21 results about "Dictionary learning" patented technology

This invention discloses a high-overload data analysis method and system based on multi-dimensional buffer protection, relating to the field of data acquisition and storage technology. The system consists of several functional modules, including: a transient impedance module, which acquires stress wave propagation parameters and material strain data collected in real time by the recorder node, constructs a dynamic digital twin model of the buffer structure, and outputs impact feature vectors and stress field distribution matrices; a signal reconstruction module, which dynamically configures the sensing signals based on the impact feature vectors, including the gain, bandwidth, and filtering parameters of the analog front end, conditions and performs analog-to-digital conversion on the sensing signals, and uses compressed sensing and dictionary learning algorithms to sparsely reconstruct abnormal data, generating a high-fidelity data stream and storage data integrity identifiers for each storage block; and a federated storage module, which inputs the stress field distribution matrix and storage data integrity identifiers into the edge-side reinforcement learning decision engine to calculate the failure risk probability of each storage block.

The application belongs to the technical field of image segmentation, and particularly relates to a complex scene target segmentation method and system based on image recognition, which comprises the following steps: performing superpixel adaptive division on an input image, fusing gray scale and texture features to determine a superpixel boundary, mapping the superpixel boundary into a graph node and calculating an edge weight, and constructing an undirected weighted graph; adaptively encoding a graph signal, utilizing a hybrid graph wavelet-Fourier joint transform to optimize and separate features, and obtaining purified graph frequency domain features; sparsely reconstructing features through adaptive super-complete dictionary learning, and obtaining target enhanced features; extracting topological parameters based on an improved persistent homology, constructing a topological constraint feature graph, mapping the topological constraint feature graph into an initial contour field, iteratively optimizing a level set and a contour through an adaptive partial differential equation, and obtaining a high-fidelity coarse segmentation result; extracting geometric features to construct a joint constraint model to repair an occluded area, and outputting a precise segmentation result. In the application, sparse topological modeling is adopted, weak features are strengthened, and target discrimination accuracy is improved.

The present application relates to the technical field of image reconstruction, and more particularly to a spectral super-resolution reconstruction method based on dynamic dictionary learning. It comprises: building a DLTN network architecture, including a feature encoding unit, a spatial down-sampling unit, a multi-level dictionary learning and sparse coding unit, a feature decoding unit and a global feature fusion mechanism; obtaining a hyperspectral training dataset, preprocessing the input low-resolution RGB image, and inputting the network for training; inputting the low-resolution RGB image to be processed into the trained network, and sequentially processing it through feature encoding, spatial down-sampling, multi-level sparse coding and feature enhancement, multi-depth feature global fusion and feature decoding, and outputting the reconstructed hyperspectral image. The advantage is that dynamic dictionary learning and lightweight Transformer architecture are fused, feature sparse representation is realized through the DLTSC module, and the calculation cost is reduced; while ensuring the reconstruction accuracy, the model parameter quantity and the floating point operation frequency are significantly reduced.

This invention discloses a method for restoring Cauchy noise images using convolutional sparse coding and low-rank constraints, belonging to the field of digital image processing technology. This invention integrates local convolutional sparse coding and low-rank regularization of structure groups to construct a joint optimization restoration model for images contaminated by Cauchy noise. First, the preprocessed noisy image undergoes convolutional sparse decomposition and dictionary learning to obtain the global sparse features of the image. Simultaneously, the image is divided into blocks, and image blocks similar to reference image blocks are extracted within a search window to construct structure groups. The kernel norm minus the Frobenius norm is used as a regularization term to impose low-rank constraints on the structure groups, suppressing noise while preserving image structural information. This invention employs the alternating direction multiplier method to efficiently solve the joint restoration model, significantly suppressing Cauchy noise, effectively restoring the edge contours and texture details of the image, and improving the visual quality and recognizability of the image. Therefore, it can be used for the restoration of Cauchy noise images.

The present application relates to the technical field of signal sparse representation, and specifically provides a separation type redundant dictionary learning algorithm for 3D signals, which comprises the following steps: S1: initialization process: initializing redundant dictionaries in three dimensions; S2: main iteration process: comprising a sparse coding stage and a dictionary updating stage; the sparse coding stage comprises: S21: reducing the sparse expression process to a two-dimensional matrix; S22: calculating the expression of samples in the sparse domain according to the initialized redundant dictionaries; S23: calculating the sparse expression error of all samples; the dictionary updating stage comprises: S24: arranging the 3D training sample data blocks into two-dimensional matrices after being respectively unfolded according to three dimensions; S25: updating the redundant dictionaries in three dimensions by solving a formula and performing atom normalization on the dictionaries. The learning algorithm in the present application reduces the time consumption of separation type dictionary training and reduces the sparse expression error of target signals.

