121 results about "Quantization matrix" patented technology

The invention provides a model training method and device, equipment and a storage medium, and relates to the technical field of computers, in particular to the technical field of neural network models and model training. The specific implementation scheme is as follows: a calculation unit executes quantization matrix multiplication based on Hadamard pre-transformation on an activation tensor and a weight tensor of a target model stored in a memory so as to generate an output tensor of a linear layer based on a low-precision tensor with smaller data bit width; using the output tensor and a subsequent network layer of the target model to complete forward propagation so as to obtain a loss value; and according to the loss value, updating model parameters of the target model stored in a memory through a back propagation algorithm. By means of the technical scheme, on the premise that the model training precision is guaranteed, memory resource occupation and the calculation amount in the calculation process can be remarkably reduced, and the training cost is reduced.

The invention discloses a network security situation awareness method based on artificial intelligence, and the method comprises the following steps: collecting multi-source heterogeneous data, and generating a standardized data set; spatial-temporal feature decoupling is carried out, and spatial-temporal dimension features are separated; fusing the time-space cross attention, and outputting a fused time-space feature vector; constructing a causal inference engine, and outputting a dynamic causal graph and an anti-fact inference result set; constructing a dynamic risk propagation model, and outputting a whole asset risk value matrix and a risk propagation path diagram; generating a situation quantization matrix, constructing an adversarial training decision network, and outputting a defense strategy set verified by adversarial training; automatically generating a strategy; and a man-machine cooperative verification closed loop is realized. According to the method, dynamic reconstruction of a threat propagation path is realized through spatial-temporal feature decoupling and a causal reasoning engine, and a risk positioning error is reduced; and the adversarial training decision network is combined, so that the misjudgment rate of the defense strategy in the simulation APT attack test is reduced.

The embodiment of the invention provides a data processing method and device for large model parameters, equipment and a medium, and belongs to the technical field of artificial intelligence. The method comprises the following steps: obtaining a quantization matrix and a meta-parameter of a target large model from different partitions of a first data storage area of a memory, reading memory access granularity representing the data processing capability of a processor, and determining the size of a filling area between the insertion meta-parameter and the quantization matrix based on the memory access granularity; dividing a second data storage area different from the first data storage area in the memory, and sequentially writing the meta-parameter, preset filling information matched with the filling area in size and the quantization matrix into the second data storage area to obtain an initial data atomic block; and continuously arranging all the initial data atomic blocks in the second data storage area to obtain a target data atomic block. By optimizing the data storage mode, the memory bandwidth utilization rate of the processor in the reasoning process is improved, and then the data calculation efficiency of the processor is improved.

The invention discloses an automobile wire harness detection device based on artificial intelligence and machine vision, and relates to the technical field of visual inspection, and the device comprises a film covering module which enables an automobile wire harness to be immersed in a control groove of magnetic fluid to form a nano film; the marking module projects interference fringes to the nano-film through a laser interference projector, and marks timestamps and capture numbers of fringe images; the image module is used for applying magnetic field pulses in the timestamps of the adjacent stripe images to generate a turbulent flow image sequence; the quantization module is used for respectively carrying out interference fringe phase analysis on the turbulence images in the turbulence image sequence to generate a curvature quantization matrix; the defect module is used for analyzing the curvature quantization matrix through a pre-trained convolutional neural network and identifying a defect type and a coordinate position; and the result module is used for superposing the defect type and the coordinate position to the stripe image to generate a defect distribution thermodynamic diagram. According to the invention, high-precision intelligent defect identification of wire harness detection is realized through cooperation of the nano-film and the convolutional neural network.

