Results 31 to 40 of about 549 (197)

Nonlinear System Identification of Soft Robot Dynamics Using Koopman Operator Theory [PDF]

2019 International Conference on Robotics and Automation (ICRA), 2019
Soft robots are challenging to model due in large part to the nonlinear properties of soft materials. Fortunately, this softness makes it possible to safely observe their behavior under random control inputs, making them amenable to large-scale data collection and system identification. This paper implements and evaluates a system identification method
Daniel Bruder, C. David Remy, Ram Vasudevan   +2 more
openaire   +2 more sources

Does Participating in Agricultural Global Value Chains Promote Agricultural Growth?

Agribusiness, EarlyView.
ABSTRACT This study examines the relationship between GVC participation and agricultural value‐added growth in 43 countries over the period 1995–2022. In contrast to prior literature, we disaggregate the agricultural sector into four sub‐sectors namely crop cultivation, animal production, forestry and fishing.
Taner Turan, Halit Yanıkkaya, Hüseyin Alperen Özer   +2 more
wiley   +1 more source

Dynamical systems and complex networks: a Koopman operator perspective

Journal of Physics: Complexity
The Koopman operator has entered and transformed many research areas over the last years. Although the underlying concept—representing highly nonlinear dynamical systems by infinite-dimensional linear operators—has been known for a long time, the ...
Stefan Klus, Nataša Djurdjevac Conrad
doaj   +1 more source

Koopman-Based Control System for Quadrotors in Noisy Environments

IEEE Access
It is well known that identification of the complete system dynamics is challenging, especially in noisy environments. The Koopman operator theory provides a linear representation of a nonlinear system using only the input/output data acquired from the ...
Yuna Oh, Myoung Hoon Lee, Jun Moon
doaj   +1 more source

DeepMapper: Attention‐Based AutoEncoder for System Identification in Wound Healing and Stage Prediction

Advanced Intelligent Discovery, EarlyView.
The authors develop a deep learning model for real‐time tracking of wound progression. The deep learning framework maps the nonlinear evolution of a time series of images to a latent space, where they learn a linear representation of the dynamics. The linear model is interpretable and suitable for applications in feedback control.
Fan Lu, Ksenia Zlobina, Michael Briden, Oscar Avalos, Héctor Carrión, Sebastian Osorio, Hsin‐ya Yang, Alexandra Nava, Michelle D. Bagood, Marco Rolandi, Roslyn Rivkah Isseroff, Marcella Gomez   +11 more
wiley   +1 more source

Neural Koopman forecasting for critical transitions in infrastructure networks

Intelligent Systems with Applications
We develop a data-driven framework for long-term forecasting of stochastic dynamics on evolving networked infrastructure systems using neural approximations of Koopman operators.
Ramen Ghosh
doaj   +1 more source

Decentralized Stability Enhancement of DFIG-Based Wind Farms in Large Power Systems: Koopman Theoretic Approach

IEEE Access, 2022
This paper proposes a data-centric model predictive control (MPC) for supplemental control of a DFIG-based wind farm (WF) to improve power system stability.
Ahmed Husham, Innocent Kamwa, M. A. Abido, Hussein Supreme   +3 more
doaj   +1 more source

A Review on Recent Trends of Bioinspired Soft Robotics: Actuators, Control Methods, Materials Selection, Sensors, Challenges, and Future Prospects

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
This article reviews the current state of bioinspired soft robotics. The article discusses soft actuators, soft sensors, materials selection, and control methods used in bioinspired soft robotics. It also highlights the challenges and future prospects of this field.
Abhirup Sarker, Tamzid Ul Islam, Md. Robiul Islam   +2 more
wiley   +1 more source

Decarbonization in Financial Turbulent Times: Global Value Chains and Regulatory Framework

Business Strategy and the Environment, EarlyView.
ABSTRACT This study examines how participation in global value chains (GVCs) influences carbon emissions amid financial turbulence, with attention to cross‐country heterogeneity and distributional dynamics. Although existing research has explored trade–environment linkages, limited attention has been given to how GVC integration interacts with ...
Xiaoyong Xu, Lu Zhang, Kingsley Imandojemu, Felix Orole, Romanus Osabohien   +4 more
wiley   +1 more source

Randomized Projection Learning Method for Dynamic Mode Decomposition

Mathematics, 2021
A data-driven analysis method known as dynamic mode decomposition (DMD) approximates the linear Koopman operator on a projected space. In the spirit of Johnson–Lindenstrauss lemma, we will use a random projection to estimate the DMD modes in a reduced ...
Sudam Surasinghe, Erik M. Bollt
doaj   +1 more source