In the field of satellite imagery, remote sensing image captioning (RSIC) is a hot topic with the challenge of overfitting and difficulty of image and text alignment. To address these issues, this paper proposes a vision-language aligning paradigm for RSIC to jointly represent vision and language. First, a new RSIC dataset DIOR-Captions is built for augmenting object detection in optical remote (DIOR) sensing images dataset with manually annotated Chinese and English contents. Second, a Vision-Language aligning model with Cross-modal Attention (VLCA) is presented to generate accurate and abundant bilingual descriptions for remote sensing images. Third, a cross-modal learning network is introduced to address the problem of visual-lingual alignment. Notably, VLCA is also applied to end-to-end Chinese captions generation by using the pre-training language model of Chinese. The experiments are carried out with various baselines to validate VLCA on the proposed dataset. The results demonstrate that the proposed algorithm is more descriptive and informative than existing algorithms in producing captions.

A distributed combat system consists of various types of combat units interconnected through the network, which can be abstracted as a complex heterogeneous combat network (HCN). It is of great military value to study the disintegration of the operational capability of HCNs for operational mission planning and improving the survivability of operational networks. Heterogeneous networks can effectively fuse more information than homogeneous networks can. The function implementation of heterogeneous networks is also more complex. Finding the key nodes and obtaining an efficient attack sequence is our focus. Most current research on network disintegration has difficulty achieving efficiency and timeliness. In this article, we specifically propose searching heterogeneous combat network attack sequences through network embedding and reinforcement learning (SHATTER) model, which uses the induction algorithm combined with the graph neural network and reinforcement learning to solve the network disintegration sequence problem. Through experiments with synthetic heterogeneous network datasets and real heterogeneous networks, we determined that SHATTER significantly outperformed existing technologies.

The Hypersonic Glide Vehicle (HGV) has become a focal point in military competitions among nations. Predicting the real-time trajectory of an HGV is of significant importance for aerospace defense interception and assessing enemy combat intentions. Existing prediction methods are limited by the need for large data samples and poor general applicability. To address these challenges, this paper presents a novel trajectory forecasting approach based on the Sparse Association Structure Model (SASM). The SASM can uncover the relationship among known data, transfer associative relationships to unknown data, and improve the generalization of the model. Firstly, a trajectory database is established for different maneuvering modes based on the six-degree-of-freedom motion equations and models of attack and bank angles of the HGV. Subsequently, three trajectory parameters are selected as prediction variables according to the maneuvering characteristics of the HGV. A parameters prediction model based on the SASM is then constructed to predict trajectory parameters. The SASM model demonstrates high accuracy and generalization in forecasting the trajectories of three different HGV types. Experimental results show a 50.35% reduction in prediction error and a 48.7% decrease in average processing time compared to the LSTM model, highlighting the effectiveness of the proposed method for real-time trajectory forecasting.

This study addressed a problem of rapid velocity consensus within a swarm of unmanned aerial vehicles. Our analytical framework was based on tools using matrix theory and algebraic graph theory. We established connections between algebraic connectivity and the speed of converging on a velocity. The relationship between algebraic connectivity and communication cost was established. To deal with the trade-off among algebraic connectivity, convergence speed and communication cost, we propose a distributed small world network construction method. The small world network characteristics expedite the convergence speed toward consensus in the unmanned aerial vehicle swarm. Eventually, our method greatly sped up the consensus velocities in the unmanned aerial vehicle swarms at a lower communication cost than other methods required.

The Monte Carlo Tree Search (MCTS) has demonstrated excellent performance in solving many planning problems. However, the state space and the branching factors are huge, and the planning horizon is long in many practical applications, especially in the adversarial environment. It is computationally expensive to cover a sufficient number of rewarded states that are far away from the root in the flat non-hierarchical MCTS. Therefore, the flat non-hierarchical MCTS is inefficient for dealing with planning problems with a long planning horizon, huge state space, and branching factors. In this work, we propose a novel hierarchical MCTS-based online planning method named the HMCTS-OP to tackle this issue. The HMCTS-OP integrates the MAXQ-based task hierarchies and the hierarchical MCTS algorithms into the online planning framework. Specifically, the MAXQ-based task hierarchies reduce the search space and guide the search process. Therefore, the computational complexity is significantly reduced. Moreover, the reduction in the computational complexity enables the MCTS to perform a deeper search to find better action in a limited time. We evaluate the performance of the HMCTS-OP in the domain of online planning in the asymmetric adversarial environment. The experiment results show that the HMCTS-OP outperforms other online planning methods in this domain.

Distributed heterogeneous multiagent systems (DHMASs) link geographically dispersed agents through networks, harnessing information technology to foster collaboration. Considering the mainstream status of wireless communication in modern multiagent systems and the differences in the performance of interagent communication devices, we believe that it is appropriate to use directed heterogeneous networks (DHNs) to model distributed heterogeneous multiagent systems. This model not only reflects the directionality of interagent communication but also reflects the complexity of communication due to performance differences, thus providing a more accurate framework for understanding and optimizing system behavior. The study of disintegration in DHNs is vital for enhancing the decision-making agility of DHMAS. We introduce Directed heterogeneous network Attack sequence inference through graph attention MAtrix Generation Embedding and reinforcement learning (DAMAGE), an algorithm that integrates graph neural networks and reinforcement learning within an inductive reasoning framework. DAMAGE is designed to optimize the generation of disintegration strategies, improving the efficiency of network breakdown processes. Our approach includes a directed network embedding technique with a graph attention matrix generation module, which enhances the utilization of imperfect network structure information. Through ablation studies, we demonstrate that DAMAGE not only increases the effectiveness of network disintegration under perfect topological conditions but also maintains robustness in scenario with imperfect topological information.