The invention provides a model quantification method and device, a model reasoning method and device, equipment, a medium and a product. Performing sensitivity evaluation on each network channel based on the weight matrix to obtain the channel sensitivity of each network channel; based on the channel sensitivity of the network channel corresponding to each element in the weight matrix, rearranging and dividing each element in the weight matrix to obtain a plurality of weight sub-matrixes; based on the channel sensitivity of the network channel corresponding to each element in each weight sub-matrix, allocating quantitative resources to each weight sub-matrix; performing quantization processing on the weight sub-matrixes based on the quantization resources to obtain a plurality of quantization sub-matrixes; and correcting the plurality of quantization sub-matrixes to obtain a target quantization matrix. Elements in a weight matrix can be accurately and orderly gathered according to channel sensitivity, different weight parameters are finely classified, different weight sub-matrixes are compatible for quantification, the problem of weight heterogeneity caused by multiple modes is solved, and the accuracy of model quantification is improved.

The invention relates to the field of image encryption, in particular to a chaos image encryption method based on DNA coding, which comprises the following steps: S1, calculating a hash value of a plaintext grayscale image by using a hash algorithm, and generating an initial key based on the hash value, S2, iterating a chaos sequence by using the initial key as an initial value and a control parameter of an iteration variable of two-dimensional chaos mapping 2D-CSMM, performing segmentation and quantization processing on the chaotic sequence to obtain a quantization matrix; performing reduction XOR operation on the quantization matrix to obtain a bit matrix; performing scrambling processing on a plaintext grayscale image based on the quantization matrix and the bit matrix to obtain a ciphertext image; s5, performing DNA diffusion processing on the ciphertext image based on the quantization matrix and the bit matrix to obtain an encrypted image; s6, decrypting the encrypted image according to the inverse process of image encryption to obtain a decrypted image; according to the encryption method provided by the invention, the security of image encryption is greatly improved, and the security performance and the encryption effect are good.

The invention discloses an image compression method and system based on edge gradient self-adaption, and the method comprises the steps: converting an input image into a YCbCr color space, calculating an edge gradient value of a brightness component, and employing iteration processing including a feedback mechanism: dividing an edge region and a non-edge region based on a current self-adaption edge gradient threshold value, the blocks are divided according to different sizes; dynamically updating an edge gradient threshold according to a block statistics result, and adaptively optimizing region division through multiple iterations; and finally, based on the optimized blocking result, processing the edge region and the non-edge region by adopting different quantization strategies, generating a quantization matrix, and then carrying out entropy coding to output a compressed code stream. According to the method, adaptive adjustment of compression parameters is realized through an iterative optimization mechanism, image edge and texture details are effectively reserved while a high compression rate is ensured, and the subjective visual quality of a compressed image is remarkably improved.

The invention relates to the technical field of video image compression, in particular to a video image compression algorithm based on an H.266 / VVC standard, which comprises the steps of generating a visual attention thermodynamic diagram through time-space domain saliency detection, adjusting a coding tree unit QP value in a partitioned manner to realize adaptive quantization, and designing an asymmetric quantization matrix to optimize a transformation quantization effect. And the generative adversarial network is used to enhance the quality of the reconstructed frame. According to the method, the blocking effect of a complex texture region can be effectively inhibited, the quantization distortion of a human eye sensitive region is reduced, the edge preserving capability is improved, the subjective visual quality and compression efficiency of the video are remarkably improved, and the high-definition video transmission requirement is met.

The invention belongs to the technical field of vehicle-road cloud integrated systems, and discloses a method for analyzing functional requirements of a vehicle-road cloud integrated system based on LLM, and the method comprises the following steps: S1, collecting and preprocessing basic data; s2, structured extraction of VRCIS requirements: semantic analysis is performed on the design requirement document based on LLM, a data structure is converted, and functional requirements and non-functional requirements are extracted; s3, constructing a VRCIS demand and benefit-critical person matrix: based on the output of the S2, mapping the function demand of each VRCIS benefit-critical person and the system design key dimension, and generating a standardized quantization matrix which can be identified by a machine; s4, quantitative analysis of demand priorities: identifying the demand priorities through quantitative analysis by utilizing the benefit-critical person matrix constructed in the S3 and fusing cost-benefit analysis; and S5, outputting a demand analysis result. The method is high in analysis efficiency, and the problems that in the analysis process, demand decomposition is not in place, the demand priority is not clear, and a general demand template cannot fully express VRCIS complexity are solved.

