10 results about "Shape vector" patented technology

A computer-implemented method and apparatus for generating a quadrilateral grid from an image with an alpha channel. The method includes receiving an image with an alpha channel depicting a shape mapped onto a 3D surface, vectorizing the shape to create a vector path, determining corners by calculating a centroid and identifying the points, correcting a winding order to ensure a clockwise orientation, constructing a quadrilateral grid by setting sub-paths based on detected corners, resampling the sub-paths to match specified rows and columns, performing weighted interpolation across the grid using inverse square distance weighting, and outputting the generated quadrilateral grid for display on a computer device. The apparatus comprises a processor configured to execute these steps, including vectorizing the shape, determining corners, correcting the winding order, constructing and resampling the grid, performing weighted interpolation, and subdividing the 3D mesh for flexible manipulation and extrapolation.

The application discloses a large language model low-delay inference method based on dynamic inference graph optimization, and proposes a low-delay inference method based on dynamic inference graph optimization; a rewritable and replayable template inference graph is constructed, and a template library is established according to an input shape vector; during inference, a template is matched according to a distance threshold value, and only attention / forward subgraphs are locally recaptured when the threshold value is exceeded; a forward execution graph injects a key-value cache page pointer, a numerical precision and an adapter identifier, and is replayed; pre-populated and decoded subgraphs are divided to implement a graph-level scheduling; a key operator is switched between a standard / quick kernel and different precisions during operation; a page-level rollback of a speculative branch is realized through a shadow page table and a reference count, and a batch and a template selection are adaptively adjusted based on online indicators; compared with existing schemes, the application reduces recapture and startup overhead, suppresses tail delay and jitter, and improves hardware utilization and service stability.

A method for analyzing change in a mode of a unidirectional composite material includes applying a physical force of a predetermined pattern onto a unidirectional composite material specimen; sensing a vibration signal generated by the physical force at at least one sensed position thereof; performing modal analysis of a frequency response at a corresponding measurement location, based on the physical force applied to the specimen and the vibration signal measured at the sensed position of the specimen, and calculating at least one mode shape vector of the specimen based on the modal analysis result; compensating the calculated mode shape vector based on a distance between a normal line passing through a center of the specimen and the sensed position, thereby calculating a modified mode shape vector; and calculating a first modal assurance criterion (MAC) of the specimen based on the modified mode shape vector.

The invention provides a modal parameter identification method, and relates to the technical field of structural health monitoring, vibration engineering and signal processing. The method comprises the following steps: acquiring an acceleration time domain signal; decomposing the acceleration time domain signal, and performing signal reconstruction on a plurality of multivariate intrinsic mode function components; smoothing each channel of data of the reconstructed signal, and constructing a Toeplitz matrix by using a cross-correlation function sequence; singular value decomposition is carried out on the matrix, and a discrete state space matrix is obtained through calculation by adopting a covariance-driven random subspace method; the discrete state space matrix is decomposed, and the inherent frequency, the damping ratio and the vibration mode vector of the ith-order mode are obtained through calculation; and carrying out statistical analysis on all modal parameters, and verifying to obtain a finally confirmed real modal parameter set. According to the method, multi-channel signals can be comprehensively processed, excitation interference of a complex environment is effectively suppressed, and a robustness method of full-automatic high-precision modal recognition is realized.

The invention discloses a welding seam identification method and system based on single-view point cloud feature extraction, and relates to the technical field of welding identification, and the method comprises the steps: obtaining a workpiece three-dimensional cleaning point cloud data set under a current view point; acquiring a sphere neighborhood point cloud set corresponding to each single point cloud, and acquiring single point cloud geometric features corresponding to the single point cloud; performing binary clustering on the feature vectors in the feature vector set by adopting a clustering algorithm; obtaining a viewpoint vector and a shape vector; and identifying non-weld convex points and weld concave points according to the included angle between the viewpoint vector and the shape vector. According to the method provided by the invention, the real welding seam point cloud can be identified without initial welding seam fitting, the real welding seam point is identified by adopting the same concept for a straight welding seam and a curve welding seam, and the universality is high; meanwhile, the technical problem that in the prior art, after the potential welding seam area is recognized, distinguishing of the real point cloud of the welding seam is lacked, and consequently the welding seam recognition precision is low is solved.

