94 results about "Matrix encoding" patented technology

The invention provides a network traffic data security assessment method and system based on deep learning. The method comprises the following steps: converting original network traffic data into a graph structure data set comprising a topological structure, node attributes and time sequence behaviors; in the process, the time-space fusion input tensor is formed through the association strength between adjacent matrix and Laplacian matrix coding network entities and the fusion of time sequence characteristics extracted by time window slices. Compared with traditional flow analysis which only pays attention to a single protocol or a rate threshold value, the method achieves global relevance expression of network behaviors through graph structure modeling. Through graph structure modeling, multi-dimensional feature fusion and probabilistic risk assessment, the method can adapt to dynamic change of network topology and continuous evolution of an attack mode, so that a final assessment result is more accurate.

High resolution human imaging using neural network is described. In one example, a described method comprises: assembling a k1-dimensional (k1-D) imaging matrix by a processor based on arranging and concatenating a plurality of k2-dimensional (k2-D) input imaging matrices; encoding the k1-D imaging matrix by an encoder, which is a k1-D encoding neural network, to generate a (k1+k3)-D first intermediate matrix; generating a (k2+k3)-D second intermediate matrix based on the (k1+k3)-D first intermediate matrix; and decoding the (k2+k3)-D second intermediate matrix by a decoder, which is a k2-D decoding neural network, to generate a k2-D output imaging matrix. There is at least one skip connection between the encoder and the decoder. An imaging resolution of the k2-D output imaging matrix is greater than the imaging resolution of any one of the plurality of k2-D input imaging matrices.

A system for matrix-based homomorphic encryption including a processor of a computing node configured to host a homomorphic encryption module and connected to at least one cloud server and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: acquire plaintext M required to be encrypted; select a size of a matrix and modulus n; select invertible × matrix S1 over n, wherein n is a residue ring modulo n; compute an invertible × matrix S2 over n; set a secret key (S1, S2); and encode the plaintext M by an integer m over n, wherein m is encoded by an envelope matrix comprising a formV⁡(m)=(α0βm),wherein α and β are numbers chosen at random α, β∈n.

The invention relates to the technical field of resource regeneration, and discloses a retired battery disassembly scheduling method based on reinforcement learning. The method comprises the following steps: acquiring a machine state of a disassembling machine, constructing a graph matrix code based on battery disassembling process steps, constructing an MGCN unit for extracting connectivity and a topological structure between different parts of a battery based on a Conv2d k1 block and a Conv2d k3 block, and constructing a multi-graph convolutional network according to the MGCN unit; inputting the machine state and the graph matrix code into a multi-graph convolutional network to obtain a disassembly weight; and obtaining the optimized disassembly path according to the disassembly weight in combination with deep search, and completing disassembly of the retired battery according to the optimized disassembly path, thereby solving the problem of low efficiency when the existing models such as reinforcement learning are applied to battery disassembly scheduling.

The present application relates to the technical field of network routing, and discloses a method and system for adaptively adjusting network routing, the method comprising: performing detection data packet transmission and hierarchical clustering on a network interface to obtain a path cluster set; performing double-circular matrix encoding on the path cluster set to obtain a routing path encoding set; constructing a spatiotemporal feature tensor based on the routing path encoding set, and performing tensor decomposition and feature pattern extraction to obtain a time-varying weight coefficient; performing sparse-aware proportional iterative optimization calculation based on the spatiotemporal feature tensor and the time-varying weight coefficient to obtain an optimal routing configuration scheme, thereby achieving differentiated processing of different business flows and improving network resource utilization efficiency.

The invention relates to the technical field of power battery disassembly and recovery, and discloses a decommissioned battery dynamic disassembly path decision-making method based on deep search. Constructing a disassembly path based on battery disassembly process steps, and constructing a graph matrix code according to the disassembly path; obtaining a machine state, and inputting the machine state and the graph matrix code into the constructed reinforcement learning model to obtain a disassembly weight; obtaining a first optimized disassembling path according to the disassembling weight in combination with deep search, performing disassembling according to the first optimized disassembling path, obtaining battery information, obtaining a second optimized disassembling path according to the battery pack information in combination with the reinforcement learning model and deep search, and distributing machines according to the second optimized disassembling path to complete disassembling of the retired battery. The problem that an existing disassembling path cannot be adjusted in real time is solved.