This disclosure relates generally to a field of industrial machine inspection, and, more particularly, to method and system for acoustic based industrial machine inspection using Delay-and-Sum beamforming (DAS-BF) and dictionary learning (DL). The disclosed method presents a two-stage approach for anomaly detection using a multi-channel acoustic mixed signal. In the first stage, separation of a plurality of acoustic signals corresponding to the spatially distributed acoustic sources is performed at a coarser level by using the DAS-BF. Subsequently, dictionaries pre-trained using the plurality of acoustic signals of the individual source machines are utilized for generating a plurality of separated acoustic source signals. The generated plurality of separated acoustic source signals are analyzed for the anomaly detection by comparing them with a corresponding normal machine sound template.

The application discloses a reservoir physical property parameter prediction method driven by a rock physical model and a dictionary, applied to the field of rock physical inversion, and aims at solving the problem that the solution of the physical property parameter in the prior art is deviated from the actual situation in details, wherein the application uses a linearized rock physical inversion method to build the framework of reservoir physical property parameter inversion, uses a joint dictionary learning method to acquire data characteristics and the relationship among different parameters, and adds the rock physical inversion process, so that the physical property parameter inversion result is more accurate and conforms to the actual underground stratum distribution rule.

The invention provides an intelligent refrigerator peculiar smell detecting and processing device, and belongs to the technical field of refrigerator production.The intelligent refrigerator peculiar smell detecting and processing device is characterized in that a multi-dimensional data acquisition system comprising a gas sensor array and a temperature and humidity sensor is constructed, and low-frequency trend components of sensor signals are extracted through wavelet transformation; a sparse coding-based dictionary learning and deep expansion network combined temperature drift compensation model is used to realize self-adaptive correction of a sensor baseline, and feature matrix construction and cosine similarity calculation are combined to complete peculiar smell type identification and concentration level quantification. And the power of the ultraviolet light emitting diode and the rotating speed of the fan are dynamically adjusted through a thermodynamic entropy increase minimization purification control algorithm to realize efficient purification, so that the technical problem of insufficient detection accuracy caused by serious baseline drift of the refrigerator odor sensor in a temperature fluctuation environment is solved.

The application discloses an image classification method, system, device, medium and product based on Wasserstein discriminative dictionary learning, relates to the field of image classification, and comprises the following steps: acquiring a training data set; constructing a target function of Wasserstein discriminative dictionary learning based on the training data set; the target function comprises a reconstruction error term based on a Wasserstein distance, an improved local constraint term and an intra-class sharing term; an alternating iterative optimization algorithm is used to solve the target function, so that an optimal dictionary and an optimal coefficient matrix are obtained; a test coefficient vector is determined based on the optimal dictionary and an image to be classified; a category membership matrix of a dictionary atom in the optimal dictionary is constructed based on the optimal coefficient matrix; category scores of the image to be classified are calculated based on the test coefficient vector and the category membership matrix, and the category of the image to be classified is determined according to the maximum score. The application can improve the precision and efficiency of image classification.

The application discloses a multi-sound source positioning and imaging method based on sparse representation and variational Bayesian inference, and steps are as follows: (1) an initial sound intensity matrix is generated under low resolution by using a conventional beamforming algorithm to estimate the sound source position; (2) the signal intensity gradient is calculated, the search position is updated along the gradient direction, and the accurate sound source position and high-resolution sound intensity matrix are obtained; (3) a sparse dictionary learning algorithm is used for sparse coding and dictionary update optimization of the high-resolution sound intensity matrix; (4) a variational Bayesian inference model is constructed based on the sparse coefficient, the lower bound of variation is optimized, and the posterior positioning estimation of the multi-sound source is carried out; and (5) the positioning result is fused with the camera image to obtain the visualized position of the sound source. The method realizes high-precision real-time positioning and imaging in a complex sound field by combining low-resolution positioning, gradient optimization, sparse dictionary learning and Bayesian inference, and has high spatial resolution and strong anti-interference capability.

