75 results about "Mesh point" patented technology

The methods may include obtaining a frequency-domain seismic wavefield associated to grid points and organized into slices. Each slice is organized into a sequence of overlapping windows. The methods may further include, for each slice, determining a value of a first intermediate function using the frequency-domain seismic wavefield for each grid point in a first overlapping window, determining a value of the first intermediate function using the frequency-domain seismic wavefield for each incremental grid point in each n-th overlapping window, where n is an integer greater than one, and determining a value of the first intermediate function by selecting the previously-determined value of the first intermediate function for each common grid point in each n-th overlapping window. The methods may still further include determining, using a second intermediate function, a Radon-transformed seismic wavefield using the value of the first intermediate function for the grid points. A non-transitory computer-readable memory and systems are also disclosed.

The application discloses a kind of distributed array high-precision time synchronization method based on compressed sensing (CS) and secondary least square (QLS), belong to wireless communication and radar signal processing technical field.For under the restriction hardware bandwidth and strong multipath environment, traditional cross-correlation method is limited by Rayleigh resolution and sampling grid, there is the problem of serious peak aliasing, insufficient synchronization accuracy, the application proposes CS-QLS joint decoupling strategy.The method constructs frequency domain observation matrix and high-density time delay over-complete dictionary by transmitting sparse multi-tone probe signal, reconstructs sparse channel impulse response using CS, separates multipath aliasing and determines direct wave coarse synchronization boundary;Further combined with main path and adjacent grid point amplitude information, QLS is used to fit continuous domain peak position, and grid mismatch error is corrected.The method breaks through the limitation of physical bandwidth and sampling grid, and realizes nanosecond-level high-precision time synchronization in complex non-line-of-sight environment with very low computational complexity.

The application provides a multi-probe array obstacle positioning method and device. The method comprises: in the case that multiple ultrasonic probes enter a transmitting mode of transmitting ultrasonic waves in turn and the multiple ultrasonic probes all enter a receiving mode of receiving ultrasonic waves, generating multi-dimensional time data corresponding to each transceiver pair; the transceiver pair comprises a pair of ultrasonic probes in the transmitting mode and an ultrasonic probe in the receiving mode; performing grid processing on the front area of the ultrasonic probe to obtain multiple grid points; determining a voting value corresponding to each grid point based on the multi-dimensional time data by using a spatial voting algorithm; and in the case that the voting value corresponding to the grid point is greater than a first threshold, determining positioning information of an obstacle based on the grid point. In this way, in a multi-obstacle scene, the positioning information of the obstacle can be confirmed by the voting values of the multiple grid points corresponding to the multiple grids in the front area of the ultrasonic probe, so that the purpose of accurate positioning and judgment in a multi-obstacle scene can be achieved.

The application discloses an engineering simulation method and system based on a spectral weight sparsification neural operator. The method collects original working condition information of an engineering PDE, discretizes the grid by means of a high-fidelity numerical solver, and generates a sample data set. The sample data set is subjected to Gaussian standardization, and the grid points are spliced to obtain a structured data. An SSNO model with a multi-layer spectral weight sparse Fourier layer is built, a learnable frequency domain transform spectral weight is set for each layer, a joint loss function is constructed by fusing a relative L2 prediction loss and a spectral weight L1 penalty term, and model training is completed by iteration relying on the function. The trained model is connected to a simulation platform, new working condition parameters are input into the model after the same preprocessing, and the full-field solution is output. Newly measured and simulated data continuously supplement the data set, and the model is cyclically updated and optimized, forming a closed-loop simulation link. The application solves the problems of the prior art, such as no built-in frequency domain screening mechanism, frequency domain parameter redundancy, and inability to simultaneously realize model precision improvement and frequency domain weight autonomous sparse optimization.

The application relates to the technical field of electromagnetic simulation, and provides a time-domain electromagnetic field simulation calculation method based on GPU parallel acceleration and a corresponding product.The method comprises the following steps: setting simulation parameters; precalculating physical coordinates of all grid points, generating an observation point coordinate array and a source point coordinate array, and transmitting the arrays to GPU device memory; initializing a GPU solver, allocating GPU memory resources, and configuring a CUDA thread block and a grid structure, so that the number of threads matches the number of observation points; for each time step corresponding to a time step, source field data of the current time step is asynchronously updated into a ring buffer; when the simulation time exceeds the time required for electromagnetic waves to propagate from a source area to an observation area, a GPU kernel function without atomic operation is started, the electromagnetic field values of each observation point are calculated in parallel based on the precalculated coordinate array and the source field data in the ring buffer; and the calculation results are transmitted from the GPU device back to the host end for storage and post-processing verification.