The invention discloses a digital twin network resource optimization method based on a multi-target genetic algorithm, which adopts a fast genetic algorithm to solve a multi-target problem so as to cope with the fast changing resource allocation requirement of a mobile ad hoc network, adopts a matrix coding mode and uses the two-dimensional data structure. According to the method, modeling is carried out on frequency domain, space domain and energy domain problems, time domain information is taken into consideration, the conflict problem of nodes of the whole network is converted into a problem model with a subnet as a unit, the decision space of an algorithm is greatly reduced, and the single operation time of iteration is shortened. The power control and frequency point allocation problem is modeled as a network transmission capacity maximization problem, so that power optimization allocation and frequency point interference minimization are unified into a multi-objective optimization problem with relevance, and the probability of power consumption and frequency point conflict can be minimized while maximization of system communication performance and frequency point utilization efficiency can be realized.

The invention relates to a security encryption sorting method and system, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: realizing key processing and data set encryption based on a preset Paillier encryption algorithm; the server performs matrix coding and comparison operation on the encrypted data set and outputs a sorting result matrix; and the two servers cooperate with each other to complete decryption of the sorting result matrix, and a plaintext sorting result is fed back to the data holder. By adopting the method, the encrypted data is sorted, the privacy leakage risk is avoided, the approximate error in the traditional technology is eliminated, and the encrypted data analysis capability which supports any data scale, is higher in accuracy and is better in calculation efficiency is realized by designing a matrix coding technology and a security comparison protocol.

The invention discloses a diet health index prediction method fusing M-One coding and deep learning. The method comprises the steps of collecting diet data of a target user; preprocessing the collected data, and performing multi-dimensional influence matrix M-One coding on discrete features to generate a two-dimensional influence matrix; determining parameters of a three-dimensional data format according to the characteristics of the input data; constructing a CNN-LSTM model, performing convolution operation on the data by using a one-dimensional convolutional neural network 1D-CNN, and extracting features of time series data; optimizing the size of a CNN convolutional layer, the number of neurons of the two LSTM layers and the learning rate through a sparrow algorithm; according to the method, M-One coding and a mixed model of 1D-CNN and LSTM are fused, deep learning optimization of multi-dimensional features is realized, and a scientific basis is provided for user health management.

The invention provides a double-input-signal virtual power system stabilizer optimization method, and relates to the technical field of power system stability control. Multi-source signals during VPSS operation are collected in real time and subjected to normalization processing, a one-dimensional time sequence signal is converted into a two-dimensional image matrix through a GASF and a GADF, and the time dependence and the dynamic phase relation of the signals are completely reserved; the physical variable frequency-power coupling is coded through a multi-channel GAF matrix; a lightweight convolutional neural network is adopted, optimization features are extracted from a GAF matrix, and VPSS control parameters are dynamically adjusted by combining a near-end strategy optimization algorithm with Lyapunov stability guarantee; introducing an online learning mechanism and an abnormal switching strategy; a closed-loop framework of signal acquisition, feature coding, mode extraction, parameter optimization, control output and abnormity recovery is adopted, system response is fed back in real time, parameters are adjusted to balance dynamic performance and steady-state efficiency of the system, and the VPSS3B is made to adapt to various operation conditions of access of the network-building type converter.

The invention discloses a feature selection method and a data reconstruction method for shoreline image feature selection. The method comprises the following steps: S1, integrating original data into a sample data set matrix X; s2, initializing a reconstruction transformation matrix W, a transformation matrix Q, an eigenvalue regression coefficient matrix A, a coordinate basis matrix B, a coding matrix E, an auxiliary matrix F and an auxiliary matrix D as unit matrixes, initializing a weight vector p and a mean vector v of a data set as unit vectors, and initializing a weight matrix P = diag (p); and S3, updating the coding matrix E, the auxiliary matrix F and the like based on the data set matrix X, the mean vector v of the data set, the transformation matrix Q, the eigenvalue regression coefficient matrix A, the coordinate basis matrix B and the weight matrix P. According to the invention, the accuracy of data reconstruction and the validity of feature selection can be improved.

