A novel data-based decentralized control algorithm based on value iteration for nonlinear interconnected DC microgrids. The nonlinearity of the system is illustrated through the inclusion of constant power loads. The proposed algorithm addresses unknown nonlinear dynamics and ensures voltage stability by integrating advanced techniques from nonlinear system control. Specifically, it combines three key components: (1) adaptive dynamic programming for nonlinear optimal control, (2) a decentralized framework for interconnected systems, and (3) integral reinforcement learning to handle unknown dynamics. By employing function approximators, the decentralized algorithm reduces computational complexity, enabling rapid deployment. Simulations on a three-sub-system interconnected DC microgrid demonstrate the algorithm’s effectiveness. Using linear function approximation, the approach successfully derives a stabilizing control solution for the microgrid.

Recently, deep reinforcement learning has shown promising results for learning fast heuristics to solve routing problems. Meanwhile, most of the solvers suffer from generalizing to an unseen distribution or distributions with different scales. To address this issue, we propose a novel architecture, called Invariant Nested View Transformer (INViT), which is designed to enforce a nested design together with invariant views inside the encoders to promote the generalizability of the learned solver. It applies a modified policy gradient algorithm enhanced with data augmentations. We demonstrate that the proposed INViT achieves a dominant generalization performance on both TSP and CVRP problems with various distributions and different problem scales. Our source code and datasets are available in supplementary materials.

Object detection in aerial images is a critical and essential task in the fields of geoscience and remote sensing. Despite the popularity of computer vision methods in detecting objects, these methods have been faced with significant limitations of aerial images such as appearance occlusion and variable object sizes. In this letter, we explore the limitations of conventional neck networks in object detection by analyzing information bottlenecks. We propose an enhanced neck network to address the information deficiency issue in current neck networks. Our proposed neck network, which serves as a bridge between the backbone network and the head network, comprises a global semantic network (GSNet) and a feature fusion refinement module (FRM). The GSNet is designed to perceive contextual surroundings and propagate discriminative knowledge through a bidirectional global pattern. The FRM is developed to exploit different levels of features to capture comprehensive location information. We validate the efficacy and efficiency of our approach through experiments conducted on two challenging datasets, DOTA and HRSC2016. Our method outperforms existing approaches in terms of accuracy and complexity, demonstrating the superiority of our proposed method.