An encoder, includes: memory; and circuitry coupled to the memory, in which in a first encoding process, the circuitry quantizes a current block using a first quantization matrix in both of a first case where the circuitry performs primary transform and the circuitry does not perform secondary transform and a second case where the circuitry performs both the primary transform and the secondary transform, in a second encoding process, the circuitry quantizes the current block using a second quantization matrix in a third case where the circuitry performs primary transform and the circuitry does not perform secondary transform, and in a third encoding process, the circuitry quantizes the current block without using a quantization matrix in both of a fourth case where both the primary transform and the secondary transform are skipped and a fifth case where both the primary transform and the secondary transform are performed.

A system and method for energy-efficient approximate digital JPEG and MJPEG-compression. The system includes a controller unit to control a processing loop for processing image blocks based on a comparison of a current image block to a previous image block. The system includes a quantization unit configured to quantize the frequency domain representation using an approximate quantization process and a quantization (Q) matrix. The quantization unit is configured to: identify, a nearest power of two value for each element of the quantization matrix; generate an updated Q matrix by assigning each element of the quantization matrix with the identified nearest power of two value; and shift each element of the updated Q matrix by a number of bits to generate a quantized frequency domain representation. The number of bits corresponds to the identified nearest power of two for the corresponding element of the updated Q matrix.

Disclosed is an operation method for executing an addition operation on quantized matrices. This operation method comprises the steps in which: an NPU matrix multiplication unit executes a matrix multiplication operation on a first matrix and a second matrix, wherein the first matrix has a size of R0*2C0 and is obtained by concatenating a first input tensor and a second input tensor, each of which is composed of quantized values and has a size of R0*C0, and the second matrix is obtained by concatenating a first diagonal matrix having a size of C0*C0 and having all main diagonal element values equal to integer N1 and a second diagonal matrix having a size of C0*C0 and having all main diagonal element values equal to integer N2; and an NPU vector processing unit calculates elements of a non-quantized tensor obtainable by adding the second input tensor to the first input tensor, by adding real number B to a product of real number R and a third matrix calculated by the matrix multiplication operation.

The invention relates to the technical field of artificial intelligence model optimization, in particular to a large model reasoning acceleration method and device for packet sensing quantization and residual correction, and the method comprises the steps: carrying out the statistical analysis of the weight and activation of each layer of a large model, and generating a channel feature matrix; constructing a learnable grouping mapping matrix, dividing channels into different groups, and dynamically distributing quantized bit width to obtain a grouping weight matrix; on the basis of the channel weight-activation joint sensitivity, calculating each grouping error contribution on line by using a small prediction model, and adjusting a grouping weight matrix to generate an optimized weight matrix; constructing an error control matrix to dynamically adjust the quantization error along the propagation path, and generating a correction matrix; dynamically adjusting the sparse rate and the quantization precision according to the channel feature matrix and the hardware constraint by combining a structured sparse strategy, and generating a sparse quantization matrix; in the reasoning process, model reasoning is carried out according to the correction matrix and the sparse quantization matrix, and the weight matrix and the correction matrix are updated and optimized in a closed-loop mode.

The application discloses a network security situation awareness method based on artificial intelligence, comprising the following steps: collecting multi-source heterogeneous data to generate a standardized data set; decoupling space-time characteristics to separate space-time dimension characteristics; fusing space-time cross attention to output fused space-time characteristic vectors; constructing a causal reasoning engine to output a dynamic causal diagram and a counterfactual reasoning result set; constructing a dynamic risk propagation model to output a full asset risk value matrix and a risk propagation path diagram; generating a situation quantization matrix, constructing an adversarial training decision network, and outputting a defense strategy set verified by adversarial training; automatically generating a strategy; and man-machine collaborative verification closed loop. Through space-time characteristic decoupling and the causal reasoning engine, the application realizes dynamic reconstruction of a threat propagation path, reduces risk positioning error; in combination with the adversarial training decision network, the application makes the defense strategy have a reduced misjudgment rate in the simulation APT attack test.

