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37 results about "Singular value thresholding" patented technology
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Singular Value Thresholding (SVT) is an algorithm to minimize the nuclear norm of a matrix, subject to certain types of constraints. It has been successfully used in many matrix-completion problems (for more on the matrix completion problem, see Exact matrix completion via convex optimization by E.J. Candès and B. Recht).
Low-rank structure-based sparse compressive sensing MRI (Magnetic Resonance Imaging) image reconstruction method
InactiveCN103400402ARefactoring Method RobustnessReconstructed image accurately2D-image generationDiagnostic recording/measuringSingular value decompositionPattern recognition
The invention discloses a low-rank structure-based sparse compressive sensing MRI (Magnetic Resonance Imaging) image reconstruction method, which mainly solves the problem of difficulty in accurate recovery of an MRI image existing in the conventional technology. The method comprises the following implementation steps: initially recovering the MRI image by using the conventional compressive sensing and looking for a similar block matrix from the image to form an index set; performing singular value decomposition on the similar block matrix and calculating a threshold, and performing threshold calculation on a singular value by using the threshold to obtain an after-threshold singular value; and optimizing the MRI image by using the after-threshold singular value, i.e., circularly performing processes of updating of the similar block matrix and the index thereof, similar block matrix singular value decomposition and threshold and singular value threshold calculation on the MRI image to obtain a final recovery image. The recovered MRI image is clearer and has sharper edges; and the method can be used for processing a medical image.
Owner:XIDIAN UNIV
Channel estimation method of frequency domain filtering mobile WiMAX (world interoperability for microwave access) downlink system based on singular value decomposition
InactiveCN102118350AReduce the impact of noiseReduce "edge effect" problemsBaseband system detailsMulti-frequency code systemsSingular value decompositionMicrowave
The invention relates to a channel estimation method of a frequency domain filtering mobile WiMAX (world interoperability for microwave access) downlink system based on singular value decomposition, which comprises the following steps: using the least-square estimation method to get channel frequency response estimation of an actually distributed pilot symbol subcarrier in each OFDM (orthogonal frequency division multiplexing) symbol; getting the channel frequency response estimation of the subcarriers of the adjacent OFDM symbols by channel frequency response linear interpolation in the corresponding subcarriers in the adjacent OFDM symbols; merging the actual pilot subcarriers with the channel frequency response estimation got by linear interpolation, arranging index values of all the subcarriers in an ascending order, and then sending into a frequency domain filter for denoising after judging a singular value threshold; and finally re-utilizing the linear interpolation to get the final channel frequency response estimation of all the data subcarriers. By using the frequency domain filter, the impacts of noise in a frequency domain least-square channel estimator can be reduced; by performing the judgment of the threshold based on the singular value decomposition on the frequency domain filter, the stability of frequency domain filtering is increased, and the problem of edge effect caused by non-uniform distribution of the pilot symbols is overcome.
Owner:HENAN UNIV OF SCI & TECH
Periodic long-short code direct sequence spread spectrum code division multiple access signal multi-pseudo-code estimation method
ActiveCN105680903AAchieve estimatesEliminate the effects ofTransmissionCode division multiple accessMathematical model
The invention relates to a periodic long-short code direct sequence spread spectrum code division multiple access signal multi-pseudo-code estimation method. The existing direct sequence spread spectrum code division multiple access signal pseudo code blind estimation technology cannot be applied to periodic long-short code direct sequence spread spectrum code division multiple access signals adopting short code spread spectrum and long code scrambling. The method comprises the steps of: constructing periodic long-short code direct sequence spread spectrum code division multiple access signals with complicated structures into a deficit matrix model of short code direct sequence spread spectrum code division multiple access signals, and modeling compound code matrix estimation as a blind source signal separation problem; applying a matrix filling theory to the compound code matrix estimation, and estimating user compound code sequences based on a singular value threshold algorithm and a fast independent component analysis algorithm; and finally proposing a delay triple correlation algorithm by utilizing a displacement stacking feature of an m sequence, and estimating long-short pseudo code sequences contained in the user compound code sequences. The periodic long-short code direct sequence spread spectrum code division multiple access signal multi-pseudo-code estimation method fully utilizes the matrix filling mathematic model and the triple correlation peak feature of the m sequence, and achieves blind estimation of the user compound code sequences, a long scrambling code sequence and a short spread spectrum code sequence of the signals.
