155 results about "Sparse coefficient" patented technology

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 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 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 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 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 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 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.

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 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 relates to the technical field of data processing and state monitoring, and discloses an intelligent ring main unit fault real-time diagnosis method and system, and the method comprises the steps: constructing a fault feature over-complete dictionary; collecting and preprocessing high-frequency operation data streams of the ring main unit in real time; carrying out online sparse decomposition and manifold mapping based on the dictionary, and converting a high-dimensional signal into a low-dimensional sparse coefficient and manifold features; and a lightweight classification model is used for rapid reasoning, and fault early warning and data uploading are triggered when abnormity is detected. The system comprises a data acquisition module, a preprocessing unit, an edge computing engine, an intelligent diagnosis module and a communication gateway. According to the method, depth dimension reduction and feature enhancement are realized through a manifold sparse coding technology, the bandwidth pressure and the calculation overhead are reduced on the premise of ensuring the fault feature integrity, and the real-time performance and the accuracy of diagnosis are improved.

The invention relates to a phased array radar reflectivity factor simulation method based on sparse sounding data, belongs to the field of radio, designs a sparse reconstruction simulation scheme based on acquired multi-point multi-height-layer sparse sounding data, and realizes high-quality restoration and three-dimensional continuous field construction of original sparse observation data. A multivariate dictionary base matrix is constructed; through a sparse representation theory and a Bayesian optimization model, a sparse coefficient reflecting rainfall essential characteristics is obtained, and high-spatial-resolution reconstruction of a temperature and humidity field and a rainfall micro-physical parameter field is realized. Thereafter, a meteorological parameter-driven phase judgment and probability field calculation model is constructed, and three-dimensional simulation of liquid, solid and mixed rainfall echoes is realized. And finally, calculating a unit direction vector and an azimuth guidance coding matrix of a beam ray of the phased array radar, introducing a radar volume scanning data implicit sampling simulator, and realizing high-precision reflectivity factor simulation of the phased array radar in a three-dimensional scanning mode.

The invention discloses an existing concrete beam bridge crack diagnosis method based on an influence line sparse coefficient, which is characterized in that force method graph multiplication and a crack nonlinear damage function are substituted into an analytical solution for solving, and finite element simulation relates to respiration crack simulation. A nonlinear spring is adopted to simulate crack opening and perform grid division and encryption processing on the tip of the crack, bridge section rigidity change caused by the breathing crack is accurately simulated by constructing a crack rigidity damage function, and the model is introduced into an influence line analytical solution to obtain a nonlinear influence line of the cracked bridge. In combination with a sparse coefficient representation method, damage position and damage degree information is extracted from influence line data, and accurate positioning and quantitative evaluation of beam bridge section cracking are achieved; the method not only improves the accuracy and efficiency of damage identification, but also provides a reliable technical means for rapid detection and state evaluation of the existing beam bridge.

The invention relates to the technical field of ion beam physical calculation, in particular to an ion beam optical property evaluation method based on a CPU-GPU hybrid parallel stable double-gradient conjugate algorithm. According to the technical scheme, the method comprises the following steps that a CPU-GPU hybrid parallel architecture is initialized, and CPU end and GPU end memory allocation, CUDA related object creation and GPU equipment initialization and parameter setting are completed; constructing a linear equation set, a sparse coefficient matrix A and a right-end vector b, and setting an initial solution vector, convergence precision epsilon and related parameters of the number of iterations; and analyzing the sparseness of the coefficient matrix A. Through combination of three core mechanisms of CPU-GPU cooperative calculation, dynamic load balancing and intelligent preprocessor selection, efficient, stable and universal evaluation of the ion beam optical characteristics is successfully realized, and an excellent solution is provided for solving the solving problem of a large-scale sparse linear equation set in the field of high-performance calculation.

