13 results about "Shape constraint" patented technology

The invention relates to the technical field of strategy optimization, in particular to an ASIC (Application Specific Integrated Circuit) layout optimization method based on hybrid shaping programming, which comprises the following steps of: analyzing a circuit netlist, and establishing a signal path physical model containing static topological connectivity; a candidate layout scheme is generated based on the model, and layout state data describing the state of the candidate layout scheme is generated through physical effect simulation; based on the layout state data, calculating a signal processing performance index and a wiring cost value, and generating a group of dynamic shaping constraints for guiding subsequent layout according to the index and the cost value, the dynamic shaping constraints being quantitative adjustment of a path congestion perception graph in a signal path physical model; and updating the model through the constraint, evaluating the stability of a Pareto optimal boundary formed by the performance index and the cost value so as to judge convergence, and finally outputting a layout scheme associated with a stable state. According to the method, dynamic shaping constraints are generated through a strategy optimization algorithm, and global collaborative optimization of multiple targets such as time sequence and wiring performance is achieved.

The embodiment of the invention provides a peanut seed measurement method and device based on three-dimensional modeling, and the privacy of data processing is ensured through innovatively designing an image pyramid structure and through multi-scale decomposition and shape constraint. A multi-view registration model is constructed, and a reliable three-dimensional reconstruction system is established in combination with projection distortion correction. Inter-layer depth mapping is introduced, and through point cloud indexing and continuity correction, a precise three-dimensional model is provided while the security of measurement data is protected. According to the method, the defects of the traditional technology in the aspects of image processing, spatial reconstruction, data management and the like are effectively overcome.

The invention provides a railway line shape constraint perception type deep learning generation method based on a B2C framework, and belongs to the technical field of railway engineering design and deep learning. 2) carrying out B2C constraint compiling; 3) preparing multi-source geographic data; according to the method, the multi-main-body game logic and the deep learning model are organically fused, a new path is provided for solving the multi-target conflict problem in linear infrastructure line selection, and the line selection efficiency and the scheme reasonability can be effectively improved.

The invention discloses a pipeline circumferential weld defect intelligent detection method fusing nondestructive testing field knowledge. According to the method, by combining dynamic film evaluation operation knowledge, weld defect forming mechanism knowledge and long-scale image local film evaluation knowledge, a novel intelligent detection normal form of'lossless domain knowledge + data + AI 'is constructed, and efficient and high-precision recognition of weld defects is achieved. The method mainly comprises the following steps: 1) preprocessing a long-scale image based on a semi-overlapping sliding window, and keeping original resolution details; 2) performing defect detection based on multi-image decomposition and key frame screening of dynamic film evaluation knowledge in combination with a YOLOv8 network; 3, final type decision making is carried out based on the position and shape constraint rule of the defect forming mechanism. According to the method, dynamic film evaluation operation knowledge, weld defect forming mechanism knowledge and long-scale image local film evaluation knowledge are deeply fused, the detection rate and classification accuracy of weak and low-contrast defects are remarkably increased, and misjudgment and missing detection are reduced.

The present application relates to the field of complex equipment design, and specifically relates to a complex equipment modular configuration design knowledge graph construction method, comprising: constructing configuration design module meta-knowledge ontology, proposing a configuration design event class knowledge multi-element representation method and a constraint rule class constraint expression and design parameter strong association knowledge representation method, and designing a constraint rule expression and design meta-parameter matching fusion algorithm to integrate module meta-knowledge into the configuration design knowledge graph. With the help of shape constraint language, an algorithm for automatically converting constraint rules into entity path constraints and design parameter attribute value constraints is designed to generate a constraint resource description architecture diagram; all project configuration design instances are extracted and converted into project resource description architecture diagrams; the constraint resource description architecture diagram is used for integrity checking of the project resource description architecture diagram, and the project configuration design instances that pass the checking are integrated into the knowledge graph to realize the integration of project instance knowledge and module meta-knowledge.

The application discloses a rigid object 6D pose estimation method of shape constraint combined vector screening, which can effectively improve 6D pose estimation precision. The application defines a kind of shape for describing object in image. The application uses the geometric relationship between different 2D key points to assist 2D key point positioning. At the same time, when calculating the assumed key point by selecting any two vectors, the prior art does not consider the rationality of the used vector, resulting in a large deviation of the generated assumed key point position. The application proposes a vector screening strategy to prevent unreasonable vectors from calculating the assumed key point, thereby reducing the position deviation of the assumed key point and ultimately improving the performance of pose estimation.

The invention relates to a VR-based personalized cultural and creative product virtual customization method and device, equipment and a medium. The method comprises the following steps: acquiring gesture and language data of VR interaction of a user, extracting behavior, acoustic and semantic features, and fusing the behavior, acoustic and semantic features through an attention mechanism to generate user intention data; extracting abstract intention features and mapping the abstract intention features into vectors, and mining potential requirements such as size expansion and material replacement through a time sequence prediction model; and finally, fusing the intention vector, the potential demand and the creative product shape data to generate an initial feature set, and generating a design scheme meeting the demand through genetic algorithm optimization and in combination with shape constraint. According to the method, the explicit intention is accurately captured through multi-modal fusion, time sequence prediction breaks through traditional customization limitation, multi-data optimization and constraint guarantee scheme personalization and feasibility, and customization efficiency and user satisfaction are effectively improved.

