Design And Optimization Of Gas Sensor Based On Machine Learning

Gas sensors are mainly used in Internet of Things systems.Since the environment of gas monitoring is complex and variable,and many factors such as temperature,humidity,air pressure,and cross-interference can lead to sensor drift,which in turn affects the performance of gas identification,it is worthwhile to study the optimization techniques of gas sensors.In recent years,machine learning has played an important role in the field of gas sensor optimization.Machine learning can deeply analyze and extract effective features from a large amount of response data,thus optimizing the detection performance of sensors.In this context,this paper investigates the application of machine learning in the optimization of gas sensors,focusing on gas recognition and drift compensation.The article firstly compares the performance of different pattern recognition algorithms in gas recognition.Response data of LC gas sensors are generated using the Monte Carlo simulation in ADS,and the resonant frequency and half-power points,which represent the steady-state and transient characteristics of the gas response respectively,are selected to construct the data set.Four common pattern recognition algorithms are used for gas identification experiments: support vector machine,K-nearest neighbor algorithm,random forest model,and self-organizing mapping network.The training process starts with normalization of the original data,followed by adjustment of the hyperparameters of the model to optimize the performance of the model.Comparing the classification performances of each model,the results show that the three features in the dataset can well reflect the gas response information,and support vector machine has the best recognition performance among the four models due to its strong nonlinear expression ability.Drift phenomenon severely restricts the application of gas sensors.In order to reduce the drift problems,which influence on gas sensor working performance,a drift compensation method based on adversarial domain adaption network is proposed.This model formalizes the drift problem as a problem of differences in sample data distribution caused by drift phenomenon.The model uses the Correlation Alignment as a measure of the domain difference,and introduces an adversarial learning strategy to reduce the distance and enhance inter-domain distribution consistency.The model is used to perform drift compensation experiments on a public dataset.The results show that under two experimental schemes,the compensation effect of the proposed model is the best compared to other drift compensation methods,with the highest average gas identification accuracy.Moreover,by comparing the data distribution before and after drifting,it also shows that the difference of data distribution between source and target domains is reduced,and this model can effectively compensate for gas sensors drift and improve the reliability of the gas sensor.