The invention discloses a double-layer optimization method and system for real-time flood control scheduling of a series reservoir group. The method comprises the steps that the maximum feasible impounding boundary of each time period is calculated based on the discharge capacity and storage capacity constraint of each reservoir; constructing a space-time risk response kernel matrix, encoding lag response and attenuation intensity of flood routing, constructing a dynamic risk potential energy index in combination with real-time reservoir capacity, and solving space distribution water quantity of each reservoir by taking minimization of the index as a target under physical boundary constraint; the space distribution water quantity is used as a guide target, an evolutionary algorithm embedded with a physical consistent projection operator is adopted, and iterative optimization is carried out on hourly output flow of each reservoir. According to the method, through matrix coding of the space-time risk and introduction of manifold projection solution, the problem that upstream and downstream hydraulic space-time coupling and strong physical constraint are difficult to solve is effectively solved, and the physical consistency and peak clipping efficiency of a scheduling scheme are improved.

The invention relates to a distributed power supply-containing power distribution network fault recovery global optimization method based on ring matrix coding, and the method comprises the steps: constructing a distributed power supply model, and obtaining a distributed power supply-containing power distribution network according to the distributed power supply model; the method comprises the following steps: establishing an ordered annular matrix according to a topological structure of a power distribution network, and performing integer coding on each loop in the ordered annular matrix to obtain an annular matrix code; constructing a power distribution network fault recovery model based on the load recovery rate, the network loss and the utilization rate of the distributed power supply; and generating an initial population according to the ring matrix coding, and performing optimization solution on the power distribution network fault recovery model by adopting an intelligent optimization algorithm to obtain a final fault recovery scheme. According to the method, ring matrix coding and a one-stage fault recovery strategy are combined, all solutions generated by utilizing the ring matrix coding are feasible solutions, and the feasibility of the final solution obtained by carrying out island division on the basis is also ensured.

The invention provides a communication method and a related device. The decoding performance can be improved. The method comprises the steps that a check matrix of a target SC-LDPC code is acquired, the number of rows of the check matrix is M, the number of columns of the check matrix is N, the value of an element is 0 or 1, and the check matrix of the target SC-LDPC code comprises a second matrix composed of the ith row to the (i + (Z + 1) * m-1) th in the M rows and the 0th column to the (n-1) th column in the N columns, the second matrix is obtained by sequentially arranging (Z + 1) third matrixes with the row number of m and the column number of n according to rows, Z third matrixes exist in the (Z + 1) third matrixes, and the number K of the elements 1 included in the Z third matrixes and the number L of the elements 1 included in the second matrix meet the following relation: K / L = Z * z%; and on the basis of the check matrix coding information bit of the target SC-LDPC code, obtaining a first SC-LDPC code word.

The invention provides a communication method and device, equipment and a storage medium. The method comprises the steps that a check matrix corresponding to a target code length is determined based on a 16 * 32 basis matrix, first information obtained through coding based on the check matrix is sent, the first basis matrix supporting a first code length is associated with a second basis matrix supporting a second code length, for example, the position of a cyclic displacement matrix represented by a first element of the first basis matrix, and the position of a cyclic displacement matrix represented by a second element of the second basis matrix is associated with the position of the cyclic displacement matrix; the position of the cyclic displacement matrix represented by the first element in the first basis matrix is the same as the position of the cyclic displacement matrix represented by the first element in the second basis matrix, so that the processing complexity is reduced for the communication equipment to determine the check matrix, and the data transmission reliability of the system is improved. Furthermore, in the embodiment of the invention, in the check matrix determined based on the 16 * 32 basis matrix, the first element in the 17th column is zero, the xth element and the 16th element in the 17th column are the same non-zero elements, and x is an integer greater than 1 and less than 16, so that the coding flexibility is improved.

