Effects of typhoon events on coastal hydrology, nutrients, and algal bloom dynamics: Insights from continuous observation and machine learning in semi-enclosed …

P Zhang, H Long, Z Li, R Chen, D Peng… - Science of the Total …, 2024 - Elsevier
Typhoons can induce variations in hydrodynamic conditions and biogeochemical
processes, potentially escalating the risk of algal bloom occurrences impacting coastal …

A model-free switching and control method for three-level neutral point clamped converter using deep reinforcement learning

P Qashqai, M Babaie, R Zgheib, K Al-Haddad - IEEE Access, 2023 - ieeexplore.ieee.org
This paper presents a novel model-free switching and control method for three-level neutral
point clamped (NPC) converter using deep reinforcement learning (DRL). Our approach …

A deep learning-based modeling of a 270 V-to-28 V DC-DC converter used in more electric aircrafts

G Rojas-Duenas, JR Riba… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
This article presents a novel approach for black-box modeling of 270 V-to-28 V dc–dc step-
down converters used in more electric aircrafts. These converters normally feed constant …

Black-box large-signal average modeling of DC-DC converters using NARX-ANNs

A Zilio, D Biadene, T Caldognetto, P Mattavelli - IEEE Access, 2023 - ieeexplore.ieee.org
This paper investigates the use of non-linear autoregressive exogenous (NARX) artificial
neural networks (ANNs) to achieve black-box average dynamic models of dc-dc converters …

Modeling of a DC-DC bidirectional converter used in mild hybrid electric vehicles from measurements

G Rojas-Duenas, JR Riba, M Moreno-Eguilaz - Measurement, 2021 - Elsevier
This paper presents a non-intrusive approach for modeling a bidirectional DC-DC converter
used in mild hybrid electric vehicles. A black-box identification methodology is proposed to …

Effinformer: A deep-learning-based data-driven modeling of DC–DC bidirectional converters

Q Shang, F **ao, Y Fan, W Kang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
This article presents a data-driven modeling method founded on deep learning (DL) to
precisely model and identify a dc–dc bidirectional converter that is widely used in renewable …

GRU and LSTM comparison for black-box modeling of power electronic converters

P Qashqai, R Zgheib… - IECON 2021–47th Annual …, 2021 - ieeexplore.ieee.org
Considering the vast application of Power Electronic Converters (PEC) in numerous modern
technologies, modeling their behavior is of immense importance. These components are …

Modeling Methodology Based on Fast and Refined Neural Networks for Non-Isolated DC–DC Converters With Configurable Parameter Settings

H Ge, Z Huang, Z Huang - … on Emerging and Selected Topics in …, 2023 - ieeexplore.ieee.org
Compared with conventional physics-based methods, eg, analytical modeling and
numerical modeling, data-driven methods can extract input-to-output relationships from the …

Deep neural network-based black-box modeling of power electronic converters using transfer learning

P Qashqai, K Al-Haddad… - 2022 IEEE Energy …, 2022 - ieeexplore.ieee.org
Black-box modeling of power electronic converters (PECs) is an essential tool for studying
commercial converters. Neural-networks-based modeling techniques are data-driven based …

X-in-the-Loop Validation of Deep Learning-Based Virtual Sensing for Lifetime Estimation of Automotive Power Electronics Converters

S Chakraborty, SK Bhoi, F Hosseinabadi… - IEEE Journal of …, 2024 - ieeexplore.ieee.org
Health degradation issues in automotive power electronics converter (PEC) systems are
present due to repetitive thermomechanical stress endured while the vehicle is in real-field …