94 results about "Sparse constraint" patented technology

The invention discloses a road cavity volume calculation method and device based on a three-dimensional FK migration algorithm, equipment and a medium, and relates to the technical field of road cavity detection, and the method comprises the steps: obtaining three-dimensional ground penetrating radar data, and carrying out the preprocessing of the data, and obtaining target radar data; converting the target radar data to a frequency wavenumber domain to obtain frequency wavenumber domain radar data; establishing a road hierarchical velocity model, and performing hierarchical wave field extrapolation and double weight correction based on the frequency wave number domain radar data and the road hierarchical velocity model to obtain corrected wave field data; performing sparse constraint optimization imaging processing on the corrected wave field data to obtain a target imaging result; and carrying out boundary extraction processing on the target imaging result to obtain a three-dimensional cavity boundary point set, and carrying out voxelization processing based on the three-dimensional cavity boundary point set to obtain a road cavity volume. According to the invention, high-precision restoration of the shape of the road cavity and automatic extraction of the volume can be realized in three-dimensional GPR imaging.

The invention relates to the technical field of big data processing, and discloses a data extraction method and system for geological mineral exploration, and the method comprises the steps: carrying out the standardization preprocessing of original data containing space coordinates, lithology texts, mineral components, logging curves and mineralization labels; spatial, semantic, concentration and time sequence deep representations are extracted in parallel through a multi-modal geologic feature encoder and fused into high-dimensional vectors; applying structured sparse constraint to the features by using a graph attention mechanism guided by an expert knowledge graph, and strengthening a mineralization association dimension; a dynamic incremental learning engine is combined with an elastic weight solidification mechanism to realize local fine tuning of model parameters and historical knowledge retention; and finally outputting a mineralization potential score, a mineralization factor sequence and an abnormal element combination. According to the method, through triple mechanisms of multi-modal fusion, knowledge embedding and incremental evolution, the accuracy, interpretability and timeliness of data extraction are improved, and the real-time analysis requirement of large-scale mineral exploration is met.

The invention discloses a million-frame-level industrial vision system and method based on event driving and compressed sensing, and the system is characterized in that an event camera imaging module in the system captures the brightness change of each pixel in a field of view of the event camera imaging module in an asynchronous manner, and generates an event containing a pixel coordinate, a timestamp and change polarity for each change; a compressed sensing coding module constructs sparse image vectors for events in a time window, and the sparse image vectors are projected to low-dimensional observation vectors through an observation matrix phi; the sparse image reconstruction module is used for optimizing an objective function through sparse constraint and total variation regularization; a dynamic ROI compression module controls a compression mask function according to the event density and a gradient threshold. The method can break through the limitation of the traditional frame rate, has the advantages of high precision, high efficiency, low power consumption, strong robustness and the like, and has a wide industrial application prospect.

The invention discloses a three-dimensional Gaussian splashing method and system for sparse view curve reconstruction, and the method reconstructs a three-dimensional curve scene through a sparse image: firstly, obtaining a dense point cloud and a camera pose, extracting a two-dimensional line segment graph, employing a line segment guide Gaussian initialization strategy, and obtaining a three-dimensional curve scene; generating an initial Gaussian set on the basis of the dense point cloud and the two-dimensional line segment graph; structure perception Gaussian pruning is executed in the optimization process of the initial Gaussian set, and spatial outliers and visibility redundant gauss are removed; and finally, calculating the total loss containing sparse regular terms through the difference between the rendered image and the sparse image, and iteratively optimizing Gaussian parameters until the total loss converges, thereby obtaining a final three-dimensional curve reconstruction result. According to the method, the problem of geometric prior deficiency under a sparse view is solved through line segment guide initialization, overfitting and artifacts are effectively inhibited by using structure pruning and sparse constraint, and high-quality three-dimensional curve reconstruction is realized.

The invention discloses a screening method of a protein marker combination, which is used for risk prediction of abdominal aortic aneurysm or ruptured abdominal aortic aneurysm, and optimizes the stability of a protein expression profile by constructing an initial protein expression profile; screening differential expression proteins by adopting statistical analysis, and preliminarily locking candidate proteins remarkably associated with a disease endpoint based on survival analysis or a risk regression model; performing cross validation by combining a sparse constraint algorithm and a nonlinear feature selection algorithm, and extracting a robust core marker; the independent contribution degree of each marker is evaluated through the standardized weight, and finally the optimal protein combination for disease prediction or diagnosis is determined. According to the method, six core proteins obtained through multi-algorithm cross screening can be detected in serum, multivariable logistic regression and external verification set performance both show good robustness, and an aorta protein fingerprint feature set which can be popularized and explained is formed.