Owner:浙江知多多网络科技有限公司
A finite angle projection reconstruction method based on L0 norm and singular value threshold decomposition for double-regular-term optimization
ActiveCN109697691AReduce artifactsEstore contourGeometric image transformationSingular value decompositionData set
The invention relates to a finite angle projection reconstruction method based on double regular item optimization of L0 norm and singular value threshold decomposition, and belongs to the field of image processing. The method specifically comprises the following steps: S1, establishing an optimization problem target equation according to a CT imaging principle, a regularization framework and a projection data set P; S2, initializing parameters; S3, performing iteration by adopting a SART algorithm to obtain an image X, and correcting the X through error feedback; S4, performing gradient L0 norm optimization on the corrected image X to obtain an image XL0, and updating an error d1; S5, performing singular value decomposition on the image optimized in the step S4, adding a soft threshold constraint to optimize the image to obtain an XSVT, and updating an error d2; and S6, carrying out next round of iteration on the image obtained in the step S5 according to the step S3 until an iteration termination condition is met. According to the method, the CT image contour can be effectively recovered, finite angle artifacts are reduced, and therefore the finite angle CT imaging quality and applicability are improved.
Owner:CHONGQING UNIV
A super-resolution image reconstruction method and system based on multi-feature learning
ActiveCN109559278APreserve edge textureInhibitionImage enhancementImage analysisSingular value thresholdingImaging Feature
The invention discloses a super-resolution image reconstruction method and system based on multi-feature learning, and the method makes full use of the rich information contained in a single input image for reconstruction, and does not depend on an external database. According to the method, a mapping relation between image features is established based on cross-scale similarity of images, and a high-resolution image containing high-frequency information is reconstructed for an input image directly by using the mapping relation, so that the defect of high-frequency information loss caused by image reconstruction by using an interpolation amplification method is well overcome. According to the method, effective high-frequency information is acquired by using singular value thresholding, andthe high-frequency information is amplified by using a gradient feature mapping relation and then is overlapped on a high-resolution image in a blocking manner, so that a final image reconstruction result is obtained. According to the method for reconstructing the image by utilizing the image feature combination, noise points of the reconstructed image are effectively inhibited, image edge and texture information is well kept, and detail enhancement of the image is realized.
Owner:SHANDONG UNIV OF FINANCE & ECONOMICS
Temperature field data reconstruction method
ActiveCN108038149ASave storage spaceOvercome deficienciesSpecial data processing applicationsSingular value decompositionSignal-to-noise ratio (imaging)
Provided is a temperature field data reconstruction method. The temperature field data reconstruction method includes the steps of 1, obtaining a temperature field data matrix; 2, conducting random sampling on the temperature field data matrix; 3, on the basis of sample data, filling the temperature field data matrix to reconstruct temperature field data. Before random sampling is conducted on thetemperature field data matrix, rank reduction processing can be conducted on an original matrix by adopting such a singular value decomposition algorithm. A singular value threshold algorithm or a singular value projection algorithm can be adopted to fill the temperature field data matrix on the basis of the simple data to reconstruct the temperature field data. By the adoption of the temperaturefield data reconstruction method and a computer readable storage medium, the problem is solved that in traditional methods, only mathematic relations among stations or correlations among locals are taken into consideration; therefore, global temperature field data can be recovered by using little temperature field station data; at the same time, reconstruction errors are low, the signal-to-noiseratio is extremely high, and not only is a traditional interpolation method overcome, but also data storage space can be saved.
