Gas Absorption Spectroscopy Data Correction for Nonlinear Detector Signals
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Solution Overview
Problem
Existing methods for correcting measurement data in gas absorption spectroscopy using a model function fail to accurately account for non-linear components in photodetector data, leading to decreased measurement accuracy.
Innovation Solution
A data processing method that generates an interpolation function based on reference data without using a model function, by extracting signals with low intensity and deriving a correspondence relationship to correct target data, thereby removing non-linear components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a model function is used to correct measurement data, then the correction process can be simplified, but the measurement accuracy decreases because the non-linear component of photodetector data does not fit the model function
Solution Approach 1:
The patent creates a lookup table (LUT) that stores pre-calibrated correspondence relationships between raw detector signals and corrected concentration values. This copying approach stores actual measurement data patterns in a table structure, allowing direct reference during correction without requiring mathematical model fitting, thus maintaining both ease of operation and measurement accuracy
Solution Approach 2:
The patent transforms the correction approach by changing from continuous mathematical modeling to discrete parameter storage. The LUT stores specific parameter pairs (raw signal, corrected concentration) that can be directly referenced, converting a complex continuous correction problem into a simpler discrete lookup operation that maintains precision
2Measurement precision
If vacuum state measurements are performed frequently to update correction data, then the accuracy of the interpolation function is maintained, but the measurement time and productivity decrease
Solution Approach 1:
The patent performs correction data calibration in advance during vacuum state measurements and stores the results in a lookup table. This preliminary action captures the detector's non-linear characteristics when known, allowing subsequent measurements to use this pre-prepared correction data without requiring frequent vacuum recalibrations, thus maintaining accuracy while improving productivity
Solution Approach 2:
The system uses the detector's own response characteristics captured during initial calibration to correct subsequent measurements. The lookup table stores the detector's self-characterized non-linear behavior, allowing the system to self-correct without external reference measurements or frequent vacuum state recalibrations
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Improves measurement accuracy by effectively correcting non-linear components in photodetector data without requiring a model function, reducing the need for frequent vacuum state measurements and maintaining high precision in gas component quantification.
Implementation Method 1
a resonator (cavity) configured to include high-reflectivity mirrors
Implementation Method 2
Cavity ring-down spectroscopy (CRDS) is known as one type of gas absorption spectroscopy
Implementation Method 3
the attenuation of the light leaking from the resonator after the light is blocked is measured by a photodetector
Implementation Method 4
the concentration of a target component contained in a gas with high sensitivity by lengthening the effective optical path length for light absorption by the gas
Data Source
AI summary
A data processing method includes a step of acquiring reference data, a step of acquiring target data, a step of generating an interpolation function based on the reference data, a step of acquiring corrected data by correcting the target data based on the interpolation function, and a step of quantifying a target component based on the corrected data. The step of generating the interpolation function includes a step of extracting a reference signal from a region where a signal intensity is equal to or less than a threshold in the reference data, a step of predicting the signal intensity of the reference data based on the reference signal and acquiring prediction data, and a step of deriving a correspondence relationship between the prediction data and the reference data as the interpolation function.