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相关概念视频

Maximum Power Transfer01:16

Maximum Power Transfer

396
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.
By substituting the entire circuit with...
396
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

178
The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
178
Reducing Line Loss01:18

Reducing Line Loss

193
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.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
193
Energy Conservation and Bernoulli's Equation01:16

Energy Conservation and Bernoulli's Equation

9.4K
Applying the conservation of energy principle or the work-energy theorem to an incompressible, inviscid fluid in laminar, steady, irrotational flow leads to Bernoulli's equation. It states that the sum of the fluid pressure, potential, and kinetic energy per unit volume is constant along a streamline.
All the terms in the equation have the dimension of energy per unit volume. The kinetic energy per unit volume is called the kinetic energy density, and the potential energy per unit volume is...
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Production Efficiency01:01

Production Efficiency

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Net production efficiency (NPE) is the efficiency at which organisms assimilate energy into biomass for the next trophic level. Due to low metabolic rates and less energy spent on thermoregulatory processes, the NPE of ectotherms (cold-blooded animals) is 10 times higher than endotherms (warm-blooded animals).
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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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相关实验视频

Updated: Sep 10, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

657

基于混合优化的深度学习,用于在MIMO支持的无线网络中进行能源效率资源分配

Mian Muhammad Kamal1, Ijaz Khan2, M A Al-Khasawneh3,4

  • 1School of Electronics and Communication Engineering, Quanzhou University of Information Engineering, Quanzhou, 362000, China. mianmuhammadkamal@qzuie.edu.cn.

Scientific reports
|August 27, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了HGGO_XCovNet,用于优化5G无线网络的资源配置,提高多个用户的能源效率和数据速率. 新的深度学习方法提高了系统的性能和可靠性.

关键词:
5G网络深度学习能源效率多输入多输出系统资源分配

相关实验视频

Last Updated: Sep 10, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

657

科学领域:

  • 无线通信
  • 人工智能
  • 优化算法

背景情况:

  • 在支持多输入多输出 (MIMO) 的无线网络中,资源分配对于优化网络性能和能源效率至关重要.
  • 现有的方法在满足MIMO用户的高资源需求方面面临挑战,需要先进的技术.
  • 深度学习可以提高5G网络资源分配的可靠性和准确性.

研究的目的:

  • 引入一种新的混合优化技术,HGGO_XCovNet,用于在MIMO支持的无线网络中有效地分配资源.
  • 通过最大限度地提高能源效率,数据速率和吞吐量来提高系统性能.
  • 用深度学习来实现准确可靠的资源分配.

主要方法:

  • 考虑使用多个用户的基站 (BS) 方案来分配资源.
  • 使用Xception卷积神经网络 (XCovNet) 来分配资源,并通过混合优化算法进行训练.
  • 河马灰斑优化 (HGGO) 算法,结合了灰斑优化 (GGO) 和河马优化 (HO),被开发用于训练XCovNet.

主要成果:

  • 通过HGGO_XCovNet技术实现了74.943 kbits/joule的最大能效.
  • 该系统的总数据速率为269.93Mbps.
  • 记录了551.262 Mbps的最大吞吐量.

结论:

  • 拟议的HGGO_XCovNet技术有效地优化了5G MIMO网络中的资源配置.
  • 混合深度学习方法显著提高了能源效率,总量和吞吐量.
  • 这种方法为先进的无线系统的资源管理提供了可靠而准确的解决方案.