Owner:EAST CHINA JIAOTONG UNIVERSITY
Partial discharge signal time estimation method based on singular value decomposition and improved wavelet packet
PendingCN113283297AEliminate narrowband interferenceFilter out white noise interferenceTesting dielectric strengthCharacter and pattern recognitionSingular value decompositionMixed noise
The invention relates to a partial discharge signal time estimation method based on singular value decomposition and an improved wavelet packet. Comprising the following steps: 1) acquiring GIS actual partial discharge ultrahigh-frequency signals containing mixed noise by using an ultrahigh-frequency sensor, and constructing a corresponding mathematical model; 2) constructing the obtained partial discharge signals into a Hankel matrix, and performing singular value decomposition on the matrix; 3) selecting a reasonable singular value threshold value through a support vector machine, filtering narrow-band interference in the actually measured partial discharge signal, and obtaining a partial discharge signal only containing white noise; 4) setting a reasonable number of decomposition layers and a threshold function by using a denoising method based on an improved wavelet packet threshold, and filtering white noise in the partial discharge signal obtained in the step 3) to obtain a relatively pure GIS partial discharge signal; and 5) obtaining the starting time of partial discharge according to the short-time energy change of the denoised GIS signal. According to the method, the partial discharge starting time can be accurately obtained, and the subsequent requirements for positioning and pattern recognition of GIS partial discharge signals are met.
Owner:SHANGHAI UNIVERSITY OF ELECTRIC POWER
Power distribution network load missing data recovery method based on approximate low-rank matrix completion
PendingCN111159638AImprove relevanceImprove low-rank and low-rank propertiesData processing applicationsComplex mathematical operationsImproved algorithmSingular value thresholding
The invention discloses a power distribution network load missing data recovery method based on approximate low-rank matrix completion. The power distribution network load missing data recovery methodcomprises the following steps: S1, dividing load historical data into two types including a data loss vector and a data integrity vector; S2, forming an original matrix; S3, performing interpolationon the missing elements to form a preliminary recovery matrix; S4, solving a Pearson correlation coefficient matrix for the preliminary recovery matrix, and screening out a vector with high correlation with a data missing vector; s5, performing Pearson correlation coefficient matrix screening on the original vectors containing data loss to obtain vectors; s6, performing recovery completion on missing elements in the recovery matrix by using a singular value threshold shrinkage algorithm; and S7, taking out the next vector with data loss, and repeating the above steps. Compared with a traditional low-rank matrix completion algorithm, the improved algorithm provided by the invention has the advantages that a better overall recovery effect and stability are achieved, and the improved algorithm is more excellently applied to completion of the load missing data of the power distribution network.
Owner:SOUTH CHINA UNIV OF TECH
Intersection arrival rate estimation method based on sampling trajectory data
ActiveCN112201037AStrong generalityStrong graftingDetection of traffic movementAlgorithmSingular value thresholding
The invention relates to an intersection arrival rate estimation method based on sampling trajectory data. The method comprises the following steps: 1) obtaining sampling vehicle trajectory data, andpreprocessing the arrival rate between queuing trajectories into an initial arrival rate matrix; 2) according to the initial arrival rate matrix, filling null values in the initial arrival rate matrixthrough a singular value threshold algorithm to obtain a filled arrival rate matrix; and 3) correcting and repairing the filled arrival rate matrix to obtain a final arrival rate matrix, and accumulating and multiplying the arrival rate vector of each period by the length of the unit time interval to obtain the flow of each period. Compared with the prior art, the method has the advantages of being high in universality, good in expansibility, wide in applicability and the like.
Owner:TONGJI UNIV
Video foreground separation method and system based on adaptive robust principal component analysis
ActiveCN109002802AImprove iteration efficiencyAccurate separationCharacter and pattern recognitionSingular value decompositionAugmented lagrange multiplier method
The invention relates to a video foreground separation method and system based on adaptive robust principal component analysis. The method comprises the following steps: firstly, each image in an image sequence is pulled into a column vector according to a row, and then the column vector is combined into a new matrix M. The matrix M is decomposed by singular value decomposition, and the rth singular value is used as the initial threshold of singular value threshold operation. Then, the first r singular values are reconstructed to form a new matrix Mr, and the ratio of the information containedin the matrix Mr reconstructed by each singular value to the information contained in the matrix Mr is calculated. The singular values are adaptively sparsed according to their proportional magnitude. Finally, according to the inexact augmented Lagrange multiplier method, the matrix M is decomposed into a low rank matrix and a sparse matrix by the singular value threshold operation model. Experiments show that the method of the invention takes into account the influence of information fusion between the foreground target and the background, and accurately separates the low-rank background part and the sparse foreground part.
