Adaptive Probing of Piecewise Continuous Surfaces
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for imaging piecewise continuous surfaces are inefficient, requiring a larger number of probe data points, leading to increased probing time and a higher risk of sample damage due to prolonged exposure.
Innovation Solution
A machine learning algorithm is employed to optimize the selection of probe locations for adaptive probing, using a jump Gaussian process to iteratively refine the selection of probe points until sufficient data is obtained for accurate image reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods are used to probe continuous surfaces, then image reconstruction can be achieved, but the number of probe data points required increases, leading to longer probing time and higher risk of sample damage
Solution Approach 1:
The patent applies parameter changes by transitioning from uniform probe distribution to adaptive probe selection based on surface characteristics. The machine learning algorithm dynamically adjusts probe location parameters, selecting only critical points that provide maximum information gain for piecewise continuous surfaces, thereby reducing the total number of probes while maintaining reconstruction accuracy.
Solution Approach 2:
The patent implements local quality by treating different regions of the piecewise continuous surface differently. Instead of uniform probing, the system identifies and focuses probes on specific local features such as discontinuities, edges, and regions of high curvature, while reducing probe density in smooth, well-understood regions. This localized approach optimizes information gathering efficiency.
2Measurement precision
If existing methods are used to probe continuous surfaces, then image reconstruction can be achieved, but the number of probe data points required increases, leading to higher risk of sample damage
Solution Approach 1:
The patent changes the probing parameter strategy from fixed-density uniform sampling to adaptive sampling driven by machine learning. The algorithm calculates information gain metrics and selects probe locations that maximize reconstruction accuracy while minimizing the total number of probes, thereby reducing cumulative exposure and damage risk to the sample.
Solution Approach 2:
The patent substitutes mechanical/probe-based sampling with an intelligent decision-making system. The machine learning algorithm acts as a virtual selector that determines optimal probe locations based on surface characteristics, replacing brute-force uniform sampling with intelligent, adaptive selection that minimizes physical interaction with the sample.
3Productivity
If existing methods are used to probe continuous surfaces, then probing can be performed, but the method is inefficient for piecewise continuous surfaces requiring additional probe data points
Solution Approach 1:
The patent transforms the probing approach by changing from uniform parameter distribution to adaptive parameter selection. The machine learning algorithm dynamically determines probe locations based on surface discontinuities and features, optimizing the distribution of probe data points to match the actual information needs of piecewise continuous surfaces, thereby improving efficiency.
Solution Approach 2:
The patent applies preliminary action by using an initial set of probe data to train the machine learning algorithm and identify surface characteristics before performing the main probing task. This preliminary training phase enables the algorithm to optimize subsequent probe selection, avoiding the need for exhaustive uniform sampling throughout the entire process.
Data Source
AI summary
Systems and methods are provided for image reconstruction of a sample via adaptive probing of piecewise continuous surfaces. A machine learning algorithm can be employed with scanning-based measurement instruments or experimental probers to optimize the selection of probe locations for effectively scanning piecewise continuous surfaces. A limited number of initial probes may first be obtained to estimate the piecewise continuous surface. The machine learning algorithm may then be leveraged to identify any subsequent probe locations used to obtain additional data points about the piecewise continuous surface. The selection of the probe locations may be performed iteratively until sufficient data has been obtained to generate an accurate image reconstruction.