The invention relates to a security element (21) for a security or value document (20), the security element comprising a substrate and a combined 2D matrix code (1) that is printed on a substrate surface of the substrate and has an arrangement of modules (3) which are arranged on the substrate surface so as to define a matrix code area (2), wherein printed modules (4) of the arrangement of modules (3) are printed using colours that luminesce when illuminated with excitation light and each correspond to a colour channel of a multicolour channel system. The 2D matrix code comprises a first 2D matrix code (5), in which a first item of matrix-coded information is provided so as to be independently optically readable by means of first printed modules, and the first printed modules are printed onto the substrate in a first colour which corresponds to a first colour channel of the multicolour channel system; and a second 2D matrix code (6) which at least partially overlaps the first 2D matrix code in the region of the matrix code area (2), and in which a second item of matrix-coded information is provided so as to be readable by means of second printed modules, and the second printed modules are printed onto the substrate in a second colour which corresponds to a second colour channel of the multicolour channel system, which second colour channel differs from the first colour channel. When optically reading the combined 2D matrix code (1), the first 2D matrix code (5) can be read separately by reading the first colour channel, and the second 2D matrix code (6) can be read separately by reading the second colour channel. The invention also relates to a security or value document (20), to a method for producing a security element for a security or value document, to a method for optically reading a security element, and to a method for verifying a security or value document (20).

The embodiment of the invention provides a method and a device for recovering disk data, which are applied to a disk array, the disk array is composed of a plurality of disks, the maximum number of simultaneously invalid disks allowed by the disk array is r, and r is an integer greater than 2, and the method comprises the following steps: under the condition that the number of simultaneously invalid disks of the disk array is n, obtaining coded data from the disk array, each piece of coding data in the coding matrix respectively belongs to a diagonal check chain, a reverse diagonal check chain and a newly added check chain, and n is an integer greater than or equal to 1 and less than or equal to r; obtaining a transformation matrix of the disk array; and recovering the data in the invalid disk through the transformation matrix and the coding matrix. According to the invention, the problem that the fault-tolerant capability of the RAID6 array is relatively low is solved.

The invention provides an automatic component classification method oriented to multi-level category labels and related equipment. Comprising the following steps: preprocessing to-be-identified component data with a plurality of fields; extracting a text feature in the to-be-identified component data with the first format, and fusing the hierarchical structure information of the category label into the text feature to obtain a fused feature vector; loading a preset coding matrix based on the DECOC algorithm; the coding matrix is provided with a plurality of corresponding binary classifiers; fusing prediction probabilities of a plurality of pre-trained base classifiers based on a soft voting mechanism in each binary classifier to obtain corresponding weighted prediction probabilities so as to obtain a plurality of predicted weighted probabilities corresponding to the plurality of binary classifiers; combining the plurality of weighted prediction probabilities into a prediction code word; and determining a prediction category label group according to the prediction code word and the Hamming distance of each row in a preset coding matrix. And the types of the components can be quickly and accurately predicted.

The present application provides a communication method, an apparatus, a device, and a storage medium. The method comprises: on the basis of a 16*32 base matrix, determining a check matrix corresponding to a target code length, and sending first information obtained by encoding on the basis of the check matrix, wherein a first base matrix supporting a first code length is associated with a second base matrix supporting a second code length, for example, the position of a cyclic shift matrix represented by a first element of the first base matrix is the same as the position of a cyclic shift matrix represented by a first element in the second base matrix. For determination of a check matrix by a communication device, the processing complexity is reduced, thereby improving the data transmission reliability of a system. Further, in embodiments of the present application, in the check matrix determined on the basis of the 16*32 base matrix, the first element in the 17th column is zero, and the xth element and the 16th element in the 17th column are the same non-zero element, x being an integer greater than 1 and less than 16, thereby improving the encoding flexibility.

The invention relates to the technical field of wireless communication, and discloses a matrix coding and multi-hop link state sensing routing protocol suitable for underwater acoustic communication, which comprises the following steps of: initializing each node, and collecting own basic information of each node; each node periodically sends information messages which exist in a hop-count hierarchical coding matrix form and are used for information exchange to neighbor nodes so as to preliminarily construct a network topology view of each node; integrating and perceiving the network topology view of each node so as to form a network topology database of each node; finding a routing path between the source node and the destination node according to a preset rule for dynamic query; carrying out data transmission based on the queried routing path, and monitoring a link state and a state of a one-hop neighbor node of the node to be subjected to data transmission in real time in a data transmission process; judging whether the receiving node receives the data sent by the sending node or not; and judging whether the sending node receives the confirmation message. According to the invention, the convergence of the routing table can be quickly completed.