The application discloses a three-dimensional image reconstruction method and system based on dictionary learning, and relates to the technical field of image processing.The method comprises the following steps: obtaining original ultrasonic field data of a rotating scanning workpiece to be measured; obtaining a low-resolution image through filtering, frequency domain transformation, secondary filtering by a ramp function and inverse transformation; constructing an external dictionary and an internal dictionary based on an open-source high-resolution CT data set and the low-resolution image respectively; coupling dictionary features in the time domain and the frequency domain by using an optimization function, iteratively optimizing a mapping relationship by using a FISTA algorithm, and reconstructing a tomographic image based on compression sensing; and finally, splicing high-resolution three-dimensional images in height order.The application realizes the super-resolution reconstruction of ultrasonic images under the double constraints of the frequency domain and the time domain by combining dictionary learning and compression sensing, and significantly improves the three-dimensional imaging quality.

The application discloses a bearing fault classification method and related device. The method comprises the following steps: inputting a first vibration signal collected under a plurality of known fault types of a bearing into a preset dictionary learning algorithm model after processing, and constructing a sparse dictionary containing a plurality of fault type training samples; processing a second vibration signal collected for fault classification to obtain a test sample, performing sparse optimization solving on the test sample, and obtaining a first sparse coefficient; respectively reconstructing the first sparse coefficient and the training sample under a plurality of fault types, and obtaining a plurality of fault type corresponding reconstruction signals; comparing the test sample with the plurality of fault type corresponding reconstruction signals, and determining the fault type corresponding to the test sample according to the comparison result. The application can accurately and effectively realize bearing fault classification diagnosis.

This invention provides a method for detecting minor process faults based on sliding window shared dictionary learning, belonging to the field of data-driven technology. It solves the technical problems of domain shift in existing dictionary learning methods and insufficient sensitivity of the KNN algorithm for minor fault detection. The technical solution includes the following steps: acquiring multivariate time series data of an industrial process; extracting the mean and variance statistical features from the sliding window; constructing a shared dictionary learning dataset; learning the shared dictionary using a clustering algorithm; calculating the reconstruction error; setting adaptive detection control limits; and performing minor fault detection and evaluation. The proposed method has been applied to the detection of several different types of minor faults in the Eastman Chemical Company in Tennessee. Simulation results show that, compared with principal component analysis and the KNN method, the proposed shared dictionary learning method has a higher fault detection rate and a lower false alarm rate.

This invention relates to the field of detection and imaging technology, specifically to a muon quasi-scattering imaging device and method based on sparse representation and dictionary learning. The device includes a modular muon detector array deployed on one side, an FPGA high-speed data acquisition system, and a computational control unit, and can also integrate a momentum spectrometer module. The method includes offline dictionary preparation and online sparse reconstruction steps. First, a complete dictionary is trained using GEANT4 simulation and the K-SVD algorithm. Then, data is collected to construct an enhanced objective function containing block sparseness and anisotropic total variational regularization. The reconstructed image is obtained by optimizing the solution using the ADMM algorithm. This invention overcomes the limitations of two-sided detection, adapts to single-sided detection scenarios in geotechnical engineering, significantly improves imaging efficiency and quality under low-throughput data, has strong algorithm robustness and a scalable framework, and can accurately identify anomalies such as underground cavities and isolated boulders.

In a wireless communication system, a user equipment (UE) may report channel state information (CSI) using a learned dictionary defining a set of sparse vectors. The UE determines a learned dictionary for CSI reporting. For example, the UE receives a shared dictionary from a similar and nearby UE or the UE trains the learned dictionary based on logged CSI measurements. The UE indicates the learned dictionary to a serving base station. The UE measures CSI for a plurality of channels. The UE reports a sparse vector representing the CSI based on the learned dictionary to the serving base station.

The application belongs to the technical field of marine exploration, and particularly relates to a marine exploration wave field reconstruction method based on dictionary learning, which comprises the following steps: preprocessing original wave field data to obtain preprocessed wave field data; separating the preprocessed wave field data into non-overlapping block sample matrices; converting all the block sample matrices into a sample vector set; selecting a training sample set from the sample vector set; constructing a double sparse dictionary model by using the training sample set; iteratively updating the double sparse dictionary model by using an alternating optimization method to obtain a final sparse dictionary and a sparse coefficient matrix corresponding to the final sparse dictionary; and performing sparse reconstruction on an actual observed wave field by using the final sparse dictionary and the sparse coefficient matrix corresponding to the final sparse dictionary. The application has strong adaptability to wave field data containing noise or under-sampling, and can still restore continuous and high-fidelity wave field structures even in the case that the data acquisition conditions are limited.

