238 results about "Sparse coefficient" patented technology

The invention relates to the technical field of mechanical fault diagnosis, and discloses a gearbox fault diagnosis method and system based on lightweight variational Bayesian learning, and the method comprises the steps: collecting a to-be-diagnosed vibration signal of a gearbox, carrying out the preprocessing of the to-be-diagnosed vibration signal, and obtaining an original vibration signal and the fault feature frequency of the original vibration signal; determining amplitude modulation-frequency modulation sparse combination representation of the original vibration signal according to the fault characteristic frequency of the original vibration signal, and constructing a joint probability model according to classification distribution containing sparse vectors and the amplitude modulation-frequency modulation sparse combination representation of the original vibration signal; performing variational Bayesian inference solution on the joint probability model by using the non-overlapping sub-sequence of the original vibration signal and natural gradient optimization to obtain posterior probability estimation of a sparse coefficient; and determining an activation component according to the posterior probability estimation of the sparse coefficient and the sparse precision parameter, and matching the activation component with a pre-established multi-scale amplitude modulation-frequency modulation sparse dictionary to obtain a fault type and a confidence coefficient thereof.

The invention discloses a data processing method for industrial equipment for industrial control Internet of Things, which comprises the following steps: acquiring industrial equipment sensor data, and carrying out standardization processing to obtain a standardized matrix; initializing a sparse dictionary learning model, setting an initial dictionary structure and a sparse coefficient, and executing preliminary sparse decomposition; constructing a flower pollination algorithm search space, optimizing a layering number, a regular weight and a switching threshold, and updating dictionary parameters; calculating an optimal sparse coefficient matrix, and performing sparse reconstruction processing; and carrying out data denoising, compression and feature extraction, and outputting a final processing result. According to the method, the flower pollination optimization algorithm and the sparse representation dictionary learning model are fused, so that efficient denoising, compression and abnormal feature extraction processing of the industrial equipment data are realized.

The invention discloses a method for establishing a high-voltage circuit breaker fault detection model, and the method comprises the following steps: collecting current, voltage, mechanical response, temperature and other multi-dimensional signals of a circuit breaker under different working conditions, and unifying the signals into standardized time sequence data; a nonlinear dynamic sparse identification method is utilized to establish a dynamic model for describing equipment state evolution, and sparse coefficients reflecting physical change rules are extracted from the dynamic model to serve as health features. And the features are fused with current monitoring data to generate a joint feature input vector, and a health prediction model based on a TabPFN architecture is introduced for training and discrimination. And finally, accurate prediction of the current health state or the potential fault of the circuit breaker is realized, and the model self-adaptive updating capability is realized. According to the method, physical modeling and data analysis are combined, so that the accuracy and interpretability of fault prediction are improved.

The invention relates to a multi-dimensional transmission data compression sensing method and system for urban emergency rescue, and the method comprises the steps: obtaining multi-modal data, carrying out the normalization processing and sparse representation, and obtaining the sparse coefficient of each modal; constructing a block diagonal random matrix for each modal sparse coefficient to perform independent compression sampling, generating a low-dimensional observation value before coding, and performing arithmetic coding and low-density parity check code error correction coding to generate an anti-interference code stream; and performing low-density parity check code decoding and arithmetic decoding on the anti-interference code stream to obtain a decoded low-dimensional observation value, performing iteration by using an orthogonal matching pursuit algorithm, recovering sparse coefficients of each mode, performing reconstruction to obtain text, image and audio features, and projecting the text, image and audio features to a shared semantic space to obtain a shared semantic space. And calculating a joint semantic weight through a cross-modal attention mechanism, and generating a multi-modal fusion result with consistent semantics. The method disclosed by the invention provides an efficient, robust and semantic collaborative end-to-end solution for urban emergency rescue.

