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相关概念视频

Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

645
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
645
Distributed Loads01:19

Distributed Loads

534
Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...
534
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

191
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
191
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

629
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
629
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

107
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.
107
Maximum Power Transfer01:16

Maximum Power Transfer

255
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...
255

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UAV Based Relay for Wireless Sensor Networks in 5G Systems.

Sensors (Basel, Switzerland)·2018
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Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
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两层边缘智能用于任务卸载和计算能力分配,在车载网络中使用无人机协助.

Xiaodan Bi1, Lian Zhao1

  • 1Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada.

Sensors (Basel, Switzerland)
|March 28, 2024
PubMed
概括

本研究介绍了一种使用无人机 (UAV) 处理无线设备任务的移动边缘计算框架. 该系统优化了资源分配,以提高车辆网络的性能.

科学领域:

  • 计算机科学 计算机科学
  • 无线通信无线通信
  • 人工智能的人工智能

背景情况:

  • 无线设备的指数增长和实时处理需求挑战了传统的服务器架构.
  • 现有的边缘计算解决方案在动态和移动环境中面临局限性.

研究的目的:

  • 提出使用无人机作为移动边缘计算 (MEC) 服务器的协作边缘计算框架.
  • 提高车辆系统中无线设备的任务卸载和处理效率.
  • 为了减少路边单位 (RSU) 的计算负担.

主要方法:

  • 一个双层边缘智能方案用于网络计算资源分配.
  • 智能任务卸载和分配在第一层.
  • 部分可观测的随机游戏 (POSG) 通过对抗深度Q学习来解决处理节点 (PN) 在第二层的资源分配.
  • 一个加权的位置优化算法,用于无人机移动.

主要成果:

  • 拟议的框架有效地从无线设备中卸载和处理任务.
  • 两层边缘情报方案优化了资源分配.
  • 决斗深度Q学习有效地分配计算资源.
  • 无人机运动优化有助于任务卸载和处理.
关键词:
决斗的深度Q学习.移动边缘计算 (MEC) 是一个资源分配的资源分配.任务卸载 任务卸载

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结论:

  • 拟议的与无人机的协作边缘计算框架显著提高了性能.
  • 智能资源分配方案解决了高计算需求的挑战.
  • 这种方法为在动态车辆环境中高效的边缘计算提供了可行的解决方案.