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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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Distributed Loads01:19

Distributed Loads

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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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Parallel Processing01:20

Parallel Processing

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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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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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Quantifying Work02:30

Quantifying Work

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As a system undergoes a change, its internal energy can change, and energy can be transferred from the system to the surroundings, or from the surroundings to the system. 
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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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相关实验视频

Updated: May 21, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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在云环境中使用深度学习进行节能动态工作流程调度.

Sunera Chandrasiri1, Dulani Meedeniya1

  • 1Department of Computer Science and Engineering, University of Moratuwa, Moratuwa 10400, Sri Lanka.

Sensors (Basel, Switzerland)
|March 17, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种使用图形神经网络和深度强化学习的新云调度框架,以尽量减少任务完成时间和能源使用. 这种方法比传统方法显著提高了效率.

关键词:
图形神经网络的神经网络人工智能的人工智能是人工智能.云计算工作流程安排多目标优化多目标优化强化学习是一种强化学习.

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科学领域:

  • 云计算 云计算 云计算 云计算
  • 人工智能的人工智能
  • 运营研究 运营研究

背景情况:

  • 云环境中的动态工作流调度是复杂的,因为依赖,可变的工作负载和资源波动.
  • 在云资源管理中,平衡 makespan (总完成时间) 和能源消耗是一个关键的挑战.

研究的目的:

  • 提出一个新的调度框架,集成图形神经网络 (GNN) 和深度强化学习 (DRL) 以实现多目标优化.
  • 为了最大限度地减少制作时间,并减少云工作流中的能源消耗.

主要方法:

  • 利用GNN来建模适应性资源分配的任务依赖性.
  • 使用近接政策优化 (PPO) 算法进行深度强化学习.
  • 在基于CloudSim的模拟环境中使用合成数据集评估框架.

主要成果:

  • 拟议的框架实现了689.22s的最低制程,比基准方法高出13.92%.
  • 与HEFT,Min-Min和Max-Min等传统启发式方法相比,在制造量和能源消耗方面取得了持续的改进.
  • 保持了10964.45 J的竞争性能源消耗.

结论:

  • 集成GNN和DRL为云环境中的动态任务调度提供了强大的方法.
  • 该框架有效地平衡了多个目标,包括制造商减少和能源效率.
  • 研究结果强调了先进的人工智能技术的潜力,以优化云资源管理.