergence speed and accuracy, can resist noise interference well, and has strong robustness.

The application discloses a product NURBS curved surface large-scale space projection solution and geometric design manufacturing method. Product NURBS curved surface information and a group of three-dimensional space query points are acquired; an original curved surface model is generated from the input curved surface information by a general three-dimensional CAD software; the generated curved surface model is subjected to vectorization expression of the NURBS curved surface by a GPU, and is saved in a matrix form; the NURBS curved surface matrix processed in the receiving step is received, and then a subdivision tree of the NURBS curved surface and a discrete point cloud representation are constructed; the three-dimensional points required to be projected and the subdivision tree are traversed, the curved surface section where the target point is possibly projected and the point cloud representing the curved surface section are acquired, then the KNN algorithm is used to find the approximate projection of the target point; finally, the projection of the target point on the NURBS curved surface is obtained by means of Newton iteration. The method can process the super large-scale three-dimensional space point projection solution problem, and achieves real-time in speed.

The invention discloses an electric power system main distribution cooperative operation scheduling method, system, device and medium, and relates to the technical field of electric power system planning, and the method comprises the steps: collecting a power grid parameter, a power parameter and an input cost parameter; constructing a target and a constraint function with the minimum operation cost; the method comprises the following steps: constructing a matrix form of an operation scheduling model, splitting the matrix into an upper layer model UP, a plurality of middle layer problems MP and a plurality of lower layer models LP, and obtaining a feasible region projection of each LP and a feasible region projection of each MP by using a time sequence operation domain equivalent projection rapid generation algorithm; and obtaining an optimal value during operation scheduling through the upper layer model UP, the middle layer problem MP and the lower layer model LP, and obtaining an optimal operation scheduling mode. According to the method, a large-scale operation optimization problem is divided into a plurality of small-scale problems to be solved respectively, and the defect that a large amount of repeated calculation needs to be carried out when the time sequence coupling operation optimization problem is processed through a traditional method is overcome.

The application discloses a fruit and vegetable video foreground detection method based on adaptive updating parameters, and the method is characterized in that: through an iterative process, parameters of different hypergeometric distributions for different frames are given, and a normal equation set and a matrix form are given by making the first derivative of the weight of all hypergeometric distributions in a frame equal to zero through a square error weighted sum function, so as to obtain a matrix expression of the weight of different hypergeometric distributions in the frame, wherein the parameters of the hypergeometric distribution can be adaptively updated after a certain frame in an interval period, and then the size of the weight corresponding to different hypergeometric distributions is selected in turn for a certain pixel point in the frame in the fruit and vegetable video sequence, so that fruit and vegetable video foreground detection is completed.

The application relates to the CAD technical field and discloses a linear engineering sensitive point corresponding mileage and distance calculation and statistics method, which comprises the following steps: S101, based on text coordinates and text contents in a building, a layer is created and a closed multisection line is generated to perform rapid modeling of the building; S102, plane data of a route center line and sensitive points are read, the route center line is converted into a matrix form containing coordinates, convexity, corresponding pile numbers and curvatures of each point, and the sensitive point data is converted into a matrix form containing coordinates and radii; S103, projection relations of the sensitive points and the route center line are calculated to determine mileage ranges, azimuth relations, minimum distances and curve radii of the sensitive points; and S104, based on calculation results of the step S103, the number of buildings contained by each sensitive point, start and end pile numbers, nearest distances and curve radii are counted. From the whole engineering cycle, the method can greatly improve work efficiency and accuracy.

The application belongs to the technical field of scientific computing and artificial intelligence, and in particular to a random partial differential equation regular structure feature construction method and system. The integral operation of the regular structure feature is transferred from the physical space to the Fourier space. The Fourier diagonalization property of the Laplace operator is used to convert the integral of the partial differential equation coupled at each space point into the accurate solution of the scalar ordinary differential equation independent of each wave number. On the applicable periodic domain or the region processed by the periodization, the spectral accuracy is realized. Further, the serial recursive format is expanded into the causal convolution matrix form in the Fourier space. The integral factor power is pre-calculated and the three-dimensional causal convolution tensor is assembled. Through the parallel pipeline of batch fast Fourier transform, de-aliasing, tensor contraction causal convolution and batch inverse fast Fourier transform, the memory occupation is significantly reduced and the calculation efficiency is improved. The feature calculation efficiency and approximation accuracy of the machine learning system based on the regular structure feature construction are improved.