Tao Yang, Li Chunyan, Liang Zhifang, Yang Haocheng, Xu Juan
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
Sensors (Basel). 2019 Aug 26;19(17):3703. doi: 10.3390/s19173703.
Electronic nose (E-nose), a kind of instrument which combines with the gas sensor and the corresponding pattern recognition algorithm, is used to detect the type and concentration of gases. However, the sensor drift will occur in realistic application scenario of E-nose, which makes a variation of data distribution in feature space and causes a decrease in prediction accuracy. Therefore, studies on the drift compensation algorithms are receiving increasing attention in the field of the E-nose. In this paper, a novel method, namely Wasserstein Distance Learned Feature Representations (WDLFR), is put forward for drift compensation, which is based on the domain invariant feature representation learning. It regards a neural network as a domain discriminator to measure the empirical Wasserstein distance between the source domain (data without drift) and target domain (drift data). The WDLFR minimizes Wasserstein distance by optimizing the feature extractor in an adversarial manner. The Wasserstein distance for domain adaption has good gradient and generalization bound. Finally, the experiments are conducted on a real dataset of E-nose from the University of California, San Diego (UCSD). The experimental results demonstrate that the effectiveness of the proposed method outperforms all compared drift compensation methods, and the WDLFR succeeds in significantly reducing the sensor drift.
电子鼻是一种将气体传感器与相应模式识别算法相结合的仪器，用于检测气体的种类和浓度。然而，在电子鼻的实际应用场景中会出现传感器漂移，这会导致特征空间中数据分布的变化，并导致预测精度下降。因此，漂移补偿算法的研究在电子鼻领域受到越来越多的关注。本文提出了一种基于域不变特征表示学习的漂移补偿新方法，即瓦瑟斯坦距离学习特征表示（WDLFR）。它将神经网络视为域判别器，以测量源域（无漂移数据）和目标域（漂移数据）之间的经验瓦瑟斯坦距离。WDLFR通过以对抗方式优化特征提取器来最小化瓦瑟斯坦距离。用于域适应的瓦瑟斯坦距离具有良好的梯度和泛化界。最后，在来自加利福尼亚大学圣地亚哥分校（UCSD）的电子鼻真实数据集上进行了实验。实验结果表明，所提方法的有效性优于所有比较的漂移补偿方法，并且WDLFR成功地显著减少了传感器漂移。