The invention discloses an image compression method and device, and the method comprises the steps: comparing a threshold matrix with a frequency domain coefficient matrix, so as to calculate the number of effective coefficients of each frequency point in each frequency domain coefficient matrix, forming an effective coefficient matrix, and achieving the independent calculation of the scaling of each frequency point based on the effective coefficient matrix and a scaling calculation formula. According to the method, a frequency domain coefficient matrix is obtained, a corresponding scaling matrix is formed, a corresponding optimized quantization matrix is generated according to the scaling matrix, the frequency domain coefficient matrix is quantized by using the optimized quantization matrix, scanning, data rearrangement and entropy coding operation are executed, and image compression is completed.

Disclosed are an image encoding / decoding method and a non-transitory computer-readable recording medium. The image decoding method includes: determining whether transform skipping can be applied to a current block; determining whether the current block is a transform skipping block based on the above determination; deriving a scaling factor of the current block depending on whether the current block is a transform skipping block; performing inverse quantization by scaling quantized transform coefficients of the current block based on the scaling factor; obtaining residual samples of the current block by selectively performing inverse transform on the scaled transform coefficients; performing prediction on the current block to generate prediction samples of the current block; and reconstructing the current block based on the residual samples and the prediction samples. When the current block is not a transform skipping block, the scaling factor is derived based on a quantization matrix and locations of transform coefficients within the current block. When the current block is a transform skipping block, the scaling factor is derived to be equal to a fixed constant value. The transform skipping block is specified based on information indicating whether an inverse transform is applied to the current block.

Disclosed herein is a video decoding method including determining one or more adaptation parameter sets including a quantization matrix set including a plurality of quantization matrices, determining an adaptation parameter set including a quantization matrix set applied to a current picture or a current slice from among the one or more adaptation parameter sets, dequantizing transform coefficients of a current block of a current picture or a current slice based on the quantization matrix set of the determined adaptation parameter set, and reconstructing the current block based on the dequantized transform coefficients, wherein the adaptation parameter set includes coding information applied to one or more pictures or slices.

The invention relates to an electromagnetic component performance analysis method and device, equipment, a medium and a product, and the method comprises the steps: constructing a judgment matrix according to the relative importance degree of a plurality of quantitative indexes affecting the performance of a to-be-analyzed electromagnetic component; the to-be-analyzed electromagnetic components comprise a plurality of electromagnetic components in the target substation; determining a weight matrix corresponding to each quantitative index according to the value of each element in the judgment matrix; determining a membership matrix corresponding to each quantitative index according to the membership between each quantitative index and a preset quantitative level; determining a comprehensive quantization matrix according to the weight matrix and the membership matrix; each element value in the comprehensive quantization matrix represents an electromagnetic protection level quantization value corresponding to the to-be-analyzed electromagnetic component. By adopting the method, the accuracy and reliability of a final electromagnetic component performance analysis result can be improved, and a scientific basis is provided for operation management, equipment type selection and the like of a transformer substation.

The present disclosure provides a network training method and device, a network optimization method and a data processing method. The network training method comprises: quantizing a sample neural network to obtain a sample quantization matrix in a first state, the matrix being used to indicate the type of an operator in the network and the connection relationship between the operators; performing optimization function prediction on the sample quantization matrix in the mth state through a reinforcement network in the mth state to determine a sample optimization function in the mth state; optimizing the sample neural network in the mth state through the sample optimization function in the mth state to obtain a sample neural network in the m+1th state; training the reinforcement network in the mth state according to the reward values of the sample neural networks in the mth state and the m+1th state to obtain a reinforcement network in the m+1th state; and determining the trained reinforcement network under the condition that a training condition is met. The embodiments of the present disclosure can improve the training effect of the reinforcement network.