The invention belongs to the field of magnetic resonance measurement signal noise filtering, and particularly relates to a ground magnetic resonance signal sparse denoising method of a combined dictionary, which comprises the following steps: reconstructing a power frequency harmonic component of a magnetic resonance signal according to frequency bands, and eliminating power frequency harmonics from the magnetic resonance signal to obtain a power frequency-removed signal; constructing a track matrix Z by adopting a Gaussian random matrix, constructing a track matrix Y by adopting a power frequency-removed signal, taking first K columns of the track matrix Z and normalizing to obtain an initial dictionary D, and using the track matrix Y as an original sample for constructing a sparse coefficient matrix X; updating the initial dictionary D and the sparse coefficient matrix X through K-SVD dictionary learning, and reconstructing a track matrix by adopting the updated dictionary # imgabs0 # and the sparse coefficient matrix # imgabs1 # to obtain a track matrix W; and obtaining a pure magnetic resonance signal by taking the first row of the track matrix W, and realizing effective removal of MRS signal noise in a complex electromagnetic interference scene under the condition of single signal acquisition.

The invention discloses a soil heavy metal inversion method and system integrating satellite remote sensing and near-end sensing, and the method comprises the steps: collecting a soil sample, and measuring the soil heavy metal content and a soil visible light-near infrared spectrum; obtaining a time sequence multispectral image of a research area, calculating a spectral index, and selecting and screening bare soil pixels through a threshold value to obtain a bare soil image; performing spectrum correction on the bare soil image; obtaining a joint dictionary and a sparse coefficient through sparse representation and dictionary learning, and reconstructing a hyperspectral image of the bare soil image; converting the one-dimensional spectral data into a two-dimensional spectrogram by using continuous wavelet transform, extracting spectral features in combination with a 2D-CNN algorithm, and constructing a soil heavy metal inversion model; and using the trained inversion model to predict the soil heavy metal content of the research area based on the reconstructed hyperspectral image. According to the method, satellite remote sensing and near-end sensing are integrated to obtain a large-scale accurate soil heavy metal content distribution map, deep features are extracted in combination with a 2D-CNN algorithm, and the inversion model precision and model efficiency are improved.

The invention discloses an image super-resolution reconstruction method based on compressed sensing, and belongs to the field of image reconstruction. The problems that the updating speed of existing hardware cannot meet the requirement for signal precision, and on the premise that the hardware cost is not increased, an image with higher definition cannot be reconstructed are solved. The invention discloses an image super-resolution reconstruction method based on compressed sensing, and the method comprises the steps: designing a dictionary learning process, and obtaining high-frequency component parameters needed in image restoration; comprising the steps of constructing a training data set and performing dictionary training; wherein a corresponding low-resolution image is obtained by carrying out quality reduction and scale reduction calculation on an image so as to carry out a process of training a data set; constructing an over-complete dictionary pair by using a K-SVD algorithm, and training the dictionary; firstly, a reduced low-frequency component is obtained, and then the resolution is reconstructed. According to the method, the GPSR algorithm is utilized, the limitation of fixed sparseness of a traditional OMP algorithm is overcome, a more accurate sparse coefficient is obtained, and the precision of the reconstructed image is further improved by adopting a bilateral filtering mode.

The invention relates to the technical field of satellite communication signal processing, and discloses a broadband satellite signal blind demodulation reconstruction method and system based on sparse representation. The method comprises the following steps: carrying out frequency domain transformation on broadband satellite mixed signals to extract pilot frequency features to construct a beam state feature matrix, inputting the matrix into an adversarial network to generate a beam adaptive sparse dictionary, constructing a topological relation graph according to a Doppler frequency shift set, and carrying out collaborative sparse decomposition to obtain a sparse coefficient matrix; and extracting a code rate candidate set to execute parallel sparse reconstruction, determining an actual code rate through self-consistency verification to obtain a complete signal, analyzing a switching instruction, extracting time sequence statistical characteristics, predicting target domain characteristics, generating a switched sparse dictionary, and executing demodulation. The sparse decomposition precision, the code rate blind estimation accuracy, the cooperative processing efficiency and the switching continuity of the broadband satellite mixed signals are improved.