This invention belongs to the field of music data processing and relates to a symbolic music classification method and system based on Tonnetz spatiotemporal graphs. The method generates a two-dimensional grid of symbolic music data, consisting of time frames and pitches, and defines graph nodes and initial features for each grid point. Discrete note events are organized into structured nodes. Based on this two-dimensional grid, graph nodes corresponding to pitches that satisfy Tonnetz interval relationships are connected within the same time frame to form spatial edges. These spatial edges explicitly introduce consonant interval relationships between pitches, allowing the model to directly obtain structural information with musicological significance without relying on large amounts of data for self-learning, thus solving the problem of insufficient utilization of harmonic structure. Furthermore, graph nodes with the same pitch or pitch differences within a preset range are connected between adjacent time frames to form temporal edges. These temporal edges directly encode the continuous and stepwise motion of unison parts during voice progression, enabling the model to capture the dynamic evolution of music in the time dimension and solving the problem of missing voice progression characterization.

This invention discloses a method for inverting the effective elastic thickness of the lithosphere based on intelligent truncation and inflection point matching, applied to a processor. The method includes the following steps: reading gridded topographic data and Bouguer gravity data stored on a hard disk or in memory to obtain the observation curve of coherence as a function of wavenumber; obtaining the monotonically decreasing coherence spectrum after truncation; solving for the logarithmic wavenumber corresponding to the coherence value at a preset value, denoted as the observed inflection point logarithmic wavenumber; establishing a mapping relationship between the theoretical inflection point logarithmic wavenumber and the effective elastic thickness; obtaining the effective elastic thickness of the lithosphere at the target grid point; traversing all grid points within the study area to generate a full-grid effective elastic thickness distribution array, thus obtaining the inversion result of the effective elastic thickness of the lithosphere. This invention significantly improves computational efficiency, achieves robust noise suppression while preserving the effective signal, and exhibits strong noise robustness.

The application discloses a kind of non-rigid space mapping-based multi-camera multi-target tracking methods without calibration, it is related to computer vision and intelligent video analysis technical field.The method includes: obtaining the position of target bounding box in each camera and the corresponding appearance feature representation;Extract the effective area that can be used for geometric modeling, generate binary effective area mask;Get dense displacement field and spatial correspondence confidence;Fixed step is constructed on the source camera image Sampling grid, the grid point is screened, and the high-confidence control point set from source view to target view is formed;According to the control point set, establish the cross-camera space mapping relationship under non-calibration condition;The spatial geometric similarity of target bounding box in different cameras is calculated, and the cross-camera target association is carried out by combining the appearance feature similarity, to realize the multi-camera multi-target tracking under non-calibration condition.The method can establish the cross-camera space mapping relationship, realize stable multi-camera multi-target tracking.

The application discloses a kind of arbitrary area water vapor balance calculation method and system considering surface curvature, belong to remote sensing science and earth information science field.The method includes: collecting three-dimensional atmospheric data and preprocessing;Calculate the water vapor transport flux of each grid point;Construct the sequence of discrete nodes of regional boundary, calculate the boundary arc length based on the curvature of the earth using spherical distance;Calculate the midpoint normal vector of each arc segment and point to the inside of the region;Using bilinear interpolation to obtain the water vapor transport flux at the midpoint of the arc segment, and project to the inside normal direction;Integrate the boundary arc segment, calculate the regional water vapor balance.This application considers the curvature of the earth in the calculation of boundary length, improves the accuracy of flux by node encryption and bilinear interpolation, and preserves the actual boundary trend, overcomes the defects such as distance calculation deviation, boundary forced approximation and flux representative error of traditional method, significantly improves the accuracy of arbitrary area water vapor balance estimation.

The application provides an improved sparse learning out-of-grid direction-of-arrival estimation method based on variational Bayesian. The method is characterized in that: the original data received by a hydrophone array is preprocessed, a real value transformation is used to convert a vectorized covariance matrix signal in a complex number field to a real number field, and the idea of variational sparse Bayesian learning and grid evolution is combined to make the grid evolve from an initial uniform grid to a non-uniform grid adaptively in an iteration process. The evolution process includes grid updating and grid fission. The evolved grid points are gradually close to the real source position through the alternately iterative grid updating process and grid fission process. Compared with the traditional compressed sensing method, the method has higher DOA estimation accuracy, reduces the operation complexity, optimizes the operation efficiency, improves the resolution capacity of the source, and has higher application value in actual engineering, especially in the case of few snapshots and low signal-to-noise ratio.