An accelerated erasure coding system includes a processing core for executing computer instructions and accessing data from a main memory, and a non-volatile storage medium for storing the computer instructions. The processing core, storage medium, and computer instructions are configured to implement an erasure coding system, which includes: a data matrix for holding original data in the main memory; a check matrix for holding check data in the main memory; an encoding matrix for holding first factors in the main memory, the first factors being for encoding the original data into the check data; and a thread for executing on the processing core. The thread includes: a parallel multiplier for concurrently multiplying multiple entries of the data matrix by a single entry of the encoding matrix; and a first sequencer for ordering operations through the data matrix and the encoding matrix using the parallel multiplier to generate the check data.

The invention discloses an image segmentation method and device for a fuel particle layered micro-nano structure. The method comprises the steps of data preprocessing, wherein 3D CT data are converted into 2D Cartesian slices, polar coordinate images are obtained, and strips are divided by means of particle radial symmetry; feature extraction: constructing a main network by U-Net, and adding a position coding module (strengthening angle position modeling) and a disturbance module (improving generalization ability); the multi-class segmentation is divided into boundary dichotomy subtasks, and boundary perception weighting loss is combined with weighted binary cross entropy training. And feature fusion: defining a structure priori matrix coding coating sequence, and training the weight by classifying cross entropy through a conditional random field fusion result (ensuring topological consistency between layers). And finally, splicing strip blocks, and converting polar coordinates into a Cartesian image. The method is applied to automatic nuclear fuel detection and radiation imaging, multi-layer accurate segmentation is achieved, and the detection efficiency and precision are improved.

The application discloses a waiting time constraint batch processing pipeline crane scheduling method based on an improved slime algorithm and belongs to the field of intelligent scheduling of industrial production. The method comprises the following steps: a scheduling model is constructed, the waiting time fuzzy interval is defined by a trapezoidal fuzzy number, and the maximum completion time is minimized as the target; the waiting time uncertainty is accurately characterized by the fuzzy interval, and the fluctuation range of the waiting time in actual production is completely covered; based on the model, a hybrid improved slime algorithm is used for solving; a matrix coding and One-Hot mapping are adopted to adapt to the discrete scheduling scene; a good point set initialization method is introduced to construct an initial population, and multiple scheduling schemes are obtained; and a double tabu table strategy is combined to optimize the local search efficiency and convergence stability. The application is superior to traditional genetic algorithms, discrete slime algorithms and other comparative algorithms in terms of solution quality, convergence speed and robustness, can efficiently adapt to the crane scheduling demand of process-type industries, and provides a stable and reliable solution for multi-crane collaborative scheduling in a complex uncertain environment.

The invention provides an interference communication integrated signal design method based on MIMO-OCDM (Multiple Input Multiple Output-Optical Code Division Multiplexing). The implementation steps are as follows: constructing an application scene of an interference communication integrated signal and a binary communication data matrix; performing OCDM on the binary communication data matrix, and obtaining an MIMO-OCDM communication signal and a frequency shift interference signal; and finally, an MIMO-OCDM interference communication integrated signal is obtained. According to the method, Hadamard matrix coding is carried out on the time domain signal matrix obtained after discrete Fresnel inverse transformation is carried out on the frequency domain QPSK symbol matrix so as to obtain the MIMO-OCDM communication signal matrix, it is guaranteed that multiple time domain signals have the high anti-interference capacity, interference of signals between MIMO transmitting channels is reduced, and the communication performance of the MIMO-OCDM communication signal matrix is improved. Moreover, a non-partner needs to obtain the interference communication integrated signals transmitted by all MIMO transmission channels and correctly analyze and recombine the interference communication integrated signals to obtain the communication data, thereby improving the anti-interference difficulty of the radar of the opposite side.

