Sensor Tuning for IoT Electronic Noses Using Gradient Boosting
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Solution Overview
Problem
Current electronic noses lack selectivity for different applications, leading to reduced accuracy in identifying odors and VOCs across various environments and uses.
Innovation Solution
A system and method for tuning a gas sensor array by using a processor with algorithms to rank and assign importance scores to sensors based on extracted features, selecting sensors with scores above a threshold value for specific applications, utilizing gradient boosting decision trees for feature ranking and importance calculation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a full sensor array is used for all applications, then the electronic nose can detect a broad range of VOCs, but the selectivity and accuracy for specific applications deteriorates
Solution Approach 1:
The patent segments the full sensor array into application-specific subsets by ranking sensors based on their importance scores for different target applications. This allows the system to select only the most relevant sensors for each specific use case, improving selectivity while maintaining the option for broad detection when needed.
Solution Approach 2:
The system dynamically adjusts the sensor array configuration by changing which sensors are active based on the target application. The processor selects different sensor subsets for different applications, making the system adaptable and selective rather than static and generic.
2Reliability
If all sensors in the array are used, then comprehensive VOC detection is achieved, but the complexity of data processing and pattern recognition increases
Solution Approach 1:
The patent extracts and removes unnecessary sensors from the data processing pipeline by ranking sensors according to their importance for the specific application. Only the top-ranked sensors are included in the pattern recognition process, reducing computational complexity while maintaining detection reliability.
Solution Approach 2:
Instead of processing data from all sensors, the system processes only the essential subset of sensors that provide the most information for the target application. This partial action approach reduces computational burden while maintaining sufficient detection capability.
3Ease of operation
If a generic sensor array configuration is used, then the system is simple to operate, but the accuracy for specific target applications is reduced
Solution Approach 1:
The system performs self-configuration by automatically ranking and selecting the appropriate sensors for each target application based on pre-calculated importance scores. This eliminates the need for manual sensor selection while achieving application-specific optimization, maintaining ease of operation without sacrificing accuracy.
Solution Approach 2:
The system changes the operational parameters by adjusting which sensors are active based on the target application. The processor modifies the sensor array configuration dynamically, allowing the same physical hardware to be optimized for different applications without requiring manual reconfiguration.
Data Source
AI summary
Provided is a system and method for tuning an array of sensors to enable selection of the most suitable sensors for a target application. After extracting features from sensor raw data, the extracted features are ranked with gradient boosting decision trees to assign an importance value to each extracted feature. A threshold value for the entire set of extracted features is calculated and an importance score is calculated for the individual sensors of the array. Individual sensors with an importance score on or above the threshold value are selected for the target application.