As a type of information, connectivity in a mesh poses challenges not only in designing an efficient AI architecture, but also in training a learning-based compression system for the mesh. In one implementation, we present the entropy bottleneck layer as an enabler by allowing training of an end-to-end learning-based compression system for images / video / point clouds. In particular, we present ways of modeling the distortion introduced by compression. We also present ways of modeling the distribution of mesh points and estimating the rate required to encode the mesh.

A system and method (100) for estimating range of motion of a joint (3) connecting a first bone (5) to a second bone (7), the joint comprising a point of rotation (9) defining rotation of the first bone relative to the second bone. The method comprises: obtaining (110) a digital model of the joint that includes, for each bone (5, 7), respective mesh models with sets of mesh points that represent the bone; determining (120) polar coordinates for the set of mesh points, with the point of rotation (9) as a pole (21) of the polar coordinates; and calculating (130), based on the polar coordinates, multiple angular distance values relating to rotation of the joint, where the multiple angular distance values are indicative of an angular distance between points in the set of mesh points, and the angular distance being indicative of the range of motion of the joint.

The application discloses a cross-dimension multi-source satellite altimeter sea level anomaly joint estimation method and system, and relates to the field of ocean remote sensing.The joint estimation method comprises the following steps: obtaining multi-source observation data of a target sea area about a target period, forming a corrected joint observation data set through cross-dimension data processing and reconstruction operation and bias correction operation, and mapping the corrected joint observation data set to a corresponding regular space-time grid of the target sea area; based on a preset space-time search radius, constructing an observation operator, a background covariance matrix and an observation error covariance matrix corresponding to each grid point in the regular space-time grid, and generating a regular gridded sea level anomaly product of the target sea area about the target period through an optimal interpolation method without performing explicit system error correction.The application can consider large-scale background consistency and small and medium-scale structure resolution capability on multiple spatial scales, and improves the spatial continuity, statistical consistency and overall stability of the sea level anomaly estimation result.

The invention discloses a hydraulic fracturing ground microseismic event positioning method, a storage medium and equipment, and the method comprises the following steps: 1) carrying out the space grid division of a fracturing operation region through employing a multi-stage grid, building a multi-stage index file, and initializing a dynamic correction library file; 2) establishing a 3D layered velocity model; (3) ray tracing is completed according to the node coordinate parameters of space gridding, the speed model and the coordinates of the detection points, and a time difference curve of each grid point is obtained; 4) establishing a dynamic correction library according to the time difference curve of each grid point; 5) performing time difference adjustment on the original microseism profile, and performing event detection on the adjusted profile; and 6) performing energy scanning and positioning on the microseism event by adopting a mesh generation offset superposition method. According to the method, the 3D speed model with the occurrence and the static dynamic correction library are adopted, the positioning data processing efficiency can be improved, and the overall form of the positioning result can be closer to the real fracturing condition.

The present application belongs to the technical field of marine information analysis, and particularly relates to a method for estimating the sound velocity of a high-resolution three-dimensional sound field in a marine search and rescue area and application thereof. The given search and rescue area is gridded, and surface temperature and salinity data are filled into the corresponding grid. According to the temperature, salinity and depth profile values of the search and rescue area, the surface sound velocity is calculated and resampled into the constructed grid. Similarly, the existing sound velocity data on each depth layer in the vertical direction are resampled into the nearest grid point. A vertical-space joint feature dataset is constructed. A neural network model is designed and trained, and the complete vertical structure information from the surface to the target layer is used to reconstruct the sound velocity data of the lower layer. When the data of the next layer is reconstructed, the newly constructed layer is used as the new upper layer data. The present application performs high-resolution three-dimensional numerical layer-by-layer fast estimation on the sound field of the given marine search and rescue area, provides high-precision data support for search and rescue decision-making, and is suitable for large-scale search and rescue sea areas.

