48 results about "Function representation" patented technology

The invention discloses a method for evaluating the possibility that complementary regions among primers in a candidate primer group trigger generation of dimers, complementary subregions in primer sequences are HBJ regions, the possibility that the HBJ regions trigger the dimers is represented by a function LHSm of a mathematical model (1), the higher the value of the LHSm is, the higher the possibility that the dimers are triggered is, and the lower the value of the LHSm is, the higher the possibility that the dimers are triggered is. According to the evaluation and optimization method disclosed by the invention, the formation of primer dimers can be reduced to the maximum extent.

The invention discloses an intelligent dynamics rapid simulation method. The invention relates to the technical field of finite element simulation, and solves the problem that real-time prediction of high-dimensional nonlinear structural dynamics and online inversion of impact acceleration cannot be met in the prior art. According to the invention, based on a deep learning algorithm, structure-level reconstruction of an existing operator learning normal form is realized through a mapping thought from a graph structure depth operator function to a function. According to the method, the double-tower type branch-trunk topology on which DeepONet leans is thoroughly got rid of; according to the method, a space correlation mode and a time evolution rule are synchronously coded in a hidden space and are directly projected to a function at a target moment to be expressed without following a frequency domain kernel integral path of the FNO instead of an end-to-end serial progressive single calculation path. According to the design, the function-to-function micro-mapping capability is reserved on the semantics of operator approximation, and induction deviation caused by structure splitting of a frame is overcome in a graph domain time sequence task.

The invention discloses a non-linear constraint coupling driven implicit modeling optimization method and system and a memory. The method comprises the steps of firstly obtaining nonlinear constraint data of a target geological interface, then generating initial implicit function representation through a Hermite radial basis function based on an initial control point of the target geological interface, and then obtaining a nonlinear coupling driving optimization objective function about implicit function representation of the target geological interface according to the data type of nonlinear constraint, and optimizing through an implicit gradient flow algorithm to obtain a geological interface reconstruction model and a global optimization target geological interface model. The invention further provides a computer readable memory, the memory contains a computer program for realizing the implicit modeling optimization method for nonlinear constraint coupling driving, the computer program can be read and executed, and the computer program, a programmable logic controller and a processor form an optimization system. According to the system, nonlinear constraint data of a large number of geological interfaces are fully coupled, an implicit modeling framework is constrained, and therefore high-precision modeling of the three-dimensional geological interfaces is achieved.

A method for modeling a wafer profile by a function is provided, in which the function is used to calculate a displacement z in a thickness direction of a wafer and is a sum of multiple functions including a first function g(r), a second function Ar×h(Nθ), and a third function Br×i(M(θ-φ)). The first function g(r) indicates a first-order or higher-order polynomial with a distance r from the center of the wafer as a variable. The second function Ar×h(Nθ) indicates multiplying a sine or cosine function h(Nθ) by a first angle θ with respect to a predetermined position in a circumferential direction of the wafer as a variable and an integer N as a constant with a coefficient A and the distance r.The third function Br×i(M(θ-φ)) indicates multiplying a sine or cosine function i(M(θ-φ)) with the first angle θ as a variable, a second angle φ with respect to the predetermined position as a constant, and an integer M as a constant with a coefficient B and the distance r.

The present invention discloses a method for generating a printable 3D model from a single photo based on a generative adversarial network, comprising the following steps: S1, obtaining a single input image and preprocessing the input image; S2, inputting the preprocessed image into an encoder, extracting multi-scale features, and outputting a deep feature representation; S3, generating a 3D model using a conditional generative adversarial network and optimizing the 3D model quality through comparison; S4, refining the 3D model at multiple scales using an implicit neural representation network and converting it into a continuous function representation; S5, optimizing the 3D model using an improved conditional diffusion inversion module, combining multi-scale discrete wavelet transform and rendering feedback correction; S6, optimizing the feature extraction, 3D model generation, and refinement processes using an end-to-end joint training system; and S7, post-processing the 3D model to output a 3D model that meets printing requirements. By integrating generative adversarial networks and implicit neural representation technology, the present invention achieves the rapid generation of high-precision 3D models with rich details suitable for 3D printing from a single photo.