The invention discloses a face recognition method based on sparse coding and dictionary learning. The method comprises the following steps: S1, collecting and preprocessing a standardized training image and a to-be-recognized face image; s2, training a dictionary set, labeling categories, and initializing and optimizing the dictionary; s3, performing sparse coding, and solving a sparse coefficient by using the optimization dictionary; s4, carrying out regional feature division and fusion of multi-region sparse coding, weighted compression and interactive modeling; s5, performing fusion feature normalization and classification, and performing joint discrimination by a reconstruction error and a discrimination classifier; s6, performing confidence evaluation and dictionary updating, and triggering an incremental learning updating mechanism with low confidence; and S7, performing model deployment and real-time identification, and deploying an optimization dictionary and a classification module to perform identification output. The invention provides a face recognition method suitable for a complex environment, and the method has the advantages of high precision, strong robustness and good practicability.

The invention discloses a deep reinforcement learning-based valve flow anomaly detection method. The method comprises the following steps of: obtaining a marked space-time synchronization industrial valve operation data set; generating an enhanced industrial valve image; inputting the enhanced industrial valve image into the improved YOLOv9 detection network, and outputting a candidate micro abnormal region set and corresponding time sequence anchor point information; forming a visual feature set and a time sequence feature set; inputting the visual feature set and the time sequence feature set into an improved coupling convolution sparse coding model to obtain a visual domain reconstruction residual error, a time sequence domain reconstruction residual error and a sparse coefficient consistency deviation; obtaining a normalized abnormal score; and comparing the normalized abnormal score with an abnormal threshold dynamically updated according to the valve flow physical prior constraint, and when the normalized abnormal score exceeds the abnormal threshold, generating micro-abnormal alarm information and an abnormal level. According to the method, false alarm and missing alarm caused by working condition change can be remarkably reduced in practical application, and the reliability of abnormal judgment is improved.

The invention discloses a video data compression and reconstruction method, system and device and a medium, and relates to the technical field of video data compression, and the method comprises the steps: extracting a feature vector of a video frame in video data; performing sparse coding by taking the feature vector as a linear combination of a base vector in a dictionary to obtain a sparse coefficient of the video frame; coding the sparse coefficient of the video frame by using entropy coding to obtain a coded character string; reading the coded character string, searching each bit of the character string, moving the corresponding node according to the bit of the current character string, and adding the corresponding sparse coefficient feature vector into the decompressed sparse coefficient feature vector when the node is a leaf node; and reconstructing the sparse coefficient feature vector into an original feature vector by using inverse sparse coding, and outputting a reconstructed video frame. According to the method, the compression ratio far higher than that of a traditional compression standard is achieved, and the characteristic enables the higher compression ratio to be achieved on the premise that the video quality is guaranteed.

The invention belongs to the field of road vehicle control systems, and relates to a blind area recognition system and a road vehicle control system and method based on blind area recognition, and the blind area recognition system comprises a laser emission module, a photon-level echo receiving module, an algorithm processing module, a target reconstruction module and a recognition and classification module. The algorithm processing module represents the echo signal as a linear combination of a series of sparse basis functions, reconstructs reflectivity information of the non-vision-field target surface, and extracts geometric information of the non-vision-field target surface; the target reconstruction module is used for performing joint optimization on the non-vision-field target surface information and the echo signal by using a regularization technology, and recovering a sparse coefficient by solving a joint regularization problem to obtain a reconstruction signal; and the identification and classification module is used for judging the type and probability of the non-vision target. According to the system, the hidden target is observed in real time through the non-vision field imaging technology, the physical characteristics of the non-vision field target are combined during signal processing, the recognition accuracy is high, and the safety risk caused by a blind area is reduced.

The invention discloses an image classification method based on intra-class difference suppression dictionary learning. The method comprises the following steps: S1, selecting an image data set; s2, preprocessing the image by using a feature extraction method to generate an image sample set; s3, constructing a supervised dictionary learning model; s4, constructing a category label matrix; s5, constructing a Laplacian matrix by using the sample data; s6, carrying out regularization on the training sample by adopting Laplacian feature mapping; s7, calculating an intra-class sparse mean vector; s8, optimizing the dictionary model by using intra-class deviation between minimum sparse coefficients; s9, solving the objective function by adopting an analytical method, and performing iterative updating on the sparse coefficient and the dictionary; s10, calculating an error according to the updated sparse coefficient and the dictionary; s11, judging whether an iteration convergence condition is met or not, if yes, outputting an optimal discrimination dictionary, and if not, executing a step S7; and S12, carrying out image classification by adopting a kNN classifier. The method is good in generalization performance and robustness and high in image classification accuracy.