The application discloses a multi-radar power envelope fusion method, comprising: single-radar envelope modeling, constructing envelope fusion foundation; determining non-coherent field fusion, constructing multi-radar envelope model; constructing gradient diffusion model, realizing envelope fusion smooth transition; displaying Euler method iteration convergence, realizing multi-radar envelope complete fusion; adopting adaptive octree structure to dynamically divide three-dimensional space. Advantageous effects: the multi-radar power envelope fusion method firstly calculates single-station detection probability of each radar unit at a grid point in a three-dimensional space voxel according to a radar equation, a propagation loss model and target scattering characteristics; then adopts a probability union or a more complex dependent relationship model to generate a full-space joint detection probability field; finally, an isosurface extraction (such as an improved Marching Cubes algorithm) and a probability gradient driven color / transparency mapping are adopted to generate a visual continuous, mathematically accurate fusion envelope surface.

This invention discloses a method and system for testing impedance matching and signal integrity of circuit boards, relating to the field of circuit board testing and inspection technology. Instead of directly calculating the straight-line distance, this invention extracts the topological test path from the test sensor to each grid point along the actual wiring topology of the raw test signal data collected by the test sensor on the circuit board. It then generates grid test feature values ​​by inversely weighting the path length, avoiding blind interpolation across wiring areas. This improves the authenticity of the test data, the accuracy of defect location, and the accuracy of the test results. It solves the problem of amplified test errors and inaccurate defect location caused by ignoring wiring paths when using pure geometric spatial distance for grid interpolation in existing technologies. This solution can achieve automated testing of circuit board impedance and signal integrity, accurate defect location, and test report output, and is suitable for high-speed circuit board factory inspection and quality verification scenarios.

The application discloses a neural network-based electromagnetic field simulation grid self-adaptive generation method. The application performs grid partitioning on a semiconductor device simulation model, obtains original partitioning data, and constructs node features; a non-directed graph is constructed, the graph nodes of which adopt the node features of the grid points, and the connecting edges adopt the Euclidean distances between the grid points and adjacent grid points; the non-directed graph is input into a double-branch neural network, and a predicted node position is output; each node of the original partitioning data is corrected by using the predicted node position obtained by the double-branch neural network, and new partitioning data is formed to perform electromagnetic field simulation. The method can not only efficiently adjust the node density by fusing physical field information, but also strictly maintain the geometric boundary and topological structure characteristics of the device.

This invention relates to an indoor positioning system and method based on a multi-waveguide, multi-clamping antenna. The positioning system includes an access point, a central processor, three waveguides, several clamping antennas, and a transmitter. The access point is located in a corner of the room's ceiling and connected to the central processor. The three waveguides are connected to the access point and extend along the ceiling. x axis, y Axial and diagonal distribution, with [missing information] distributed on each waveguide. N The clamping antennas described herein have randomly distributed transmitters. The positioning method includes: first, simultaneously activating multiple clamping antennas on different waveguides; establishing a position estimation equation based on the received power closed-form expression and the actual received power at the access point; then, dividing the room into grid points and calculating the theoretical received power of all grid points; then, selecting the region with the smallest error to refine the grid and repeatedly evaluating the error function, gradually reducing the grid size to achieve the required positioning resolution; finally, selecting the grid point with the smallest error as the estimated user position. This invention enables accurate positioning in indoor scenarios.

This invention provides a distributed radar noncoherent direct localization method, device, terminal, and medium for data anomaly scenarios, relating to the field of radar technology. The method includes: acquiring echo signal data received by each observation array; constructing a signal model characterizing the mapping relationship between the echo signal data and potential source locations, where the common detection area of ​​each observation array is a two-dimensional grid, the grid point coordinates correspond to the potential source locations, the signal model does not contain phase synchronization parameters between observation arrays, and is adapted to the noncoherent characteristics of the signal data; and constructing a system based on the signal model... 2,1 This invention addresses the group sparse optimization problem of norms. A pre-defined optimization algorithm is used to solve the problem, yielding a row sparse matrix. Based on this matrix, the true source localization result is determined, and the column vector elements of the row sparse matrix correspond to the potential source reflection energy estimation results at the grid points. This invention utilizes echo signal data from various observation arrays to achieve primary localization, eliminating secondary localization errors and reducing abnormal noise interference.

