Model-based control of soft robots: A survey of the state of the art and open challenges
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 …
compared with rigid robots, animals' bodies look overly redundant, imprecise, and weak …
Quantitative phase imaging based on holography: trends and new perspectives
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 …
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 …
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
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 …
communication systems for various purposes, such as deployment of cognitive radio and …
Physics-guided convolutional neural network (PhyCNN) for data-driven seismic response modeling
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 …
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
This paper presents a comprehensive study on developing advanced deep learning
approaches for nonlinear structural response modeling and prediction. Two schemes of the …
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
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 …
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 …
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 …
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 …
application of deep artificial neural networks to robotic systems, with at least 30 papers …