19 results about "Linear representation" patented technology

The invention discloses a reweighted inversion method and system for a magnetic anomaly modulus under a residual magnetism condition and a storage medium, and belongs to the technical field of magnetic exploration, and the method comprises the following steps: converting the magnetic anomaly modulus to obtain the magnetic anomaly modulus; carrying out nonlinear forward modeling on the magnetic anomaly modulus; and linearizing a forward modeling formula of the magnetic anomaly modulus, constructing a reweighted minimum model objective function, solving the reweighted minimum model objective function, finally obtaining the magnetization intensity of an inversion space, and delineating a target geologic body. According to the scheme, the nonlinear forward modeling formula of the magnetic anomaly modulus is subjected to linear representation, and the sensitivity matrix serving as depth weighting is further updated by utilizing the magnetic anomaly modulus kernel function matrix updated along with iteration, so that the reweighting minimum model objective function is constructed, the precision of magnetic anomaly modulus inversion under the residual magnetism condition is improved, and the accuracy of the magnetic anomaly modulus inversion under the residual magnetism condition is improved. And a more reliable inversion result is provided for more accurate depiction of a deep magnetic structure, mineral resource exploration and the like.

A cross-subject motion intention recognition method, system and medium relate to the technical field of human motion intention recognition. The foregoing method comprises: collecting physiological signals and / or motion signals of a source subject and a target subject under different motion intentions, and preprocessing the same; extracting time domain features and frequency domain features from the preprocessed signals; constructing a kernel space projection function, mapping the extracted features to a reproducing kernel Hilbert space to realize linear representation of nonlinear relationship; constructing a joint objective function comprising a classification loss, a source subject fusion loss and a domain adaptation loss; and optimizing and solving the joint objective function in the reproducing kernel Hilbert space to obtain a classification model for motion intention recognition. The present application can solve the cross-subject generalization problem.

Approaches presented herein provide for the generation of image content including fine objects, such as those that may be less than one pixel in width. Fine objects such as hairs can be represented by curve equations, and curves defined by these equations can be given an inflated width in order to perform conservative hit testing to efficiently identify pixels that a hair might intersect. Once candidate pixels are identified through ray tracing or sampling, for example, false positives or pixels with little hair representation can be removed from consideration. For remaining pixels, a linear representation of the inflated curve can be used to determine intersection points and vertices of a pixel quad corresponding to a hair, which can be used to generate a convex geometric shape representative of the object. The percentage of the pixel area occupied by this geometric shape can be used to determine an alpha or blending value for blending the pixel value of this hair or object with the background pixel color.

The present disclosure relates to signaling quantization parameters. A method for reconstructing coded data using one or more processors includes: receiving a coded bitstream comprising one or more coded pictures, wherein each picture comprises a luma component, a first chroma component, and a second chroma component; extracting syntax parameters from the coded bitstream to determine a chroma quantization parameter (QP) table, wherein the chroma QP table maps an input luma QP value to a corresponding chroma QP value; and decoding the one or more coded pictures based on the chroma QP table, wherein the syntax parameters include a starting luma QP value and one or more offset parameters to determine a mapping of luma QP values to chroma QP values using a piecewise linear representation.

The invention discloses a power grid loss distribution robust optimization method considering output uncertainty, and belongs to the technical field of power grid optimization control. The method comprises the following steps of: constructing a power grid loss optimization model by taking a minimum power grid loss expectation as a target and taking a node voltage chance constraint linear expression based on a security domain hyperplane coefficient and an origin moment as a constraint condition; secondly, considering the node output uncertainty, re-representing the active power injection vector of each node of the power grid based on a distributed robust method, and carrying out linear representation on the target function of the power grid loss optimization model so as to establish a power grid loss quadratic optimization model; and solving the power grid loss quadratic optimization model based on a quadratic programming method to realize power grid loss optimization. According to the method, the power grid loss optimization model considering the node injection uncertainty is established mainly based on the Wasserstein fuzzy set, the objective function of the power grid loss optimization model is subjected to quadratic processing, the model is solved based on the quadratic programming method, and the power grid loss optimization precision and efficiency are improved.

