Results 61 to 70 of about 5,231 (215)

Liquid Metals in Radio Frequency Applications: A Review of Physics, Manufacturing, and Emerging Technologies

open access: yesAdvanced Electronic Materials, EarlyView.
This paper reviews the physics of liquid metals in RF devices, including the influence of mechanical strain on resonance as well as fabrication methods and strategies for designing tunable and strain‐tolerant inductors, capacitors, and antennas.
Md Saifur Rahman, William J. Scheideler
wiley   +1 more source

Simulations of Particle Dynamics in Magnetorheological Fluids [PDF]

open access: yesJournal of Computational Physics, 1999
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ly, H. V.   +4 more
openaire   +2 more sources

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

open access: yesAdvanced 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

A Deep Neural Network Based Model for a Kind of Magnetorheological Dampers

open access: yesSensors, 2019
In this paper, a deep neural network based model for a set of small-scale magnetorheological dampers (MRD) is developed where relevant parameters that have a physical meaning are inputs to the model.
Carlos A. Duchanoy   +3 more
doaj   +1 more source

Rheology and microstructural evolution in pressure-driven flow of a magnetorheological fluid with strong particle-wall interactions [PDF]

open access: yes, 2012
The interaction between magnetorheological (MR) fluid particles and the walls of the device that retain the field-responsive fluid is critical as this interaction provides the means for coupling the physical device to the field-controllable properties of
Gareth H. McKinley   +3 more
core   +1 more source

Towards Advanced Intelligent and Perceptive Soft Grippers

open access: yesAdvanced Intelligent Systems, EarlyView.
Implementing soft yet strong and intelligent soft grippers request innovative and creative solutions in designing soft bodies and seamlessly integrating actuated systems with hierarchical sensing. This review systematically analyses soft grippers with a deep understanding of core components, from fundamental design principles to actuation and sensing ...
Haneul Kim   +4 more
wiley   +1 more source

Semi-Active Vibration Control for High-Speed Elevator Using Magnetorheological Damper

open access: yesMagnetism
This paper presents the results of investigating the application of magnetorheological fluids in controlling the lateral and angular vibrations of a high-speed elevator.
Marcos Gonçalves   +4 more
doaj   +1 more source

New nonlinear dielectric materials: Linear electrorheological fluids under the influence of electrostriction

open access: yes, 2004
The usual approach to the development of new nonlinear dielectric materials focuses on the search for materials in which the components possess an inherently large nonlinear dielectric response.
A. Kawai   +5 more
core   +3 more sources

Yield stress in magnetorheological suspensions near the limit of maximum-packing fraction [PDF]

open access: yes, 2012
International audienceThis work deals with the magnetic field-induced static yield stress of magnetorheological (MR) suspensions with concentration near the limit of maximum-packing fraction.
Bossis, Georges   +5 more
core   +4 more sources

PPO‐Based Reinforcement Learning for the Semi‐Active Vibration Control of MDOF Platform

open access: yesAI &Innovation, EarlyView.
ABSTRACT Aiming at the coupled vibration problem of a multi‐degree‐of‐freedom (MDOF) vibration isolation platform under eccentric excitation, this paper proposes a semi‐active vibration control strategy based on Proximal Policy Optimization (PPO) ‐based reinforcement learning (PPO RL).
Wei Huang, Jian Xu
wiley   +1 more source

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