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53 results about "Linear reconstruction" patented technology
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A task unloading method based on user experience in edge computing network
ActiveCN109240818AImprove experienceReduce energy consumptionProgram initiation/switchingResource allocationService experienceEnergy expenditure
The invention discloses a task unloading method based on user experience in an edge computing network. The method comprises the following steps: a scheduler receives a task unloading request submittedby a user; a task unloading strategy model is jointly optimized based on time and energy expenditure; the model is solved and calculated based on a branch and bound algorithm of linear reconstruction; the task unloading strategy model is optimized based on time and energy expenditure. Based on the results of the calculation, the decision is made whether the task is unloaded to the edge server, and the tasks unloaded to the edge server are communicated and the computational resources are allocated. The invention combines two key indexes of optimization time delay and energy consumption, and solves the problem by branch and bound algorithm based on linear reconstruction technology, which can reduce the energy consumption of mobile intelligent device under the condition of ensuring the completion of the task, reduce the task processing time delay, and achieve the purpose of maximizing the optimization of user experience.
Owner:CENT SOUTH UNIV
Process for reconstructing human face image super-resolution by position image block
InactiveCN101477684AQuality improvementAvoid the search stepImage enhancementFeature extractionComputer graphics (images)
The invention provides a method for reconstructing face image super-resolution by utilizing position image blocks. The method comprises the following steps: dividing a low-resolution face image and face images in high-low resolution training sets into mutually overlapped image blocks; calculating the optimal value of each divided image blocks input in the low-resolution face image during the linear restoration of the position block of each sample image in the low-resolution training set; replacing the position blocks of the sample images in the low-resolution training set by the position blocks of the sample images in the high-resolution training set, which correspond to each position block of the sample images in the low-resolution training set, and compositing image blocks of high-resolution in a weighting manner; and splicing the composited image blocks of high-resolution into a whole image according to the position of the image blocks in the face image. The method which reconstructs a high-resolution image block in the same position by utilizing the image block in the same position of each sample image in a training set directly has the advantage that the manifold learning step or the feature extraction step which are common in similar algorithms are avoided, thereby greatly saving operation time, reducing complexity; and the quality of the composited high-resolution image is improved.
Owner:XI AN JIAOTONG UNIV
Data subspace clustering method based on multiple view angles
ActiveCN103400143AImprove clustering effectLow similarityCharacter and pattern recognitionSpecial data processing applicationsSpectral clustering algorithmLinear representation
The invention discloses a data subspace clustering method based on multiple view angles, which comprises the steps of extracting multi-view-angle characteristics in a multi-view-angle database; for the multi-view-angle database, selecting a specific linear reconstruction expression method and determining a regularization constraint method corresponding to the linear reconstruction expression method; determining reconstruction error weight of each view angle characteristic in multi-view-angle characteristics; according to the selected reconstruction expression method and the obtained reconstruction error weights of different view angle characteristics, learning to obtain a linear expression matrix for reconstructing all samples in the multi-view-angle database, wherein the linear expression matrices are used for expressing a relationship among the samples in the database and element values are used for expressing reconstruction coefficients for corresponding samples in the line to reconstruct corresponding samples in the row; correspondingly processing the linear expression matrix to obtain an affinity matrix for measuring the similarity of the samples in the multi-view-angle database; and using a spectral clustering algorithm to partition the affinity matrix to obtain multi-view-angle data subspaces.
Owner:INST OF AUTOMATION CHINESE ACAD OF SCI
Face super-resolution reconstruction method based on K-neighboring re-recognition
ActiveCN103824272AQuality improvementAccurate acquisitionImage enhancementImage resolutionOptimal weight
The invention discloses a face super-resolution reconstruction method based on K-neighboring re-recognition, the method comprises the following steps: respectively dividing a to-be-reconstructed low-resolution face image and sample images in a high-resolution training set and a low-resolution training set into overlapped image blocks, for the image blocks of the to-be-reconstructed low-resolution face image, according to the priority that geometrical information with high-resolution manifold is relatively credible and relatively representative, updating the recognized neighboring image by using geometrical information with low-resolution manifold and the high-resolution manifold, computing an optimal weight coefficient when the re-recognized neighboring image blocks are used for linear reconstruction, replacing the re-recognized neighboring image blocks by using one-to-one corresponding position image blocks of corresponding images in a high-resolution training set, weighting to synthesize the high-resolution image block, fusing as the high-resolution face image according to the position of a synthesized image on the face. The method has the relatively high reconstruction precision and reconstruction efficiency, and can be used for reconstructing high-quality face image.
