Gas Detection Intelligence Training System Using Multi-Condition Sensor Arrays
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Solution Overview
Problem
Current gas detection systems face challenges in accurately identifying gas particles in various environmental conditions due to limitations in sensing modalities and conditions, leading to inefficiencies in training prediction models for gas detection.
Innovation Solution
A gas detection intelligence training system that mixes environmental gases with target gases under multiple sensing conditions, using a first and second sensor array to generate measurement data, and trains an ensemble prediction model using a processor to improve detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single dedicated sensor is used for gas detection, then the device complexity is reduced, but the detection accuracy and adaptability to various environmental conditions deteriorates
Solution Approach 1:
The system divides the gas detection function into multiple sensor arrays (first sensor array and second sensor array), each sensing under different conditions. This segmentation allows the system to achieve high detection accuracy across various environmental conditions while maintaining manageable complexity through modular sensor configuration
Solution Approach 2:
The patent introduces multiple sensing dimensions by employing sensor arrays that operate under different sensing conditions (first sensing condition and second sensing condition). This multi-dimensional approach enables the system to detect target gases more accurately by analyzing gas responses across multiple conditions rather than relying on a single sensor dimension
2Measurement precision
If gas detection is performed under multiple sensing conditions with multiple sensor arrays, then the gas detection accuracy and model training quality improve, but the device complexity and data processing requirements worsen
Solution Approach 1:
The sensing system is designed with multi-functionality, where the first sensor array and second sensor array can detect target gases under different sensing conditions. This universal sensing capability allows a single system to handle various detection scenarios and environmental conditions, improving accuracy without requiring separate dedicated sensors for each condition
Solution Approach 2:
The system creates multiple copies of sensing data by measuring the same target gas under different sensing conditions with multiple sensor arrays. These replicated measurements serve as comprehensive training data for the prediction model, enabling the model to learn patterns across different conditions and improve its generalization capability
3Adaptability or versatility
If environmental gases are mixed with target gases for training, then the adaptability to real-world conditions improves, but the difficulty of detecting and measuring target gases in mixed gas worsens
Solution Approach 1:
The system employs feedback mechanisms where sensor arrays measure target gases in mixed environmental gases under various conditions, and the results are used to train and refine prediction models. This feedback loop enables the system to learn how to distinguish target gases from environmental gases, progressively improving detection capability in complex mixtures
Solution Approach 2:
The patent utilizes parameter changes by varying sensing conditions (such as temperature, humidity, or sensor operating parameters) while measuring target gases in mixed environmental gases. These parameter variations create distinct measurement signatures that help the prediction model learn to identify target gases despite the presence of environmental gases, improving detection in realistic conditions
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
The system enhances gas detection accuracy by generating a large amount of training data through mixed gas sensing under various conditions, allowing for precise identification of target gases without relying on single dedicated sensors, thereby improving convenience and precision.
Implementation Method 1
generates a mixing gas based on the collected environmental gas and a target gas
Implementation Method 2
gas particles included in the air chemically stimulate olfactory receptors in his/her nose. Accordingly, olfactory nerves are excited and then deliver a signal to an olfactory center in a temporal lobe of a brain
Implementation Method 3
An olfactory sensor may measure the electrical resistance of a product resulting from a chemical reaction caused when gas particles are combined with a substance, such as a specific metal
Data Source
AI summary
Disclosed are a gas detection intelligence training system and an operating method thereof. The gas detection intelligence training system includes a mixing gas measuring device that collects an environmental gas from a surrounding environment, generates a mixing gas based on the collected environmental gas and a target gas, senses the mixing gas by using a first sensor array and a second sensor array under a first sensing condition and a second sensing condition, respectively, and generates measurement data based on the sensed results of the first sensor array and the second sensor array, and a detection intelligence training device including a processor that generates an ensemble prediction model based on the measurement data.