The application provides a kind of structural modal parameter intelligent screening method and system based on feature fusion, it is related to engineering structure health monitoring and machine learning cross technical field. Including the following steps: S1, utilize preset time window to carry out block processing to structure response signal;S2, by SSI-Cov, modal parameter identification is carried out to the data block, and the initial modal parameter set containing frequency, damping ratio and mode shape vector is obtained;S3, based on the initial modal parameter set and the structure response signal, construct multi-dimensional feature fusion vector, input the multi-dimensional feature fusion vector into pre-trained classification model, output modal true and false probability, and screen;S4, based on the stable modal parameter obtained by screening, repeat execution for subsequent data block, and output final modal parameter;The application improves the false modal rejection rate, solves the problem that false modal rejection is not thorough;Reduce operation and maintenance cost, improve error identification accuracy.

The present application relates to the technical field of image segmentation, and particularly relates to a quantitative evaluation method of momordica grosvenori based on image segmentation. The method comprises the following steps: performing spectral decomposition on momordica grosvenori data image information by using a spatial-frequency feature component decoupling algorithm to generate decoupled light and shadow features; extracting cross-scale deep semantic information to construct a semantic probability distribution field, performing edge tensor correction and generating a pixel-level fruit body lesion semantic deconstruction set; performing contour topology mapping and spatial domain gravity field analysis to obtain a geometric shape vector represented by a geometric moment operator, a lesion distribution density and a surface texture loss weight value; and performing a preset trait residual energy level discriminant to perform nonlinear dimension reduction mapping and output an evaluation grade. Through deep coupling of signal decoupling, semantic field analysis and geometric topology measurement, the present application establishes a logical mapping link from pixel semantic features to physical energy levels, realizes image signal representation of momordica grosvenori irregular edges in a complex background, and realizes nonlinear coupling between pixels and physical trait residual energy level attributes.

A face drive generation method based on neural radiance field includes the following steps: S1, extracting feature points of a face region from image frames of a video, calculating expression features and posture features of a corresponding three-dimensional face model according to the feature points, and extracting speech features from a speech part of the video; S2, for each position in a three-dimensional space where the three-dimensional face is located, predicting a mixed shape vector for controlling the three-dimensional face, a posture correction coefficient, and a linear mixed skinning weight through a neural network; S3, based on a deformation technology of the three-dimensional face model, calculating a deformed position of each current position according to the posture features and the speech features obtained in step S1 and the parameters predicted in step S2; S4, predicting a flow field according to each calculated position, aggregating feature information of adjacent time through a multi-frame set manner, and optimizing a face reconstruction loss by using time aggregation, so as to constrain smooth transformation of a dynamic neural radiance field in time. The method improves the accuracy of expression deformation, and makes the generated video more smooth and consistent.

The application discloses a kind of based on single view point cloud feature extraction of weld seam identification method and system, it is related to welding identification technical field, including steps: obtaining the workpiece three-dimensional cleaning point cloud dataset under current view point;Obtain the spherical neighborhood point cloud set corresponding to each single point cloud, obtain the single point cloud geometry feature corresponding to single point cloud;Using clustering algorithm, the feature vectors in feature vector set are classified clustering;Obtain view point vector and shape vector;According to the included angle between view point vector and shape vector, identify non-weld seam convex point and weld seam concave point.The method provided by the application can identify real weld seam point cloud without initial weld seam fitting, uses the same concept for real weld seam point identification for straight-line weld seam and curve weld seam, and has strong versatility;Meanwhile, the technical problem that the present art lacks distinction of real weld seam point cloud after identifying potential weld seam area, resulting in low weld seam identification precision, is solved.

The present application relates to the technical field of intelligent manufacturing, and more particularly to a plasticizing order matching method, which comprises the following steps: first, encoding the order and production line data events, generating a shape vector and an energy flow vector through a neural network; constructing a coupling matrix in a hyperbolic space and matching once using an Ising model; if the output gradient is over the threshold, refining through neural morphology inhibition and calculating the local entropy gradient; feeding the refined results and production line feedback into a causal generation graph to determine conflicts, and if necessary, modulating a solver to generate a correction matching; finally, writing the matching vector and conflict markers into a blockchain, and each node updates the model through federated learning, and drives the curvature and threshold adaptively through the shape entropy. The method realizes real-time production scheduling with low energy consumption, less line change and sustainable learning.