Trajectory-user linking (TUL), whereby a trajectory is linked to its owner in location-based social networks, is a fundamental and critical task in spatio-temporal data mining. It plays a key role in personalized recommendation, anomaly detection, and semantic trajectory mining. Existing methods for TUL are either rule-based methods, which link trajectories and users based on conventional trajectory similarities, or learning-based methods, which learn a classification model to map trajectories to their owners. However, rule-based methods ignore the semantic information in the trajectory sequence, and learning-based methods require retraining the model each time a new user is added. In this paper, we propose a Siamese network-based model for trajectory-user linking (TULSN), which uses a Siamese network to capture semantic information in the trajectory, and instead of retraining the model, it requires only a few labeled trajectories per user to identify the user category of the trajectory. The experimental results show that the TULSN outperforms existing baselines and state-of-the-art methods on real-world datasets.

Timely and effective maintenance is imperative to minimize operational disruptions and ensure the reliability of marine vessels. However, given the low early warning rates and poor adaptability under complex conditions of previous data-driven fault prediction methods, this paper presents a hybrid deep learning model based on multi-layer attention mechanisms for predicting faults in a marine diesel engine. Specifically, this hybrid model first introduces a Convolutional Neural Network (CNN) and self-attention to extract local features from multi-feature input sequences. Then, we utilize Long Short-Term Memory (LSTM) and multi-head attention to capture global correlations across time steps. Finally, the hybrid deep learning model is integrated with the Exponential Weighted Moving Average (EWMA) to monitor the operational status and predict potential faults in the marine diesel engine. We conducted extensive evaluations using real datasets under three operating conditions. The experimental results indicate that the proposed method outperforms the current state-of-the-art methods. Moreover, ablation studies and visualizations highlight the importance of fusing multi-layer attention, and the results under various operating conditions and application scenarios demonstrate that this method possesses predictive accuracy and broad applicability. Hence, this approach can provide decision support for condition monitoring and predictive maintenance of marine mechanical systems.