To solve the problem of data accuracy degradation of vehicle GNSS/INS integrated navigation systems when the GNSS signal is unavailable or there is a GNSS outage, this paper improves the existing GNSS/INS integration methodology for land vehicle navigation based on the AI method. First, a GNSS/INS integration methodology for land vehicle navigation based on position update architecture (PUA) using LightGBM regression for predicting the position of a vehicle during a GNSS outage is presented. It uses LightGBM to model the relationship between INS data and vehicle position changes. On-board INS and GNSS data are collected when the GNSS signal is available and are used to train the PUA-LightGBM model; in the event of a GNSS outage, INS data are used as the input to the PUA-LightGBM to predict the change in vehicle position. Second, a vehicle navigation data acquisition system was designed for model validation. This included a self-developed GNSS/INS integrated navigation system and a Novatel pwrpak7-e1 GNSS/INS integrated navigation system for data acquisition on six road segments. Finally, the collected data were used for machine learning training of the PUA-LightGBM model and the existing PUA-RandomForest model. As a result, the PUA-LightGBM predicts the vehicle position with less error in the event of a GNSS outage and takes less time to train. It was also demonstrated that by allowing the model to be dynamically trained or updated while the vehicle is moving the PUA-LightGBM could adapt perfectly to the predictions of vehicle position changes in different complex road segments.

In industrial scenarios, bearing fault diagnosis often suffers from data scarcity and class imbalance, which significantly hinders the generalization performance of data-driven models. While generative adversarial networks (GANs) have shown promise in data augmentation, their efficacy deteriorates in the presence of multi-category and structurally complex fault distributions. To address these challenges, this paper proposes a novel fault diagnosis framework based on a Multi-Domain Feature Transformer GAN (MDFT-GAN). Specifically, raw vibration signals are transformed into 2D RGB representations via joint time-domain, frequency-domain, and time–frequency-domain mappings, effectively encoding multi-perspective fault signatures. A Transformer-based feature extractor, integrated with Efficient Channel Attention (ECA), is embedded into both the generator and discriminator to capture global dependencies and channel-wise interactions, thereby enhancing the representation quality of synthetic samples. Furthermore, a gradient penalty (GP) term is introduced to stabilize adversarial training and suppress mode collapse. To improve classification performance, an Enhanced Hybrid Visual Transformer (EH-ViT) is constructed by coupling a lightweight convolutional stem with a ViT encoder, enabling robust and discriminative fault identification. Beyond performance metrics, this work also incorporates a Grad-CAM-based interpretability scheme to visualize hierarchical feature activation patterns within the discriminator, providing transparent insight into the model’s decision-making rationale across different fault types. Extensive experiments on the CWRU and Jiangnan University (JNU) bearing datasets validate that the proposed method achieves superior diagnostic accuracy, robustness under limited and imbalanced conditions, and enhanced interpretability compared to existing state-of-the-art approaches.

This study proposes a novel network modeling approach, called sliding window limited penetrable visibility graph (SLPVG), for transforming time series into networks. SLPVG takes into account the dynamic nature of time series, which is often affected by noise disturbances, and the fact that most nodes are not directly connected to distant nodes. By analyzing the degree distribution of different types of time series, SLPVG accurately captures the dynamic characteristics of time series with low computational complexity. In this study, the authors apply SLPVG for the first time to diagnose compensation capacitor faults in jointless track circuits. By combining the fault characteristics of compensation capacitors with network topological indicators, the authors find that the betweenness centrality reflects the fault status of the compensation capacitors clearly and accurately. Experimental results demonstrate that the proposed model achieves a high accuracy rate of 99.1% in identifying compensation capacitor faults. The SLPVG model provides a simple and efficient tool for studying the dynamics of long time series and offers a new perspective for diagnosing compensation capacitor faults in jointless track circuits. It holds practical significance in advancing related research fields.

Key nodes have a significant impact, both structural and functional, on complex networks. Commonly used methods for measuring the importance of nodes in complex networks are those using degree centrality, clustering coefficient, etc. Despite a wide range of application due to their simplicity, their limitations cannot be ignored. The methods based on degree centrality use only first-order relations of nodes, and the methods based on the clustering coefficient use the closeness of the neighbors of nodes while ignore the scale of numbers of neighbors. Local structural entropy, by replacing the node influence on networks with local structural influence, increases the identifying effect, but has a low accuracy in the case of high clustered networks. To identify key nodes in complex networks, a novel method, which considers both the influence and the closeness of neighbors and is based on local structural entropy and clustering coefficient, is proposed in this paper. The proposed method considers not only the information of the node itself, but also its neighbors. The simplicity and accuracy of measurement improve the significance of characterizing the reliability and destructiveness of large-scale networks. Demonstrations on constructed networks and real networks show that the proposed method outperforms other related approaches.