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The increasing discovery of underwater methane leakage underscores the importance of monitoring methane emissions for environmental protection. Underwater remote sensing of methane leakage is critical and meaningful to protect the environment. The construction of sensor arrays is recognized as the most effective technique to increase the accuracy and sensitivity of underwater remote sensing of methane leakage. With the aim of improving the reliability of underwater methane remote-sensing sensor arrays, in this work, a deep learning method, specifically an explainable sparse spatio-temporal transformer, is proposed for detecting the failures of the underwater methane remote-sensing sensor arrays. The data input into the explainable sparse block could decrease the time complexity and the computational complexity (O (n)). Spatio-temporal features are extracted on various time scales by a spatio-temporal block automatically. In order to implement the data-driven early warning system, the data-driven warning return mechanism contains a warning threshold that is associated with physically disturbing information. Results show that the explainable sparse spatio-temporal transformer improves the performance of the underwater methane remote-sensing sensor array. A balanced F score (F1 score) of the model is put forward, and the anomaly accuracy is 0.92, which is superior to other reconstructed models such as convolutional_autoencoder (CAE) (0.81) and long-short term memory_autoencoder (LSTM-AE) (0.66). [ABSTRACT FROM AUTHOR]

In exploring pattern recognition for electronic noses via deep neural networks, traditional networks encounter key challenges, such as low training efficiency, and neglect of spatial-temporal attributes of gas sensor response sequences. In this study, an unmanned gas-sensing test system is used to generate a large dataset to ensure robust model training. The Gramian angular field-Markov transition field is utilized to convert time sequences into images. Using advanced image processing tools, the images are then subsequently compressed with data augmentation. This method fully preserves temporal and spatial features within the sequences, thus enhancing model performance. Furthermore, the proposed multi-task learning (MTL) framework competently performs simultaneous classification and regression tasks. The primary component of the MTL network, majorly constituted of convolutional neural networks, emphasizes the spatial features of the sequences. The integration of a long short-term memory layer ensures the preservation of temporal feature analysis of the input data, thereby enhancing predictive performance. When images are compressed to only 3.9 % of the original data, substantial information can still be preserved. Subsequently, the model trained by such compressed images attains an accuracy of 95.31 % and an R 2 score of 0.9510 for classification and regression tasks, respectively. Our work reveals the remarkable potential of integrating temporal and spatial features in pattern recognition, promoting the potency of multi-tasking deep learning networks in electronic nose technology. • The GASF, MTF, GADF are used for transforming time series into images, retaining spatial-temporal traits of time series. • This study enhances training, prediction with efficient image compression, preserving vital spatial-temporal details. • A multi-task learning (MTL) model is introduced for gas analysis, predicting gas type and concentration simultaneously. • The LSTM is integrated into CNN layers, enhancing spatial analysis with temporal feature handling in the model. • A large-scale gas-sensing dataset is employed for thorough model training and validation of capabilities. [ABSTRACT FROM AUTHOR]

Recent years have witnessed the splendid performance of deep learning methods used in gas recognition for electronic noses (E-nose). In addition to effective feature extraction, the architecture of the deep neural network plays a vital role. Currently, most applied network structures are hand-crafted by human experts, which is time-consuming and problem-dependent, making it necessary to design the structures of neural networks according to specific demands. In this work, a genetic algorithm with particle swarm optimization (GA-PSO), which possesses promising optimization capabilities, is applied to search for effective deep convolutional neural networks (CNNs) for gas classification based on E-nose technology. A novel image transformation strategy using Gramian angular field and a hybrid cost-saving method is employed in the search process, enabling adaptive and efficient CNN search on gas datasets. With the proposed methods, we can achieve an average classification accuracy of over 90 % on two public gas datasets, while also significantly reducing the model size compared to state-of-the-art CNNs. By using these novel strategies, our approach surpasses random search and basic PSO algorithm in achieving the global optimal solution, higher and more stable accuracy, and faster convergence in pattern recognition using E-nose. Our work suggests that the proposed method can quickly identify excellent CNN structures for E-nose applications. • A new Gramian Angular Field method transforms 1D time series into 2D images. • Kernel decomposition and depthwise separable convolution optimize CNN training. • A fast and robust hybrid algorithm searches for gas-identification models. [ABSTRACT FROM AUTHOR]