This invention discloses a noise reduction method, a bearing fault diagnosis method, and a system based on dual sparse dictionary adaptive learning. The method performs wavelet decomposition on the signal to be denoised to obtain high-frequency and low-frequency signals, constructing high-frequency and low-frequency matrices. Then, threshold-adaptive DDTF dictionary learning is applied to both the high-frequency and low-frequency matrices. Specifically, an initial dictionary is set and learned to obtain an initial sparse coefficient matrix. An adaptive threshold update is then applied to the initial sparse coefficient matrix, selecting sparse coefficients from the initial sparse coefficient matrix in descending order as dynamic thresholds during the iteration process to update the initial sparse coefficients. After determining the final threshold, the final sparse coefficient matrix is ​​obtained, and the dictionary is updated again to obtain sub-signals. Finally, inverse transformation and matrix rearrangement inverse operations are performed on the obtained sub-signals to obtain the denoised signal. This invention combines wavelet decomposition and DDTF to construct a dual sparse pattern, effectively improving the sparse representation capability of a fixed basis.

The application provides an abnormal audio detection method and a computer device, which can be applied to the field of device detection, and comprises the following steps: obtaining first audio data (including running audio of a to-be-detected device and surrounding environment audio), respectively performing time domain representation and frequency domain representation on the first audio data, then calculating correlation according to obtained first time domain data and first frequency domain data, and obtaining second audio data according to the correlation, extracting target features of the second audio data through a trained first model, obtaining sparse coefficients based on the target features, and judging whether the second audio data is abnormal audio according to the sparse coefficients. The application describes audio change conditions from two dimensions of time domain and frequency domain, simultaneously smoothes a sound scene through correlation comparison of the two dimensions, removes interference, and corrects a starting point and an ending point of current audio data. In addition, key features in the second audio data are identified by using the trained first model, so that various types of abnormal sound can be identified.

The invention relates to the technical field of image fusion, in particular to a hesitant fuzzy variable granularity dictionary learning-based multi-modal image fusion method, which comprises the following steps of: firstly, adaptively selecting division granularity according to image quality, and partitioning a source image into blocks; then extracting image block features and calculating hesitant fuzzy membership degrees of the image block features so as to quantitatively represent uncertainty information in the image; obtaining a joint over-complete dictionary and a sparse coefficient through dictionary learning, and fusing the hesitant fuzzy entropy and a granularity coefficient to construct an adaptive weight; and finally, fusing the sparse coefficient by using the weight and reconstructing a fused image. The problems of image fuzzy processing, structure multi-scale expression and insufficient adaptive feature extraction capability are effectively solved.

The application discloses an aero-engine bearing fault diagnosis method and system, which carries out amplitude normalization preprocessing on the vibration signal of the aero-engine bearing, unifies the signal data magnitude, and eliminates the analysis interference caused by the signal amplitude difference; a convolution sparse coding optimization objective function is built relying on a non-separable learnable sparse regularizer, the limitation of traditional norm and other separable regularization methods is broken, the sparse coefficients are no longer regarded as independent individuals, the structural interaction relationship between the multi-channel sparse coefficients can be fully mined, the interference caused by strong background noise, multi-source vibration coupling and complex transmission path under actual working conditions can be effectively stripped, the weak impact characteristics of the early bearing damage submerged by noise can be accurately separated, the early fault signal extraction effect under complex operating conditions is greatly improved, and the local impact fault characteristics in the aero-engine bearing vibration signal are effectively enhanced, so that the fault diagnosis is completed without any label.

The invention belongs to the technical field of ocean exploration, and particularly relates to an ocean exploration wave field reconstruction method based on dictionary learning, and the method comprises the steps: carrying out the preprocessing of original wave field data, and obtaining the preprocessed wave field data; separating the preprocessed wave field data into non-overlapping block sample matrixes; converting all block sample matrixes into a sample vector set; selecting a training sample set from the sample vector set; adopting the training sample set to construct a double-sparse dictionary model; iteratively updating the double sparse dictionary model by adopting an alternate optimization mode to obtain a final sparse dictionary and a sparse coefficient matrix corresponding to the sparse dictionary; and carrying out sparse reconstruction on the actual observation wave field by adopting the final sparse dictionary and the sparse coefficient matrix corresponding to the final sparse dictionary. The method has high adaptability to noisy or under-sampled wave field data, and a continuous and high-fidelity wave field structure can still be recovered even under the condition that data acquisition conditions are limited.

