Meilin Wu^1,2, Lianggui Tang^1,2,*, Qingda Zhang^1,2, Ke Yan^1,2

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 179-198, 2023, DOI:10.32604/iasc.2023.036684 - 29 April 2023

Abstract As COVID-19 poses a major threat to people’s health and economy, there is an urgent need for forecasting methodologies that can anticipate its trajectory efficiently. In non-stationary time series forecasting jobs, there is frequently a hysteresis in the anticipated values relative to the real values. The multilayer deep-time convolutional network and a feature fusion network are combined in this paper’s proposal of an enhanced Multilayer Deep Time Convolutional Neural Network (MDTCNet) for COVID-19 prediction to address this problem. In particular, it is possible to record the deep features and temporal dependencies in uncertain time series, More >

Dongsheng Du^1,*, Sheng Shi¹, Weizhi Xu¹, Chen Kong², Shuguang Wang¹, Weiwei Li¹

CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.1, pp. 265-285, 2020, DOI:10.32604/cmes.2020.09151 - 19 June 2020

Abstract The intensity non-stationarity is one of the most important features of earthquake records. Modeling of this feature is significant to the generation of arti- ficial earthquake waves. Based on the theory of phase difference spectrum, an intensity non-stationary envelope function with log-normal form is proposed. Through a tremendous amount of earthquake records downloaded on Kik-net, a parameter fitting procedure using the genetic algorithm is conducted to obtain the value of model parameters under different magnitudes, epicenter distances and site conditions. A numerical example is presented to describe the procedure of generating fully non-stationary ground motions More >