The invention discloses a dust monitoring method and monitoring system for an out-of-service wind power blade, and relates to the technical field of dust monitoring. The method comprises the following steps: marking a tracing material on an ex-service wind power blade, establishing a mapping relation between spectral characteristics and blade information, transmitting a coaxial composite light detection light field to a to-be-monitored ex-service wind power blade processing area through a multi-frequency laser synergistic excitation source, receiving a returned multi-mode light response mixed signal, and determining the ex-service wind power blade to be monitored according to the received multi-mode light response mixed signal. The method comprises the following steps: performing multi-spectral feature calculation on a multi-modal light response mixed signal through a sub-channel spectral decoupling mechanism to obtain a component-form-traceability three-dimensional feature set, generating a dust hazard level dynamic quantization matrix through a cross-spatio-temporal data coupling knitting algorithm, and constructing a global dust situation monitoring panoramic map through a virtual-real mapping visualization technology. And a directional traceability regulation and control instruction is generated. The dust risk management and control efficiency and precision in the decommissioning wind power blade treatment process can be improved, and harm of dust to personnel health and the environment is reduced.

A system and method for energy-efficient approximate digital JPEG and MJPEG-compression. The system includes a controller unit to control a processing loop for processing image blocks based on a comparison of a current image block to a previous image block. The system includes a quantization unit configured to quantize the frequency domain representation using an approximate quantization process and a quantization (Q) matrix. The quantization unit is configured to: identify, a nearest power of two value for each element of the quantization matrix; generate an updated Q matrix by assigning each element of the quantization matrix with the identified nearest power of two value; and shift each element of the updated Q matrix by a number of bits to generate a quantized frequency domain representation. The number of bits corresponds to the identified nearest power of two for the corresponding element of the updated Q matrix.

An orthogonal transform unit orthogonally transforms prediction residuals in a block of a P×Q array of pixels, thereby generating an N×M (N is an integer satisfying N<P, and M is an integer satisfying M<Q) array of orthogonal transform coefficients. A quantization unit quantizes the N×M array of the orthogonal transform coefficients using at least a quantization matrix of an N×M array of elements, and thereby generates an N×M array of quantized coefficients.

The present disclosure relates to an image processing device and method that enable suppression of an increase in the amount of coding of a quantization matrix.An image processing device of the present disclosure includes an up-conversion unit configured to up-convert a quantization matrix limited to a size less than or equal to a transmission size that is a maximum size allowed for transmission, from the transmission size to a size that is identical to a block size that is a processing unit of quantization or dequantization. The present disclosure is applicable to, for example, an image processing device for processing image data.

The present disclosure relates to the technical field of computer system, and provides a fine-grained quantization matrix multiplication device and method based on systolic array, which comprises: an input scheduling module, configured to receive input matrix data and corresponding quantization factor data, and to attach type identifiers to the input matrix data and the quantization factor data; a systolic array, connected by multiple processing units in a two-dimensional mesh structure, and each processing unit is configured to select to perform a multiply-accumulate operation or a dequantization operation according to the data received from an upstream and the type identifier of the data; wherein the input scheduling module is configured to schedule the data with the attached type identifiers to the systolic array by rows and by columns; and an output module, configured to receive and output the calculation results calculated by the systolic array. The problems of low efficiency of quantization factor insertion, fragmentation of dequantization and calculation pipeline, and large task switching overhead are solved, and the data multiplexing rate, calculation efficiency and overall energy efficiency ratio of the systolic array in the fine-grained quantization scenario are significantly improved.

The present application provides an image decoding apparatus and method, an image encoding apparatus and method, and a storage medium. A decoding unit decodes data corresponding to a first array of quantized coefficients including an N x M array of quantized coefficients corresponding to a first block and data corresponding to a second array of quantized coefficients including an N x M array of quantized coefficients corresponding to a second block. An inverse quantization unit derives a first array of orthogonal transform coefficients from the first array of quantized coefficients using at least a first quantization matrix of elements of the N x M array and derives a second array of orthogonal transform coefficients from the second array of quantized coefficients using at least a second quantization matrix of elements of the N x M array. An inverse orthogonal transform unit inverse orthogonally transforms the first array of orthogonal transform coefficients to generate a first prediction residual of pixels of a P x Q array and inverse orthogonally transforms the second array of orthogonal transform coefficients to generate a second prediction residual of pixels of the N x M array.

