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相关概念视频

Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

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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...
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Classification of Systems-II01:31

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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Classification of Systems-I01:26

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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
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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...
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Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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一个基于多个分类器的算法,用于节能任务在雾下卸载计算计算.

Moteb K Alasmari1, Sami S Alwakeel1, Yousef A Alohali1

  • 1College of Computer and Information Sciences, King Saud University, Riyadh 11362, Saudi Arabia.

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概括
此摘要是机器生成的。

为物联网 (IoT) 设备优化任务卸载至关重要. MCEETO战略提高了能源效率,并减少了混合物联网,雾和云环境中的网络使用.

关键词:
能源效率是指能效的能源效率.雾计算 雾计算 雾计算如果FogSim.物联网的东西互联网.模块放置位置模块放置位置任务卸载 任务卸载

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科学领域:

  • 计算机科学 计算机科学
  • 网络化 网络化 网络化
  • 分布式系统 分布式系统

背景情况:

  • 物联网 (IoT) 设备产生大量数据,使得远程云卸载效率低下且成本高昂.
  • 雾计算提供了近距离和较低的延迟,但资源有限,导致频繁的云卸载.
  • 在混合环境中优化能源消耗和满足物联网设备的最后期限是一个重大挑战.

研究的目的:

  • 在雾计算环境中探索有效的任务卸载策略.
  • 提出一个智能能源意识的分配策略,以在混合物联网,雾和云范式中有效卸载任务.
  • 改进物联网设备任务的分类和分配,以实现最佳的资源利用.

主要方法:

  • 该研究提出了MCEETO,一种使用多分类算法的智能能源意识分配策略.
  • MCEETO根据任务属性,雾节点特征,网络延迟和带宽来选择模块放置的最佳雾设备 (FD).
  • 使用iFogSim模拟器评估和比较边缘化和仅云计算策略.

主要成果:

  • MCEETO表现出卓越的能源效率,与仅云计算相比节省了约11.36%,与边缘战略相比节省了9.30%.
  • MCEETO算法实现了网络使用的显著减少:67%与边缘相比,96%与仅云相比.
  • 该策略有效地优化了混合物联网,雾和云设置中的任务卸载.

结论:

  • 拟议的MCEETO战略为物联网任务卸载提供了更节能和网络优化的解决方案.
  • 基于多个分类器的智能分配策略在混合云环境中是有效的.
  • 进一步的研究可以探索异质物联网生态系统的先进资源管理技术.