This paper proposes a new method of mixed gas identification based on a convolutional neural network for time series classification. In view of the superiority of convolutional neural networks in the field of computer vision, we applied the concept to the classification of five mixed gas time series data collected by an array of eight MOX gas sensors. Existing convolutional neural networks are mostly used for processing visual data, and are rarely used in gas data classification and have great limitations. Therefore, the idea of mapping time series data into an analogous-image matrix data is proposed. Then, five kinds of convolutional neural networks—VGG-16, VGG-19, ResNet18, ResNet34 and ResNet50—were used to classify and compare five kinds of mixed gases. By adjusting the parameters of the convolutional neural networks, the final gas recognition rate is 96.67%. The experimental results show that the method can classify the gas data quickly and effectively, and effectively combine the gas time series data with classical convolutional neural networks, which provides a new idea for the identification of mixed gases.

Drift is an important issue that impairs the reliability of sensors, especially in gas sensors. The conventional method usually adopts the reference gas to compensate for the drift. However, its classification accuracy is not high. We propose a supervised learning algorithm that is based on multi-classifier integration for drift compensation in this paper, which incorporates drift compensation into the classification process, motivated by the fact that the goal of drift compensation is to improve the classification performance. In our method, with the obtained characteristics of sensors and the advantage of Support Vector Machine (SVM) in few-shot classification, the improved Long Shot Term Memory (LSTM) is integrated to build the multi-class classifier model. We tested the proposed approach on the publicly available time series dataset that was collected over three years by the metal-oxide gas sensors. The results clearly indicate the superiority of multiple classifier approach, which achieves higher classification accuracy as compared with different approaches during testing period with an ensemble of classifiers in the presence of sensor drift over time.

Dysarthric speech recognition helps speakers with dysarthria to enjoy better communication. However, collecting dysarthric speech is difficult. The machine learning models cannot be trained sufficiently using dysarthric speech. To further improve the accuracy of dysarthric speech recognition, we proposed a Multi-stage AV-HuBERT (MAV-HuBERT) framework by fusing the visual information and acoustic information of the dysarthric speech. During the first stage, we proposed to use convolutional neural networks model to encode the motor information by incorporating all facial speech function areas. This operation is different from the traditional approach solely based on the movement of lip in audio-visual fusion framework. During the second stage, we proposed to use the AV-HuBERT framework to pre-train the recognition architecture of fusing audio and visual information of the dysarthric speech. The knowledge gained by the pre-trained model is applied to address the overfitting problem of the model. The experiments based on UASpeech are designed to evaluate our proposed method. Compared with the results of the baseline method, the best word error rate (WER) of our proposed method was reduced by 13.5% on moderate dysarthric speech. In addition, for the mild dysarthric speech, our proposed method shows the best result that the WER of our proposed method arrives at 6.05%. Even for the extremely severe dysarthric speech, the WER of our proposed method achieves at 63.98%, which reduces by 2.72% and 4.02% compared with the WERs of wav2vec and HuBERT, respectively. The proposed method can effectively further reduce the WER of the dysarthric speech.

As one of the main pollution sources in China, haze can blur the railway monitoring and threaten railway safety. In this paper, we propose an end-to-end multi-scale residual network (MSRN) which can achieve remarkable dehazing effect on railway monitoring images. The method optimizes the image dehazing algorithm in three aspects: network structure, loss function and hazy dataset. Firstly, inspired by the residual network, the paper presents a method of fusing multi-scale feature information based on the residual network, which can extract more effective information at different scales. Secondly, a combined loss function is designed to achieve better convergent results by balancing training time, training calculations, and precision. Thirdly, the paper synthesizes an outdoor dataset specifically for railway scenarios, which relies on real depth maps and various outdoor images. Extensive experimental results on both full reference image quality assessment and no reference image quality assessment of image restoration demonstrate that the proposed algorithm shows higher performance than the state-of-the-art algorithms. Moreover, the haze of railway monitoring images is removed under hazy weather, and the detection algorithm achieved higher detection accuracy on the images after dehazing. The proposed network structure, loss function, and hazy dataset are discussed and analyzed in detail to verify the effectiveness of the proposed method.