The present invention relates to a high-speed maneuvering target tracking method based on a continuous space-time trajectory function random process, and relates to the field of target tracking. The method comprises: modeling the continuous space-time trajectory of a high-speed maneuvering target with an unknown motion model as a random process based on a continuous-time trajectory function; modeling the mean function of the target trajectory random process as a continuous space-time trajectory function; and fitting and estimating the continuous space-time trajectory function to output the target state. The method of the present invention can achieve higher-precision real-time target tracking in complex maneuvering scenarios, overcoming the shortcomings of existing random process target tracking methods, which often add a constant mean function and rely solely on covariance kernel function representation for trajectory estimation.

This invention provides a method and related apparatus for establishing a nonlinear mathematical model of a hybrid DC transmission system considering discontinuous characteristics. The method includes: representing the dynamic processes of discontinuous components in the main and control loops of the hybrid DC transmission system using a step function; approximating the step function as a continuous function using a linear combination of hyperbolic tangent functions to obtain a continuous expression; obtaining modular nonlinear differential equations for the rectifier, inverter, and AC / DC networks, and replacing the discontinuous dynamic parts involved in the equations with the continuous expression; and interconnecting the modules according to their input-output relationships to obtain a complete nonlinear mathematical model of the hybrid DC transmission system. This invention establishes a nonlinear mathematical model of a hybrid DC transmission system that accurately reflects discontinuous dynamic characteristics by employing the continuous approximation method using step functions and hyperbolic tangent functions, thereby improving the accuracy and practicality of system modeling.

The application discloses a kind of based on three periodic minimal surface double-fluid porous heat exchanger optimization and design method, comprising: step 1: the function representation of double-fluid porous heat exchanger;Step 2: establish optimization mathematical model;Step 3: optimization process and improvement optimization solver.The application is mainly aimed at the problem of enhancing the heat exchange efficiency of double-fluid heat exchanger, based on the porous structure of three periodic minimal surface has natural heat exchange advantage, for example, the specific surface area ratio is increased to increase the contact area of double-fluid, full connectivity and high smoothness can reduce flow resistance;In optimization model, the wall thickness between double-fluid is directly controlled by geometric parameter variable, to avoid adding complex wall thickness constraint;Secondly, the optimization solver of adding monotone programming operator is more suitable for the optimization of complex structure, and accelerates the convergence speed of optimization;In addition, the function representation of porous heat exchanger can guarantee the accuracy of model, good smoothness, connectivity and self-supporting are also conducive to additive manufacturing.

The embodiment of the invention provides a training method of a three-dimensional reconstruction prediction model and a three-dimensional reconstruction method, and relates to the technical field of image processing, and the training method of the three-dimensional reconstruction prediction model comprises the steps: obtaining a plurality of image groups obtained by photographing an object through a camera, each image group comprises a plurality of images with different relative angles between the object and the light source and the same shooting view angle, different image groups have different shooting view angles, generating a first feature map by using a pre-trained photometric stereo prediction model according to the plurality of image groups, and obtaining a second feature map according to a first loss function and a second loss function; model parameters in the three-dimensional reconstruction prediction model are trained, the first loss function represents the difference between the first feature map and the second feature map, the second feature map is determined according to the middle layer output of the three-dimensional reconstruction prediction model, and the second loss function represents the difference between the three-dimensional object information and the three-dimensional object reference information; and the reconstruction result of the three-dimensional reconstruction prediction model for the object carries more surface details.

