Results 101 to 110 of about 172,286 (287)
Slow–fast n-dimensional piecewise linear differential systems
Journal of Differential Equations, 2016 The paper deals with the \(n\)-dimensional singularly perturbed differential slow-fast system \[ \begin{aligned}&\dot {\mathbf u}=\frac{d{\mathbf u}}{dt}=\varepsilon(A{\mathbf u}+{\mathbf a}v+{\mathbf b}),\\ &\dot v=\frac{dv}{dt}=u_1+|v|,\end{aligned} \] where \({\mathbf u}\in\mathbb{R}^{n-1}\) is the slow variable (\(n\geq 2\)), \(v\in\mathbb{R}\) is ...
R. Prohens, A.E. Teruel, C. Vich
openaire +2 more sources
Machine‐Learning‐Assisted Onset‐Time Determination in Transient Luminescence Thermometry
Advanced Intelligent Discovery, EarlyView.Artificial neural networks enable autonomous extraction of onset times from transient heating curves in luminescence thermometry. Using Ln3+‐doped upconverting nanoparticles as luminescent thermometers, we combine experimental transients with physically motivated synthetic curves to enhance data diversity and improve generalization.
David J. Sousa +3 more
wiley +1 more source
Calculation of nonlinear vibrations of piecewise-linear systems using the shooting method
Vietnam Journal of Mechanics, 2012 In this paper, an explicit formulation of the shooting scheme for computation of multiple periodic attractors of a harmonically excited oscillator which is asymmetric with both stiffness and viscous damping piecewise linearities is derived.
Nguyen Van Khang +2 more
doaj +1 more source
Stability-preserving model order reduction for nonlinear time delay systems [PDF]
, 2013 Delay elements are needed to model physical, industrial and engineering systems as action and reaction always come with latency. In this paper, we present an algorithm to obtain the reduced-order models (ROMs) while preserving the stability of nonlinear ...
Wang, Q, Wong, N
core
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 +2 more
wiley +1 more source
Global analysis of piecewise linear systems using impact maps and surface Lyapunov functions [PDF]
, 2003 This paper presents an entirely new constructive global analysis methodology for a class of hybrid systems known as piecewise linear systems (PLS). This methodology infers global properties of PLS solely by studying the behavior at switching surfaces ...
Dahleh, Munther A. +2 more
core
Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.In this research, a paradigm of parameter estimation method for pneumatic soft hand control is proposed. The method includes the following: 1) sampling harmonic damping waves, 2) applying pseudo‐rigid body modeling and the logarithmic decrement method, and 3) deriving position and force control.
Haiyun Zhang +4 more
wiley +1 more source
Enabling Stochastic Dynamic Games for Robotic Swarms
Advanced Intelligent Systems, EarlyView.This paper scales stochastic dynamic games to large swarms of robots through selective agent modeling and variable partial belief space planning. We formulate these games using a belief space variant of iterative Linear Quadratic Gaussian (iLQG). We scale to teams of 50 agents through selective modeling based on the estimated influence of agents ...
Kamran Vakil, Alyssa Pierson
wiley +1 more source
Three crossing limit cycles in planar piecewise linear systems with saddle-focus type
Electronic Journal of Qualitative Theory of Differential Equations, 2015 This paper presents an analysis on the appearance of limit cycles in planar Filippov system with two linear subsystems separated by a straight line. Under the restriction that the orbits with points in the sliding and escaping regions are not considered,
Liping Li
doaj +1 more source
A Flexible and Energy‐Efficient Compute‐in‐Memory Accelerator for Kolmogorov–Arnold Networks
Advanced Intelligent Systems, EarlyView.This article presents KA‐CIM, a compute‐in‐memory accelerator for Kolmogorov–Arnold Networks (KANs). It enables flexible and efficient computation of arbitrary nonlinear functions through cross‐layer co‐optimization from algorithm to device. KA‐CIM surpasses CPU, ASIC, VMM‐CIM, and prior KAN accelerators by 1–3 orders of magnitude in energy‐delay ...
Chirag Sudarshan +6 more
wiley +1 more source