Convolutional Neural Network for Impedance Spectroscopy Analysis
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Solution Overview
Problem
Current impedance spectroscopy methods face challenges in relating impedance data characteristics to parameters of interest due to non-linear relationships, signal overlap, and difficulty in setting measurement parameters, leading to laborious data interpretation and redundant information.
Innovation Solution
The use of artificial neural networks (ANNs), specifically convolutional neural networks (CNNs), processes impedance spectroscopy data to classify and quantify constituents in materials by training on categorical, ordinal, or quantitative data sets, reducing error through iterative optimization and providing accurate predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional impedance spectroscopy methods are used to analyze material constituents, then measurement can be performed, but data interpretation is laborious and accuracy is reduced due to non-linear relationships and signal overlap
Solution Approach 1:
The patent introduces machine learning models as an intermediary between impedance spectroscopy measurements and constituent analysis. The ML models process the complex impedance data, automatically handling non-linear relationships and signal overlap, thereby improving measurement precision while reducing interpretation time.
Solution Approach 2:
The patent replaces manual data interpretation methods with automated machine learning algorithms. This substitution transforms the laborious manual analysis process into an automated computational system that rapidly processes impedance data with higher accuracy.
2Ease of manufacture
If manual data interpretation methods are used, then analysis can be performed, but it requires laborious trial and error and produces redundant information
Solution Approach 1:
The machine learning models perform self-service by automatically learning from training data and independently analyzing new impedance measurements without requiring manual trial and error. The models self-optimize through training, eliminating the need for laborious manual interpretation while reducing redundant information.
3Adaptability or versatility
If conventional analysis methods are used, then measurement parameters can be set, but it is difficult to determine optimal parameters a priori
Solution Approach 1:
The patent applies preliminary action by training machine learning models on diverse impedance data before actual analysis. This pre-training enables the models to automatically determine optimal measurement parameters and configurations for different materials, eliminating the need for difficult a priori parameter setting while maintaining measurement flexibility.
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
This approach enables accurate classification and quantification of material constituents with high accuracy, such as predicting soluble solids content in fruits with approximately 90% accuracy, and real-time analysis of beer constituents, improving product consistency and reducing laboratory testing needs.
Implementation Method 1
obtain a plurality of impedance spectroscopy measurements of a material under test over a predetermined frequency range
Data Source
AI summary
A method for analyzing constituents of a material under test is disclosed. The method includes obtaining a plurality of impedance spectroscopy measurements of the material under test over a predetermined frequency range. A data set is passed to a machine learning system, the machine learning system having been trained to return an output related to one or more constituents in the material under test based on impedance spectroscopy measurement data over the predetermined frequency range. An output of the machine learning system is received and outputted. Related systems, methods and devices are also disclosed.