Machine Olfaction Gas Detection Using SLDA and Markov Discriminant
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Solution Overview
Problem
Current methods for detecting and identifying toxic and harmful gases, such as PH test paper and gas chromatographs, lack a specific and real-time detection process, failing to effectively identify gases in industrial settings.
Innovation Solution
A method utilizing a machine olfactory system with Selected Linear Discriminate Analysis (SLDA) and a two-dimensional distance discriminant method to analyze gas samples, constructing an odor information base for identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional detection methods (PH test paper, gas chromatographs) are used, then gas detection is possible, but real-time detection and identification capability is lacking
Solution Approach 1:
The patent replaces traditional mechanical/chemical detection methods (PH test paper, gas chromatographs) with an electronic sensor array system. Multiple gas sensors detect gas components simultaneously, and electronic signal processing enables real-time identification, eliminating the time-consuming sequential analysis of traditional methods while maintaining detection accuracy.
Solution Approach 2:
The patent segments the gas detection task into multiple parallel detection channels using an array of different gas sensors. Each sensor targets specific gas components, and their results are integrated through signal processing. This segmentation enables simultaneous detection of multiple gas types, achieving real-time identification capability.
2Adaptability or versatility
If a sensor array with multiple sensors is used, then gas detection capability is improved, but device complexity increases
Solution Approach 1:
The patent employs a universal signal processing system that handles outputs from multiple different gas sensors. The same data acquisition, feature extraction, and pattern recognition algorithms process signals from various sensor types, enabling the system to detect multiple gas kinds without requiring separate processing circuits for each sensor, thus controlling complexity.
Solution Approach 2:
The patent merges the detection functions of multiple gas sensors into a single integrated system. The sensor array, data acquisition module, and pattern recognition system work as a unified whole, combining their capabilities to achieve broad gas detection coverage while managing system complexity through integrated architecture.
3Productivity
If data processing complexity is reduced, then system simplicity is improved, but recognition efficiency may be affected
Solution Approach 1:
The patent performs preliminary feature extraction on sensor data before final gas identification. By extracting key features (such as peak positions, areas, and ratios) from raw sensor signals in advance, the system reduces the complexity of subsequent pattern recognition while maintaining high recognition efficiency. This preliminary processing simplifies the decision-making process without sacrificing accuracy.
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
Enables real-time detection and identification of toxic and harmful gases with high recognition efficiency and low complexity, suitable for industrial applications.
Implementation Method 1
A method for detecting and identifying toxic and harmful gases based on machine olfaction
Data Source
AI summary
Disclosed is a method for detecting and identifying toxic and harmful gases based on machine olfactory. Information about the toxic and harmful gases is firstly collected through the machine olfactory system and then analyzed through a Selected Linear Discriminate Analysis (SLDA) combined with a Markov two-dimensional distance discriminant method to identify various toxic and harmful gases. The algorithm disclosed in the invention extracts the characteristic information of the sample data, and then fast processes and identifies the information as a linear recognition algorithm does, having wide applications in the field of machine olfaction, especially in detecting and identifying the toxic and harmful gases in real-time based on machine olfaction. The algorithm involves low complexity and high recognition efficiency.