The application is suitable for the technical field of radar signal processing, and provides a deep unfolding ISAR super-resolution imaging method based on sparse-neighborhood joint constraint, comprising: firstly, constructing an ISAR image degradation model, constructing an ISAR super-resolution imaging problem based on the ISAR degradation model and the compressed sensing theory, using ADMM to solve the ISAR super-resolution imaging problem, unfolding the process of solving the ISAR super-resolution imaging problem by ADMM into a multi-level neural network, and finally inputting low-resolution ISAR echoes into the trained neural network to obtain an ISAR super-resolution imaging result. The application combines sparse constraint and neighborhood amplitude constraint, deduces a corresponding signal model and an ADMM solving algorithm, effectively improves the reconstruction ability of the algorithm to complex target details and the representation ability of the algorithm to structural information, and can realize effective super-resolution imaging based on narrowband short-aperture echoes.

The application discloses a kind of double-channel label guide multi-label feature selection method and system, belong to feature engineering technique.Method mainly includes: obtaining the feature matrix and positive label matrix of multi-label data set, by performing logical negation to positive label matrix, generate mirror negative label matrix, and construct graph Laplacian matrix based on feature matrix;Based on the data after pre-processing, a multi-label model is constructed, and the objective function of the multi-label model includes at least positive label regression loss term, negative label regression loss term, label alignment constraint term, graph regularization term and sparse constraint term;The constraint is processed by relaxation, and the optimization function is constructed by combining the Lagrange multiplier method, and then the objective function is iteratively solved according to the KKT condition, and after iterative convergence, the feature importance is evaluated based on the projection matrix used to associate features and positive labels;The application meets the demand of multi-label learning for accurate and efficient feature selection, can fully utilize label information, enhance anti-interference ability and consider efficiency.

The application discloses a hyperspectral sparse unmixing method of a learnable implicit variable iterative unfolding network, and comprises the following steps: constructing an unmixing model by sparse constraint optimization; constructing an alternating direction iteration by variable splitting and an augmented Lagrangian method; modeling an alternating direction iteration step as an implicit unfolding network module, including a learnable layer of abundance variable and multiplier variable; constructing a learnable smooth convolution layer to improve the smoothness of abundance patches; realizing spectral unmixing and reconstruction by a coding-decoding structure; and realizing model training by adopting an unsupervised loss function. The application uses the optimization mechanism of a classical sparse unmixing model to design a learnable network, the network layer is designed based on algorithm iteration steps, the sparsity and patch smoothness of hyperspectral abundance are fully met, and the explainability and transparency are enhanced; an unsupervised training mechanism is introduced, the network availability is enhanced; the model parameter size and overfitting phenomenon are reduced through model driving and network parameter sharing, and the model is lightened.

This invention discloses a sparse array optimization method and related equipment based on amplitude mapping. The method includes: acquiring array information and preset sparse constraints of an antenna array; initializing an amplitude weight matrix based on the array information and sparse constraints; mapping the amplitude weight matrix to a sparse binary layout; quantizing the sidelobe level based on the sparse binary layout; if the sidelobe level meets preset requirements, the corresponding sparse binary layout is used as a sparse array; otherwise, the amplitude weight matrix is ​​updated through an optimization algorithm, and the step of mapping the amplitude weight matrix to a sparse binary layout is returned to be executed until the sidelobe level meets the preset requirements. This invention transforms the discrete, massive cell position combination search problem into a continuous, relatively low-dimensional amplitude weight optimization problem, which can greatly compress the solution space and thus efficiently complete the optimization of large-scale arrays. It can be widely applied in the field of data processing technology.