Owner:WUHAN UNIV
Interactive image segmentation method for multiple foreground targets
The invention provides an interactive image segmentation method for multiple foreground targets. The method comprises the following steps of: performing linear reconstruction on pixel colors in an image local window, and repeatedly modifying color reconstruction coefficients by using linear projection; repeatedly performing the linear reconstruction on pixel class label vectors in the image localwindow by using the modified color reconstruction coefficients, and estimating to acquire local reconstruction errors; accumulating the local reconstruction errors to acquire a global reconstruction error; building an interactive image segmentation model of the multiple foreground targets; and performing cluster analysis on the same class of pixels which are labeled by a user to acquire a clustering center; acquiring a group of polynomial functions by adopting regression estimation by taking the clustering center as a training sample; mapping the unlabelled pixels by using the polynomial functions to acquire an initial solution; solving the segmentation model; and determining the class attribution of the unlabelled pixels, and outputting a segmentation result. The interactive image segmentation method has a wide application prospect, and the problem that the multiple foreground targets are difficult to segment simultaneously in the prior art is solved.
Owner:INST OF AUTOMATION CHINESE ACAD OF SCI
Linear reconstruction method of standard number 12 lead electrocardiogram segments based on self-adaptive electrocardiosignal region segmentation
ActiveCN107616791AReduce the cost of trainingReduce length requirementsDiagnostic recording/measuringSensorsEcg signalST-T segment
The invention discloses a linear reconstruction method of standard number 12 lead electrocardiogram segments based on self-adaptive electrocardiosignal region segmentation. The method comprises the following steps of self-adaptive electrocardiosignal region segmentation, wherein standard number 12 lead electrocardiosignals are subjected to self-adaptive segmentation, and according to wave characteristics of the electrocardiosignals and different heartbeat stages, the standard number 12 lead electrocardiosignals are divided into a head and tail segment, an ST-T segment, an R-P segment and a QRSsegment; linear regression training and reconstruction, wherein a least squares criterion is used for conducting linear modeling and linear reconstruction on an existing electrocardio sample; sub region electrocardio deserialization, wherein due to the fact that signals to be reconstructed are subjected to linear reconstruction respectively after electrocardiosignal region segmentation, the reconstructed electrocardio sub region sequences need to be restored to common sequential electrocardio signals. The linear reconstruction method of the standard number 12 lead electrocardiogram segments based on self-adaptive electrocardiosignal region segmentation is fast and accurate.
Owner:ZHEJIANG UNIV
Infrared and visible light image fusion method and system, computer equipment and application
The invention belongs to the technical field of image fusion, and discloses an infrared and visible light image fusion method and system, computer equipment and application, and the method comprises the steps: carrying out super-resolution reconstruction of an original infrared image based on a convolutional neural network to acquire a reconstructed high-resolution infrared image; anisotropicallydiffusing the visible light image and the high-resolution infrared image respectively to obtain a corresponding base layer and a detail layer; obtaining a maximum feature vector by utilizing KL transformation to fuse detail layers, and fusing a base layer by utilizing saliency adaptive extraction; and carrying out linear reconstruction on the fused detail layer and the base layer to obtain a finalfused image. A large number of experiments show that compared with four fusion algorithms, the algorithm provided by the invention has a better fusion effect no matter from subjective judgment or from objective evaluation indexes, and has richer detail information and contour textures while enabling a fused image to have higher definition.
