Traditional algorithms and single predictive models often face challenges such as limited prediction accuracy and insufficient modeling capabilities for complex time-series data in fault prediction tasks. To address these issues, this paper proposes a combined prediction model based on an improved temporal convolutional network (TCN) and bidirectional long short-term memory (BiLSTM), referred to as the TCN-BiLSTM model. This model aims to enhance the reliability and accuracy of time-series fault prediction. It is designed to handle continuous processes but can also be applied to batch and hybrid processes due to its flexible architecture. First, preprocessed industrial operation data are fed into the model, and hyperparameter optimization is conducted using the Optuna framework to improve training efficiency and generalization capability. Then, the model employs an improved TCN layer and a BiLSTM layer for feature extraction and learning. The TCN layer incorporates batch normalization, an optimized activation function (Leaky ReLU), and a dropout mechanism to enhance its ability to capture multi-scale temporal features. The BiLSTM layer further leverages its bidirectional learning mechanism to model the long-term dependencies in the data, enabling effective predictions of complex fault patterns. Finally, the model outputs the prediction results after iterative optimization. To evaluate the performance of the proposed model, simulation experiments were conducted to compare the TCN-BiLSTM model with mainstream prediction methods such as CNN, RNN, BiLSTM, and A-BiLSTM. The experimental results indicate that the TCN-BiLSTM model outperforms the comparison models in terms of prediction accuracy during both the modeling and forecasting stages, providing a feasible solution for time-series fault prediction.

To address critical challenges in tiny object detection within remote sensing imagery, including resolution–semantic imbalance, inefficient feature fusion, and insufficient localization accuracy, this study proposes Hierarchical Feature Compensation You Only Look Once 11 (HFC-YOLO11), a lightweight detection model based on hierarchical feature compensation. Firstly, by reconstructing the feature pyramid architecture, we preserve the high-resolution P2 feature layer in shallow networks to enhance the fine-grained feature representation for tiny targets, while eliminating redundant P5 layers to reduce the computational complexity. In addition, a depth-aware differentiated module design strategy is proposed: GhostBottleneck modules are adopted in shallow layers to improve its feature reuse efficiency, while standard Bottleneck modules are maintained in deep layers to strengthen the semantic feature extraction. Furthermore, an Extended Intersection over Union loss function (EIoU) is developed, incorporating boundary alignment penalty terms and scale-adaptive weight mechanisms to optimize the sub-pixel-level localization accuracy. Experimental results on the AI-TOD and VisDrone2019 datasets demonstrate that the improved model achieves mAP50 improvements of 3.4% and 2.7%, respectively, compared to the baseline YOLO11s, while reducing its parameters by 27.4%. Ablation studies validate the balanced performance of the hierarchical feature compensation strategy in the preservation of resolution and computational efficiency. Visualization results confirm an enhanced robustness against complex background interference. HFC-YOLO11 exhibits superior accuracy and generalization capability in tiny object detection tasks, effectively meeting practical application requirements for tiny object recognition.

Wargames are essential simulators for various war scenarios. However, the increasing pace of warfare has rendered traditional wargame decision-making methods inadequate. To address this challenge, wargame-assisted decision-making methods that leverage artificial intelligence techniques, notably reinforcement learning, have emerged as a promising solution. The current wargame environment is beset by a large decision space and sparse rewards, presenting obstacles to optimizing decision-making methods. To overcome these hurdles, a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) based wargame decision-making method is presented. The Partially Observable Markov Decision Process (POMDP), joint action-value function, and the Gumbel-Softmax estimator are applied to optimize MADDPG in order to adapt to the wargame environment. Furthermore, a wargame decision-making method based on the improved MADDPG algorithm is proposed. Using supervised learning in the proposed approach, the training efficiency is improved and the space for manipulation before the reinforcement learning phase is reduced. In addition, a policy gradient estimator is incorporated to reduce the action space and to obtain the global optimal solution. Furthermore, an additional reward function is designed to address the sparse reward problem. The experimental results demonstrate that our proposed wargame decision-making method outperforms the pre-optimization algorithm and other algorithms based on the AC framework in the wargame environment. Our approach offers a promising solution to the challenging problem of decision-making in wargame scenarios, particularly given the increasing speed and complexity of modern warfare.

