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Rolling resistance, also known as rolling friction, is the force that resists the motion of a rolling object, such as a wheel, tire, or ball, when it moves over a surface. It is caused by the deformation of the object and the surface in contact with each other, as well as other factors like internal friction, hysteresis, and energy losses within the materials. Rolling resistance opposes the object's motion, requiring additional energy to overcome it and maintain movement. In practical...
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相关实验视频

Updated: Jul 6, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

基于元学习和强化学习的分区RIS辅助车辆安全通信.

Hui Li1, Fengshuan Wang2, Jin Qian1

  • 1College of Information Engineering, Taizhou University, Taizhou 225300, China.

Sensors (Basel, Switzerland)
|September 27, 2025
PubMed
概括

这项研究通过使用分区可重新配置的智能表面 (RIS) 来引导信号和干扰来增强车辆网络安全. 这种适应性方法显著提高了对窃听者的安全通信速率.

关键词:
超级学习是一种超级学习.物理层的安全性是物理层的安全性.可重新配置的智能表面.强化学习是一种强化学习.车辆特设网络 车辆特设网络

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Online Virtual Reality Networked Control Laboratory Applied in Control Engineering Education
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Online Virtual Reality Networked Control Laboratory Applied in Control Engineering Education

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相关实验视频

Last Updated: Jul 6, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

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04:15

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Published on: February 23, 2024

科学领域:

  • 无线通信无线通信
  • 网络安全 网络安全
  • 人工智能的人工智能

背景情况:

  • 车辆特设网络 (VANET) 面临着动态的窃听威胁.
  • 适应式窃听器挑战了在VANET中的安全通信.
  • 可重新配置的智能表面 (RIS) 提供了信号操纵的潜力.

研究的目的:

  • 为VANETs开发一个安全的通信方案,以防止动态窃听.
  • 优化分区RIS的使用,以增强信号和人工噪声 (AN) 传输.
  • 整合元学习和强化学习 (RL) 进行自适应性安全优化.

主要方法:

  • 一个分区的RIS被用来同时增强合法的信号,并将AN指向窃听者.
  • 超级学习用于快速适应RIS分区,以适应新的窃听场景.
  • 强化学习 (RL) 优化束形向量和RIS反射系数,以提高安全性.
  • 一个联合优化框架整合了元学习和RL,以实现动态性能提升.

主要成果:

  • 与传统的RIS辅助方法相比,拟议的方案实现了28%更高的保密率.
  • 该框架显示了比传统的深度学习方法更快的趋同.
  • 该系统有效地平衡了信号增强与干扰干扰,以确保强大的安全性.
  • 模拟证实了这种方法在动态车辆环境中的有效性.

结论:

  • 集成的meta-learning和RL框架为VANETs提供了强大且能效的安全性.
  • 分区的RIS战略有效地减轻了动态窃听威胁.
  • 适应性优化方法在快速变化的网络条件下确保了高保密率.