The application discloses a gearbox fault diagnosis method based on adaptive decomposition and transfer learning, comprising: collecting multi-condition vibration signals as original input; adopting an exponential coupling resonance search-Fourier adaptive modal decomposition method to adaptively decompose the signals, accurately decoupling multi-component signals and suppressing modal aliasing; extracting multi-domain features and utilizing weighted multi-objective probability principal component analysis dimension reduction to obtain a sensitive low-dimensional feature set; constructing a sparse excitation residual network with a sparse constraint channel attention mechanism as a feature extractor and pre-training; adopting a confidence-aware incremental open set transfer learning strategy to fine-tune the high layer of the network, realizing known fault diagnosis and open set identification of unknown fault samples. The method effectively improves the fault feature decoupling capability and the generalization performance of the diagnosis model, and is suitable for intelligent fault diagnosis and state monitoring of the gearbox under multiple conditions.

This invention relates to the field of seismic data processing technology, specifically disclosing a method for removing strong reflection shielding in hidden river channels based on adaptive hybrid L0-L1 norm. The method includes: first, establishing an optimization objective function based on Bayes' theorem using hybrid L0-L1 norm; second, dynamically adjusting the L0-L1 norm weights using an adaptive weight function driven by the seismic signal, combined with reflection coefficient amplitude and residual information, enhancing sparsity constraints in strong reflection zones and reducing constraint strength in weak reflection zones; then, constructing a convex upper bound for the objective function using a minimization framework, and solving it iteratively in stages using an accelerated rapid iterative threshold shrinkage algorithm, while incorporating prior knowledge of seismic wave propagation laws and river channel deposition patterns to ensure the geological rationality of the solution. This invention solves the technical problems of traditional sparse processing methods, such as fixed parameters, lack of geological constraints, and inability to simultaneously address strong reflection suppression and weak signal protection, significantly improving the separation accuracy of strong reflections and the recovery rate of weak signals, effectively overcoming the strong reflection shielding effect.

The invention discloses a robust speech enhancement method based on adaptive beam forming and sparse spectrum constraint. The robust speech enhancement method comprises the following steps: receiving a multichannel observation signal through a microphone array and constructing a signal model of a generalized sidelobe canceller structure; based on the signal model, a beam forming optimization model is constructed in combination with the least square criterion and the speech spectrum sparse constraint; performing iterative solution on the beam forming optimization model by adopting a Lagrange multiplier alternating direction method to obtain an adaptive filter weight vector in a generalized sidelobe canceller; and calculating and outputting an enhanced voice signal based on the weight vector. According to the method, the voice quality and intelligibility can be effectively improved in a strong interference and reverberation environment, interference and reverberation are remarkably suppressed, and the robustness of an algorithm is improved.

The invention discloses a two-dimensional wave field reconstruction method based on compressed sampling, and belongs to the technical field of signal processing and sensors. According to the method, original time domain signals of a two-dimensional wave field are expressed as a three-dimensional matrix, random sampling is carried out through a Bernoulli distribution random sampling matrix, after the three-dimensional matrix is reduced into a two-dimensional matrix, a group sparse perception convolutional neural network is constructed to serve as a non-convex optimization solver, a real part and an imaginary part of a complex signal are processed, group sparse constraint is applied, and a non-convex optimization solver is constructed. And reconstructing the basis coefficient matrix to obtain an original signal reconstruction result, and finally raising the dimension to restore the original signal into a three-dimensional wave field. According to the method, the sampling rate is reduced, the reconstruction precision is improved, large-scale data set training is not needed, each training is an independent reconstruction process, and the method is suitable for the fields of acoustic imaging, seismic exploration and the like.

The application is suitable for the technical field of device management, and provides a behavior recognition method based on space-time relationship and an electronic device. The method comprises the following steps: receiving target video data to be recognized; introducing the target video data into a preset inter-frame action extraction network to obtain inter-frame action feature data; introducing the inter-frame action feature data into a feature extraction network to output sparse feature data corresponding to the target video data; the feature extraction network is generated by performing sparse constraint processing on each convolution kernel in a pooling fusion network through selected weights; introducing the target video data into a context attention network to determine gait behavior data of a target object in the target video data; and obtaining a behavior category of the target object according to the gait behavior data and the sparse feature data. The above method can greatly reduce the calculation cost of video data in the behavior recognition process, thereby improving the operation efficiency.