The invention discloses an aircraft shape high-precision aerodynamic design method and device based on a quantum discrete adiabatic algorithm, and relates to the technical field of quantum computing. The method comprises the following steps: constructing a linear system model according to target aerodynamic data based on a computational fluid mechanics method; based on the cyclic shift basis matrix, according to the sparse coefficient matrix and the source item vector, performing block coding by using a linear combination unitary operator technology to obtain a block coding unitary operator; based on a preset iteration operator, constructing a walking operator according to the block coding unitary operator; according to the walking operator, line preparation is carried out through a cyclic calling method, and a quantum solving line is obtained; executing a quantum solving line, and measuring flow field quantum state information; and based on the target aerodynamic data, according to the flow field quantum state information, carrying out high-precision aerodynamic optimization design on the shape of the aircraft by utilizing an aerodynamic integration method. The high-precision aerodynamic design method for the aircraft profile is efficient and accurate based on the quantum discrete adiabatic algorithm.

The invention relates to the technical field of bearing fault detection, in particular to a bearing fault sparse feature extraction method based on local feature online learning, and the method comprises the steps: collecting a bearing vibration original signal of warehouse logistics equipment, and carrying out the normalization processing; performing sliding window segmentation on the processed bearing vibration signal based on a kurtosis index, and selecting a preset number of signal segments with the maximum kurtosis as an initial atom set; carrying out orthogonalization operation on atoms in the initial atom set in sequence to form an online learning dictionary based on local feature learning; carrying out convolution operation on atoms in the online learning dictionary and the normalized bearing vibration signal to obtain a sparse coefficient matrix, and executing soft threshold operation; kurtosis values of sparse coefficient vectors in the sparse coefficient matrix are calculated respectively, the sparse vector with the maximum kurtosis value is selected as a target feature vector for envelope spectrum analysis, and finally whether the bearing of the warehouse logistics equipment breaks down or not is judged through an envelope spectrum.

The invention belongs to the technical field of optical fiber sensing, and relates to an optical time domain reflection monitoring method and device based on forward and reverse signal joint optimization, and the method comprises the steps: obtaining reverse and forward OTDR observation signals of a to-be-measured optical fiber; constructing a reverse OTDR linear signal reconstruction item based on a product of the orthogonal transformation matrix and a to-be-solved first sparse coefficient vector; constructing a forward OTDR linear signal reconstruction item based on the product of the orthogonal transformation matrix and a to-be-solved second sparse coefficient vector; constructing a data fidelity item based on the OTDR observation signal and the linear signal reconstruction item; constructing a joint sparse regular term based on the structural sparsity of the first sparse coefficient vector and the second sparse coefficient vector to be solved; and constructing a spatial constraint regular term based on the continuous smoothness of the reconstructed OTDR linear signal in the space, thereby constructing a joint optimization function and solving the joint optimization function to obtain a first sparse coefficient vector and a second sparse coefficient vector, and further reconstructing the OTDR linear signal to detect the optical fiber to be detected.

The invention belongs to the field of magnetic resonance sounding signal noise filtering, and particularly relates to a magnetic resonance sounding signal de-noising method based on backtracking generalized OMP, and the method comprises the steps: employing a magnetic resonance sounding water detector to carry out the sampling of a sparse signal at a frequency lower than the Nyquist frequency, and obtaining a noisy MRS signal; converting the noisy MRS signal into a two-dimensional matrix signal; dictionary learning is carried out on the two-dimensional matrix signals through a KSVD algorithm, and an updated dictionary is obtained after iteration; calculating a recovery matrix and measurement data by using an independently distributed Gaussian random matrix; performing iterative operation on the measurement data by using a backtracking generalized orthogonal matching pursuit algorithm, selecting an atomic sequence of maximum projection of the measurement data and a recovery matrix module, and obtaining a sparse coefficient matrix of the measurement data under the recovery matrix; and calculating a recovery signal according to the dictionary and the sparse coefficient matrix of the measurement data under the recovery matrix.

