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Related Experiment Video

Updated: Jul 25, 2025

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization

Published on: August 27, 2021

Research on Improved Quantitative Identification Algorithm in Odor Source Searching Based on Gas Sensor Array.

Yanru Zhao¹, Dongsheng Wang¹, Xiaojie Huang¹

¹The College of Mechanical and Power Engineering, Henan Polytechnic University, Jiaozuo 454003, China.

|June 28, 2023

Summary

This summary is machine-generated.

This study introduces an improved quantitative identification algorithm for gas detection using a gas sensor array. The enhanced algorithm significantly improves odor source searching precision and speed.

Keywords:

gas sensor array neural network odor source searching quantitative identification

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Area of Science:

Chemical Sensing
Artificial Olfaction
Computational Chemistry

Background:

Gas detection systems require precise identification algorithms for accurate odor source searching.
Cross-sensitive properties of gas sensor arrays necessitate advanced algorithms for quantitative analysis.

Purpose of the Study:

To research and develop an improved quantitative identification algorithm for gas detection and odor source searching.
To enhance the precision and efficiency of gas detection systems using artificial olfactory principles.

Main Methods:

Development of a gas sensor array mimicking the artificial olfactory system.
Implementation of an improved Back Propagation algorithm combined with cuckoo and simulated annealing algorithms.
Design of a gas detection system using MATLAB for concentration analysis and prediction.

Main Results:

The improved algorithm achieved optimal solutions with 0% error on the Schaffer function.
The gas sensor array demonstrated effective detection of alcohol and methane concentrations.
A zigzag searching strategy with a 45° initial angle proved faster and more precise for locating high concentration points.

Conclusions:

The developed gas sensor array and improved algorithm significantly enhance gas detection precision and odor source searching efficiency.
The study validates the effectiveness of the proposed computational methods in real-world gas sensing applications.
The zigzag search strategy offers a superior approach for rapid and accurate identification of gas concentration peaks.