The invention provides a sparse triangle solution parallel optimization method and system based on a GPU, and relates to the technical field of high-performance parallel computing. Obtaining a to-be-solved linear equation set; decomposing a sparse coefficient matrix in the system of linear equations to obtain a triangular sparse matrix; based on the triangular sparse matrix, sparse triangular parallel solving is carried out, and a final solution is obtained; wherein the sparse triangle parallel solution is characterized in that the hierarchy of each non-zero element is determined according to the dependency relationship among the non-zero elements, sub-tasks executed in sequence are divided based on the hierarchy, parallel solution is carried out by adopting the non-zero elements in the same hierarchy in a single sub-task, and synchronous solution is carried out among blocks among different hierarchies; obtaining a final solution through the kernel of the GPU; according to the method, the multi-thread parallelism of sparse triangle solution on the GPU can be further improved, and the problems of load imbalance and low resource utilization rate can be effectively avoided.

The invention relates to the technical field of signal processing, and discloses a flip chip vibration signal denoising method and system, and the method comprises the steps: obtaining a vibration signal responded by a to-be-detected flip chip under the action of external ultrasonic excitation, carrying out the segmentation processing of the vibration signal, constructing an initial sparse dictionary according to the segmented vibration signal, and iteratively updating the sparse dictionary; in the iteration updating process, sparseness parameters are adjusted in real time along with the number of iterations, and soft threshold denoising is conducted on residual terms; introducing a feature projection matrix to perform joint modeling on each section of vibration signal, and constructing a joint optimization model according to the updated sparse dictionary and the corresponding sparse coefficient; alternately optimizing the sparse coefficient and the feature projection matrix in the joint optimization model to obtain an optimal solution of the sparse coefficient; and combining the updated sparse dictionary and the optimal solution of the sparse coefficient to reconstruct the vibration signal to obtain a denoised vibration signal. According to the method, transient features can be effectively extracted, expression of key features is enhanced, noise is suppressed, and denoising robustness and reconstruction precision are improved.

The invention provides a logarithmic bus bearing roller defect detection method and system based on an ultrasonic technology, and the method comprises the steps: obtaining an ultrasonic detection signal of the surface of a logarithmic bus bearing roller, and carrying out the preprocessing of the ultrasonic detection signal, and obtaining a first echo signal sequence; obtaining an average value sequence, a skewness sequence, a kurtosis sequence and a first high-order cumulant sequence based on the first echo signal sequence; correcting the first high-order cumulant sequence based on the average value sequence to obtain a second high-order cumulant sequence; optimizing the first echo signal sequence based on the dictionary matrix and the signal sparse coefficient vector to obtain a second echo signal sequence; and fusing the sequences to obtain a multi-dimensional feature vector sequence, and comparing the multi-dimensional feature vector sequence with a preset vector threshold to detect whether the logarithmic bus bearing roller has defects or not. According to the method, the problem of beam distortion of traditional ultrasonic waves during detection of the roller defects of the logarithmic bus bearing is solved, and the detection precision is remarkably improved.

The present invention belongs to the field of rehabilitation therapy technology and discloses a longitudinal analysis method and system for magnetic resonance imaging data of mild brain injury. The method extracts BOLD signals from the magnetic resonance imaging data of subjects; constructs a symmetric positive definite sparse brain functional connectivity network set of subjects based on sparse inverse covariance matrix estimation; determines the brain functional connectivity network dictionary and sparse coefficient matrix in kernel space based on Riemannian manifold sparse coding; performs spatial distribution analysis of brain functional connectivity atomic networks; and performs longitudinal analysis of magnetic resonance imaging data of mild brain injury. By analyzing the differences in the spatial distribution of these highly present brain functional connectivity atomic networks in the brain, the present invention digs out brain functional connectivity imaging markers for distinguishing the three mild brain injury rehabilitation treatment stages: acute phase, subacute phase, and complete recovery, thereby realizing longitudinal analysis of the mild brain injury rehabilitation process.