This invention relates to a method and system for EEG emotion recognition based on an adaptive multi-view graph neural network, belonging to the field of brain-computer interface and emotion computing technology. The method includes: dividing multi-channel EEG signals into continuous time windows, and using four adjacent time windows as temporal input samples; extracting multi-band differential entropy features of each time window as initial node features; fusing prior knowledge of electrode spatial proximity and brain biological symmetry to construct a basic matrix, and modulating and applying sparse constraints through a learnable attention mechanism to generate an individualized brain functional connectivity topology; designing a parallel bi-branch deep network, where a graph convolutional branch extracts global spatiotemporal features from the graph structure sequences corresponding to the four time windows, and a one-dimensional convolutional branch extracts and fuses local frequency-spatial features; and during training, comprehensively applying node-level domain adversarial and graph structure collaborative regularization to output the emotion category. This invention is beneficial for improving cross-subject recognition performance.

The application provides an industrial network security situation prediction method and system based on a generative large model, relates to the technical field of industrial network security, and comprises the following steps: acquiring time series network behavior data of a plurality of monitoring nodes in an industrial network, and extracting multi-dimensional features to obtain a security feature vector; the feature vector is decomposed into a periodic baseline component and a transient disturbance component through frequency domain transformation, and a decomposition situation feature is obtained through sparse constraint screening; semantic space mapping is performed by using a generative large model to obtain a semantic enhanced situation representation, the correlation between security anomalies among nodes is calculated based on the decomposition situation feature, and a dynamic correlation matrix is constructed; a propagation operator is constructed based on the dynamic correlation matrix, the semantic enhanced situation representation is subjected to multi-step iterative propagation, a decay factor is introduced to simulate abnormal influence diffusion, and a security situation prediction result in a future time window is obtained.

The application relates to the technical field of visual text tamper detection, and specifically discloses an open set visual text tamper detection method based on sparse constraint rectified flow, which comprises the following steps: acquiring a real image and constructing training data; constructing a neural network, including a multi-modal forensic tokenizer, an F-DiT encoder and an F-DiT decoder; wherein the multi-modal forensic tokenizer extracts RGB visual block features, spatial rich model filtering features and block discrete cosine transform frequency domain features from an image state, and fuses the features to obtain a multi-modal forensic information feature vector; the neural network is trained by using the training data to obtain a forensic feature network; a query image is acquired, the forensic feature network is used to predict a recovery velocity vector field, a pixel-level tamper probability graph is calculated according to the recovery velocity vector field, and tamper area positioning is realized. The application can realize high-precision positioning of unknown open set text tampering without supervised paired data.

The application discloses a Cauchy noise image restoration method based on a ratio type sparse constraint, and belongs to the technical field of digital image processing. The method uses the non-local similarity of an image, takes a structure group formed by similar blocks as a training object, learns an orthogonal dictionary, performs sparse representation on the structure group, and applies a non-convex constraint on representation coefficients. Firstly, a Cauchy noise image after preprocessing is blocked, a most similar group of image block vectors is extracted in a search window with a reference block as a center after being structured, then the orthogonal dictionary is trained by using the structure group, the correlation in the structure group is enhanced by using joint coding, and a norm is used as a regularization term to perform sparse constraint on a coefficient matrix, and finally, the Cauchy noise is removed and local texture details are restored. The proposed model is solved by using an alternating direction multiplier method, most of the noise can be effectively removed and image texture information can be reserved, and therefore, the method can be used for Cauchy noise image restoration.

The invention relates to an electroencephalogram emotion recognition method and system based on a self-adaptive multi-view neural network, and belongs to the technical field of brain-computer interfaces and emotion calculation. The method comprises the following steps: dividing a multi-channel electroencephalogram signal into continuous time windows, and forming a time sequence input sample by four adjacent time windows; extracting a multi-band differential entropy feature of each time window as a node initial feature; fusing electrode space proximity and brain biological symmetry prior to construct a basic matrix, modulating and applying sparse constraint through a learnable attention mechanism, and generating an individualized brain function connection topological graph; a parallel double-branch deep network is designed, a graph convolution branch extracts global spatial-temporal features from graph structure sequences corresponding to four time windows, and a one-dimensional convolution branch extracts local frequency-space features and fuses the local frequency-space features; node-level domain confrontation and graph structure collaborative regularization are comprehensively applied in training, and emotion categories are output. According to the invention, the cross-subject identification performance can be improved.

