Resonance Mixture Model for Spectrum Feature Extraction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods struggle to accurately extract features from noisy or low-resolution segments of a measurement spectrum, such as peaks and areas, which are crucial for identifying material traits.
Innovation Solution
An electronic device is configured to determine initial values of parameters for a resonance mixture model based on a spectrum and the number of clusters, then iteratively refine these values to minimize the loss between the spectrum and the model, generating a denoised model by removing noise information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional spectrum analysis methods are used on noisy or low-resolution segments, then the analysis process is simple, but the accuracy of feature extraction (peaks and areas) deteriorates
Solution Approach 1:
The patent transforms the spectrum analysis problem by changing parameters from direct measurement to model-based parameter estimation. It uses a resonance mixture model with parameters (amplitude, frequency, damping) that are optimized to match the measured spectrum, thereby extracting features more accurately even from noisy data
Solution Approach 2:
The patent introduces a resonance mixture model as an intermediary between the raw spectrum measurement and feature extraction. This model acts as a mediator that filters noise and provides a structured framework for identifying peaks and areas through parameter optimization rather than direct measurement
2Measurement precision
If a resonance mixture model is used to model the spectrum, then the accuracy of spectrum representation is improved, but the computational complexity increases
Solution Approach 1:
The patent segments the complex spectrum analysis problem into multiple resonance components, each represented by a simple resonance function with three parameters. By dividing the spectrum into additive resonance components, the model achieves high accuracy while keeping individual component calculations computationally efficient
Solution Approach 2:
The patent reduces computational complexity by parameterizing each resonance component with only three parameters (amplitude, frequency, damping) and using optimization algorithms to determine these parameters efficiently, rather than requiring complex full-spectrum analysis
3Measurement precision
If the number of clusters is increased to capture more spectrum features, then the model accuracy is improved, but the model complexity and processing time increase
Solution Approach 1:
The patent segments the spectrum into a predetermined number of resonance clusters, where each cluster represents a distinct feature or group of features. This segmentation allows the model to capture multiple spectrum characteristics while limiting the total number of optimization iterations required
Solution Approach 2:
The patent uses a predetermined number of clusters that may be fewer than the total number of actual spectrum features, accepting that not all features will be perfectly captured. This partial action approach balances model complexity with sufficient accuracy for practical applications
Data Source
AI summary
A method and device with spectrum modeling are disclosed. The electronic device includes one or more processors and a memory electrically connected to the processor and storing instructions configured to cause the one or more processors to: determine, based on a spectrum and a number of clusters, initial values of respective parameters included in a resonance mixture model that models a resonance function; determine, for each of the number of clusters, values of the parameters based on a loss between the spectrum and the resonance mixture model; and generate a model corresponding to the spectrum based on the determined values of the parameters.