Results 91 to 100 of about 160,813 (290)
Advanced Intelligent Systems, EarlyView.An online learning control framework with a data cache pool based on a constant‐curvature model inspired neural network (CCMINN) model to obtain the inverse kinematics model of tendon‐actuated continuum manipulators is proposed. Combining the fast‐converging CCMINN with an online learning control framework enables high‐precision and highly robust ...
Jinyu Duan +5 more
wiley +1 more source
Cyclic spacetimes through singularity scattering maps. The laws of quiescent bounces
Journal of High Energy Physics, 2022 For spacetimes containing quiescent singularity hypersurfaces we propose a general notion of junction conditions based on a prescribed singularity scattering map, as we call it, and we introduce the notion of a cyclic spacetime (also called a multiverse)
Bruno Le Floch +2 more
doaj +1 more source
Reprogrammable, In‐Materia Matrix‐Vector Multiplication with Floppy Modes
Advanced Intelligent Systems, EarlyView.This article describes a metamaterial that mechanically computes matrix‐vector multiplications, one of the fundamental operations in artificial intelligence models. The matrix multiplication is encoded in floppy modes, near‐zero force deformations of soft matter systems.
Theophile Louvet +2 more
wiley +1 more source
Advanced Intelligent Systems, EarlyView.Herein, a deep reinforcement learning‐based multi‐UAV formation control approach is proposed. By optimizing the utilization of historical data through correcting of offline samples, the past experience is better leveraged and learning performance is improved.
Zhongkai Chen +4 more
wiley +1 more source
Robotic Needle Steering for Percutaneous Interventions: Sensing, Modeling, and Control
Advanced Intelligent Systems, EarlyView.This review examines recent advances in robotic needle steering for percutaneous interventions, highlighting closed‐loop sensing, physics‐informed tissue‐needle interaction modeling, and real‐time trajectory planning and control. It synthesizes innovations in deep learning, fiber‐optic feedback, and adaptive control strategies, and outlines emerging ...
Fangjiao Zhao +5 more
wiley +1 more source
The strong $ABC$ conjecture over function fields (after McQuillan and Yamanoi) [PDF]
, 2008 The $abc$ conjecture predicts a highly non trivial upper bound for the height of an algebraic point in terms of its discriminant and its intersection with a fixed divisor of the projective line counted without multiplicity.
Gasbarri, Carlo
core +1 more source
Low‐Energy Atomic Scattering: S‐Wave Relation Between the Interaction Potential and the Phase Shift
Annalen der Physik, EarlyView.The validity of the on‐shell approximation for s‐wave scattering is examined across one, two, and three dimensions using exactly solvable model interaction potentials. By comparing exact and approximate s‐wave components of the interaction potential, the analysis reveals that the approximation improves with increasing momentum and decreasing ...
Francesco Lorenzi, Luca Salasnich
wiley +1 more source
Annalen der Physik, EarlyView.A set of particle representations, familiar from the Standard Model, collectively form a superalgebra. Those representations mirroring the behaviour of the Standard Model's gauge bosons, and three generations of fermions, are each included in this algebra, with exception only to those representations involving the top quark.
N. Furey
wiley +1 more source
Full‐order observer design for quadratic port‐controlled Hamiltonian systems
Asian Journal of Control, EarlyView.Abstract The full‐order observer design problem for a particular class of port‐controlled Hamiltonian systems is approached in this paper. The proposed full‐order observer scheme belongs to the structure preserving class of dynamic estimators as it preserves the natural stability properties of the approached class of systems that are useful for the ...
Michael Rojas +2 more
wiley +1 more source
Risk‐aware safe reinforcement learning for control of stochastic linear systems
Asian Journal of Control, EarlyView.Abstract This paper presents a risk‐aware safe reinforcement learning (RL) control design for stochastic discrete‐time linear systems. Rather than using a safety certifier to myopically intervene with the RL controller, a risk‐informed safe controller is also learned besides the RL controller, and the RL and safe controllers are combined together ...
Babak Esmaeili +2 more
wiley +1 more source