Owner:XIAN UNIV OF SCI & TECH
Active learning big data mark method and system
ActiveCN106169095AHigh precisionPrecise screeningKnowledge representationMachine learningData setProactive learning
The present invention relates to an active learning big data mark method and system. The method comprise: performing linearity reconstruction of each data point according to the anchor data set to be marked in the data set to be marked; calculating the distance between data points; taking the distances as the weight construction regular items of reconstruction parameters, wherein the distances are inversely proportional to the reconstruction parameters; constructing and obtaining a data mark model to perform corresponding processing and correction of the data mark model; and performing optimizing and solution to determine the anchor data for active learning. Because the distances are inversely proportional to the reconstruction parameters, the data mark model is sensitive to the distance among data points, and it is easier to determine whether the corresponding data points have representativeness or not in the solution and optimization process according to the size of the infinite norm value to accurately screen out the anchor data set for active learning in the data set to be marked so as to improve the big data anchor mark accuracy.
Owner:广州图普网络科技有限公司
Anchor graph structure-based semi-supervised data classification method of double Laplacian regularization
InactiveCN108830301AAvoid influenceImprove accuracyCharacter and pattern recognitionData setAnchor point
The invention discloses an anchor graph structure-based semi-supervised data classification method of double Laplacian regularization. The method mainly comprises the following steps: firstly, carrying out clustering on a data set to obtain anchor point data which can approximatively indicate the entire data set, and calculating linear reconstruction weights between sample points and adjacent anchor points thereof through an FLAE method; then respectively constructing Laplacian regularization terms on the anchor points and Laplacian regularization terms on the sample points on the basis of a weight matrix between the sample points and the anchor points, and establishing an anchor graph structure-based semi-supervised classification model of double Laplacian regularization; and finally, using a zero-gradient method to parse and solve the model to obtain category soft-labels of the anchor point data, and using feature codes of unlabeled samples to linearly combine the category soft-labels of the anchor points, and discriminating categories of the unlabeled samples. Double-Laplacian-regularization constraints established by the method can better describe graph structure information among the samples, and thus realize higher classification and discriminating ability, and the method has very good application prospects.
Owner:温州大学苍南研究院
Convolutional neural network initialization method based on pre-training model filter extraction
InactiveCN108108806AMeet memory overheadFulfil requirementsNeural architecturesTask networkNetwork structure
The invention provides a convolutional neural network (CNN) initialization method based on pre-training model filter extraction and relates to the technical field of video processing. The method extracts a filter parameter in a pre-training model by using a minimum entropy loss and a minimum reconstruction error in order to initialize a target task network model and achieve the small and medium-sized network initialization that meets practical application problems. By using the minimum entropy loss and the minimum linear reconstruction method, the method extracts the filter parameter from thepre-training model to initialize the target task network model, does not require that the target task network structure is consistent with the pre-training network structure, flexibly design the network structure of the target task according to practical applications so as to meet the memory overhead and calculation speed requirements in practical application problems.
Owner:NORTHWESTERN POLYTECHNICAL UNIV
Vehicle bearing fault diagnosis method based on CEEMDAN and APSO-SVM
PendingCN114818780AImprove computing efficiencyReconstruction error 0Machine part testingCharacter and pattern recognitionTime domainFeature vector
The invention discloses a vehicle bearing fault diagnosis method based on CEEMDAN and APSO-SVM, and the method comprises the steps: collecting a vibration signal of a rolling bearing, measuring the parameters of the bearing, carrying out the decomposition of the collected vibration signal of the rolling bearing, carrying out the screening of IMF modal components after the decomposition, carrying out the linear reconstruction of the screened components, and removing invalid information; then singular entropy, power spectrum entropy and energy entropy calculation is carried out on the screened IMF modal components, and principal feature extraction is carried out on the reconstructed signals by using WPCA based on calculation results to obtain feature vectors; making the feature vectors into a training set and a test set of an SVM, adding a category label, and constructing and optimizing an SVM classifier model on the basis; and finally, performing fault diagnosis on the vehicle bearing by using the optimized SVM classifier. According to the method, time domain and frequency domain information is comprehensively considered, fault features can be accurately extracted, the problem that the SVM optimal parameters are difficult to manually select is solved, popularization in engineering application is facilitated, and practicability is high.