The implementations of present disclosure provides a coder, based on the way of updating the message by way of using inter-block serial coding for a plurality of cyclic permutation matrices in a layer of a check matrix corresponding to a frame of code words, the coder includes: a posterior probability storage module configured to: store the posterior probability message respectively corresponding to each of the cyclic permutation matrices; a node message storage module configured to: store a message transmitted to a variable node by a check node respectively corresponding to each of the cyclic permutation matrices; and a message updating module configured to: sequentially receive the posterior probability message and the message transmitted to the variable node by the check node respectively corresponding to each of the cyclic permutation matrices in a first order, and sequentially output the updated message transmitted to the variable node by the check node and updated posterior probability message respectively corresponding to each of the cyclic permutation matrices in the second order; wherein the first order is the same as or different from the second order.

The invention discloses a non-negative matrix factorization face clustering method and device based on graph learning and pseudo tag guidance and a medium, and relates to the technical field of data analysis and machine learning. The method comprises the following steps: acquiring a data matrix, a pseudo tag matrix, a regularization parameter and a clustering number; the data matrix comprises face image data; randomly initializing a basis matrix, a coding matrix, a self-expression coefficient matrix and an auxiliary matrix; determining an affinity matrix and a diagonal matrix, and determining a graph Laplacian matrix according to the affinity matrix and the diagonal matrix; and carrying out iteration based on the set maximum number of iterations, and in each iteration process, sequentially updating each matrix and minimizing the target function until the target function converges or the number of iterations reaches the maximum number of iterations, so as to obtain a clustering result. According to the method, by combining a self-representation method in subspace learning, the representation coefficient is generated, the Laplacian matrix is further calculated, and the Laplacian matrix is updated in the iteration process, so that generation of the optimal solution is guaranteed.

The invention relates to a security element (21) for a security or valuable document (20) and a method for manufacturing the security element (21), which comprises a substrate and a combined 2D matrix code (1), wherein the combined 2D matrix code (1) is: printed on a substrate surface; comprises an arrangement of modules formed with printed modules printed using inks that are optically readable under illumination with light; comprises a first 2D matrix code (2) in which a first matrix-coded information is provided by means of first printed modules, and the first printed modules are printed on the substrate with a first ink that is optically readable under illumination with light from a first spectral range; and comprises a second 2D matrix code (3).which is formed in the area of ​​the matrix code surface (2) and is formed at least partially overlapping with the first 2D matrix code, and in which a second matrix-coded information is provided by means of second printed modules, and the second printed modules are printed on the substrate with a second color, which can be optically read under illumination with light from a second spectral range that differs from the first spectral range. When optically reading the combined 2D matrix code (1), the first matrix-coded information can be read by reading the first 2D matrix code (2) under illumination with light from the first spectral range, and the second matrix-coded information can be read by reading the second 2D matrix code (3) under illumination with light from the second spectral range. Furthermore, a security or valuable document (20),a method for optically reading a security element and a method for checking a security or value document (20) as well as a method for optically reading are provided.

This invention discloses an automatic data repair method for missing traffic loop data based on latent feature learning. The method involves matrix encoding of traffic flow data collected by loops to obtain a traffic flow parameter value matrix. By setting latent feature matrices, temporal latent feature matrices, and loop latent feature matrices, and establishing a traffic flow parameter value calculation model, the optimal temporal latent feature matrix and loop latent feature matrix suitable for the traffic flow data are solved using the stochastic gradient descent method. Multiplying these matrices yields the missing traffic flow parameter values, thereby improving data quality and effectively enhancing the accuracy of various traffic statistics and model precision. This invention is the first to propose a traffic flow matrix encoding method, the first to propose traffic temporal latent feature learning and loop latent feature learning methods, and the first to apply them to repair missing traffic flow parameter data.

Embodiments are disclosed for a matrix coded stereo signal with periphonic elements. A mixing matrix, suitable for processing a multi-channel audio input signal, is constructed so that the resulting multi-channel output signal contains the same audio elements from the input signal, wherein the spatial relationships between audio elements, as defined by panning rules associated with the input signal format, are preserved in the output signal, as defined by matrix encoding rules associated with the output signal format. The choice of the coefficients of the mixing matrix is governed by a phase-preference rule that is used to determine the appropriate phase to apply to each input signal channel.