Owner:WUHAN UNIV OF SCI & TECH
Wireless sensor node task time slot scheduling method based on matrix completion
ActiveCN111954305AImprove performanceIncrease the number of sleep slotsPower managementNetwork topologiesSingular value thresholdingWireless sensor networking
The invention discloses a wireless sensor node task time slot scheduling method based on matrix completion, and the method comprises the steps: dividing the continuous working time of a node into different task time slots through taking a sampling time slot as a unit based on the time correlation and low-rank characteristics of sampling data of a wireless sensor node in an environment monitoring application; the energy consumption of the node is reduced and the battery life of the node is prolonged by reducing the frequency of executing a sampling task time slot by the node; constructing a sampling matrix of wireless sensor nodes at a coordinator end, reconstructing a node sampling matrix subjected to data reduction by utilizing a singular value threshold algorithm, ensuring that completenode sampling data is provided for a user, and meanwhile, meeting the requirement of low-power-consumption operation of the wireless sensor network nodes; designing a singular value threshold algorithm of dynamic correction amplitude, adjusting the correction amplitude according to the reconstruction recovery precision of the sampling matrix, reducing the iteration frequency of matrix reconstruction, shortening the reconstruction time of the sampling matrix, and improving the algorithm performance.
Owner:UNIV OF ELECTRONIC SCI & TECH OF CHINA
Image repair method and image repair device
ActiveCN106651776AImprove accuracyTo achieve the purpose of repairImage enhancementGeometric image transformationPattern recognitionSingular value thresholding
The invention provides an image repair method and an image repair device. The image repair method includes determining at least one missing block containing unknown pixel points in an image to be repaired; for any missing block among the at least one missing block, clustering to obtain a similar block of the any missing block, so as to form a category obtained by clustering of the any missing block; forming a matrix having low-rank property according to the category obtained by clustering of the any missing block; adopting a nuclear norm to approach the rank of the matrix, and adopting a singular value threshold method to solve the matrix; and repairing any missing block in the image to be repaired according to the value after solving the matrix. The technical scheme of the invention improves accuracy of repairing the missing blocks in the image, and effectively achieves the purpose of repairing the image with the missing blocks.
Owner:PEKING UNIV
Thick cloud remote sensing removal method and device
InactiveCN103729831AEasy to keepGuaranteed smoothnessImage enhancementImaging processingSingular value thresholding
The invention discloses a thick cloud remote sensing removal method and device and belongs to the technical field of image processing. The thick cloud remote sensing removal method comprises the following steps of s101, acquiring a reliable element set from a matrix of images to be recovered through deterministic sampling; s102, setting step length and soft threshold parameters according to a singular value and threshold value shrinkage iterative algorithm; s103, performing shrinkage iteration through the singular value and threshold value shrinkage iterative algorithm to reconstruct an original image matrix; s104, judging whether a relative reconstruction error is smaller than the preset error; s105, adding one time on iterations; s106, judging whether the number of iterations is the largest; s107, outputting the reconstructed image matrix. By means of the thick cloud remote sensing removal method, defect information caused by pollution, damage, blocking or the like can be repaired well, information can be recovered, detail texture information is remained, and consistency of image structures is kept.
Owner:INST OF GEOGRAPHICAL SCI & NATURAL RESOURCE RES CAS
SAR image compression method based on robust tensor decomposition
PendingCN113689513AAchieve compressionImprove image compressionImage enhancementImage analysisAlgorithmTensor decomposition
The invention discloses an SAR image compression method based on robust tensor decomposition, and the method comprises the following steps: converting an original multi-channel SAR image into a tensor; performing mode-n expansion on the tensor to obtain a mode-n matrix, and verifying sparse distribution characteristics of SAR multi-channel image tensor decomposition singular values; setting a singular value threshold value, retaining singular values exceeding the threshold value, performing zero setting on the residual singular values, and performing singular value truncation processing; obtaining approximate tensor representation based on singular value truncation and serving as an initial iteration value of multichannel SAR image compression; calculating robust tensor decomposition based on an augmented Lagrangian multiplier method, and performing dimensionality reduction on the singular value matrix; and performing tensor reconstruction by using the data obtained by dimension reduction to obtain a final multi-channel SAR image compression result. According to the method, the SAR multi-channel image is converted into a tensor form, and the robust tensor decomposition technology is utilized to carry out high-dimensional principal component sparse representation on the image, so that remote sensing SAR image compression is realized, and the influence of an outlier on the image compression effect is effectively inhibited.
