Neural network-enhanced contaminant measurements
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Solution Overview
Problem
Current contaminant measurement sensors face challenges with accuracy due to linear calibration models that fail to account for non-linear drifts and cross-sensitivities between contaminants, leading to measurement uncertainties and inaccuracies in real-world conditions.
Innovation Solution
A method utilizing a neural network-based system where a contaminant measurement sensor is connected to a remote server, allowing for non-linear calibration and real-time data enhancement, accounting for environmental factors and cross-sensitivities through a trained neural network model that adapts to the sensor's location and usage conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If linear calibration models are used for contaminant measurement sensors, then the calibration process is simple and fast, but measurement accuracy deteriorates due to non-linear drifts and cross-sensitivities
Solution Approach 1:
The patent transforms the calibration approach from linear parameter relationships to non-linear parameter relationships by implementing a neural network model. The neural network learns complex non-linear mappings between sensor readings and actual contaminant concentrations, accounting for non-linear drifts and cross-sensitivities between different contaminants. This resolves the contradiction by accepting increased computational complexity in exchange for significantly improved measurement accuracy.
Solution Approach 2:
The patent introduces a neural network model as an intermediary between the raw sensor measurements and the final contaminant concentration values. This intermediary layer processes the sensor data through multiple non-linear transformations, enabling the system to compensate for non-linear drifts and cross-sensitivities that direct linear calibration cannot handle, thereby improving measurement accuracy without requiring complex hardware modifications.
2Productivity
If laboratory calibration is performed, then initial measurement setup is complete, but measurement accuracy deteriorates over time due to sensor drift in field conditions
Solution Approach 1:
The patent implements a dynamic calibration system where the neural network model is continuously updated with field measurement data. Instead of static laboratory calibration, the system adapts to changing field conditions by learning from ongoing measurements, compensating for non-linear drifts that occur over time. This dynamic approach maintains high measurement accuracy throughout the sensor's operational life without requiring frequent manual recalibration.
Solution Approach 2:
The patent incorporates feedback mechanisms where measured contaminant concentrations are used to update and refine the neural network model. The system continuously compares sensor readings with expected values based on environmental conditions and other sensors' data, using this feedback to adjust calibration parameters and compensate for drift, thereby maintaining measurement accuracy over extended periods in field conditions.
3Device complexity
If linear calibration models are used, then cross-sensitivities between contaminants are ignored, but measurement accuracy deteriorates when multiple contaminants are present
Solution Approach 1:
The patent implements a universal calibration model using neural networks that can simultaneously handle multiple contaminants and their cross-sensitivities. The neural network model processes inputs from multiple sensors targeting different contaminants and learns the complex interrelationships between them, enabling accurate measurement of individual contaminants even when multiple are present. This multi-functional approach replaces multiple simple linear calibrations with a single robust model that accounts for all interactions.
Solution Approach 2:
The patent creates a composite calibration model that integrates data from multiple sensor types and multiple contaminants into a unified neural network framework. This composite approach combines information from various sensor readings and environmental parameters, allowing the system to distinguish between signals from different contaminants and compensate for cross-sensitivities, thereby achieving high measurement accuracy in complex multi-contaminant environments.
Data Source
AI summary
A method for enhancing measurements from a contaminant measurement sensor, the measurement sensor being connected to a specialized remote server including a neural network, the method including the following steps: creating a trained neural network model based on the neural network, and for each measurement performed by the measurement sensor: measuring at least one raw data item by means of the measurement sensor, sending the at least one raw data item to the specialized remote server, and enhancing the at least one raw data item by means of the trained neural network model, making it possible to obtain the measurement of the concentration of the contaminant.