The invention discloses a battery health state prediction model training method and device and a storage medium, and relates to the technical field of batteries, and the method comprises the steps: determining a source domain spatial-temporal characteristic matrix and a target domain spatial-temporal characteristic matrix according to source domain target time sequence data and target domain target time sequence data; performing battery health state prediction based on the source domain spatial-temporal characteristic matrix to obtain a source domain SOH prediction value; iteratively solving a source domain dictionary and a source domain sparse coefficient matrix of the source domain features, reconstructing target domain features based on the source domain dictionary, and determining the difference between the two domains; and obtaining a total loss value based on the source domain SOH predicted value, the source domain SOH true value and the difference between the two domains, and updating parameters of the health state prediction model based on the total loss value until a trained battery health state prediction model is obtained. The problem that in the prior art, a battery health state prediction model is poor in generalization performance when facing cross-domain data distribution offset is solved, and the prediction stability and accuracy of the model under different working conditions are improved.

The invention relates to the technical field of wavelength division multiplexing, and particularly provides a sparse representation-based lower wave enhancement method of a reconfigurable optical add-drop multiplexer, which comprises the following steps of: performing conversion processing on a lower wave mixed optical signal to obtain a digital electric signal vector; wherein the lower wave mixed optical signal at least comprises a target signal and an interference signal; performing sparse decomposition processing on the electric signal vector based on the over-complete dictionary to obtain a sparse coefficient vector; wherein the over-complete dictionary at least comprises target signal atoms and interference atoms; identifying and extracting a target coefficient corresponding to the target signal atom from the sparse coefficient vector; and combining the target coefficient with the target signal atoms to obtain an enhanced target signal. According to the sparse representation-based lower wave enhancement method for the reconfigurable optical add drop multiplexer, provided by the invention, the accuracy of extracting a target signal in ROADM lower waves can be improved, and adjacent channel interference and noise are effectively inhibited, so that the performance is improved.

The invention belongs to the field of radars, and relates to a multi-frequency diversity radar target distance and speed estimation method. According to the method, distance and speed estimation of multi-frequency diversity radar echoes is realized based on a distance and speed dimension super-resolution orthogonal matching pursuit (SRC-OMP) sparse processing algorithm. The high-resolution distance and speed estimation of a multi-frequency diversity radar echo signal is generally realized by combining sparse representation and sparse processing, and a traditional OMP algorithm based on a matched inner product is easy to judge a plurality of targets as one target for estimation when the targets are close to each other due to the characteristics of a sinker function. A distance-velocity two-dimensional spectrum is constructed according to an observation vector and a dictionary matrix, atoms corresponding to peak values in the two-dimensional spectrum are estimated, the positions of the atoms are stored, a set of atomic position indexes and the atoms is updated, and then a residual vector is updated. And then calculating a sparse coefficient solution to obtain a high-resolution target distance and speed detection result. The method is beneficial to high-resolution estimation of the target distance and speed by the multi-frequency diversity radar.

The invention relates to a land surface temperature and emissivity joint inversion method and system and a medium. The inversion method comprises the following steps: acquiring radiation brightness vectors observed by a satellite sensor in a plurality of thermal infrared bands as observation radiation brightness vectors; constructing a standard emissivity spectrum dictionary database as a dictionary matrix; defining a generation rule of the surface emissivity; establishing a forward physical model, and taking an analog value of the observed radiance vector as model output; defining a likelihood function of an observation radiance vector, defining sparsity prior probability distribution for the sparse coefficient vector, and defining uniform prior probability distribution for the surface temperature; joint posterior probability distribution of the surface temperature and the sparse coefficient vector is calculated, and multiple groups of samples are extracted; calculating a posteriori estimation value and an uncertainty interval of the surface temperature; and calculating a posteriori estimation vector of the sparse coefficient vector, and multiplying the posteriori estimation vector with the dictionary matrix to obtain a posteriori estimation value of the surface emissivity. The automation level of remote sensing information extraction and the information output efficiency are improved.