Owner:SOUTHEAST UNIV
Image repairing method and image repairing device
InactiveCN106651776BImprove accuracyTo achieve the purpose of repairImage enhancementGeometric image transformationPattern recognitionSingular value thresholding
The invention provides an image repair method and an image repair device. The image repair method includes determining at least one missing block containing unknown pixel points in an image to be repaired; for any missing block among the at least one missing block, clustering to obtain a similar block of the any missing block, so as to form a category obtained by clustering of the any missing block; forming a matrix having low-rank property according to the category obtained by clustering of the any missing block; adopting a nuclear norm to approach the rank of the matrix, and adopting a singular value threshold method to solve the matrix; and repairing any missing block in the image to be repaired according to the value after solving the matrix. The technical scheme of the invention improves accuracy of repairing the missing blocks in the image, and effectively achieves the purpose of repairing the image with the missing blocks.
Owner:PEKING UNIV
Video Foreground Separation Method and System Based on Adaptive Robust Principal Component Analysis
ActiveCN109002802BImprove iteration efficiencyAccurate separationCharacter and pattern recognitionSingular value decompositionAugmented lagrange multiplier method
The present invention relates to a video foreground separation method and system based on adaptive robust principal component analysis, comprising: first pulling each image in the image sequence into column vectors by row, and then combining the column vectors into a new matrix M; Singular value decomposition is performed on the matrix M, and the rth singular value is used as the initial threshold of the singular value threshold operation; then the first r singular values are reconstructed to form a new matrix M r , and calculate the information contained in the matrix formed by the individual reconstruction of each singular value to account for the matrix M r The scale of the information contained; the singular values are adaptively thinned according to the scale. Finally, according to the inexact augmented Lagrangian multiplier method, the matrix M is decomposed into a low-rank matrix and a sparse matrix through the singular value threshold operation model. Experiments show that the method of the present invention takes into account the influence of information fusion between the foreground object and the background, and accurately separates the low-rank background part and the sparse foreground part.
Owner:WUHAN UNIV OF SCI & TECH
Super-resolution image reconstruction method and system based on multi-feature learning
ActiveCN109559278BPreserve edge textureInhibitionImage enhancementImage analysisHigh resolution imageFeature mapping
The present disclosure discloses a super-resolution image reconstruction method and system based on multi-feature learning. The method makes full use of the rich information contained in a single input image itself for reconstruction without relying on external databases. The method establishes the mapping relationship between image features based on the cross-scale similarity of the image, and directly uses the mapping relationship to reconstruct the high-resolution image containing high-frequency information from the input image, which effectively overcomes the high-frequency information caused by using the interpolation amplification method to reconstruct the image. Missing defects. The method uses singular value thresholding to obtain effective high-frequency information, and uses the gradient feature mapping relationship to amplify the high-frequency information and superimpose it into blocks on the high-resolution image to obtain the final image reconstruction result. This method of using a combination of image features to reconstruct images effectively suppresses noise points in the reconstructed image, and better maintains image edge and texture information, achieving image detail enhancement.
Owner:SHANDONG UNIV OF FINANCE & ECONOMICS
Compressive Sensing MRI Image Reconstruction Method Based on Low Rank Sparse Structure
InactiveCN103400402BRefactoring Method RobustnessReconstructed image accurately2D-image generationDiagnostic recording/measuringSingular value decompositionPattern recognition
The invention discloses a low-rank structure-based sparse compressive sensing MRI (Magnetic Resonance Imaging) image reconstruction method, which mainly solves the problem of difficulty in accurate recovery of an MRI image existing in the conventional technology. The method comprises the following implementation steps: initially recovering the MRI image by using the conventional compressive sensing and looking for a similar block matrix from the image to form an index set; performing singular value decomposition on the similar block matrix and calculating a threshold, and performing threshold calculation on a singular value by using the threshold to obtain an after-threshold singular value; and optimizing the MRI image by using the after-threshold singular value, i.e., circularly performing processes of updating of the similar block matrix and the index thereof, similar block matrix singular value decomposition and threshold and singular value threshold calculation on the MRI image to obtain a final recovery image. The recovered MRI image is clearer and has sharper edges; and the method can be used for processing a medical image.