Owner:NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
Feature selection method based on optimal reconstruction
InactiveCN107203489AEfficient solutionHas an analytical solutionComplex mathematical operationsData setFeature selection
The present invention provides a feature selection method based on optimal reconstruction, the steps of which are: 1) represent each original feature in the data set as d-dimensional data as a data matrix X, where d>1; 2) for the above data matrix X establishes an optimal linear reconstruction model, and the optimization target of the model is the representation matrix B; 3) Transpose the above data matrix X to obtain the feature matrix F, and clear the representation matrix B; 4) Use iterative alternate optimization Solve the above-mentioned optimized linear reconstruction model after step 3) in a way to obtain the optimal representation matrix B*; 5) select the optimal k-dimensional feature subset that can represent all d-dimensional features according to the optimal representation matrix B* , where k
Owner:INST OF INFORMATION ENG CAS
Hyperspectral image recognition method and device based on spatial spectrum group covariance characteristics
ActiveCN113537252AEasy extractionEasy to refactorCharacter and pattern recognitionZ eigenvalueImaging Feature
The invention discloses a hyperspectral image recognition method and device based on spatial spectrum group covariance features and a computer readable storage medium. The method comprises the following steps: acquiring a hyperspectral image to be identified, mapping the hyperspectral image to be identified to a Riemann space, and calculating the Riemann distance between data points in the Riemann space; and on the basis of a Riemann cutting space and a local linear measurement criterion, performing adaptive neighborhood calculation on each data point; projecting the data points in the Riemann space to a Riemann local tangent space according to the self-adaptive neighborhood information of each data point; obtaining the low-dimensional image features of the to-be-recognized hyperspectral image by performing eigenvalue decomposition on the reconstruction weight matrix obtained in the linear reconstruction process, and recognizing the surface features of the hyperspectral image based on the low-dimensional image features, so that the surface feature recognition accuracy of the hyperspectral image is effectively improved.
Owner:HAINAN UNIVERSITY +1
X-ray image linear reconstruction method
ActiveCN112053307ASpeed up the rebuilding processImprove density reconstruction accuracyImage enhancementReconstruction from projectionProjection imageHyperprior
The invention discloses an X-ray image linear reconstruction method, which comprises the steps: obtaining an X-ray projection image, introducing a regular term based on total variation prior, and constructing a reconstruction target function; introducing super prior parameters, and constructing a layered Bayesian model; introducing a split variable by using a variable splitting method, and separating a data fidelity term and a TV regular term to obtain joint probability density distribution in a split form; defining a super-prior variable based on Jefferys prior to obtain condition distribution of each variable; iteratively updating the super-prior parameters, and solving conditional distribution of split variables containing TV regular terms; approximating the full-condition probability density distribution of the to-be-solved parameter by utilizing the low-rank property of the forward matrix, and calculating the target distribution of low-rank approximation to obtain a closed solution about the to-be-solved parameter; and calculating the mean value of the sampling samples, and estimating the to-be-solved parameters. According to the method, the problems of high calculation overhead and the like in solving the large-scale linear inverse problem can be effectively solved.
Owner:HOHAI UNIV CHANGZHOU
Visible light and infrared image fusion method based on structure group double sparse learning
InactiveCN111080566AEnhanced ability to capture salient features of imagesImprove accuracyImage enhancementImage analysisSparse learningMedical diagnosis
The invention relates to a visible light and infrared image fusion method based on structure group double sparse learning. The method comprises the following steps: (1) carrying out sliding window processing on input visible light and infrared images, searching similar blocks of original image blocks, carrying out group vectorization, and establishing an image similar structure group matrix; (2) taking the image similar structure group matrix as a training sample, forming a base dictionary by utilizing a Kronecker product of shear wavelets, obtaining a sparse dictionary through online learning, and performing linear reconstruction on the base dictionary and the sparse dictionary to obtain a final double sparse dictionary; and (3) in combination with the double sparse dictionaries, performing group sparse solution on the image similar structure group by adopting SOMP to obtain a group sparse coefficient, and obtaining a final fused image through image reconstruction by adopting a maximum fusion rule. The method solves the problem that the existing sparse fusion algorithm ignores the correlation between the image blocks, the dictionary adaptability is poor, and the image fusion quality is low, and can be applied to the fields of remote sensing detection, medical diagnosis, intelligent driving, safety monitoring and the like.