An on-loop efficient multiplication optimization method based on a sparse ternary polynomial and precomputation belongs to the field of cryptography and comprises the following steps: compressing a cyclic matrix of a prime order domain loop into a single row to obtain a generation operator matrix temp; storing the second column of elements in the temp in an inverted sequence; according to the sparse property of a ternary polynomial and the algebraic structure characteristic of a finite ring (Z / q) [x] / (xp-x-1), through sparse coefficient traversal, table look-up operation is carried out on non-zero coefficients of the ternary polynomial. According to the method, traditional NTT multiplication is replaced by addition and subtraction, the sparse small polynomial characteristics are combined, the theoretical complexity of polynomial multiplication is reduced to be close to O (p) from O (p2), and the operation efficiency is remarkably improved; a compression type precomputation cyclic matrix and a reverse storage strategy are adopted, memory occupation is reduced, the resource utilization rate is improved, the application scene is expanded, and flexibility and practicability are remarkably improved.

The invention discloses a user health data monitoring method and system based on a smart watch, and relates to the technical field of smart wearable health monitoring, and the method comprises the steps: carrying out the optimization through Hilbert transform and cross-spectral density, carrying out the iteration of a sparse coefficient through employing MCMC, carrying out the dimension reduction construction of a track matrix through employing Ortho-SSA, carrying out the screening through ReliefF and VC-SSA, and initializing a GBTN model. NAS is used for adjustment, integration is carried out through federated average and incremental learning, and the health state is monitored. According to the method, through the VIB model, cross-spectral density optimization, GMM noise modeling and MCMC sparse iteration, the dynamic coupling relation between modes is accurately captured, the robustness of data representation is improved, and through fusion of causal reasoning and federated learning, the accuracy of the health monitoring state is improved.

The invention relates to the technical field of aero-engine rotating blade health monitoring, and provides an aero-engine rotating blade timing undersampling vibration signal reconstruction method and system, and the method comprises the steps: constructing a reconstruction model and a recovery matrix; setting an initial residual error and a support set of the recovery matrix; calculating the inner product of the column vector of the recovery matrix and the initial residual error; the column vectors with the absolute values of the inner product values exceeding a threshold value are selected as matching vectors to be combined into a first set; projecting the first set to a parameter space; clustering is carried out by adopting a K-nearest neighbor algorithm, and column vectors with maximum inner products in each class are reserved to form a second set; adding the second set into a support set, and solving a sparse coefficient by adopting a least square method by taking the minimum initial residual error as a target; a new residual error is calculated, iteration is terminated until the support set is not updated any more, and a final vibration signal sparse coefficient is obtained; and calculating to obtain a blade end vibration signal of the rotating blade of the reconstructed aero-engine. According to the invention, the non-contact online real-time health monitoring effect of the blade can be improved.

The invention discloses an orthogonal matching pursuit sound field reconstruction method combining weighted transfer matrix modality and improved cosine similarity, and the method comprises the steps: setting a measurement surface, carrying out the uniform division, obtaining a plurality of grid points, installing microphones through the grid points, and forming a holographic surface; a reconstruction surface is arranged, an equivalent source surface is arranged on the side, away from the holographic surface, of the reconstruction surface, and a sound field reconstruction model under sparse sampling based on a transfer matrix mode is established by means of the relation between the source intensity of the equivalent source and the measured sound pressure and the sound pressure of the reconstruction surface; implementing a transfer matrix modal weighting strategy, and solving a weighted sparse coefficient vector of the equivalent source intensity by using an orthogonal matching pursuit method of improved cosine similarity; and realizing sound field reconstruction under sparse sampling according to the weighted sparse coefficient of the equivalent source intensity and the transfer relationship between the reconstructed sound pressure and the equivalent source intensity. According to the invention, the sound field reconstruction precision and stability of the orthogonal matching pursuit method are improved.

