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相关概念视频

Frequency-dependent Selection01:21

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When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
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Numerous practical applications within engineering disciplines, such as telecommunications, necessitate optimizing power delivery to a connected load. This pursuit, however, entails inherent internal losses, which can either equal or exceed the power supplied to the load. The Thevenin equivalent circuit is helpful in finding the maximum power a linear circuit can deliver to a load. It is assumed in this context that the load resistance can be adjusted.
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Carrier generation is the process by which electron-hole pairs (EHPs) are created within the semiconductor. In direct-bandgap semiconductors, such as gallium arsenide (GaAs), this occurs efficiently when energy absorption prompts valence electrons to leap into the conduction band, leaving behind holes.
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Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...
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相关实验视频

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基于MAB的组合联合通道和扩散因子选择,用于LoRa设备.

Ikumi Urabe1, Aohan Li1,2, Minoru Fujisawa1

  • 1Department of Electrical Engineering, Tokyo University of Science, Tokyo 125-8585, Japan.

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概括

优化远程 (LoRa) 设备通信包括选择最佳的扩散因子 (SF) 和通道. 这项研究表明,结合SF和通道选择,考虑到设备位置,显著提高了LoRa系统的性能和可靠性.

关键词:
这就是为什么物联网物联网物联网.洛拉洛拉是什么意思轻量级的分布式强化学习学习.多重武装的强盗问题传输参数选择 传输参数选择

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科学领域:

  • 无线通信无线通信
  • 物联网 (IoT) 的物联网 (IoT) 的物联网.
  • 机器学习 机器学习

背景情况:

  • 远程 (LoRa) 设备对于低功耗,远程物联网通信至关重要.
  • 洛拉系统的可扩展性和性能在很大程度上取决于扩散因子 (SF) 和通道分配.
  • 现有的方法经常独立地对待SF和频道选择,忽视它们的相互依赖性和位置影响.

研究的目的:

  • 评估LoRa设备位置对实用,大规模系统中通信性能的影响.
  • 实施和评估基于学习的联合道和SF选择方法.
  • 提出和比较一种新的组合式多武器盗方法,用于联合SF和频道选择.

主要方法:

  • 实施并评估了基于学习的去中心化联合道和SF选择算法 (Tug of War,UCB1, ε-greedy).
  • 开发了一种组合式多臂强盗方法,其中SF和通道组合形成了"手臂".
  • 基于ACKnowledge (ACK) 信息的评估方法,考虑设备位置和SF/通道相互依赖.

主要成果:

  • 组合方法在框架成功率 (FSR) 和公平性方面明显优于独立的SF/通道选择方法.
  • 与单独选择SF相比,联合通道和SF选择可以增强FSR.
  • 最佳的通道和SF选择高度依赖于LoRa设备的空间分布 (位置情况).

结论:

  • 位置意识,联合通道和SF选择对于优化LoRa系统性能至关重要.
  • 拟议的组合多武器盗方法为密集的LoRa网络提供了卓越的可扩展性和可靠性.
  • 未来的研究应该进一步探索适应性策略,根据实时网络条件和设备位置动态调整SF和通道.