Model-based control of soft robots: A survey of the state of the art and open challenges

C Della Santina, C Duriez, D Rus - IEEE Control Systems …, 2023 - ieeexplore.ieee.org
From a functional standpoint, classic robots are not at all similar to biological systems. If
compared with rigid robots, animals' bodies look overly redundant, imprecise, and weak …

Quantitative phase imaging based on holography: trends and new perspectives

Z Huang, L Cao - Light: Science & Applications, 2024 - nature.com
Abstract In 1948, Dennis Gabor proposed the concept of holography, providing a pioneering
solution to a quantitative description of the optical wavefront. After 75 years of development …

Nonlinear system identification: A user-oriented road map

J Schoukens, L Ljung - IEEE Control Systems Magazine, 2019 - ieeexplore.ieee.org
Nonlinear system identification is an extremely broad topic, since every system that is not
linear is nonlinear. That makes it impossible to give a full overview of all aspects of the fi eld …

Deep learning for wireless physical layer: Opportunities and challenges

T Wang, CK Wen, H Wang, F Gao… - China …, 2017 - ieeexplore.ieee.org
Machine learning (ML) has been widely applied to the upper layers of wireless
communication systems for various purposes, such as deployment of cognitive radio and …

Physics-guided convolutional neural network (PhyCNN) for data-driven seismic response modeling

R Zhang, Y Liu, H Sun - Engineering Structures, 2020 - Elsevier
Accurate prediction of building's response subjected to earthquakes makes possible to
evaluate building performance. To this end, we leverage the recent advances in deep …

Deep long short-term memory networks for nonlinear structural seismic response prediction

R Zhang, Z Chen, S Chen, J Zheng, O Büyüköztürk… - Computers & …, 2019 - Elsevier
This paper presents a comprehensive study on developing advanced deep learning
approaches for nonlinear structural response modeling and prediction. Two schemes of the …

Applications of hybrid models in chemical, petroleum, and energy systems: A systematic review

S Zendehboudi, N Rezaei, A Lohi - Applied energy, 2018 - Elsevier
Mathematical modeling and simulation methods are important tools in studying various
processes in science and engineering. In the current review, we focus on the applications of …

The challenge of machine learning in space weather: Nowcasting and forecasting

E Camporeale - Space weather, 2019 - Wiley Online Library
The numerous recent breakthroughs in machine learning make imperative to carefully
ponder how the scientific community can benefit from a technology that, although not …

[HTML][HTML] Deep holography

G Situ - Light: Advanced Manufacturing, 2022 - light-am.com
With the explosive growth of mathematical optimization and computing hardware, deep
neural networks (DNN) have become tremendously powerful tools to solve many …

Deep learning in robotics: a review of recent research

HA Pierson, MS Gashler - Advanced Robotics, 2017 - Taylor & Francis
Advances in deep learning over the last decade have led to a flurry of research in the
application of deep artificial neural networks to robotic systems, with at least 30 papers …