This invention provides a method, system, and storage medium for enhancing transformer fault acoustic signature diagnostic samples. The method includes: constructing the acoustic wave equation for the vibration of the core and windings propagating through the medium and the medium interface boundary conditions; performing sound field simulation on a fault model based on the acoustic wave equation and the medium interface boundary conditions to obtain a fault simulation sound field; determining a physical simulation dataset based on the fault simulation sound field; performing sparse representation on the physical simulation dataset to obtain a sparse dictionary and a sparse coefficient matrix; performing transfer learning on real fault samples to obtain a transfer dictionary; and performing sample enhancement based on the sparse dictionary, the sparse coefficient matrix, and the transfer dictionary to obtain enhanced transformer fault acoustic signature samples. In this embodiment, sample enhancement using the sparse dictionary, the sparse coefficient matrix, and the transfer dictionary effectively enhances the samples of transformer faults.

The invention relates to the technical field of data cloud storage, in particular to a data backup method and system based on cloud storage, and the method comprises the steps: carrying out the feature collection and processing of to-be-backed-up unstructured data, so as to obtain a joint feature vector; performing matching calculation on the joint feature vector and a preset matrix structure according to a preset tracking algorithm to obtain a sparse coefficient vector; according to the sparse coefficient vector, determining whether the to-be-backed-up unstructured data is duplicated data; and if the unstructured data to be backed up is the non-duplicated data, backing up the corresponding unstructured data to be backed up to the cloud server. Content storage and transmission of repeated data are avoided, cloud storage capacity occupation and bandwidth consumption are reduced from the execution level, and repeated storage and effective metadata retention are both avoided.

The application provides a multimodal large model compression method and device based on sparse codebook quantization, and relates to the technical field of computer vision, wherein the method comprises the following steps: optimizing a visual encoder to make the weight significance distribution of an arbitrary large-scale visual-language model more concentrated; evaluating the influence of each layer weight of the optimized language model on the output through second-order information, and dynamically allocating the code word quantity of each weight group according to the significance; determining the optimal sparse combination from a large-scale codebook by adopting a two-stage strategy of high-level candidate search and low-level subset refinement; and completing model quantization by combining the combination and sparse coefficients, so as to balance the compression and inference performance. The dynamic code word allocation and hierarchical search method based on sparse coding does not need additional training, can adaptively allocate the optimal sparse code word combination, can keep the model expression ability under extremely low bits, and can improve the quantization compression efficiency and inference performance.

The present application belongs to the field of magnetic resonance signal noise filtering, specifically a sparse denoising method for ground magnetic resonance signals using a combined dictionary, which divides the magnetic resonance signal into frequency bands to reconstruct the power frequency harmonic components, and eliminates the power frequency harmonics from the magnetic resonance signal to obtain a power frequency-free signal; a trajectory matrix is ​​constructed using a Gaussian random matrix. Z , using the power frequency signal to construct the trajectory matrix Y , trajectory matrix Z Take the front K After column normalization, the initial dictionary is obtained D , trajectory matrix Y As original samples used to construct sparse coefficient matrix X ; Complete the initial dictionary through K-SVD dictionary learning D and the sparse coefficient matrix X The updated dictionary and sparse coefficient matrix are used to reconstruct the trajectory matrix to obtain the trajectory matrix W ; Take the trajectory matrix W The first row of is used to obtain a pure magnetic resonance signal, which can effectively remove the MRS signal noise in complex electromagnetic interference scenarios under the condition of a single signal acquisition.

This application discloses a method and system for compressing and reconstructing borehole elastic wave signals based on segmented windowing and sparse coding, relating to the field of geophysical signal processing. The method includes segmenting the original borehole elastic wave time-series signal into multiple signal segments; applying a combined Hann window function to each signal segment for windowing and selecting effective signal segments; obtaining the sparse coefficient vector and atom index corresponding to each effective signal segment based on a pre-built dictionary; calculating the average sparse coefficient vector of all effective signal segments to obtain the average sparsity; reconstructing any effective signal segment according to the average sparsity to obtain a reconstructed effective signal segment; and performing inverse weighting processing using the Hann window function corresponding to the reconstructed effective signal segment to obtain the final reconstructed signal. This application achieves efficient compression and high-fidelity reconstruction of borehole elastic wave time-series signals.