Owner:TAIYUAN UNIVERSITY OF SCIENCE AND TECHNOLOGY
Single-sample face recognition method based on block linear reconstruction discriminant analysis
ActiveCN111259780AImprove recognition rateCharacter and pattern recognitionSingle sampleMachine learning
The invention discloses a single-sample face recognition method based on block linear reconstruction discriminant analysis. The method comprises the steps of firstly, partitioning each face training image, then expressing any face image block as a linear combination of k1 intra-class nearest neighbor image blocks, and meanwhile expressing any face image block as a linear combination of k2 inter-class nearest neighbor image blocks; respectively solving the intra-class representation coefficient and the inter-class representation coefficient by using a least square method, and calculating the intra-class reconstruction divergence and the inter-class reconstruction divergence of the sample; solving an optimal projection matrix by maximizing the ratio of the inter-class reconstruction divergence to the intra-class reconstruction divergence, and extracting features of the training sample set and the to-be-identified sample by using the projection matrix; and finally, constructing a discrimination criterion of the class label of the to-be-recognized face image, and judging the class label of the to-be-recognized face image. According to the method, the problem of single-sample face recognition can be effectively solved, the influence of changes of image illumination, face postures, expressions and the like on the recognition effect can be effectively avoided, and the recognition rateis increased.
Owner:NANJING AUDIT UNIV
High-precision format simulation method for one-dimensional ice-water coupling motion
ActiveCN110705188AImprove forecast accuracyTightly coupledClimate change adaptationDesign optimisation/simulationIce waterDiscretization
The invention relates to a high-precision format simulation method for one-dimensional ice-water coupling motion, and belongs to the field of river ice disaster forecasting. The method comprises the steps of boundary condition acquisition, equation control, ice-water coupling motion unified discretization, surface flux calculation, shallow water wave velocity calculation under the ice-water coupling condition and a high-precision format. According to the method, ice and hydrodynamic equations are uniformly dispersed into a high-precision Godunov format; an HLL approximate Rieman solution is adopted to calculate interface flux, equivalent water depth is adopted to replace section average water depth, piecewise linear reconstruction is carried out on basic variables of each unit in space, and a prediction-correction method is adopted in time, so that the precision of a numerical solution is integrally improved to a second order. The high-precision format simulation method for ice-water coupling movement has high prediction precision for the water level and ice thickness of the riverway in the flow ice period, particularly an equivalent water depth method is adopted to replace an average water depth method to calculate the wave velocity of shallow water waves, the water level prediction precision can be obviously improved, and research results are of great significance for prevention and control of ice disasters of the riverway.
Owner:CHINA WATER NORTHEASTERN INVESTIGATION DESIGN & RES
Sensor noise and fault judging method based on sparse representation
The invention discloses a sensor noise and fault judging method based on sparse representation. The specific method includes the following steps: 1. an overcomplete atom library containing normal signals, noise and fault samples corresponding thereto is built through historical data; 2. based on a hypothesis that linear representation of unknown samples of some category can be effectively realized in a corresponding subspace by a plurality of samples of the category, sparse representation of a mixed signal collected by a sensor is performed with a built dictionary, i.e., atoms best matched with the signal to be decomposed is found out from the overcomplete atom library and are subjected to linear reconstruction to obtain a new representation mode; 3. a reconstruction error of the sample subjected to linear reconstruction is calculated, and an error of a new sample reconstructed by use of a data set of each kind of noise and fault samples is obtained; 4. data of the same kind of fault samples and data of the same kind of noise samples are used to perform training to calculate a corresponding reconstruction error value; and 5. noise and fault judgment is realized through calculation of the reconstruction error.
