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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
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The Global Positioning System (GPS) has become an indispensable tool in fieldwork, offering unparalleled precision and efficiency for surveying, navigation, and infrastructure development. By harnessing signals from a constellation of satellites, GPS receivers determine the location of objects with remarkable speed and accuracy, often completing calculations within a second.Advantages of Modern GPS TechnologyContemporary GPS receivers are designed to meet the practical demands of field...
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洛拉万与ML相遇:关于通过机器学习提高性能的调查

Arshad Farhad1, Jae-Young Pyun1

  • 1Wireless and Mobile Communication System Laboratory, Department of Information and Communication Engineering, Chosun University, Gwangju 61452, Republic of Korea.

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概括

机器学习 (ML) 通过有效地分配扩散因子和传输功率,优化了远程广域网 (LoRaWAN) 的资源管理. 这项调查指导研究人员应用ML以提高LoRaWAN性能和资源利用.

关键词:
物联网 (IoT) 的物联网 (IoT) 的物联网.洛拉洛拉是什么意思洛拉旺人 洛拉旺人人工智能的人工智能是人工智能.数据集数据集数据集深度学习是一种深度学习.机器学习 (ML) 是指机器学习.强化学习是一种强化学习.资源管理 资源管理模拟模拟是指一个模拟模拟器.扩散因子 (SF) 的使用传输功率 (TP) 是指传输功率 (TP) 的

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科学领域:

  • 无线通信技术无线通信技术
  • 物联网 (IoT) 的物联网 (IoT) 的物联网.

背景情况:

  • 远程广域网 (LoRaWAN) 对于低功耗,远程物联网通信至关重要.
  • 高效利用无线电资源 (传播因子,传输功率) 是LoRaWAN的一个关键挑战.

研究的目的:

  • 调查和分析用于LoRaWAN资源管理的机器学习 (ML) 方法.
  • 识别ML框架,数据集和优化LoRaWAN性能的功能.

主要方法:

  • 对应用到LoRaWAN资源分配的最先进的ML技术的审查.
  • 探索公开可用的LoRaWAN框架和数据集.
  • 基于网络模拟器-3的ML框架的评估.

主要成果:

  • 机器学习方法有效地解决了LoRaWAN中的资源分配挑战.
  • 确定合适的ML方法和有效管理所必需的特征.
  • 对基于机器学习的LoRaWAN研究现有数据集和模拟工具的概述.

结论:

  • 机器学习为提高LoRaWAN效率和性能提供了巨大的潜力.
  • 为研究人员和从业人员提供了全面的指南,用于将ML应用于LoRaWAN.
  • 突出了在无线物联网网络中对ML的未来研究方向.