Accurate precipitation prediction is crucial for a range of sectors, including agriculture, water resource management, and disaster preparedness. Traditional meteorological models often struggle to capture the complex spatial and temporal patterns associated with precipitation events. To address this gap, this study introduces a groundbreaking approach that combines Transformer and Generative Adversarial Network (GAN) technologies. The objective is to downscale low-resolution (25km) precipitation data to a finer resolution (8km) specifically for the Beijing region in China. Our proposed model enhances the accuracy of precipitation forecasts by leveraging a hybrid architecture that combines the strengths of Transformers and Generative Adversarial Networks (GANs). The model is particularly effective in downscaling low-resolution meteorological data to high-resolution precipitation forecasts. Comparative analyses with existing models like CorrectorGAN and ResDeepD indicate a significant improvement in forecast accuracy, validating the efficacy of our novel approach.
With the development and wide application of advanced weapons, the battlefield environment has gradually become more complex and harsher, which greatly increases the risk of rescuers performing search and rescue tasks. In view of this situation, this paper proposes a six-wheeled portable reconfigurable robot, Antibot, which is used to replace rescuers to perform life detection tasks in complex and unknown environments. This robot can adapt to the terrain through its unique three-rocker-leg passive suspension, and can be folded up for users to carry. The terrain adaptability of the robot is analyzed through mathematical modelling and numerical simulations, and verified by outdoor experiments. This robot has the advantages of simple structure and strong terrain adaptability, which has broad application prospects in battlefield rescue missions.
Air pollution is a major environmental issue that affects human health and the environment. In recent years, deep learning has been applied to the prediction of air pollution expansion with promising results. This paper provides a comprehensive review of the recent literature on the application of deep learning related algorithms to the prediction of pollution expansion. The paper focuses on the use of deep learning models such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Hybrid models for air pollution forecasting. The literature review covers studies published between 2018 and 2023, and includes articles from various journals with high impact factors. The results of the reviewed studies show that deep learning models have outperformed traditional statistical models in terms of accuracy and robustness for air pollution forecasting. The paper concludes by highlighting the challenges and opportunities for further research in this area.
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