Owner:XIDIAN UNIV
Dynamic Video Segmentation Method Based on Weighted Non-convex Regularization and Iteratively Reconstrained Low-Rank Representation
ActiveCN109145738BImprove operational efficiencyImprove accuracyCharacter and pattern recognitionSingular value thresholdingThresholding
A dynamic video segmentation method based on weighted non-convex regularization and iteratively re-constrained low-rank representation, including the following steps: (1) introduce a weighting factor W for the error matrix, and determine the constraint form of the weight matrix; (2) combine the W matrix to calculate the space Laplacian structure matrix L; (3) introduce weighted non-convex Rational function for the singular value of representation matrix Z; (4) optimize the existing GLRR framework through steps 1, 2 and 3, and propose the IRWNR model; (5) adopt the IRM framework (algorithm 1) Iteratively optimize the unknown variables W, L and Z in the target model; (6) Use the proximal gradient (EIPG) algorithm (Algorithm 2) to solve the subproblem of Z; (7) Use the block singular value threshold approximation method (Algorithm 3) Realize the SVT operation in Algorithm 2; (8) Iteratively optimize to obtain W, L and Z, which are used to realize dynamic video segmentation. It has the advantages of high operating efficiency, strong data adaptability, high accuracy and strong robustness.
Owner:ZHEJIANG UNIV OF TECH
Image compression method combining singular value decomposition and wavelet packet transformation
InactiveCN112365552AImprove refactoring qualityImprove compression qualityImage codingPictoral communicationSingular value thresholdingSingular value decomposition method
The invention discloses an image compression method combining singular value decomposition and wavelet packet transformation, and the method comprises the steps: carrying out the singular value decomposition of an original image, and decomposing the original image into a texture vector U, a geometric vector V and a singular value S; adopting an energy spectrum mode to process the characteristic value set to obtain a characteristic value set; processing the feature value set by adopting an adaptive singular value decomposition method to obtain a singular value threshold number K, and selectingthe first K feature values in the feature value set to form an approximate matrix; performing wavelet packet transformation on the first K left and right odd vectors of the approximate matrix, so thata wavelet complete binary tree can be obtained; taking the logarithm energy entropy as a cost function, sequentially comparing the logarithm energy entropy values of the child node and the father node upwards from the bottom layer of the wavelet complete binary tree, reserving the node with the smaller entropy value, deleting the node with the larger entropy value, and further obtaining an optimal wavelet tree; and reconstructing the optimal wavelet tree to obtain a reconstructed image. According to the invention, the image compression quality can be improved and a high compression ratio canbe obtained.
Owner:CHENGDU POLYTECHNIC
Expansion Debye model parameter identification method based on dynamic mode decomposition
ActiveCN119940244ASustainable transportationComputer aided designElectric power equipmentPower equipment
The invention relates to the technical field of insulation dielectric response characteristic identification and aging state evaluation of capacitive power equipment, in particular to an extended Debye model parameter identification method based on dynamic modal decomposition, which comprises the following steps of: acquiring time-varying data of depolarization current, constructing the time-varying data into a Hankel matrix, and taking front N columns and rear N columns to form a matrix A and a matrix B; carrying out economic SVD decomposition on the matrix A, setting a singular value threshold value, abandoning singular values smaller than the singular value threshold value, and reconstructing an SVD decomposition matrix; solving X = A-1B, and then solving a characteristic value and a characteristic vector of X; solving modals of the X and sorting the X according to an energy decreasing sequence; the modes are screened, the screened modes are selected to restore the depolarization current, the effect of dynamic mode decomposition is judged, and whether the singular value threshold value is adjusted or not is judged according to the restoration effect; and calculating the number of branches, a time constant and a relaxation intensity coefficient of the insulation expansion Debye model. The parameter identification result obtained through the method is clear in physical significance.