Techniques for level-set based shape optimization with shape constraints include simulating a state of a current shape of a component, calculating a sensitivity of an objective function to with respect to modification to the current shape based on the simulated shape based on the simulated state, generating a source function for an optimization equation based on the sensitivity, adding one or more constraint terms to the optimization equation based on one or more constraints on an interface of the shape to generate a constrained optimization equation, solving the constrained optimization equation to generate a solution to the constrained optimization equation, updating a velocity field based on the solution to the constrained optimization equation, solving a transport function based on the updated velocity field to generate a solution of the transport equation, and updating the current shape based on the solution to the transport equation to generate an updated shape.

The application provides a handwriting document reconstruction and seal completion method and system based on multi-modal fusion, relates to the technical field of multi-modal fusion, and extracts a primary state physical layer, a primary state structure layer and a primary state semantic layer through layering; constructs a structure boundary constraint graph and a semantic shape constraint graph; introduces the structure boundary constraint graph and the semantic shape constraint graph to repair a damaged area of the primary state physical layer based on the primary state physical layer, and generates a refined physical layer; reversely extracts new geometric elements by using the refined physical layer, optimizes the primary state structure layer, and generates an optimized structure layer; uses residual positioning rules to mark residual areas based on the primary state semantic layer and the refined physical layer; performs local image enhancement and a three-level cascading recognition strategy, and generates a corrected semantic layer; obtains a visual enhancement base map based on the refined physical layer; and generates an enhanced handwriting document image in combination with the optimized structure layer and the corrected semantic layer, thereby improving the integrity, accuracy and visual consistency of handwriting document reconstruction and seal completion.

This invention discloses an automatic method for generating shape-constrained semantic word clouds. The technical solution of this invention involves the processing of text and shapes. The text processing flow includes: extracting words from the text; converting words into word vectors using the word2vec model and calculating the similarity between words; constructing an undirected graph based on the similarity and grouping words using a network community partitioning algorithm. The shape processing flow includes: extracting shape boundary points to form simple polygons; calculating the central axis of the simple polygons; obtaining the Venn diagram of the simple polygons based on the central axis, thereby generating spirals inside the shapes; and calculating vector fields based on direction constraints. After processing the text and shapes, a word cloud layout is performed to obtain a shape-constrained semantic word cloud. The word cloud generated using this technology satisfies both the characteristic of semantically similar words being close to each other in the word cloud layout and the characteristic of shape-constrained word clouds where words are placed inside arbitrary shapes.

Techniques for level-set based shape optimization with shape constraints include simulating a state of a current shape of a component, calculating a sensitivity of an objective function to with respect to modification to the current shape based on the simulated shape based on the simulated state, generating a source function for an optimization equation based on the sensitivity, adding one or more constraint terms to the optimization equation based on one or more constraints on an interface of the shape to generate a constrained optimization equation, solving the constrained optimization equation to generate a solution to the constrained optimization equation, updating a velocity field based on the solution to the constrained optimization equation, solving a transport function based on the updated velocity field to generate a solution of the transport equation, and updating the current shape based on the solution to the transport equation to generate an updated shape.

The invention provides a pseudo-view collaborative geometric perception sparse view 3DGS reconstruction method, device and equipment and a storage medium, Gaussian representation of a three-dimensional scene is initialized through Gaussian solution pool densification, then geometric perception optimization is executed, outlier gauss deviating from the scene surface are forcibly embedded into a correct position by using a depth stamping surface reconstruction module, and the 3D scene is reconstructed. Excessive tensile deformation of the Gaussian ellipsoid is prevented through equilibrium form constraint, and redundant gauss are eliminated by using opacity constraint, so that an accurate scene geometric structure is established. Then, double-source collaborative pseudo-visual-angle regularization is implemented, a pseudo-training visual angle is intelligently generated through a visual angle interpolation dynamic scoring mechanism, and under the visual angle, a pseudo-true value generated by current Gaussian rendering and original view warping transformation is utilized at the same time for bidirectional constraint; and finally, a neighborhood smoothing module is applied, a neighborhood graph is constructed based on a Gaussian space neighborhood relation, and color jump and geometric discontinuity caused by discrete Gaussian distribution are eliminated through luminosity and depth smoothing constraints.

The invention discloses an AIGC character generation consistency control method based on clay kneading entity constraint, and belongs to the technical field of artificial intelligence generation content. The method comprises the following steps: making a mud kneading entity role as an initial form constraint source of a target person; performing digital acquisition on the entity role to obtain role digital data; performing image main body extraction based on the digital data, emphatically extracting appearance contour, posture, proportion and style features, and constructing a figure initial feature model; in the AIGC video generation process, the initial feature model serves as a condition to be input into a video generation model in the reasoning stage, time sequence consistency constraint and style guide constraint are applied to corresponding features of a character through a cross attention layer, and a character image matched with the model is guided to be generated. According to the method, the consistency of character images in multi-lens and multi-round generation is remarkably improved, the generation randomness is reduced, and low-cost and high-efficiency AIGC character generation control is realized.