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相关概念视频

Load-frequency control01:28

Load-frequency control

162
Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...
162

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相关实验视频

Updated: Jul 2, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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在软件定义网络中实现最佳负载平衡的时间深度Q学习.

Aakanksha Sharma1, Venki Balasubramanian2, Joarder Kamruzzaman2

  • 1Melbourne Institute of Technology (MIT), Melbourne, VIC 3000, Australia.

Sensors (Basel, Switzerland)
|February 24, 2024
PubMed
概括
此摘要是机器生成的。

时间深度Q学习 (tDQN) 为物联网 (IoT) 流量优化软件定义网络 (SDN). 这种智能方法最大限度地减少了网络延迟,并且在不添加控制器的情况下提高了性能.

关键词:
在SDN中,SDN是SDN.深度时间强化学习学习流动的波动 流动的波动延迟最小化 延迟最小化的数据包交付比率.

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科学领域:

  • 计算机科学 计算机科学
  • 网络工程 网络工程
  • 人工智能的人工智能

背景情况:

  • 物联网 (IoT) 产生了大量的网络流量,压倒了传统网络.
  • 软件定义网络 (SDN) 提供了更好的流量管理,但在多控制器设置中面临控制平面瓶和负载平衡问题.
  • 现有的动态控制器映射解决方案在控制器放置和实时负载平衡方面面临着波动流量的困难.

研究的目的:

  • 提出一种智能解决方案,用于多控制器SDN中的动态控制器映射,以最大限度地减少网络延迟.
  • 为应对不可预测的物联网流量增长和异质设备复杂性的挑战.
  • 通过基于自我学习强化模型,提高动态SDN (dSDN) 的性能.

主要方法:

  • 在dSDN控制器架构中实现时间深度Q学习网络 (tDQN).
  • 使用一种带有奖励-惩罚方案的强化学习代理来优化开关-控制器映射决策.
  • 制定一个多目标优化问题,以根据流量波动动动态地转移流量.

主要成果:

  • tDQN方法有效地优化动态流量映射,减少网络延迟,而不增加控制器的数量.
  • 广泛的模拟表明,与传统网络,eSDN和dSDN相比,性能优越.
  • 在各种网络场景中观察到吞吐量,延迟,动,数据包交付比率和数据包丢失的显著改善.

结论:

  • 临时深度Q学习为管理复杂和动态的SDN环境提供了智能和有效的解决方案,特别是对于物联网.
  • tDQN模型成功地平衡负载并最大限度地降低延迟,优于以前的方法.
  • 这项研究提供了一种可扩展和高效的方法,用于未来的SDN基础设施,以应对指数级流量增长.