Owner:DONGFANG ELECTRIC MACHINERY
Optically Excited Infrared Thermography Defect Detection Method Based on Structured Sparse Decomposition
ActiveCN110108754BEffective use of spatio-temporal featuresHigh-resolutionMaterial flaws investigationVertex component analysisSparse constraint
The invention discloses a light-excited infrared thermal imaging defect detection method based on structured sparse decomposition. By performing wavelet decomposition on each frame image of the heat map sequence, only the low-frequency part is reserved to form a new heat map sequence and arrange them sequentially. Restructured into a new matrix, the new matrix is decomposed into a low-rank matrix, the sum of a sparse matrix and a noise matrix, where the low-rank matrix represents the background of the thermal image, and the sparse matrix represents the defects in the thermal image. The sparse matrix is further decomposed into the product of a dictionary matrix and a weight matrix, where the dictionary matrix is used to characterize the different thermal modes of different defects on the same specimen, and the weight matrix has sparse constraints and non-negative constraints. The low-rank matrix is solved by the singular value threshold decomposition method, the dictionary matrix is solved by the vertex component analysis method, the weight matrix is solved by the multiplier alternating direction method, and finally the sparse matrix is reconstructed into a defect image matrix, so as to realize the defect detection of infrared thermal imaging .
Owner:四川沐迪圣科技有限公司
Channel estimation method of frequency domain filtering mobile WiMAX (world interoperability for microwave access) downlink system based on singular value decomposition
InactiveCN102118350BReduce the impact of noiseReduce "edge effect" problemsBaseband system detailsMulti-frequency code systemsSingular value decompositionChannel frequency response
Owner:HENAN UNIV OF SCI & TECH
A method for identifying parameters of an extended debye model based on dynamic modal decomposition
ActiveCN119940244BSustainable transportationComputer aided designElectric power equipmentPower equipment
The present application relates to the technical field of identification of dielectric response characteristics and aging state evaluation of capacitive power equipment, and particularly relates to a dynamic modal decomposition-based extended Debye model parameter identification method, which comprises the following steps: obtaining depolarization current data changing with time, constructing the data into a Hankel matrix, taking the first N columns and the last N columns to form a matrix A and a matrix B; performing economic SVD decomposition on the matrix A, setting a singular value threshold, discarding singular values less than the singular value threshold, and reconstructing the SVD decomposition matrix; solving X=A ‑1 B, then solving the eigenvalues and eigenvectors of X; solving the modes of X and sorting them in the order of decreasing energy; screening the modes, restoring the depolarization current by using the screened modes, judging the effect of dynamic modal decomposition, and judging whether to adjust the singular value threshold according to the restoration effect; calculating the branch number, time constant and relaxation intensity coefficient of the insulation extended Debye model. The parameter identification result obtained by the method has clear physical meaning.
Owner:DONGFANG ELECTRIC MACHINERY
Lithium battery charging curve reconstruction method based on capacity increment analysis and charging segment
PendingCN121749449ABatteries circuit arrangementsElectrical testingElectrical batterySingular value thresholding
The invention belongs to the technical field of charging curve reconstruction, and particularly relates to a lithium battery charging curve reconstruction method based on capacity increment analysis and a charging fragment, which comprises the following steps of: collecting charging data, performing polarization analysis based on a second-order RC equivalent circuit model, selecting the data after polarization stabilization as an effective input fragment, and calculating the charging curve of a lithium battery. Analyzing the charging curve by using a capacity increment analysis method; aligning a plurality of capacity increment curve segments along a voltage axis to construct an observation matrix, performing soft threshold contraction operation on a current estimation matrix based on a singular value threshold method, and continuously correcting a low-rank estimation matrix through an iterative optimization process; and in the correction process, calculating the relative variation of the Floribenius norm of the matrix subjected to continuous two iterations, and when the iteration stopping condition is met, outputting the currently corrected low-rank estimation matrix as a complemented reconstruction result. According to the method, the repeatable behavior characteristics of the battery in the constant-current charging process are fully utilized, so that data acquired in different periods have high comparability.
