Chemical Sensor Sample Identification via Frequency Domain Transformation
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Solution Overview
Problem
Existing methods for identifying samples using chemical sensors are limited by the need for specific sample supply methods, restricting adaptability and comparability of data, and often require fixed input conditions, which hinders universal applicability.
Innovation Solution
A novel analysis method that derives and utilizes a sensor function based on time-varying inputs and outputs from chemical sensors, allowing for sample identification regardless of supply method, through transformation of the sensor function into alternative bases and comparison with known samples, enabling flexible and universal sample analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sample supply method is fixed to ensure consistent sensor signal shapes, then identification accuracy is improved, but adaptability to different supply methods deteriorates
Solution Approach 1:
The patent applies dynamics by transforming the sensor function from time domain to frequency domain, allowing the system to adapt to different sample supply methods dynamically. The frequency domain representation captures essential characteristics regardless of temporal variations in supply methods, enabling both accurate identification and broad adaptability.
Solution Approach 2:
The patent changes the parameter basis from time to frequency through Fourier transformation. This parameter transformation allows the sensor function to represent sample characteristics in a basis that is invariant to temporal supply method variations, resolving the contradiction between maintaining identification accuracy and adapting to different supply methods.
2Adaptability or versatility
If theoretical model is constructed to extract common parameters, then adaptability to different supply methods is improved, but system complexity increases
Solution Approach 1:
The patent replaces complex theoretical modeling with a mathematical transformation approach. Instead of constructing physical or chemical theoretical models to extract parameters, the invention uses Fourier transformation to convert sensor functions to frequency domain, achieving adaptability through mathematical substitution rather than complex modeling.
Solution Approach 2:
The patent simplifies system complexity by changing from time-domain parameter extraction to frequency-domain transformation. This parameter basis change eliminates the need for complex differential equations and theoretical models, achieving adaptability through a more straightforward mathematical approach.
3Adaptability or versatility
If random input is used to contain various frequency components, then analysis flexibility is improved, but input control restriction increases
Solution Approach 1:
The patent inverts the conventional approach by not requiring random or controlled inputs to achieve frequency diversity. Instead, it transforms any input's sensor response to frequency domain, where the frequency components are extracted mathematically rather than being dependent on input randomness or control complexity.
Data Source
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AI summary
Provided is a novel analysis method that enables identification of a sample even when any sample is introduced during measurement carried out by using a chemical sensor. An input in which the amount of an unknown sample changes over time is provided to the chemical sensor, a response which is from the chemical sensor and which changes over time is measured, a sensor function (transmission function) of the chemical sensor with respect to the unknown sample is calculated on the basis of the input and the response, and the unknown sample is identified on the basis of the sensor function of the chemical sensor with respect to the unknown sample.