Owner:CHONGQING UNIV
Robust face recognition method based on secondary cooperative representation identification projection
ActiveCN110956113AEfficient expressionAccurate expressionCharacter and pattern recognitionPattern recognitionMachine learning
The invention discloses a robust face recognition method based on secondary cooperative representation identification projection. The method comprises the steps of: screening out K types of samples closely related to training samples through first-time cooperative representation; representing a linear reconstruction training sample through secondary cooperation to obtain a reconstruction coefficient; constructing the intra-class graph and the inter-class graph of the samples through the reconstruction coefficient to describe cohesiveness and separability of the samples; then obtaining a projection matrix by maximizing the inter-class divergence and minimizing the intra-class divergence at the same time, finally extracting the features of a to-be-identified sample and all the training samples by utilizing the obtained projection matrix, and judging the class label of the to-be-identified sample according to a classification criterion. According to the method, the training samples are reconstructed through cooperative representation, the problem of recognition errors caused by illumination, shielding, human face postures and expression changes can be effectively solved, the trainingsamples can be expressed more effectively and accurately, and the high-precision requirement for human face recognition in practical application can be met.
Owner:NANJING AUDIT UNIV
Method, device and storage medium for extracting news web page content
ActiveCN109271598AImprove versatilityImprove recallWebsite content managementWeb data queryingData setTree shaped
The invention discloses a method, a device and a storage medium for extracting news webpage content, which relate to the technical field of news webpage content extraction. The method comprises the following steps: obtaining HTML code of the webpage, HTML linear reconstruction of the webpage, removing HTML noise label, filtering and dividing data set, absorbing pseudo noise paragraph, and generating text paragraph. HTML linear reconstruction of web page linearizes the tree-shaped div tags nested with each other, and the linear structure can be conveniently located as a div tag, thereby eliminating the influence of nested tags on subsequent steps; HTML noise label removal will reduce the impact of noise text on paragraph clustering; Data set filtering partitioning further reduces the effectof noise on text segments; Absorbing pseudo-noise paragraphs increases the recall rate of text paragraphs. The method overcomes the shortcomings of specific crawling of specific websites and enhancesthe generality of extracting news page content. Compared with the existing technology, the method can extract news content accurately and efficiently, and has a good effect.
Owner:数据地平线(广州)科技有限公司
Transmission network utilization rate evaluation method based on power system timing coupling
ActiveCN110429591AImprove solution efficiencyQuick solveAc network circuit arrangementsElectric power systemLinearization
The invention discloses a transmission network utilization rate evaluation method based on power system timing coupling. The method comprises the steps of: obtaining system basic technical data, system operation constraint condition data and system operation prediction data; in a condition that the existing power supply installation and a wire frame are unchanged, considering (N-1) expected faultsof a power transmission device, constructing a reasonable utilization calculation model based on time series analysis, adopting a linear auxiliary method to introduce dual secondary variables for linear reconstruction so as to improve the model solution efficiency; and for a certain typical timing operation scene, maximizing the maximum electric quantity value reached by a power transmission device to be analyzed in the typical scene, selecting a plurality of typical scenes, the operation time frame of each scene being T0, repeating the steps mentioned above, analyzing the typical scenes, constructing a reasonable utilization index calculation formula to evaluate the utilization rate of the transmission network. The transmission network utilization rate evaluation method has the high adaptability to the operation analysis of the power system with high timing coupling.
Owner:XI AN JIAOTONG UNIV
Face super-resolution reconstruction method based on k-nearest neighbor re-identification
ActiveCN103824272BQuality improvementAccurate acquisitionImage enhancementImage resolutionSample image
Owner:WUHAN UNIV
Light-field image compression method based on linear reconstruction
ActiveCN107770537AReduce coded dataReduce the amount of encoded dataDigital video signal modificationVideo encodingImage compression
The invention discloses a light-field image compression method based on linear reconstruction. The light-field image compression method comprises the steps of decomposing a light-field image into viewing angle image arrays and then dividing into A and B sets; at an encoding end, performing compression on viewing angle images in the A set by adopting a first video encoder, transmitting a code stream to a video decoder at the encoding end and a video decoder at a decoding end, obtaining a relationship between viewing angle images in the B set and the viewing angle images in the A set by combining the viewing angle images in the B set with viewing angle images reconstructed by the video decoder at the encoding end in the A set and utilizing a linear reconstruction theory of light-field viewing angle images, and transmitting the relationship to a second video decoder at the decoding end; at the decoding end, reconstructing the B set by utilizing the linear reconstruction theory of the light-field viewing angle images and combining with decoding results of the first and second video decoders at the decoding end; and re-forming the light-field image by utilizing the reconstructed A set and B set. According to the method, encoding data at the encoding end can be greatly reduced, and are reconstructed at the decoding end with a relatively good quality.
