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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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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Maxwell-Boltzmann Distribution: Problem Solving01:20

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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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Centroid of a Body: Problem Solving01:03

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The centroid of a body is a crucial concept in engineering and physics. Finding the centroid of a body can help determine its stability, its balance point, and even its design. In this context, consider a thin wire bent in the form of a quarter circular arc. Polar coordinates are used to calculate the centroid. The wire is first divided into small differential elements of a length equal to the radius multiplied by the differential angle.
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Turbulent Flow: Problem Solving01:09

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Carbonation is a process used to dissolve carbon dioxide gas in a liquid, commonly used in the production of carbonated beverages. Achieving efficient carbonation requires careful control of temperature, pressure, and flow conditions. By adjusting these parameters, carbonation efficiency can be maximized, producing a higher concentration of CO2 in the liquid.
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Heuristics01:21

Heuristics

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Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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优化对边缘云连续体中的物联网应用程序的资源配置,使用混合元启发算法.

Nasiru Muhammad Dankolo1, Nor Haizan Mohamed Radzi2, Noorfa Haszlinna Mustaffa2

  • 1Faculty of Computing, Universiti Teknologi Malaysia, 81310, Johor Bahru, Malaysia. muhammaddankolo@graduate.utm.my.

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概括

本研究介绍了一种混合的花花授粉算法和 Tabu Search (FPA-TS),用于优化物联网 (IoT) 资源配置. 在边缘云环境中,FPA-TS算法有效地平衡了成本和任务完成时间.

关键词:
连续性 连续性的连续性边缘云是一个边缘云.花的授粉方式 花的授粉方式物联网的物联网,就是物联网.优化优化 优化优化资源分配资源的分配.

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 云计算 云计算 云计算 云计算

背景情况:

  • 物联网 (IoT) 应用程序需要在边缘和云计算环境中有效地分配资源.
  • 在动态边缘云系统中优化资源配置是复杂的,平衡成本和任务完成时间 (makespan).
  • 现有的算法通常为物联网场景中的多目标优化提供次优化解决方案.

研究的目的:

  • 引入一种新的混合花粉算法和禁忌搜索 (FPA-TS) 用于物联网中的多目标资源配置.
  • 通过适应性概率和动态征收飞行控制来增强鲜花授粉算法,以改进全球搜索.
  • 为了利用 Tabu Search 进行内存引导的本地改进,以最大限度地减少产量和成本.

主要方法:

  • 开发了一种混合FPA-TS算法,将增强的FPA功能与Taboo搜索集成.
  • 基于FPA的解决方案多样性和动态征收飞行控制的实现适应概率.
  • 利用 Tabu Search 进行本地搜索和改进,以优化产品范围和成本.
  • 使用代表性的物联网场景进行了广泛的模拟.

主要成果:

  • 与现有算法相比,混合FPA-TS算法表现出优越的性能.
  • 在平衡成本方面取得了显著的改进,并为物联网资源配置做出了贡献.
  • 增强的FPA组件有助于有效的全球搜索能力.
  • 塔布搜索为最大限度地减少产量和成本,有效地改进了解决方案.

结论:

  • 混合FPA-TS为物联网边缘云系统中的资源配置提供了强大的和高效的方法.
  • 这种方法解决了动态物联网环境中固有的多目标优化挑战.
  • 拟议的算法为大规模物联网部署中优化性能和成本提供了一个有希望的解决方案.