Deep learning innovations in South Korean maritime navigation: Enhancing vessel trajectories prediction with AIS data.

Predicting ship trajectories can effectively forecast navigation trends and enable the orderly management of ships, which holds immense significance for maritime traffic safety. This paper introduces a novel ship trajectory prediction method utilizing Convolutional Neural Network (CNN), Deep Neural Network (DNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). Our research comprises two main parts: the first involves preprocessing the large raw AIS dataset to extract features, and the second focuses on trajectory prediction.

Optimizing prediction accuracy in dynamic systems through neural network integration with Kalman and alpha-beta filters.

In the realm of dynamic system analysis, the Kalman filter and the alpha-beta filter are widely recognized for their tracking and prediction capabilities. However, their performance is often limited by static parameters that cannot adapt to changing conditions. Addressing this limitation, this paper introduces innovative neural network-based prediction models that enhance the adaptability and accuracy of these conventional filters.

Water-saving technologies affect the grain characteristics and recovery of fine-grain rice cultivars in semi-arid environment.

Growing rice with less water is direly needed due to declining water sources worldwide, but using methods that require less water inputs can have an impact on grain characteristics and recovery. A 2-year field study was conducted to evaluate the impact of conventionally sown flooded rice and low-water-input rice systems on the grain characteristics and recovery of fine rice. Three fine grain rice cultivars-Super Basmati, Basmati 2000, and Shaheen Basmati-were grown under conventional flooded transplanted rice (CFTR), alternate wetting and drying (AWD), and aerobic rice systems.