Spectrum Measuring Device Adaptive Weighting Function
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Solution Overview
Problem
Existing spectrum measuring devices struggle to accurately evaluate the difference between spectra, particularly when noise components and variations in spectral values are significant, and when samples consist of multiple known components.
Innovation Solution
The development of a spectrum measuring device that employs a weighting function based on gain-adjustment voltage values and spectral variations, allowing for more precise evaluation of spectral differences by adjusting weights according to noise components and spectral variability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If uniform weighting is applied to all data points in spectral difference evaluation, then calculation simplicity is maintained, but measurement precision deteriorates due to inability to account for noise variations and spectral importance across different wavelengths
Solution Approach 1:
The patent applies local quality by introducing a weighting function that assigns different weights to different data points based on their individual characteristics. The weighting function considers noise levels and spectral importance at each wavelength, allowing the evaluation to focus on reliable and significant spectral regions while minimizing the impact of noisy or less informative regions. This resolves the contradiction by improving measurement precision through localized adaptive weighting without requiring complex device hardware modifications.
2Measurement precision
If weighting based on reference spectral value size is applied, then contribution of significant spectral regions is enhanced, but noise components in high-absorbance regions are not adequately suppressed
Solution Approach 1:
The patent employs parameter changes by incorporating multiple parameters into the weighting function, including noise level estimates and spectral importance metrics. The weighting function dynamically adjusts weights based on these parameters, suppressing regions with high noise levels while enhancing regions with significant spectral features. This approach resolves the contradiction by simultaneously considering both the magnitude of spectral values and the noise characteristics, achieving precise evaluation while mitigating noise impact.
3Measurement precision
If all data points are evaluated with equal importance, then evaluation process is simplified, but sensitivity to detect subtle spectral variations deteriorates
Solution Approach 1:
The patent applies local quality by making the evaluation process sensitive to local characteristics of the spectrum. The weighting function identifies regions with subtle but significant spectral variations and assigns higher weights to these regions, thereby enhancing detection sensitivity. This is achieved without complex device modifications, only through algorithmic weighting based on spectral and noise characteristics at each data point.
Data Source
AI summary
Based on an HT voltage value of each data point applied to a light detector and a plurality of reference spectra measured, in measurement of a reference substance performed for a plurality of times, a weighting function deriver of a spectrum measuring device derives a relation between the HT voltage value (HTi) and a degree of dispersion σi of a plurality of spectral values as a weighting function (σ=f(HT)). Moreover, a numerical evaluator of the spectrum measuring device is configured to calculate a degree of individual coincidence between a sample spectrum and the reference spectrum, acquire the degree of dispersion σi by applying the HT voltage value at measuring the sample spectrum to the weighting function as a weighting value, and evaluate the difference between the reference spectrum and the sample spectrum based on the degree of individual coincidence to which the weighting value is applied.