Various embodiments provide an encoder that generates a plurality of quantization matrix elements for a current block; generates a quantization matrix using the plurality of quantization matrix elements; and quantizes, using the quantization matrix, transform coefficients of the current block. The quantization matrix includes only a subset of quantization matrix elements of the plurality of quantization matrix elements. Each of the subset of quantization matrix elements has an x-coordinate value less than a threshold x-coordinate value, a y-coordinate value less than a threshold y-coordinate value, or an x-coordinate value less than the threshold x-coordinate value and a y-coordinate value less than the threshold y-coordinate value.

The application discloses an adversarial sample generation method based on AdvDrop, relates to the technical field of machine learning security, and processes two different branches of an image input space domain and a frequency domain; for the frequency domain attack AdvDrop, first, the input image is segmented into N*N blocks, and discrete cosine transform (DCT) is used on each block to convert them to the frequency domain; a quantization matrix M is introduced to reduce some specific frequencies of the transformed image; a tangent function is introduced in the quantization process to gradually approach the quantization function, and then the quantization matrix M is accurately adjusted through the new quantization function; then, the image is converted from the frequency domain to the space domain through inverse discrete cosine transform (IDCT) operation; finally, the space domain attack and the frequency domain attack fusion module are used to iteratively update the adversarial perturbation by using the gradients from different fields, and the adversarial sample is generated; the quality of the generated adversarial sample is improved, the difference between the distribution characteristics of the adversarial sample and the distribution characteristics of the real sample is reduced, and the attack success rate is improved.

The invention discloses an energy consumption partition monitoring method and system for an intelligent mold factory, and the method comprises the steps: obtaining the energy consumption data of the intelligent mold factory through a sensor network, and generating an energy consumption data set; performing spatial clustering processing on the energy consumption data set through a spatial clustering algorithm to obtain energy consumption spatial distribution characteristics; calculating an energy consumption difference value and a physical distance between two adjacent production areas according to the plurality of energy consumption space distribution characteristics, and generating an energy consumption gradient quantization matrix; determining a high-energy-consumption area list according to the energy consumption gradient quantization matrix, and analyzing an energy flow path guided in a gradient direction by adopting a flow direction tracking algorithm to obtain position coordinates of an abnormal loss point; determining an energy consumption propagation influence range of the abnormal loss point according to the position coordinates and the energy consumption gradient quantization matrix, calculating power distribution schemes of the plurality of production areas through a dynamic programming algorithm, and generating power upper limit adjustment data according to the plurality of power distribution schemes; and updating the power distribution of the plurality of production areas through a real-time control algorithm.

Decoding corresponding to a first array of quantized coefficients including an N×M array corresponding to a first block and data corresponding to a second array including an N×M array corresponding to a second block. Deriving a first array of orthogonal transform coefficients from the first array of quantized coefficients by using at least a first quantization matrix of an N×M array of elements, and derives a second array of orthogonal transform coefficients from the second array of by using at least a second quantization matrix of an N×M array of elements. Performing inverse orthogonal transform on the first array of orthogonal transform coefficients to generate a P×Q array of pixels of first prediction residuals, and performs inverse orthogonal transform on the second array of orthogonal transform coefficients to generate an N×M array of pixels of second prediction residuals.

The application relates to the technical field of network security, and discloses a data processing method and system for network security monitoring.The method comprises the following steps: acquiring network entity connection relationship data and historical attack event data; constructing graph structure data and obtaining an initial representation vector by embedding the graph structure data in a preset graph neural network; weighting the initial representation vector according to a network entity importance index to obtain a quantization matrix; extracting risk nodes from the quantization matrix, and obtaining potential transmission paths by random walk when the number of the risk nodes exceeds a preset number threshold; processing the potential transmission paths in segments to confirm a risk core mode; marking a propagation chain set based on the risk core mode; extracting node risk values from the propagation chain set and preprocessing the node risk values to obtain a risk state vector, inputting the risk state vector into a preset time sequence prediction model to obtain a predicted diffusion distribution probability; determining an enhanced monitoring target based on a preset probability threshold and the predicted probability; and obtaining a monitoring enhancement scheme after solving a target function.