Owner:UNIV OF SCI & TECH OF CHINA
Monthly power demand prediction method based on VMD-ANFIS-ARIMA
PendingCN112991088AReduce noiseImprove forecast accuracyForecastingNeural architecturesPower usageIndependent predictor
The invention discloses a monthly power demand prediction method based on VMD-ANFIS-ARIMA, which solves the defects in the prior art, and comprises the following steps: 1, obtaining monthly power consumption sequence data, and determining the number of VMFs of VMD components; 2, taking the screened influence factors as independent variables, taking trend terms in the VMF as dependent variables, and using an ANFIS model to perform prediction; 3, carrying out sequence stability test on the VMF except the trend term, and determining the order of AR and MA according to the correlation coefficient and the partial autocorrelation coefficient of the VMF; 4, using an ARIMA model to carry out time sequence prediction on the VMFs except the trend term; and 5, performing linear reconstruction on each VMD component prediction result to obtain a final power consumption demand prediction result.
Owner:STATE GRID ZHEJIANG ELECTRIC POWER +1
An Energy-Efficient Compressed Sensing Image Coding Method
The invention relates to an energy-saving compressed sensing image coding method, which effectively solves the problem of high coding energy consumption and coding and decoding energy consumption existing in the prior art. Image block; calculate the gradient value of each image block; use the gradient value of each block to construct a measurement matrix, implement block CS measurement; use the measurement matrix of each block to construct a projection matrix; implement the inner product operation of the projection matrix and the observation vector, and reconstruct image blocks and merge them into the final reconstructed image. The invention implements adaptive CS measurement according to the gradient value of each image block, the gradient value quantifies the sparsity of the image block, and ensures that the encoder can still capture most of the information of the image block under the condition of low energy consumption; Each image block is reconstructed, which reduces decoding energy consumption while obtaining good reconstruction quality. By implementing encoding and decoding on the CIF format video sequence, the encoding energy consumption is low and the decoding energy consumption is low.
Owner:XINYANG NORMAL UNIVERSITY
Cost-sensitive linear reconstruction based optic cup localization
Owner:AGENCY FOR SCI TECH & RES
Circular workpiece surface grinding method
InactiveCN112318226AAchieving appearance contour reconstructionImprove sanding qualityRevolution surface grinding machinesGrinding drivesPoint cloudEngineering
Owner:WUHU HIT ROBOT TECH RES INST
A Standard 12-Lead ECG Segmented Linear Reconstruction Method Based on Adaptive ECG Signal Region Segmentation
ActiveCN107616791BReduce the cost of trainingFix issues that require regular updatesDiagnostic recording/measuringSensorsEcg signalMedicine
Owner:ZHEJIANG UNIV
High-energy flash X-ray image linear reconstruction method with uncertainty constraint
PendingCN114445513ASolve the noiseSolve the large amount of calculationImage enhancementReconstruction from projectionImage resolutionOptical pathlength
The invention discloses an uncertainty-constrained linear reconstruction method for a high-energy flash X-ray image. A two-stage framework is designed, the reconstruction process of a high-resolution image is constrained through uncertainty of high-stage target parameters so as to eliminate the phenomenon that a sampled sample is unstable due to noise in an optical path image, and a variable splitting form of posterior distribution is considered in the two-stage framework so as to explore more flexible and accurate sampling steps. A high-efficiency MCMC algorithm with a first-order truncation CG optimizer is adopted to approximate posterior distribution of target parameters, the algorithm sampling efficiency is improved, and finally a refining step is adopted to remove residual noise in a sample mean value. The algorithm is high in calculation efficiency, and the image reconstruction quality can be effectively improved.
Owner:中国船舶集团有限公司第七二四研究所
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