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.

A novel gas recognition and concentration detection algorithm consisting of a dynamic wavelet convolutional neural network (DWCNN) and a many-to-many long short-term memory-recurrent neural network (LSTM-RNN), respectively, is proposed as a replacement for the traditional data processing algorithm in artificial olfaction. The proposed DWCNN gas recognition algorithm does not require gas signal preprocessing, and it directly converts the raw time-domain gas signal data to 64 * 64 2-D gray images as the input layer of a convolutional neural network (CNN). The experiments show that the recognition accuracy of CO, H ₂ , and the gas mixture of CO and H ₂ is nearly 100%, and the many-to-many LSTM-RNN algorithm requires only a few labeled data from the steady-state values of the gas sensor array signals. In addition, comparisons with other neural network multilayer perceptrons (MLPs), gated recurrent unit (GRU) algorithms, and conventional algorithms, such as the Bayesian ridge, support vector machines (SVMs), decision tree, k-nearest neighbor (KNN), random forest, AdaBoost, gradient-boosting decision tree (GBDT), and bagging, revealed that the algorithm can obtain a higher concentration detection accuracy, which was evaluated using two different kernel functions for the kernel principal component analysis (KPCA) dimensionality reduction: polynomial and rbf. The experimental results demonstrated that the proposed many-to-many LSTM gas concentration detection model outperformed the abovementioned algorithms and can more accurately estimate the concentration of different gases while using less labeled data.

With the rapid development of artificial intelligence, the application of this new technology to music generation has attracted more attention and achieved gratifying results. This study proposes a method for combining the transformer deep-learning model with generative adversarial networks (GANs) to explore a more competitive music generation algorithm. The idea of text generation in natural language processing (NLP) was used for reference, and a unique loss function was designed for the model. The training process solves the problem of a nondifferentiable gradient in generating music. Compared with the problem that LSTM cannot deal with long sequence music, the model based on transformer and GANs can extract the relationship in the notes of long sequence music samples and learn the rules of music composition well. At the same time, the optimized transformer and GANs model has obvious advantages in the complexity of the system and the accuracy of generating notes.