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相关概念视频

Short-distance Transport of Resources02:12

Short-distance Transport of Resources

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Short-distance transport refers to transport that occurs over a distance of just 2-3 cells, crossing the plasma membrane in the process. Small uncharged molecules, such as oxygen, carbon dioxide, and water, can diffuse across the plasma membrane on their own. In contrast, ions and larger molecules require the assistance of transport proteins due to their charge or size. Transport across membranes also occurs within individual cells, playing a variety of essential roles for the plant as a whole.
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Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization
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通过在无人机支持的无线网络中使用基于深度学习的资源配置来增强粘模具优化.

Reem Alkanhel1, Ahsan Rafiq2, Evgeny Mokrov3

  • 1Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.

Sensors (Basel, Switzerland)
|August 26, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了用于无人机网络的基于深度学习的资源分配方法 (ESMOML-RAA) 的增强滑泥模具优化. 该方法优化了移动用户的资源配置,提高了能源效率和网络性能.

关键词:
深度学习是一种深度学习.资源分配的资源分配.粘液模具算法 粘液模具算法无人驾驶飞行器 无人驾驶飞行器 无人驾驶飞行器无线网络是无线网络.

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科学领域:

  • * 无线通信网络无线通信网络
  • * 人工智能在电信中的应用
  • * 优化算法 优化算法

背景情况:

  • *无人驾驶飞行器 (UAV) 网络对于各种应用,如公共安全和灾害管理至关重要.
  • * 在动态无人机环境中为移动用户 (MU) 提供可靠的通信,存在重大挑战.
  • *高效的资源配置 (分通道,电源,用户服务) 对于无人机网络的覆盖范围和能源效率至关重要.

研究的目的:

  • *为无人机支持的无线网络提供基于深度学习的资源分配方法 (ESMOML-RAA) 的增强的粘模具优化.
  • * 实现计算和能源效率高的资源分配决策.
  • * 提高无人机辅助传输网络的覆盖率和能源效率.

主要方法:

  • * 开发了ESMOML-RAA技术,将无人机视为资源分配的学习代理.
  • * 采用高度并行长短期记忆 (HP-LSTM) 模型来分配资源.
  • *使用了增强的粘液模具优化 (ESMO) 算法来优化HP-LSTM的超参数.

主要成果:

  • *ESMOML-RAA技术证明了高效的计算和能源使用.
  • * 这种方法通过设计的奖励函数成功地将加权资源消耗降到最低.
  • *与其他机器学习模型相比,模拟证实ESMOML-RAA的性能优越.

结论:

  • *ESMOML-RAA为无人机网络的资源分配挑战提供了有效的解决方案.
  • *ESMO和HP-LSTM的整合显著提高了网络性能和效率.
  • * 这种方法为优化无人机支持的无线通信系统提供了一个强大的框架.