This invention discloses a method, terminal device, and storage medium for noise identification and filtering in lidar, comprising: clustering line segments according to distance similarity in the scanning order for each scan line, marking suspected noise line segments in each scan line to obtain a set of suspected noise line segments; performing rapid clustering on each suspected noise line segment in the set to obtain multiple targets, and deleting point noise corresponding to targets containing fewer than a threshold line segments from the point cloud; calculating the linear representation of the suspected noise line segments and determining whether it is a centripetal line, and deleting the linear noise corresponding to the centripetal line from the point cloud. This invention is applied to the field of lidar, solving the problem of identifying and filtering point and linear noise in lidar, with low time consumption, compatibility with any multi-line mechanical rotating lidar, strong generalization ability, and the ability to retain real target point clouds with similar noise while filtering noise, resulting in extremely low false detection and false detection rates.

The invention provides a phase shift amplification modulation depth normalization method for an optical microwave interference ranging system, which relates to the field of ranging and comprises the following steps of: extracting amplitude-frequency characteristic data and phase-frequency characteristic data; in amplitude-frequency characteristic data analysis, according to the ratio of the maximum power to the minimum power, the amplitude ratio of the amplitude-frequency characteristics is obtained by combining image comparative analysis; carrying out normalization on the frequency; in the phase-frequency characteristic data analysis, a local minimum value and a local maximum value are obtained according to the phase-frequency characteristic curve, and normalized parameters corresponding to the local minimum value and the local maximum value are calculated; generating a table according to a phase shift amplification technology; querying the table to obtain an amplitude ratio of the phase-frequency characteristic; selecting an optimal amplitude ratio to obtain a normalized phase-frequency characteristic curve; and establishing a phase shift and measurement distance variation linear representation model according to a phase shift amplification technology, and calculating the distance variation according to the center frequency of the phase frequency characteristic curve. According to the technical scheme, the problem that precise distance measurement cannot be achieved due to the fact that the modulation depth is not normalized in the prior art is solved.

The present disclosure relates to signaling quantization parameters. A method for reconstructing coded data using one or more processors includes: receiving a coded bitstream comprising one or more coded pictures, wherein each picture comprises a luma component, a first chroma component, and a second chroma component; extracting syntax parameters from the coded bitstream to determine a chroma quantization parameter (QP) table, wherein the chroma QP table maps an input luma QP value to a corresponding chroma QP value; and decoding the one or more coded pictures based on the chroma QP table, wherein the syntax parameters include a starting luma QP value and one or more offset parameters to determine a mapping of luma QP values to chroma QP values using a piecewise linear representation.

The invention discloses a time series data consistency evaluation method based on dynamic mode matching and slope deviation analysis. The method comprises the following steps: firstly, loading and preprocessing actually measured data and simulation data, including format unification, sampling alignment and denoising normalization, so as to ensure data comparability; then extracting feature points based on a slope mutation threshold value, and performing piecewise linear modeling by taking the feature points as boundaries to obtain the slope, intercept and residual error of each segment, and forming linear representation of the overall trend and local change; on the basis, slope difference between actually measured data and simulation data is calculated section by section, a trend deviation index is obtained, meanwhile, a characteristic mode based on time span and amplitude difference is constructed, an improved dynamic time warping algorithm is adopted, a path normalization mechanism is introduced, and an overall form difference index is calculated; and finally, a consistency score is obtained through dual-index fusion and weighting, automatic analysis and visual display of time series data consistency can be realized, and the method is suitable for scenes such as inertial navigation and industrial monitoring.

A computer-implemented method for generating machine learning training data may include obtaining mechanism of action (MOA) data that is indicative of a hierarchical tree structure of relationships between the MOA data; generating linear representations of branches of the hierarchical tree structure; determining association rules for the MOA data by applying one or more frequent pattern mining algorithm to the linear representations; and determining, as at least a portion of the generated machine learning training data, MOA clusters by applying a clustering model to the linear representations and the association rules.