A path planning method for lattice three-dimensional (3D) printing includes generating a 3D model; slicing the 3D model to generate a 2D contour; determining a region of the 2D contour; and generating a path. The 3D model is represented by an explicit mesh or implicit function. Slicing the 3D model to generate the 2D contour includes generating the 2D contour of the 3D model defined by an explicit or implicit function. For a 3D model defined by an explicit function, a 2D contour is generated by calculating intersections between slice layers and triangular meshes. The determination of the region comprises dividing the lattice structure or the thin-wall structure into two regions including a thin wall and a wall connection. Geometric features of the two regions are defined by the tilt angle and the plate thickness.

The invention discloses a neural operator modeling method with a structure adjustment capability and a multi-stage optimization mechanism. The neural operator modeling method is suitable for efficiently solving a complex partial differential equation under a small sample condition. According to the method, a primary function regulation and control network is provided, a traditional fixed primary function is replaced, and the function representation capacity is dynamically adjusted through a structure guiding module; in the aspect of feature fusion, a collaborative modulation mechanism is introduced, and deep semantic integration of input conditions and space coordinates is realized. The training process adopts a staged design, and comprises structure adaptive preheating, self-supervised reconstruction and physical consistency collaborative optimization, so that the precision, stability and interpretability of a solution are effectively improved. Experimental verification shows that the method has good generalization and physical rationality in the problems of Darcy flow, Poisson equations and the like, and is suitable for scientific calculation tasks of complex boundaries, multi-scale modeling and the like.

The invention discloses a table structuring method, related equipment and a program product, and relates to the technical field of computer vision and table recognition. The method comprises the following steps: inputting a to-be-structured table image into a configured control parameter prediction model to obtain a group of control parameters respectively corresponding to each row and column line in the table image; generating a curvilinear function corresponding to each row and column line based on the group of control parameters corresponding to each row and column line and a preset curvilinear function containing a group of unknown control parameters; and generating a structured table of the table image according to the curvilinear function corresponding to each row and column line. The model directly predicts the function representation of each row and column line, and eliminates the additional error caused by the fitting process, thereby improving the accuracy of the structured table finally generated by the curvilinear function of the row and column lines.

A method of initial residual hypothesis derivation for joint sign prediction of transform coefficients of a residue block. According to this method, an initial residual hypothesis is derived by applying inverse transformation to de-quantized transform coefficients with the set of sign-predicted transform coefficients set to zero or by applying the inverse transformation to the de-quantized transform coefficients corresponding to non-sign-predicted transform coefficients only to form a first part of the initial residual hypothesis, and adding a second part by summing up products of normalized template functions and absolute values of respective sign-predicted transform coefficients for the set of sign-predicted transform coefficients. The normalized template functions represent approximated entry values of inverse transformation matrixes along top and left block boundaries for corresponding transform coefficient indexes associated with the respective sign-predicted transform coefficients. The initial residual hypothesis is used to derive sign prediction for the sign-predicted transform coefficients.

The invention provides a sub-function code generation method and device, and the method comprises the steps: receiving a code completion instruction which comprises a function type and a context code of a target sub-function; the target sub-function represents at least one undefined sub-function in the to-be-complemented function; determining a target completion code of the target sub-function according to the function type, the context code and a first code completion model; and complementing the to-be-complemented function through the target complementing code to obtain a first complementing code. According to the embodiment of the invention, by detecting and complementing the undefined sub-functions, the generated complementation codes can be ensured to be complete, compiling or running errors caused by the undefined sub-functions are reduced, and the code complementation accuracy and the code operability are greatly improved.

The present invention discloses a nonlinear constraint coupling-driven implicit modeling optimization method, system, and memory. The method first obtains nonlinear constraint data for a target geological interface. Then, based on the initial control points of the target geological interface, an initial implicit function representation is generated using a Hermite radial basis function. Next, based on the data type of the nonlinear constraint, a nonlinear coupling-driven optimization objective function for the implicit function representation of the target geological interface is obtained. This objective function is optimized using an implicit gradient flow algorithm to obtain a geological interface reconstruction model and a globally optimized target geological interface model. The present invention also provides a computer-readable memory containing a computer program that implements the aforementioned nonlinear constraint coupling-driven implicit modeling optimization method. The memory is readable and executable, and together with a programmable logic controller and a processor, forms an optimization system. This system achieves high-precision modeling of three-dimensional geological interfaces by fully coupling a large amount of nonlinear constraint data for the geological interface and constraining the implicit modeling framework.

