Learning representation of the enclosing subgraph of node pairs is recognized as an efficient approach for link-oriented prediction tasks in network applications. The core challenge within this subgraph encoding approach is how to effectively distinguish and then properly aggregate the contribution of nodes in the subgraph into a single vector to indicate the relation between the target node pair. In this work, we propose a novel sphere-based subgraph encoding architecture, namely BS-SubGNN, to address the challenge. In detail, we design two key building blocks, including Bicentric Sphere Node Labeling (BSNL) and Bicentric Sphere Subgraph Pooling (BSSP) to assist message passing in BS-SubGNN. BSNL endows each node a label according to the sphere it belongs to in the subgraph to distinguish the contribution of nodes, while BSSP adopts an attention mechanism to aggregate the contribution of nodes in each sphere. Theoretically, we prove that BS-SubGNN can unify existing node distance labeling methods, and yield discriminative node features with less time complexity. We evaluate the performance of BS-SubGNN in link prediction tasks over a variety of network types, including undirected networks, attribute networks, directed networks, and signed directed networks. Our experimental results demonstrate that BS-SubGNN consistently achieves significant performance improvements over the above diverse types of networks. In particular, compared to those methods with a requisite of multi-hop neighborhood information, BS-SubGNN can obtain better performance even when only one-hop neighborhood information of the node pair is utilized.

Mixed platoons, consisting of connected autonomous vehicles (CAVs) and human-driven vehicles (HDVs), are expected to dominate future roadways. However, achieving substantial improvements in fuel economy and safety under time-varying, mixed traffic conditions in real-world scenarios remains challenging. To this aim, this paper proposes a deep reinforcement learning (DRL) based decentralized control strategy, which consists of two levels: 1) At the methodology level, the hierarchical platoon formation algorithm systematically organizes the mixed platoons into local platoons with uniform car-following patterns and sub-platoons within a generic HDV-CAVs structure, enabling adaptation to time-varying traffic volume and mixed traffic heterogeneity. 2) At the operation level, the HDV-CAVs unit is embedded in the learning environment to capture uncertain HDV behaviors through state and reward propagation channels. Multiple objectives are explicitly integrated into the reward function. A safety-supervised decentralized proximal policy optimization algorithm is developed to enhance safety and training efficiency. Extensive validations demonstrate the effectiveness of the proposed strategy in improving fuel economy and safety under different traffic demand levels and penetration rates.

In this article, we consider the problem of equilibrium selection in multiplayer finite potential games with large-size action sets. Traditional learning approaches often require players to traverse the entire action set to evaluate the utility of each action, which can be computationally intensive and inefficient. To overcome this limitation, we leverage the idea of neighborhood search into the game-theoretical learning process for the first time by generating neighborhood candidate action sets for exploration and evaluation. As such, we propose a lightweight log-linear dynamics for efficient equilibrium selection in finite potential games. Asymptotic convergence is proved under both asynchronous and independent revision rules with the help of resistance tree theory. Furthermore, through the multisatellite cooperative task allocation (MSCTA) problem, we elaborate on how to encode the players’ actions and how to generate the neighborhood structure. Simulation results demonstrate that the proposed method significantly outperforms the existing game-theoretic learning methods, notably in terms of solution time.

Despite of the fast development of deep learning networks, its inexplicability poses a low credibility challenge for fault diagnosis methods based on them. This article proposes an interpretable fault diagnosis network for robotic grinding equipment driven by the knowledge of grinding process. The network consists of a vibration imaging module, a grinding quality quantification module, an auxiliary learning module, and a fault diagnosis module. In addition, the developed synchronous algorithm is embedded in the network to update the weights and coefficient matrix combinations, which can reconstruct the vibration images from the wavelet domain to be recognized by convolution kernels more easily. Besides, through auxiliary learning, the grinding knowledge can be learned by the network to endow the results with interpretability. Finally, the comparative experiment shows that the proposed method has a maximum accuracy of 4.25%, 7.25%, 2.25%, 3.25%, and 1.25% higher than the five popular state-of-the-art fault diagnosis algorithms, respectively; the ablation experiment shows that the proposed algorithm has a maximum accuracy of 6.25%, 12.5%, and 16.25% higher than each sub-algorithm, respectively; by analyzing the learned weights of the network, it can be concluded that the proposed network has successfully learned the potential features of grinding knowledge with satisfactory performance.Note to Practitioners—In the autonomous robotic grinding system, the reliability of robotic grinding equipment is a prerequisite for ensuring high-quality grinding of thin-walled parts for large equipment such as aircraft, high-speed rail, and ships. In robotic grinding manufacturing, the information of grinding quality is the most direct feedback of valuable information from the manufacturing system. Based on the causal relationship between the fault status of grinding equipment and the grinding quality of workpiece, this article develops an interpretable fault diagnosis network incorporated with the grinding knowledge, which treats grinding quality information as physical knowledge and provides credibility to the fault diagnosis results. Therefore, this article improves the interpretability of the fault diagnosis model in practical applications through the credibility of data and grinding knowledge, which improves the applicable potential of fault diagnosis methods based on deep learning in industry.