The invention provides a business load prediction method and device, equipment, a storage medium and a product, and relates to the field of financial science and technology or other related fields, and the method comprises the steps: obtaining historical business feature data and a historical business load value; converting the historical business feature data into an original sparse coefficient matrix of a sparse linear equation set; converting a first element value in the original sparse coefficient matrix into a semi-precision format for storage; generating an approximate inverse matrix of the original sparse coefficient matrix based on the first element values stored in the semi-precision format, and storing second element values in the approximate inverse matrix in the semi-precision format; converting the first element value and the second element value in the semi-precision format into a single-precision format; and based on the historical business load value, the first element value of the single-precision format and the second element value of the single-precision format, executing solving operation of the equation set, and determining a predicted value of the business load according to a solution vector obtained by solving. According to the method, the storage space occupied by the matrix data and the data transmission bandwidth pressure can be reduced.

The invention discloses a noise reduction method based on double sparse dictionary self-adaption and a bearing fault diagnosis method and system. The method comprises the following steps: performing wavelet decomposition on a signal to be subjected to noise reduction to obtain a high-frequency signal and a low-frequency signal, and constructing a high-frequency matrix and a low-frequency matrix; dDTF dictionary learning based on threshold self-adaption is carried out on the high-frequency matrix and the low-frequency matrix, namely, an initial dictionary is set, learning is carried out to obtain an initial sparse coefficient matrix, and self-adaption threshold updating is carried out on the initial sparse coefficient matrix, namely, the initial sparse coefficient matrix is updated according to the sequence from large to small; selecting a sparse coefficient as a dynamic threshold value in an iteration process to update an initial sparse coefficient; determining a final threshold value to obtain a final sparse coefficient matrix, updating the dictionary again to obtain sub-signals, and finally performing inverse transformation and matrix rearrangement inverse operation on the obtained sub-signals to obtain noise reduction signals. According to the method, wavelet decomposition and DDTF are combined to construct a double sparse mode, and the sparse representation capability of a fixed base is effectively improved.

The invention discloses a surveying and mapping geography multivariate data fusion processing method. The method comprises the following steps: S1, data preprocessing; s2, constructing a sparse representation model; s3, estimating posterior distribution, and assuming a data generation mode; s4, carrying out sparse coefficient fusion, and carrying out joint modeling by using a multi-task sparse coding model; s5, enhancing relevance, and constructing a semantic network graph model; and S6, performing global optimization and output. According to the method, the defects of a traditional method in processing diversified scales can be effectively overcome in the data preprocessing link, the data quality is greatly improved, local optimum is avoided and the convergence speed is increased through self-adaptive step length adjustment and a random restart mechanism when the sparse representation model is constructed, and meanwhile, in the global optimization and output stage, the algorithm is simple and convenient to operate. A multi-objective evolutionary algorithm is adopted, multiple optimization objectives can be considered at the same time, an optimization strategy is flexibly adjusted according to an actual application scene, deep data fusion is achieved, and the comprehensive utilization value of surveying and mapping geography multivariate data is improved.

The invention relates to the technical field of vacuum corrugated pipe detection, in particular to a high-compression-ratio vacuum corrugated pipe visual detection system which comprises an image acquisition unit, an image preprocessing unit, a feature point extraction unit and a crack defect recognition unit. According to the method, contour framing is carried out on laser stripes through a contour tracking method, key areas where crack feature points are located are set in two times through a horizontal projection method and a column-by-column search method, false feature points possibly appearing in other areas are avoided, and the crack feature points are recognized in combination with a crack defect recognition unit; the precision and efficiency of crack detection and recognition are improved through cooperation of an image acquisition unit of an electron microscope and a visual detection method, and a crack defect recognition unit can take a to-be-detected image as a linear combination of sample images by constructing a dictionary matrix composed of the sample images. The type of the to-be-detected image is obtained by solving the sparse coefficient vector, and the crack detection effect and detection quality of the vacuum corrugated pipe are improved.