The present invention discloses a method for simulating incompressible viscous fluids based on signed distance function representation, and relates to the technical fields of numerical calculation and numerical simulation. The method comprises spatially dividing a calculation area into Cartesian grids; using signed distance functions to represent virtual points in the grid at the solid wall boundary of the calculation area, calculating the perpendicular point from the virtual point to the boundary of the geometric area using a perpendicular calculation algorithm based on signed distance function representation, solving the differential information of the equation at the perpendicular point using an explicit Inverse Lax-Wendroff technique at the perpendicular point, obtaining the grid function value at the virtual point using the differential information, and simultaneously solving the algebraic relationship between the virtual point value and the internal point value, and auxiliary matrix assembly; and using the Crank-Nicolson format to advance the time step forward to realize the numerical simulation of incompressible viscous fluids based on SDF. The present invention realizes complex boundary processing while ensuring the accuracy of the algorithm.

A path planning method for three-dimensional (3D) printing of a lattice includes generating a 3D model; slicing to generate 2D contours of the 3D model; determining regions of the 2D contours; and generating a path. The 3D model is represented by explicit mesh or implicit functions. The slicing to generate 2D contours of the 3D model includes generating 2D contours of the 3D models defined by either explicit or implicit functions. For the 3D model defined by explicit functions, the 2D contours are generated by calculating intersecting points between a slicing layer and triangle meshes. The determining regions includes dividing lattice structures or thin-walled structures into two regions including thin-walls and wall joints. Geometric characteristics of the two regions are defined by inclination angles and sheet thicknesses.

The invention discloses a three-dimensional shape generation method fusing image retrieval and sketch regulation, which comprises the following steps of: firstly, mapping an input sketch to a shared intermediate feature space by constructing a sketch-image mapping network, and retrieving a plurality of images which are closest to sketch semantics from an image library based on cosine similarity; secondly, a pre-trained CLIP visual encoder is adopted to carry out feature extraction on the retrieved image, fusion is carried out through a maximum pooling mode, and final image features are obtained; introducing a shape adapter with a cross attention mechanism into a de-noising network of a diffusion generation model, and fusing the geometric features of the input sketch into the de-noising network to obtain a three-dimensional latent vector consistent with the input sketch in structure and semantics; and finally, a decoder of the three-dimensional vector quantization variational auto-encoder is reduced into a three-dimensional symbol distance function for representation. According to the method, the quality of the three-dimensional shape generated by sketch driving in the aspects of structural consistency and local detail reduction is improved, and the method has high generalization ability and practical application value.

The invention discloses a profile steel section parameterization generation method based on Autolisp, and belongs to the technical field of mechanical design. Comprising the following steps: 1, establishing a database comprising different section steel section parameters; 2, representing drawing processes of different types of section steel sections through a drawing function; 3, setting keywords for obtaining form parameters in the drawing function; 4, programming by using a language of a DLC dialog box in CAD to form interactive interfaces corresponding to different types of section steel sections, and drawing the section steel sections by selecting the keywords in the step 3; 5, a main function is defined as a drawing command in CAD, different driving functions are selected through the main function, and the driving functions are used for calling interaction interfaces of different types of section steel sections in the step 4. According to the method, automatic drawing is achieved by programming section parameters of different types of section steel through Autolisp, manual consulting and section drawing operation is not needed any more, and drawing time and drawing energy are reduced.