The invention discloses an analysis scheduling method and system based on multi-label routing. The method comprises the following steps: receiving multi-label hypergraph data, performing tuck decomposition on a hypergraph adjacency tensor, applying sparse constraint, extracting a core multi-label dependency structure, and generating a low-rank factor matrix; extracting hyperedge embedding vectors according to the low-rank factor matrix, compressing the hyperedge embedding vectors into binary hash codes through a locality sensitive hash module, and splicing the binary hash codes with the residual floating point features to form mixed hyperedge embedding vectors; for nodes in the graph neural network, attention weights are calculated through a multi-label compatibility function based on mixed embedding vectors, neighborhood aggregation is carried out, node states are updated through a gating circulation unit, and node embedding representation is obtained; and performing routing scheme generation on the node embedded representation through a path search algorithm, evaluating different schemes through a path scoring function, and outputting an optimal scheduling route according to a scoring result. According to the method, the calculation and message transmission efficiency of the large-scale multi-label graph is improved.

The present application relates to the technical field of local structure damage detection, and particularly relates to a local structure hazard analysis method using digital twinning. The method acquires measured strain signals and vehicle parameters when a standard vehicle passes over a bridge, and extracts a normalized measured feature vector. At the same time, an isomorphic simulation feature vector is generated based on the vehicle parameters and a finite element model. An optimal transmission coupling matrix between the two is calculated using the Sinkhorn algorithm, and a distribution pattern transmission cost and an adaptive sparse weight are obtained, solving the recognition error caused by the phase shift in the waveform space. By minimizing the transmission cost and applying sparse constraints, the equivalent cross-section reduction coefficient of each unit is inverted, and the precise positioning of local stiffness damage is realized. Finally, the model material parameters are updated synchronously, and the load bearing capacity reduction rate of the structure is calculated through nonlinear whole-process analysis. The present application can accurately quantify the influence of local damage on the overall safety of the structure under the conditions of unknown vehicle weight and waveform shift.

This invention discloses an AI video behavior real-time analysis system for police law enforcement recorders, relating to the field of video behavior analysis. It includes: a topology construction module S101, used to collect spatial location information of key human body points from real-time video and construct an initial human body topology map using the spatial location information; a topology reconstruction module S102, used to dynamically reconstruct the topology of the initial human body topology map using a graph convolutional network with sparse constraints on topological connections, obtaining a dynamic topology feature map; and a feature extraction module S103, used to extract local motion pattern features of human behavior based on the dynamic topology feature map and use the local motion pattern features to determine potential areas of behavioral abnormalities. This invention achieves accurate identification and real-time positioning of abnormal behaviors in police law enforcement recorder videos by constructing a human key point map that integrates dynamic topology reconstruction and local motion pattern analysis.

The invention discloses a multi-view aluminum product identification method, system, equipment and medium, and relates to the technical field of object identification, and the method comprises the steps: superposing subspace representation matrixes of different views of an aluminum product into a tensor, applying low-rank constraint to the tensor, introducing Frobenius norm constraint to process the subspace representation matrixes, and obtaining a subspace representation matrix; carrying out sparse constraint on the transpose of the consistent subspace representation matrix in the processed class and the product of the matrix, and obtaining a target function for identifying the multi-view aluminum product; and by optimizing various results in the target function and applying spectral clustering to the optimized subspace representation matrix when a convergence condition is met, a final clustering result is obtained, and the clustering result is used as an aluminum product identification result. The block diagonal structure of the subspace representation matrix is enhanced by performing sparse constraint on transpose of the subspace representation matrix and the product of the subspace representation matrix, so that the recognition performance of the construction method on the aluminum product can be improved to a certain extent.

The invention relates to the technical field of medical imaging, and discloses a sparse bimodal kernel method for low-dose high-quality PET image reconstruction, which comprises the following steps of: firstly, defining input parameters such as a PET system matrix, projection data and the like; respectively constructing PET and MRI channel kernel matrixes based on the PET initial image reconstructed by the kernel-free high-order total variation regularization method and the traditional Gaussian kernel, and the MRI original image and the transformed Gaussian kernel introduced with the gray scale difference term; generating a weighting matrix through the binary image, and fusing the two-channel kernel matrixes to obtain a bimodal kernel matrix; constructing an optimization model containing sparse constraint and non-negative constraint, and adopting a PKMA algorithm combined with a precondition technology and KM momentum acceleration for iterative solution; and finally, outputting a high-quality reconstructed image. According to the method, noise can be suppressed under the low-dose condition, focus details are reserved, the image quality and the calculation efficiency are balanced, and the high requirement of clinical diagnosis for PET images is met.

