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相关概念视频

Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

595
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...
595
Distributed Loads01:19

Distributed Loads

475
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...
475
Relation Between the Distributed Load and Shear01:23

Relation Between the Distributed Load and Shear

571
Understanding the relationship between the distributed load and shear force in structural analysis is crucial for analyzing beams subjected to various loading conditions. Consider the case of a beam experiencing a distributed load, two concentrated loads, and a couple moment.
571
Work and Energy for Variable Forces01:10

Work and Energy for Variable Forces

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When an object is acted upon by a variable force, the amount of work done and the change in energy of the object can be more complex to calculate compared to when a constant force is applied. Work is the product of force and displacement, while energy is the capacity of a system to do work. When a constant force is applied to an object, the work done can be calculated as the product of the force and the distance moved in the direction of the force. However, when a variable force is applied, the...
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Beams with Unsymmetric Loadings01:17

Beams with Unsymmetric Loadings

103
Analyzing a supported beam under unsymmetrical loadings is essential in structural engineering to understand how beams respond to varied force distributions. This analysis involves calculating the deflection and identifying points where the slope of the beam is zero, which are crucial for ensuring structural stability and functionality.
The first moment-area theorem determines the slope at any point on the beam. This theorem indicates that the change in slope between two points on a beam...
103
Energy Budgets00:51

Energy Budgets

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Organisms must balance energy intake with the energy required for growth, maintenance and reproduction. These trade-offs result in a variety of survivorship and reproductive strategies, including semelparity and iteroparity. Semelparous species, like annual plants, have only one reproductive episode in their lifetimes and consequently have short lifespans. Iteroparous species, by contrast, have many reproductive events during their lifetimes but have relatively few offspring. These two...
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相关实验视频

Updated: May 13, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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在采矿场景中,基于移动边缘计算的多用户联合任务卸载和资源配置.

Siqi Li1, Weidong Li2, Wanbo Zheng3

  • 1Faculty of Science, Kunming University of Science and Technology, Kunming, 650500, China.

Scientific reports
|May 9, 2025
PubMed
概括

本研究介绍了采矿中的工业物联网设备的部分卸载和协作移动边缘计算 (MEC) 方法. IGA-DDPG算法显著降低了延迟,能源消耗和系统成本,同时确保任务完成.

关键词:
采矿边缘计算采矿边缘计算多目标优化多目标优化资源分配资源的分配.任务卸载 任务卸载

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科学领域:

  • 事物的工业互联网 (IIoT)
  • 移动边缘计算 (MEC) 是指移动边缘计算.
  • 资源管理 资源管理

背景情况:

  • 采矿中的智能终端设备面临由于高网络流量和有限资源的性能挑战.
  • 满足低传输延迟和低能耗的要求对于这些设备来说至关重要.
  • 现有的解决方案难以平衡计算负载和资源利用.

研究的目的:

  • 为 IIoT 设备在采矿中提出一种新的方法,将部分卸载与协作移动边缘计算 (MEC) 结合起来.
  • 优化卸载决策过程,以提高资源利用效率.
  • 为了最大限度地降低整体系统成本,同时确保任务完成延迟不会超过预定义的值.

主要方法:

  • 一个部分卸载策略,利用设备对设备的通信来分割任务.
  • 一个双层交替优化框架:用于卸载决策的改进基因算法 (IGA) 和用于战略优化的深度决定性政策梯度 (DDPG).
  • 考虑延迟和能源消耗加权系数的多目标优化公式.

主要成果:

  • 拟议的IGA-DDPG算法显著超过了五个基线算法.
  • 在延迟方面实现了平均24.5%的降低,在能源消耗方面降低了26.3%,在总系统成本方面降低了44.6%.
  • 在各种系统配置中始终确保100%的任务完成率.

结论:

  • IGA-DDPG的方法有效地解决了智能终端设备在采矿IIoT中的性能挑战.
  • 该方法通过将任务卸载到协作设备和MEC服务器来提高资源利用率.
  • 这种优化的卸载策略在效率和成本效益方面取得了重大改善.