Structure Noise Reduction via Self-Analysis Boundary Conditions
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Solution Overview
Problem
Existing methods for reducing structure noise in two-dimensional or three-dimensional data are inefficient, often distorting signal components and increasing processing time due to the inability to differentiate between signal and noise components during noise removal.
Innovation Solution
A method and device that perform self-structure analysis to classify data elements as signal or noise components, allowing for a smoothing operation based on set boundary conditions without additional input, thereby reducing noise while maintaining the integrity of the structure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional noise reduction methods are applied to structure data, then noise removal is achieved, but processing time significantly increases and signal components may be distorted
Solution Approach 1:
The patent segments structure data into discrete structure elements (vertices, edges, faces) and processes them individually through boundary condition setting. This segmentation allows the system to identify and remove noise components from specific elements without reprocessing entire structures, significantly reducing processing time while maintaining noise reduction effectiveness.
Solution Approach 2:
The patent applies preliminary boundary condition setting to structure elements before performing smoothing operations. By pre-classifying elements as signal or noise components through boundary conditions, the system avoids unnecessary processing of noise-free elements during subsequent operations, thereby reducing overall processing time while preserving signal integrity.
2Measurement precision
If additional input data is provided to improve noise reduction accuracy, then classification precision increases, but system complexity and data requirements increase
Solution Approach 1:
The patent enables structure data to serve itself by automatically generating boundary conditions from the input structure data without requiring additional external information. The system analyzes the geometric and topological properties of the input data to set boundary conditions, achieving accurate signal-noise classification while maintaining system simplicity and avoiding additional data requirements.
Solution Approach 2:
The patent creates a universal boundary condition setting mechanism that can handle various types of structure data (2D/3D, different formats) using the same core algorithm. This universal approach achieves high classification accuracy across different data types without increasing system complexity, as the same multi-functional boundary condition engine adapts to different input scenarios.
3Adaptability or versatility
If boundary conditions are set by analyzing input structure data, then additional information requirements are reduced, but analysis complexity increases
Solution Approach 1:
The patent transforms the boundary condition setting process from a complex analytical task into a parameter-based classification system. By defining boundary conditions through geometric parameters (distances, angles, areas) and topological parameters (element connectivity), the system achieves high adaptability to arbitrary input structures while managing analysis complexity through standardized parameter evaluation criteria.
Data Source
AI summary
To reduce structure noise, input data representing an input structure is obtained and boundary conditions are set by classifying data of each of multiple structure elements of the input data as a signal component or a noise component. A smoothing operation is performed with respect to the input data and based on the boundary conditions. Output data representing an output structure is provided by reducing noise from the input structure.