Gas sensor arrays (GSAs) usually encounter challenges due to the cross-contamination of mixed gases, leading to reduced accuracy in measuring gas mixtures. However, with the advent of artificial intelligence, there is a promising avenue for addressing this issue effectively. In pursuit of more accurate mixed gas measurements, we proposed a measurement model leveraging neural networks. Our approach involved employing the encoder of an autoencoder network (AEN) to extract features from experimental data, while fully connected layers were utilized for predicting concentrations of mixed gases. To refine the neural network parameters, we employed a variational autoencoder to generate additional data resembling the distribution of experimental data. Subsequently, we designed a domain difference maximum entropy technique to identify optimal concentration points for the calibration data. These calibration points were instrumental in training the fully connected layers, enhancing the model’s accuracy. During practical usage, with the AEN configuration fixed, the model can be fine-tuned by using a small subset of test points across large-scale GSA deployments. Simulation and practical measurement results demonstrated the efficacy of our proposed measurement model, boasting high accuracy, with confidence intervals for relative errors of the four gas measurements below 3% at the 95% confidence level. Besides, the calibration scheme reduced the number of test points compared with traditional methods, reducing the cost of labor and equipment.

中文翻译：

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一种新颖的气体传感器阵列标定方案，用于更精确的混合气体测量模型


由于混合气体的交叉污染，气体传感器阵列 （GSA） 通常会遇到挑战，从而导致气体混合物测量的准确性降低。然而，随着人工智能的出现，有一条很有前途的途径可以有效解决这个问题。为了追求更准确的混合气体测量，我们提出了一种利用神经网络的测量模型。我们的方法涉及使用自动编码器网络 （AEN） 的编码器从实验数据中提取特征，同时利用全连接层来预测混合气体的浓度。为了优化神经网络参数，我们采用了变分自动编码器来生成类似于实验数据分布的额外数据。随后，我们设计了一种域差最大熵技术来确定校准数据的最佳浓度点。这些校准点有助于训练全连接层，提高模型的准确性。在实际使用过程中，在固定 AEN 配置的情况下，可以通过在大规模 GSA 部署中使用一小部分测试点来微调模型。仿真和实际测量结果表明了我们提出的测量模型的有效性，具有很高的准确性，在 95% 置信水平下，四种气体测量的相对误差置信区间低于 3%。此外，与传统方法相比，校准方案减少了测试点的数量，降低了人工和设备成本。