Deep learning-driven hybrid model for short-term load forecasting and smart grid information management.

Accurate power load forecasting is crucial for the sustainable operation of smart grids. However, the complexity and uncertainty of load, along with the large-scale and high-dimensional energy information, present challenges in handling intricate dynamic features and long-term dependencies. This paper proposes a computational approach to address these challenges in short-term power load forecasting and energy information management, with the goal of accurately predicting future load demand.

Research on obstacle avoidance optimization and path planning of autonomous vehicles based on attention mechanism combined with multimodal information decision-making thoughts of robots.

With the development of machine perception and multimodal information decision-making techniques, autonomous driving technology has become a crucial area of advancement in the transportation industry. The optimization of vehicle navigation, path planning, and obstacle avoidance tasks is of paramount importance. In this study, we explore the use of attention mechanisms in a end-to-end architecture for optimizing obstacle avoidance and path planning in autonomous driving vehicles.