In recent years, autonomous driving technology has made remarkable progress in urban transportation and logistics, while its application in complex off-road environments has gradually become a research hotspot. Compared to traditional manned vehicles, unmanned vehicles demonstrate higher safety and flexibility in scenarios such as rapid transportation, emergency rescue, and environmental reconnaissance. However, current research on path planning is predominantly focused on structured environments, with limited attention given to unstructured off-road conditions. This paper proposes an improved A* algorithm tailored to address the challenges of path planning in complex off-road environments. First, a grid map incorporating multi-dimensional information is constructed by integrating elevation data, risk zones, and surface attributes, significantly enhancing environmental perception accuracy. At the algorithm level, the heuristic function and search strategy of the A* algorithm are optimized to improve its efficiency and path smoothness in complex terrains. Furthermore, the method supports the flexible planning of three types of paths—minimizing time, minimizing risk, or optimizing smoothness—based on specific task requirements. Simulation results demonstrate that the improved A* algorithm effectively adapts to dynamic off-road environments, providing intelligent and efficient path planning solutions for unmanned vehicles. The proposed method holds significant value for advancing the application of autonomous driving technology in complex environments.

Unmanned aerial vehicles (UAVs) have emerged as pivotal tools in emergency communication scenarios. In the aftermath of disasters, UAVs can be communication nodes to provide communication services for users in the area. In this paper, we establish a meticulously crafted virtual simulation environment and leverage advanced deep reinforcement learning algorithms to train UAVs agents. Notwithstanding, the development of reinforcement learning algorithms is beset with challenges such as sparse rewards and protracted training durations. To mitigate these issues, we devise an enhanced reward function aimed at bolstering training efficiency. Initially, we delineate a specific mountainous emergency communication scenario and integrate it with the particularized application of UAVs to undertake virtual simulations, constructing a realistic virtual environment. Furthermore, we introduce a supplementary shaped reward function tailored to alleviate the problem of sparse rewards. By refining the DQN algorithm and devising a reward structure grounded on potential functions, we observe marked improvements in the final evaluation metrics, substantiating the efficacy of our approach. The experimental outcomes underscore the prowess of our methodology in effectively curtailing training time while augmenting convergence rates. In summary, our work underscores the potential of leveraging sophisticated virtual environments and refined reinforcement learning techniques to optimize UAVs deployment in emergency communication contexts.

Unmanned aerial vehicle UAV has become an important tool for emergency communication. When disaster happens, the roads in the disaster area always are seriously damaged, and theground infrastructure communication facilities are damaged. Under emergency conditions, UAV can go into key areas as communication nodes to provide communication services for users in the area.But it's difficult for UAV to cover the entire disaster area and the efficient rescue time will be delayed. Therefore, how to make the UAV accurately perceive the regional situation and deployed in the right position is worth studying. In this paper, a virtual simulation environment is established reasonably and deep reinforcement learning algorithm is used to train UAV agents. At the same time, a series of problems such as sparse reward and long training time are still not solved in the development of reinforcement learning algorithms,and the reward function is designed to improve training efficiency. First of all, we set a specific mountain emergency communication scenario, and combined with the specific application of the UAV to carry out virtual simulation, and build a virtual environment. Furthermore, an additional shaped reward function is designed to address the sparse reward problem. Through the improvement of deep Q-learning network DQN algorithm and the reward design based on potential function, the final evaluation index is improved, and the effectiveness of the algorithm is verified. The experimental results demonstrate our work can effectively shorten the training time and increase convergencerate.