Updating singular value decomposition for modal analysis of slow-varying non-stationary vibration structures.

This paper proposes a novel method for the modal analysis of slow-varying vibration structures based on vector autoregressive models. The basic idea of this method consists of using a short-time sliding window (STSW) to identify modal parameters for non-stationary vibrations. This method uses the recursive least-squares estimation for multivariable systems with the singular value decomposition (SVD) method to find the solutions within a segment of the data from each time window.

Accurate discharge and water level forecasting using ensemble learning with genetic algorithm and singular spectrum analysis-based denoising.

Forecasting discharge (Q) and water level (H) are essential factors in hydrological research and flood prediction. In recent years, deep learning has emerged as a viable technique for capturing the non-linear relationship of historical data to generate highly accurate prediction results. Despite the success in various domains, applying deep learning in Q and H prediction is hampered by three critical issues: a shortage of training data, the occurrence of noise in the collected data, and the difficulty in adjusting the model's hyper-parameters.