Abstract
Drift compensation is an important issue for metal oxide semiconductor (MOS) gas sensor arrays. General machine learning methods require constant calibration and a large amount of label gas data. At the same time, recalibration will cause a lot of costs, and label gas is difficult to obtain in practice. In this paper, a novel drift compensation method based on balanced distribution adaptation (BDA) is proposed. First, the BDA drift compensation method can adjust the conditional distribution and marginal distribution between the two domains through the weight balance factor, thereby more effectively reducing the mismatch between the two domains. When the BDA method performs classification tasks through machine learning, no labeled data is required in the target domain. Then, the particle swarm optimization algorithm is used to improve the accuracy of drift compensation. Individuals in the population are initialized randomly, and their fitness values are calculated. Iterative optimization of the population individuals is conducted until the optimal weight balance factor parameters are calculated. Finally, the BDA method is experimentally verified on the public gas sensor drift data set. Experimental results showed that the BDA method was significantly better than the existing joint distribution adaptation (JDA) method and other standard drift compensation methods such as K-Nearest Neighbor (KNN). In the two setting groups, the recognition accuracy was 4.54% and 1.62% ahead of the JDA method, and 12.23% and 15.83% ahead of the KNN method.
Highlights
With the advantages of small size, low cost, simple production, and high sensitivity to flammable and toxic gases, metal oxide semiconductor (MOS) sensor arrays play a vital role in the fields of environmental protection and monitoring [1,2], food and beverage production [3,4], clinical diagnosis, and process control [5,6]
When the mixed gas enters the gas chamber, the oxygen ions adsorbed on the surface of the MOS gas sensors will chemically react with them, which will cause the resistance of the MOS gas sensors to decrease sharply
The above adaptive correction methods applied in drift compensation of MOS gas sensor arrays has achieved certain effects
Summary
With the advantages of small size, low cost, simple production, and high sensitivity to flammable and toxic gases, metal oxide semiconductor (MOS) sensor arrays play a vital role in the fields of environmental protection and monitoring [1,2], food and beverage production [3,4], clinical diagnosis, and process control [5,6]. Zhang et al [14] proposed a domain-adaptive extreme learning machine (DAELM) drift compensation method based on semisupervised learning This method obtained a higher classification accuracy, and required more labeled drift samples to participate in the model construction. The above adaptive correction methods applied in drift compensation of MOS gas sensor arrays has achieved certain effects. The BDA method needs to be combined with a preprocessing device, gas measurement cell, air pump, solenoid valve, and thermostat control unit in the system. BDA and particle swarm optimization (PSO) method, a brief description of the combination tion of transfer learning, and MOS sensor array drift compensation are introduced.
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