13 citations · 15 across the 6 of their papers we have counts for
6 papers
A Deep-Learning Usability Expansion Model of Ocean Observations
Ali Muhamed Ali, Hanqi Zhuang, Yu Huang +3
Today's ocean numerical prediction skills depend on the availability of in-situ and remote ocean observations at the time of the predictions only. Because observations are scarce a…
ST-PCNN: Spatio-Temporal Physics-Coupled Neural Networks for Dynamics Forecasting
Yu Huang, James Li, Min Shi +5
Ocean current, fluid mechanics, and many other spatio-temporal physical dynamical systems are essential components of the universe. One key characteristic of such systems is that c…
Physics-Coupled Spatio-Temporal Active Learning for Dynamical Systems
Yu Huang, Yufei Tang, Xingquan Zhu +4
Spatio-temporal forecasting is of great importance in a wide range of dynamical systems applications from atmospheric science, to recent COVID-19 spread modeling. These application…
Physics-informed Tensor-train ConvLSTM for Volumetric Velocity Forecasting of Loop Current
Yu Huang, Yufei Tang, Hanqi Zhuang +2
According to the National Academies, a weekly forecast of velocity, vertical structure, and duration of the Loop Current (LC) and its eddies is critical for understanding the ocean…
North Atlantic Right Whales Up-call Detection Using Multimodel Deep Learning
Ali K Ibrahim, Hanqi Zhuang, Laurent M. Ch'erubin +3
A new method for North Atlantic Right Whales (NARW) up-call detection using Multimodel Deep Learning (MMDL) is presented in this paper. In this approach, signals from passive acous…
Detection of north atlantic right whale upcalls using local binary patterns in a two-stage strategy
Mahdi Esfahanian, Hanqi Zhuang, Nurgun Erdol +1
In this paper, we investigate the effectiveness of two-stage classification strategies in detecting north Atlantic right whale upcalls. Time-frequency measurements of data from pas…