A method for obtaining the position of an object and a method for point cloud segmentation, wherein the method for point cloud segmentation first constructs a feature vector of a point cloud using point cloud data, then obtains the initial probability distribution of the category of the point cloud, and finally inputs the initial probability distribution and feature vector of each point cloud category into a pre-trained probability graph model to obtain the probability distribution of the category of the point cloud, and takes the category of the point cloud with the highest probability in the probability distribution of each point cloud category. The random variable of the probability graph model is the category of the point cloud, and each random variable is independent of each other. Its energy function includes a unary part and a binary part. The initial probability distribution is used to initialize the unary part, and the binary part is a function representation of the feature vector pairs formed between the current point cloud and other point clouds. Therefore, when segmenting, not only the information of the current point cloud is considered, but also the relationship between the point clouds is considered, which is conducive to more complete segmentation of objects and backgrounds and improves the effect of point cloud segmentation.

The application discloses a kind of robust sparse bayesian two-dimensional boresight angle estimation method.It is characterized in that: the patent method is by introducing a new auxiliary angle, two-dimensional DOA estimation problem is converted into two one-dimensional DOA estimation problems.Through solving two sparse reconstruction problems containing amplitude and phase errors, the auxiliary angle and its corresponding pitch angle are estimated respectively, and according to the relationship between the three angles, the azimuth angle estimation and angle automatic matching process are completed simultaneously.For the sparse reconstruction problem containing amplitude and phase errors, the expectation maximum algorithm is used to derive the estimation expression of all unknown parameters, obtain a new spatial spectrum function representation method, and finally obtain the angle information.The patent method can effectively complete the angle automatic matching process, and improve the azimuth estimation accuracy and angle resolution capability, so it has high application value in practical engineering.

The present invention discloses a compact topology optimization method for porous models based on Voronoi partitioning, which directly uses explicit topology and geometric parameters as design degrees of freedom to construct a compact design space for porous model design. Different from the traditional voxel-based model representation, the porous model is represented by a continuous distance field function, which can accurately extract the model features; and this representation method remains unified during the optimization process, avoiding unstable model conversion. The proposed compact topology optimization method can optimize the global topological structure and local size distribution of the porous model based on effective gradient calculation, and can generate high-quality porous models for various complex free surface models. In addition, the present invention proposes a numerical coarsening method to construct a general polygon / polyhedron shape function. Compared with the classical numerical homogenization method, this method improves the simulation accuracy by an order of magnitude at a similar computational cost.

The invention relates to the technical field of image super-resolution reconstruction, and discloses a ridgelet-based image super-resolution reconstruction method, which comprises the following steps of: modeling an input low-resolution image into a continuous function, directly predicting a pixel value from a space coordinate through a multi-layer perceptron network, and obtaining continuous function representation of the whole image; periodic features of space coordinates are extracted, and the expression ability of a periodic structure is improved through a periodic coding enhancement network of Fourier transform; analyzing the fractal dimension of the ridgelet structure of the original image, and introducing a fractal dimension retention constraint according to the fractal dimension; constructing a high-order derivative regularization term near the ridgelet; network parameters are optimized through end-to-end training, and super-resolution images of any scale are generated; according to the invention, the image is represented as a continuous function, so that the image can be sampled under any resolution, and amplification of any scale is realized without retraining the model.

Disclosed is a method for performing a method for performing a quantum computation, referred to as initial quantum computation, the initial quantum computation being defined by at least a first set of one or more first type quantum operations, the first set of one or more first type quantum operations having as parameters one or more first rotation angles respectively, each first type quantum operation being a single-qubit rotation around a z- axis in a Bloch sphere by the respective first rotation angle. The method comprises: representing the initial quantum computation by a function f having as arguments the first rotation angles; performing a Taylor expansion of the function f around zero, resulting in foundational derivatives of the function f; for each specific derivative of at least one of the foundational derivatives: using a parameter shift rule for expressing the specific derivative as a linear combination of a set of terms.