Owner:STATE GRID ENERGY CONSERVATION SERVICE
A Method of Reconstructing Temperature Field Data
ActiveCN108038149BSave storage spaceOvercome deficienciesDigital data information retrievalSpecial data processing applicationsSingular value decompositionSingular value thresholding
Provided is a temperature field data reconstruction method. The temperature field data reconstruction method includes the steps of 1, obtaining a temperature field data matrix; 2, conducting random sampling on the temperature field data matrix; 3, on the basis of sample data, filling the temperature field data matrix to reconstruct temperature field data. Before random sampling is conducted on thetemperature field data matrix, rank reduction processing can be conducted on an original matrix by adopting such a singular value decomposition algorithm. A singular value threshold algorithm or a singular value projection algorithm can be adopted to fill the temperature field data matrix on the basis of the simple data to reconstruct the temperature field data. By the adoption of the temperaturefield data reconstruction method and a computer readable storage medium, the problem is solved that in traditional methods, only mathematic relations among stations or correlations among locals are taken into consideration; therefore, global temperature field data can be recovered by using little temperature field station data; at the same time, reconstruction errors are low, the signal-to-noiseratio is extremely high, and not only is a traditional interpolation method overcome, but also data storage space can be saved.
Owner:EAST CHINA JIAOTONG UNIVERSITY
Remote Sensing Thick Cloud Removal Method and Device
InactiveCN103729831BTroubleshoot technical issues with limited fixabilityImage enhancementImaging processingSingular value thresholding
Owner:INST OF GEOGRAPHICAL SCI & NATURAL RESOURCE RES CAS
Low-voltage distribution network phase-household relation identification method based on matrix completion
PendingCN113839384AImprove accuracyImprove the accuracy of intelligent recognitionEnergy industryDesign optimisation/simulationOccurrence dataAlgorithm
The invention discloses a low-voltage distribution network phase-household relation identification method based on matrix completion. The method comprises the following steps of collecting the current time sequence data of a low-voltage bus and all ammeters in multiple time periods in a target station area to form an original current matrix; decomposing, optimizing and combining the original current matrix with the data missing condition by adopting a singular value threshold decomposition method to form a current completion matrix; calculating a relative error of the complemented matrix relative to the original current matrix by solving the ratio of the norm of the difference between the current complementing matrix and the original current matrix to the norm of the original current matrix; and adopting a mathematical optimization method to identify the corresponding phase-household relationship in the transformer area. According to the method, the engineering condition that the acquired data of the low-voltage transformer area is not complete is effectively complemented by means of a data analysis technology, the phase-household identification accuracy is improved, and the intelligent and efficient operation and maintenance of the low-voltage transformer area are facilitated.
Owner:SOUTH CHINA UNIV OF TECH
A moving object detection method based on non-convex low-rank sparse decomposition
ActiveCN110210282BAccurate portrayalSolve the problem of decreased accuracy of foreground targetsImage analysisCharacter and pattern recognitionSingular value thresholdingThresholding
The invention discloses a moving target detection method based on non-convex low-rank sparse decomposition, the steps are: 1. 0 The non-convex surrogate function in the general form of norm is applied to the approximation of low-rank matrices; and the non-convex surrogate function with good performance is selected; 2. The non-convex low-rank sparse decomposition model is established; 3. The singular value threshold SVT uses the generalized singular value The threshold GSVT is used instead; 4. Use the alternating direction multiplier method to solve the non-convex low-rank sparse decomposition model, and obtain the foreground target according to the obtained solution. The invention solves the problem that the approximate expression of the rank function in the existing method is inaccurate, resulting in that the recovered background is not clean enough and the accuracy of the extracted foreground target decreases.
Owner:NANJING UNIV OF POSTS & TELECOMM
A Method of Intersection Arrival Rate Estimation Based on Sampling Trajectory Data
ActiveCN112201037BStrong generalityStrong graftingDetection of traffic movementAlgorithmSingular value thresholding
The invention relates to a method for estimating the arrival rate of an intersection based on sampled trajectory data, comprising the following steps: 1) obtaining sampled vehicle trajectory data, and preprocessing the arrival rate between queuing trajectories into an initial arrival rate matrix; 2) according to the initial Arrival rate matrix, filling the empty values in the initial arrival rate matrix through the singular value threshold algorithm to obtain the filled arrival rate matrix; 3) Correcting and repairing the filled arrival rate matrix to obtain the final arrival rate matrix. The arrival rate vector of each period is accumulated and multiplied by the length of the unit time interval to obtain the flow rate of each period. Compared with the prior art, the present invention has the advantages of strong generality, good expansibility, wide applicability and the like.
Owner:TONGJI UNIV