The present application relates to a kind of fusion multi-physical model and the foundation pit deformation prediction method of monitoring data, steps are as follows: S1.based on the soil parameter statistics of foundation pit deformation calculation physical model, then through calculation physical model, obtain corresponding deformation calculation response, construct dictionary model;S2. Observation matrix is gradually constructed, from S1 dictionary model, identify the atom with high correlation of observation matrix, determine important atom, and estimate the weight and its uncertainty of important atom using Bayesian inference method;S3. Based on the important atom determined in S2, using sparse dictionary learning, linear weighted combination is carried out in combination with the weight result estimated, and the approximate representation of foundation pit excavation deformation and subsequent construction stage are carried out deformation prediction;S4. Dynamic update subsequent deformation prediction result.The present application fuses physical model information and field monitoring data, can realize the accurate prediction and uncertainty quantification of foundation pit deformation, and provides technical support for foundation pit engineering safety control.

The application discloses a high-overload-resistant data analysis method and system based on multi-dimensional buffer protection, and relates to the technical field of data acquisition and storage. The system is composed of a plurality of function modules, including: a transient impedance module, which acquires stress wave propagation parameters and material strain data collected by a recorder node in real time, constructs a dynamic digital twin model of a buffer structure, and outputs an impact feature vector and a stress field distribution matrix; a signal reconstruction module, which dynamically configures a sensing signal based on the impact feature vector, including gain, bandwidth and filtering parameters of an analog front end, carries out conditioning and analog-digital conversion on the sensing signal, and carries out sparse reconstruction on abnormal data by using a compression sensing and dictionary learning algorithm, to generate a high-fidelity data stream and a storage data integrity identifier of each storage block; and a federal storage module, which inputs the stress field distribution matrix and the storage data integrity identifier into a side reinforcement learning decision engine together, to calculate failure risk probabilities of the storage blocks.

The application discloses a bearing intelligent diagnosis method based on a generalized domain data fusion strategy and kernel sparse representation, designs a generalized domain data fusion strategy for dictionary learning, specifically uses an improved Kalman filter fusion framework to project time domain and frequency domain signals to a generalized domain state space and realizes signal adaptive fusion, and secondly, in order to avoid the influence of time shift characteristics on a dictionary learning model, develops a kernel discriminative sub-dictionary learning method, specifically uses a Gaussian kernel function to map the fused generalized domain signals to a high-dimensional feature space, then learns a specific category kernel discriminative sub-dictionary in a data-driven manner through a kernel K-SVD algorithm, then uses the learned specific category kernel discriminative sub-dictionary to realize sparse representation of unknown bearing signals in a high-dimensional space, and finally realizes intelligent identification of the bearing health state according to a minimum reconstruction error criterion. The application enhances the sparse representation ability and discriminative feature mining ability of the dictionary model for nonlinear data.

The application provides a generalized S transform time-frequency analysis method based on dictionary learning and matching pursuit decomposition, comprising the following steps: step 1, inputting an original seismic signal set, and constructing a wavelet library based on K-SVD dictionary learning; step 2, combining the fast complex domain matching pursuit algorithm with the wavelet library constructed based on K-SVD dictionary learning to improve the speed and accuracy of seismic signal decomposition; step 3, obtaining the time-frequency spectrum of independent wavelets based on the generalized S transform, and superimposing the time-frequency spectrum of all independent wavelets to perform time-frequency joint analysis of the high-resolution time-frequency spectrum; step 4, studying the change of reservoir characteristics by using single-frequency attributes based on the high-resolution time-frequency spectrum; and step 5, eliminating the high-resolution time-frequency spectrum representing the change of reservoir characteristics after strong reflection. The generalized S transform time-frequency analysis method based on dictionary learning and matching pursuit decomposition forms a high-resolution time-frequency analysis technology according to the development of actual application requirements and the characteristics of non-stationary signals.

Broadly speaking, the present techniques generally relate to improving image reconstruction through dictionary learning and sparse coding algorithms and making these algorithms more effective. In particular, the present techniques relate to selecting patches, based on some criteria, that are used to construct a dictionary so that the dictionary produces reconstructions that are tailored to those criteria.