The present invention is directed to a computer implemented method for controlling a finite set of components which are fluidly connected to a common compressed air distribution system, the method comprising iteratively repeating the steps of: - receiving prediction data for said compressed air distribution system; - receiving characterising data for each component of said set of components; - determining one or more sets of continuously differentiable functions, wherein each of said sets of functions represents a unique sequence of operation of the components in said set of components; - selecting an optimal set of functions from said one or more sets of continuously differentiable functions; wherein the unique sequence of operation represented by said optimal set meets said prediction data; - deriving configuration data for said set of components from said optimal set of functions; - configuring each component of said set of components based on said configuration data.

A program according to one embodiment of the present disclosure causes a computer to execute: a first input procedure for inputting a first nonterminal node and a second nonterminal node on a zero-suppressed binary decision diagram; a second input procedure for inputting at least designation of a first function representing a condition to be satisfied by a set pair of a set included in a set group represented by the first nonterminal node and a set included in a set group represented by the second nonterminal node; and a calculation procedure for executing a set group calculation of the set group represented by the first nonterminal node and the set group represented by the second nonterminal node, on the basis of the first nonterminal node, the second nonterminal node, the first function, and a template of an algorithm for achieving the set group calculation.

The invention discloses an incompressible viscous fluid simulation method based on symbolic distance function representation, and relates to the technical field of numerical calculation and numerical simulation. The method comprises the following steps: carrying out Cartesian grid division on a calculation area in space; at the solid wall boundary of the calculation area, using a sign distance function to represent a virtual point in the positioning grid, calculating a perpendicular foot point from the virtual point to the geometric area boundary through a perpendicular foot calculation algorithm represented based on the sign distance function, and solving differential information of an equation at the perpendicular foot point through an explicit Inverse Lax-Wendroff technology on the perpendicular foot point, obtaining a lattice point function value at a virtual point through differential information, solving an algebraic relational expression between a virtual point value and an internal point value, and assisting matrix assembly; a Crank-Nicolson format is used for advancing a time step, SDF-based numerical simulation of the incompressible viscous fluid is realized, complex boundary processing is realized, and meanwhile, the precision of an algorithm is ensured.

The application relates to a trajectory prediction method and device, electronic equipment and a computer readable storage medium, and belongs to the technical field of computers. The method comprises the following steps: acquiring a semantic map corresponding to a prediction target and a historical observation trajectory sequence corresponding to the prediction target, the historical observation trajectory sequence representing the trajectory position of the prediction target at each historical moment; then, the historical observation trajectory sequence corresponding to the prediction target is represented by sequence coding to obtain sequence coding features; a function representation corresponding to the semantic map is generated based on the semantic map corresponding to the prediction target, the function representation corresponding to the semantic map representing the mapping relationship between semantics and trajectory positions; then, trajectory prediction is performed based on the sequence coding features and by using the function representation corresponding to the semantic map to obtain a future prediction trajectory sequence. The application relates to a trajectory prediction method and device, electronic equipment and a computer readable storage medium, which can improve the vehicle trajectory prediction accuracy.

The invention discloses a cross-scale feature fusion method based on quantum inspiration, and relates to the technical field of target detection, and the method comprises the steps: carrying out the up-sampling operation of a cross-scale feature map; mapping the original spatial domain features to a quantum inspiration domain through amplitude coding and phase coding; performing feature preliminary fusion by simulating quantum interference; predicting a spatial offset based on the similarity; performing resampling operation on the original feature map by using the space offset to realize feature alignment; using a quantum observation mechanism to convert the complex value features into real number representation; and performing channel dimension splicing on the aligned original feature map and the feature map after quantum interference fusion, and recovering the number of channels through convolution operation to obtain a fused cross-scale aggregation representation feature map. According to the invention, through the complex value quantum wave function representation and interference fusion formula, the feature fusion which is finer and more accordant with semantic logic is realized, semantic dislocation and cross-scale intra-class difference are reduced, and the detection precision under a small target and a complex scene is effectively improved.