Abstract
Traditional algorithms cannot readily address the fact that artificial olfaction in a dynamic ambient environment requires continuous selection and execution of the optimal algorithm to detect different gases. This paper presents a deep learning WCCNN-BiLSTM-many-to-many GRU (wavelet coefficient convolutional neural network–bidirectional long short-term memory–many-to-many-gated recurrent unit) model for qualitative and quantitative artificial olfaction of gas based on the automatic extraction of time-frequency domain dynamic features and time domain steady-state features. The model consists of two submodels. One submodel recognizes a gas by the WCCNN-BiLSTM model, and the experiments based on actual data from our fabricated artificial olfactory system demonstrate that the gas recognition accuracy is nearly 100%. The other submodel quantifies the gas by the many-to-many GRU model with less labeled data; this submodel is comparable to conventional algorithms such as DT (decision tree), SVMs (support vector machines), KNN (k-nearest neighbor), RF (random forest), AdaBoost, GBDT (gradient-boosting decision tree), bagging, and ET (extra tree) according to PCA (principal component analysis) dimensionality reduction. The experimental results of 10-fold cross-validations show that the proposed many-to-many GRU outperforms the aforementioned conventional algorithms with remarkable metrics and can maintain higher concentration estimation accuracy for different unknown gases with less labeled data.
Published Version
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