The application discloses a kind of sparse generalized W transform gas reservoir characterization time-frequency analysis method, for the first time L1 norm is introduced to the mathematical relationship between generalized W transform and seismic signal for sparse constraint, and is solved using Bregman iteration algorithm, to obtain a kind of analysis result with higher time-frequency resolution.The method absorbs the advantage that generalized W transform highlights low-frequency information of seismic signal, avoids the problem of main frequency splitting, and provides a more sparse time-frequency representation for non-stationary seismic signals. When applied to the time-frequency analysis of actual seismic data, it can provide a high-precision seismic spectral decomposition result for gas reservoirs, thereby more accurately delineating the low-frequency abnormal area of gas reservoir.

The application discloses an image denoising method based on a non-hyperparameter total variation equilibrium constraint, and comprises the following steps: first, a total variation image denoising model based on columnar processing is established; then, a total variation image denoising cost function is constructed; then, according to a covariance fitting criterion, an optimal equilibrium weighting matrix of the total variation sparse constraint is derived, and a non-hyperparameter equilibrium total variation image denoising cost function is obtained; finally, optimal iterative solution is realized through a convex optimization tool. The method solves the non-hyperparameter equilibrium total variation image denoising cost function, realizes uniform denoising, does not lose the denoising effect, and solves the optimal selection problem of the regularization parameter in the existing total variation denoising method.

The invention relates to the technical field of computer vision, in particular to a visual tracking discriminative sparse appearance model construction method and tracking method.The construction method comprises the steps that a foreground image set and a background image set are extracted from a target area; creating a discriminative sparse dictionary by applying sparse constraint to the difference between the foreground images and emphasizing the difference between the foreground images and the background image, and obtaining a group of sparse filters based on the sparse dictionary; and sparse convolution feature mapping is obtained according to the sparse filter and the candidate image set, the sparse convolution feature mapping is superposed, and the discriminative sparse appearance model is constructed. According to the scheme, layered and differentiated features can be effectively extracted to reduce external interference, so that the method is suitable for practical application in a scene which is easily influenced by scale change, shielding and background similarity.

This application relates to a method and system for reconstructing watertight meshes from building point clouds based on multi-scale graph networks, belonging to the field of 3D mesh reconstruction technology. The method for reconstructing watertight meshes from building point clouds includes: acquiring and processing the original building point cloud and combining it with a radius adaptive mechanism to generate a building point cloud map; analyzing the building point cloud map according to a multi-scale graph encoder and outputting a composite code for building features; generating initial triangular patches based on a joint classifier calculation and the composite code for topological building features, and determining the type of holes; parsing and repairing all holes according to a hole repair mechanism, projecting the building boundary to close the bottom surface, and generating an initial watertight mesh for the building; optimizing the initial watertight mesh for the building based on a mixture of curvature diffusion terms and sparse constraint terms to obtain the optimal watertight mesh for the building; verifying the optimal watertight mesh for the building through topology, and generating a material property table and mesh partition labels; and solving the problems of low efficiency, numerous holes, and poor watertightness in existing technologies by using an end-to-end fusion of geometric repair and semantic optimization algorithms.

This invention relates to the field of neural network construction technology, and more particularly to a method and system for constructing a dynamic prediction model for cerebrovascular diseases based on artificial intelligence. The invention first constructs a graph network model with continuous time slices and defines multimodal feature nodes. Based on the evolutionary correlation of historical data, pruning and sparsity constraints are applied to the fully connected matrix between layers to construct a sparse topology model. Subsequently, fact evolution nodes are activated based on patient clinical data, and reverse path backtracking is performed with these nodes as endpoints. A masking mechanism is used to block non-predetermined fact branches, locking the unique historical path. Finally, this path is transformed into a context vector and injected into subsequent layers to achieve model state reconstruction and dynamic weight updates. This invention, through a global pruning-path locking-dynamic reconstruction mechanism, effectively filters clinical noise in disease progression, solves the path drift problem of traditional time-series prediction, and significantly improves the accuracy of individualized predictions in the mid-to-late stages while greatly reducing the number of parameters.