Open-set gas recognition: A case-study based on an electronic nose dataset

• Published in: Sensors and actuators. B, Chemical Chemical, 2022-06, Vol.360, p.131652, Article 131652
• Main Authors: Qu, Cheng, Liu, Chuanjun, Gu, Yun, Chai, Shuiqin, Feng, Changhao, Chen, Bin
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Classification; Clustering; CNN; Datasets; Drift; Electronic nose; Electronic noses; Extreme values; Feature extraction; Gas recognition; Learning; Mathematical analysis; Model accuracy; Multilayer perceptrons; Neural networks; Open-set; Recognition; Sensor arrays; Sensors; Training
• ISSN: 0925-4005; 1873-3077
• DOI: 10.1016/j.snb.2022.131652

Electronic nose (E-Nose) has been widely used in detection and classification of gases. The learning models of traditional E-Noses are generally limited in closed-set environment: the training and test samples share the same label spaces. However, a more challenging and realistic scenario of E-Noses is open-set learning, where the test samples contains classes unseen during the model training. This study investigated the possibility of open-set learning models for the recognition and classification of gases based on a public electronic nose datasets. The dataset includes the response of a 72-channels MOS sensor array on 10 gaseous substances. The original data was preprocessed by two methods: one is to manually extract features from the response curve of each sample, and the other is to down-sample the original sample into a matrix. Then multilayer perceptron (MLP) and convolution neural network (CNN) were used to extract the feature vectors of the data processed by the two processing methods respectively. The performance of four different open-set recognition models, including softmax threshold (ST), OpenMax, extreme value machine (EVM) and class anchor clustering (CAC), was compared based on the feature vectors obtained from two neural networks. To understand the effect of sensor drift on the models, we also validated the models on a commonly used sensor drift dataset. The results demonstrated that for the open-set detection task, the CNN-based CAC (CAC-CNN) outperformed the other methods. For the closed-set recognition task, the CNN-based classification model achieved higher accuracy. On sensor drift dataset, the performance of open-set recognition models has decreased a lot, and it seems that drift has a large negative impact on the open-set gas recognition. •Open-set gas recognition method is proposed to classify the known and reject the unknown gases with high accuracy.•The adopted convolution neural network (CNN) can achieve better feature extraction and closed-set recognition.•The open-set recognition algorithm class anchor clustering (CAC) has the best open-set gas recognition performance.