This invention discloses a method and system for modeling the dynamics of a five-axis machine tool based on monoid coding. The method includes: S1, deriving the modular kinematics of a cascaded five-axis machine tool based on local exponential products; S2, completing the linearization modeling of the modular kinematics with respect to the kinematic parameters; S3, obtaining the linear representations of the inertia, centrifugal force, Coriolis force, and gravity terms of the dynamic model with respect to a multivariate polynomial; S4, describing the linearly represented dynamic model as a bilinear representation with respect to the inertia parameters and the multivariate polynomial; S5, eliminating redundant multivariate polynomials using matrix row correlation and eliminating redundant parameters using matrix decomposition to obtain the minimum set of inertia parameters; S6, inversely encoding the multivariate polynomial encoding numerical matrix and the parameter matrix after redundancy elimination into a symbolic representation with Homer form to achieve efficient model computation. This invention reduces the algorithm development and complexity of five-axis machine tools and is suitable for the rapid deployment of general-purpose five-axis machine tool algorithms.

The present disclosure relates to signaling quantization parameters. A method of reconstructing encoded data with one or more processors includes receiving an encoded bitstream including one or more encoded pictures, wherein each picture includes a luma component, a first chroma component, and a second chroma component; extracting syntax parameters from the encoded bitstream to determine a chroma quantization parameter (QP) table, wherein the chroma QP table maps an input luma QP value to a corresponding chroma QP value; and decoding the one or more encoded pictures based on the chroma QP table, wherein the syntax parameters include a starting luma QP value and one or more offset parameters to determine the mapping of luma QP values to chroma QP values using a piecewise linear representation.

The invention belongs to the field of artificial intelligence security, and discloses a point cloud migration attack resisting method based on prototype guidance, which comprises the following steps: S1, training a point cloud auto-encoder which comprises an encoder and a decoder; s2, acquiring a set of prototypes corresponding to each category in the data set; s3, performing linear representation on the latent vector of the clean point cloud sample based on the prototype set of the category to obtain a linear combination coefficient; s4, attacking the prototype corresponding to the category to obtain an adversarial prototype; s5, obtaining an adversarial latent vector based on the linear combination coefficient and the adversarial prototype; and S6, inputting the confrontation latent vector into a point cloud auto-encoder for calculation to obtain a confrontation point cloud sample. According to the method, the mobility of the confrontation samples is remarkably improved, and wider black box attack capability is realized. The overfitting of the agent model is effectively avoided, and the generalization ability of resisting disturbance is enhanced. The generated adversarial disturbance is more structural and semantic, and the attack efficiency is higher.

The present invention belongs to the field of natural language processing, and discloses an aspect-level sentiment analysis method based on a graph convolutional network. In this method, Chinese review texts are encoded by BERT to obtain word-level text sequence vector representations. Then, the vector representations are used to obtain the syntactic dependency tree of the sentence through CDT to get the adjacency matrix. Next, the syntactic distance and the syntactic distance weight are calculated according to the syntactic dependency tree. Using the graph convolutional neural network, an accurate aspect word vector representation is obtained by combining the syntactic distance weight. The conjunctions between multi-aspect words are found, and a weight matrix is obtained from the conjunctions and multi-aspect words. The weight matrix, conjunctions, and aspect words are passed through the graph convolutional network to obtain the vector representation of the final aspect word. The vector representation is sent to the linear representation layer to obtain the final prediction result. Through the graph convolutional network, the present invention strengthens the relationship between aspect words and other words, effectively improving the accuracy of aspect-level sentiment analysis.

The invention discloses an aero-engine load spectrum filtering method based on time sequence linear representation, and relates to the field of aero-engine load spectrums.The method comprises the steps that primary filtering processing is conducted on original data points of an aero-engine load spectrum, and upper filtering points and lower filtering points are sequentially connected according to a time sequence; extracting turning points of the original data points to form a key point sequence, removing data points which have small influence on the trend change, and retaining data points which have large influence on the trend change; carrying out secondary filtering on the key point sequence obtained by the first filtering, judging and extracting edge points based on a slope change threshold value and a distance and a difference value between two key points, and deleting small cycles in the small cycle concentrated region to obtain a final key point sequence; linear interpolation is carried out on the basis of the final key point sequence, an aero-engine load spectrum filtering spectrum is generated, and multi-section linear fitting representation is adopted. According to the method, detail interference is removed, the characteristics of the time sequence are reflected, and the efficiency and accuracy of data mining are improved.

