With the scale of networked marine surface vehicles (NMSVs) increasing, position scheduling for achieving formation connectivity becomes more challenging, particularly in large and obstacle-laden areas. However, existing works usually assume the formation isconnectedinitially. Against this backdrop, we propose ak-connectedhierarchical topology self-organization control scheme, steering NMSVs to establish a fault-tolerant collision-free network. Ak-connectedhierarchical network construction (KHNC) is developed to regulate the priorities of members. Then, ak-connectedhierarchical topology formation algorithm (KHTFA) is presented to collision-free aggregates spatially dispersed NMSVs. Meanwhile, a hierarchy-aware performance indicator function is developed to sustaink-connectivityduring collaborative maneuvers. By these designs, a collision-free cooperative optimal control scheme is proposed to achieve two objectives: 1) self-organizedk-connectivityattainment in initially disconnected NMSVs and 2) collision-free formation maneuvers with sustained k-connectivity. The effectiveness of the proposed control scheme is confirmed by theoretical analysis and semi-physical simulation results.

Developing a reliable EEG-based Brain Computer Interface (BCI) system typically requires large and diverse training datasets, but collecting sufficient data remains challenging due to subject fatigue and inter-individual variability. To address these limitations, this study proposes a reinforcement learning-enhanced EEG diffusion (RLED) framework for adaptive data augmentation in endogenous EEG tasks, with a focus on motor imagery and emotion recognition. The framework integrates a reinforcement learning mechanism to dynamically regulate the diffusion training process and achieve a flexible balance among temporal, spectral, and class-related features. Experiments on four datasets demonstrate that the proposed method generates high-quality synthetic EEG signals and consistently improves classification performance. These findings show that the proposed RLED framework may serve as a promising tool for EEG data augmentation and generalization in practical BCI applications.

For continuing tasks, average cost Markov decision processes have well-documented value and can be solved using efficient algorithms. However, it explicitly assumes that the agent is risk-neutral. In this work, we extend risk-neutral algorithms to accommodate the more general class of dynamic risk measures. Specifically, we propose a relative value iteration (RVI) algorithm for planning and design two model-free Q-learning algorithms, namely a generic algorithm based on the multi-level Monte Carlo (MLMC) method, and an off-policy algorithm dedicated to utility-based shortfall risk measures. Both the RVI and MLMC-based Q-learning algorithms are proven to converge to optimality. Numerical experiments validate our analysis, confirm empirically the convergence of the off-policy algorithm, and demonstrate that our approach enables the identification of policies that are finely tuned to the intricate risk-awareness of the agent that they serve.

Growing demands for marine missions are driving the trend of maneuvering the large-scale fleet of electric marine surface vehicles (LSF-EMSVs) through obstacle-laden environments. Against this backdrop, this article investigates the collision-free distributed flocking control problem for LSF-EMSVs under the fully intermittent communication scenario. Existing collision-free works considering intermittent communication would like to assume that the transmission of relative position is continuous. In contrast, we discuss the case where the transmissions of all states are interrupted and use the term fully intermittent to distinguish it. To address this challenge, a weight-based adjacency matrix is developed to assess the communication connectivity and collision risk simultaneously. Accordingly, the collision avoidance analysis can be isolated from the dynamic layer, simplifying the control design. Then, a switching estimator based on intermittent relative positions is introduced for collision avoidance and communication maintaining, even on the period of communication interruption. Based on these designs, a completely distributed flocking control scheme is proposed for LSF-EMSVs, which is solely rely on the relative position and self course and velocities. Effectiveness of the proposed control schemes is demonstrated by theoretical analysis and semi-physical simulation results.