In mixed traffic of human-driven vehicles (HDVs) and connected and automated vehicles (CAVs), it is essential to predict the driving intention of HDVs to avoid potential risks. Data quality is crucial to intention prediction under a cooperative vehicle-infrastructure system, whereas the data collection of HDVs relies on the vehicle-infrastructure cooperative perception, which is inevitably exposed to perception errors. In this paper, a robust driving intention prediction framework based on multi-stage learning is proposed in mixed traffic under the vehicle-infrastructure cooperative perception situation. To address this issue, different information sources from vehicles and traffic are considered to derive the implied vehicle dynamic interaction relation and traffic flow context. A feature extraction module is developed to respectively capture the local and global features based on convolutional neural network (CNN) for reducing the impact of noise, which ensures the prominent detailed and overall descriptions of driving intention can be comprehensively acquired. Then, the deep multi-scale technique and multi-layer perceptron network are introduced to further extract deep features, and improve the model adaptability by complementary feature learning mechanism and nonlinear mapping ability. Experiment results on a real-world dataset confirm the effectiveness of our proposal in reducing the impact of poor data quality and accurately predicting driving intention.

Highway on-ramps are typical bottlenecks for the deployment of connected and automated vehicles (CAVs). However, few studies have considered time-varying traffic volume and dynamic uncertainties in real-world scenarios. This paper proposes an adaptive robust formation control strategy (ARFC) for CAVs to address these challenges. The ARFC consists of two stages: 1) In the tactical stage, the constraint-oriented platoon formation algorithm classifies approaching CAVs into multiple local virtual platoons (LVP), assigns the collision-free merging sequences and driving modes, thereby enabling the strategy to adapt to time-varying traffic volume. Unified spatial-dependent constraints are formulated to construct this geometry topology, where the spatial dependence ensures collision avoidance in the merging conflict zone. 2) In the operational stage, an adaptive robust control scheme is designed based on the Lyapunov min-max approach, rendering uniform boundedness, uniform ultimate boundedness performance of tracking errors, and string stability of LVP, regardless of time-varying uncertainties. Comprehensive validations demonstrate that the proposed strategy performs effectively under different traffic demands, and improves ride comfort and fuel economy compared to baseline methods.

Although multisource sensor (MS) signal-based mechanical fault diagnosis (MFD) can significantly improve the diagnostic performance, the existing methods often lack sufficient adaptability and generalization when retraining on single-sensor signals or inferring from partial sensor signals. Thus, a general two-stage signal representation contrastive learning fault diagnosis framework (T-SCF) is proposed to adapt the trained model to varying numbers of sensor signals. This framework enhances model robustness and data fusion by comparing sensor signal views, offering a new approach for information fusion, fault detection, and classification in MFD. In the first stage, an adaptive contrastive algorithm is proposed to generate contrastive samples (C-Ss) and contrastive labels (C-Ls) for MS signals. Then, a supervised contrastive loss (SCL) is designed to minimize the similarity between different fault MS signals while maximizing the similarity between identical ones. By designing a parallel encoder architecture, SCL enables it to merge contrasting the features of different sensor signals during training. This strategy preserves the time-domain dimension properties of different sensors during the training of the second-stage classifier, thereby improving the adaptability of the model to different sensor signals without affecting the global information. The effectiveness of the method was verified from multiple different evaluation dimensions using two public datasets and one self-built dataset.

This paper concerns the robust guaranteed synchronization of chaotic systems subject to unknown but amplitude-bounded parameter perturbation, process disturbance, and measurement noise. The incremental quadratic constraints are adopted to provide a unified description of chaotic systems, based on which a circle criterion-based observer with$\ell _{\infty }$performance is design to attenuate the impacts of unknown uncertainties on synchronization error. Based on the designed observer, a zonotope-based guaranteed synchronization algorithm is proposed via mean value extension and zonotope inclusion techniques, where all the uncertainties from parameter perturbation, process disturbance, measurement noise, and chaotic behavior are taken into consideration. Finally, simulation results on the hyper-chaotic Chen system are presented to demonstrate the effectiveness of the proposed method.