The present disclosure relates to signaling quantization parameters. A method for reconstructing coded data using one or more processors includes: receiving a coded bitstream comprising one or more coded pictures, wherein each picture comprises a luma component, a first chroma component, and a second chroma component; extracting syntax parameters from the coded bitstream to determine a chroma quantization parameter (QP) table, wherein the chroma QP table maps an input luma QP value to a corresponding chroma QP value; and decoding the one or more coded pictures based on the chroma QP table, wherein the syntax parameters include a starting luma QP value and one or more offset parameters to determine a mapping of luma QP values to chroma QP values using a piecewise linear representation.

The application discloses a kind of based on the band gap calculation method of periodic pile arrangement structure of area decomposition and linear expression method, the method adopts area decomposition to divide pile soil coordinate system, establishes by periodic boundary constraint condition matrix and pile soil contact interface constraint condition matrix composition pile soil cell total constraint condition matrix;Pile soil cell total constraint condition matrix is handled by linear expression method, and the basic solution set is obtained;According to the basic solution set, the total energy functional of pile soil cell and the motion equation of pile soil cell are established;According to the motion equation of pile soil cell, the wave number of first Brillouin zone is scanned, and the vibration dispersion curve of periodic pile arrangement structure is obtained;The corresponding relationship between wave vector and the frequency of periodic pile arrangement structure is analyzed, and the band gap characteristics of periodic pile arrangement structure are obtained.The application avoids the problem of pile soil parameter distortion and the boundary dependence problem in shape function construction, and has low implementation complexity, accurate calculation result and significantly improved calculation efficiency.

An optical interference phase extraction method based on principal component analysis and vector inverse orthogonalization, which regards the light intensity values of the same pixel points in interference images as a multi-dimensional light intensity vector, according to the double-beam interference light intensity formula, regards the light intensity vectors of different pixel points as the result of linear representation by a group of unknown initial basis vectors, solves the real phase shift between the interference images by solving the unknown initial basis vectors, and finally solves the wrapped phase of the object surface height; when calculating, firstly, a group of standard orthogonal bases of the interference light intensity vector is solved by the principal component analysis method, and the first two principal components of the standard orthogonal bases are considered as the result of orthogonalization of the unknown initial basis vectors; then the constraint conditions are set to establish the equation group, the initial basis vectors are calculated by vector inverse orthogonalization, so as to calculate the real phase shift between the interference images, substitute into the linear over-determined equation group constructed according to the double-beam interference light intensity theory, and solve the least square solution of the wrapped phase of the object surface height.

Provided in the present disclosure are a method and apparatus for determining a planetary lithospheric magnetic field model coefficient. The method comprises: acquiring at a magnetic-field observation point a vertical magnetic-field component which points to a planetary core and is represented by a spherical harmonic function; for the vertical magnetic-field component, obtaining a partial derivative in the geographical south direction, so as to determine a first linear representation corresponding to a spherical harmonic coefficient, and obtaining a partial derivative in the geographical east direction, so as to determine a second linear representation corresponding to the spherical harmonic coefficient; on the basis of the first linear representation and the second linear representation, determining the absolute value of a horizontal gradient corresponding to the vertical magnetic-field component to be a regular term; determining a third linear representation for the spherical harmonic coefficient corresponding to the regular term, and on the basis of the third linear representation, constructing a target function for solving a system of linear equations corresponding to a planetary lithospheric magnetic field model coefficient; and performing optimization processing on the target function, so as to determine a target model coefficient corresponding to the minimum value of the target function. The physical characteristics of a planetary lithospheric magnetic field model can be achieved while effectively solving the problem of model overfitting, and the accuracy of the calculation of a model coefficient is relatively high.