This disclosure relates to the field of computer graphics technology, and provides a method, apparatus, device, medium, and program product for clothing adaptation of virtual characters. The method for clothing adaptation of virtual characters includes: first, establishing a three-dimensional spatial mesh containing a source virtual character model and a target virtual character model with the same topological structure, as well as a source clothing model; then, based on the positional correspondence between the source and target virtual character models, and combined with smoothing constraints to limit the difference in deformation vectors between adjacent mesh points, calculating the deformation vector of each mesh point in the three-dimensional spatial mesh; finally, using the deformation vector of each mesh point, mapping and transforming the source position of each vertex in the source clothing model to the target position, thereby generating a target clothing model adapted to the target virtual character model. This disclosure can effectively maintain the geometric characteristics of the clothing model, reduce distortion during the adaptation process, and improve visual effects.

This invention discloses a method for locating the void between the surface layer and base layer of an asphalt pavement, belonging to the field of pavement inspection technology. The method includes: scanning the pavement with ground-penetrating radar to obtain radar data; performing horizontal filtering on the radar data; and applying an improved back projection imaging algorithm to the horizontally filtered radar data to obtain the true location of the void. This invention performs horizontal filtering on the acquired radar data to reduce the influence of reflected waves from the surface layer and base layer interface on void identification, minimizing the influence of direct waves and interface reflections. This invention only superimposes radar signals adjacent to grid points, greatly reducing the computational load, and can accurately locate the void position.

This invention relates to the field of large-scale multiple-input multiple-output (MIMO) communication technology, and particularly to a hierarchical beamforming training method and apparatus based on channel map assistance. The method includes: constructing a channel map guided by beamforming codebooks, wherein the channel map records the equivalent channel gain corresponding to each grid point within a preset area and each beamforming codeword; acquiring partial location information of the user equipment, including multiple mutually exclusive spatial sub-regions where the user is located and the prior probabilities of each sub-region; calculating the beam potential energy of all beamforming codewords based on the channel map and partial location information; pruning the complete binary search tree based on the beam potential energy of each beamforming codeword; and traversing the simplified search tree according to a hierarchical beamforming training strategy to obtain near-optimal beamforming codewords. This solves the problems of unknown precise user location before beamforming training, unclear coordination mechanism between channel map and observation information, and difficulty in balancing training overhead and computational complexity.

The application provides a wireless signal loss prediction method and device, equipment, medium and program product, relates to the technical field of artificial intelligence, and is used for improving the accuracy of indoor space wireless signal loss prediction results. The specific technical scheme is as follows: obtaining a penetration loss cumulative value of a first path corresponding to each grid point in an indoor space; obtaining a reflection eigenvalue corresponding to each grid point; based on the penetration loss cumulative value of the first path corresponding to each grid point and the reflection eigenvalue corresponding to each grid point, predicting a loss value of the wireless signal in the indoor space. The application is applied to the scene of indoor space wireless channel modeling and prediction.

The application discloses an atmospheric pollution tracing method fusing a numerical model and an artificial intelligence algorithm, and relates to the technical field of atmospheric pollution tracing. The method preprocesses meteorological data and simulates a high-precision meteorological field, converts a result into a readable format of a Lagrangian diffusion model, generates a high-resolution space-time footprint matrix based on the driving model, identifies a dominant air mass path, combines pollutant concentration data of receptor points, constructs and verifies a random forest regression model, takes the footprint matrix as features, takes standardized concentration as labels, quantifies the contribution of grid points through feature importance and Shapley explanation methods, and identifies key pollution source areas. The application can achieve the technical effect of significantly improving the accuracy and efficiency of atmospheric pollution tracing and providing more scientific and accurate decision-making basis for atmospheric pollution prevention and control.

This application provides a distributed computing system, device, and medium for predicting the pressure distribution of an external flow field in a vehicle. Applied to a distributed computing system with multiple computing nodes, the system first obtains a global computational domain for the external flow field of the vehicle, consisting of grid points carrying physical parameters and geometric information. An attention dependency graph is constructed based on the hydrodynamic relationships between grid points, completing the partitioning of the computational domain and assigning it to corresponding computing nodes. Each node generates a candidate set based on its spatial neighborhood, calculates attention weights, and filters to obtain the final attention dependency set. Each node completes cross-node data transmission according to the dependency set, obtains the dependency data required for computation, performs attention-weighted summation within the corresponding range, and finally summarizes the computation results of all nodes to complete the prediction of the pressure distribution of the external flow field in the vehicle. Using this method, the parallel computing efficiency and pressure prediction accuracy of vehicle external flow field pressure simulation under large-scale unstructured grids can be effectively improved.