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相关概念视频

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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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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Distributed Loads: Problem Solving01:21

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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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Applying the conservation of energy principle or the work-energy theorem to an incompressible, inviscid fluid in laminar, steady, irrotational flow leads to Bernoulli's equation. It states that the sum of the fluid pressure, potential, and kinetic energy per unit volume is constant along a streamline.
All the terms in the equation have the dimension of energy per unit volume. The kinetic energy per unit volume is called the kinetic energy density, and the potential energy per unit volume is...
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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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相关实验视频

Updated: Jun 18, 2025

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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基于低碳背景下的CNN-BiLSTM模型的云计算负载预测方法.

HaoFang Zhang1, Jie Li2, HaoRan Yang3

  • 1Ninghe Campus Management Center, Civil Aviation University of China, Tianjin, 300000, China.

Scientific reports
|August 3, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了CNN-BiLSTM模型,用于预测云计算负载和碳排放,这对于"双碳"目标至关重要. 该模型显著减少了预测错误,帮助减少低碳排放的努力.

关键词:
碳排放 碳排放 碳排放这就是为什么BiLSTM.在美国,CNN是CNN.大声计算负载预测这是一个低碳的低碳.

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科学领域:

  • 计算机科学 计算机科学
  • 环境科学 环境科学
  • 人工智能的人工智能

背景情况:

  • 云数据中心在与负载请求相匹配的资源供应方面面临挑战,导致显著的碳排放.
  • 全球必须要实现这一目标.
  • 双碳二氧化碳的使用情况
  • 这些目标需要创新的解决方案来减少跨行业的排放.
  • 云计算中的资源分配效率低下有助于能源浪费和碳足迹增加.

研究的目的:

  • 开发和验证一种用于预测云计算环境碳排放的新方法.
  • 建立一个动态的服务器碳排放预测模型,将排放与CPU利用率相关联.
  • 为实现云计算领域的低碳减排目标做出贡献.

主要方法:

  • 利用结合的卷积神经网络 (CNN) 和双向长期和短期内存 (BiLSTM) 神经网络模型用于云计算负载预测.
  • 集成预测负载与功率计算,以估计实时碳排放.
  • 开发了一个基于CPU利用率的动态服务器碳排放预测模型.
  • 使用谷歌集群数据用于模型培训和验证.

主要成果:

  • 与传统模型相比,CNN-BiLSTM模型在云计算负载预测方面表现出卓越的性能.
  • 与BP,LSTM,BiLSTM和CEEMDAN-ConvLSTM模型相比,实现了平均平方误差 (MSE) 的显著减少,分别为52%,50%,34%和45%.
  • 动态服务器碳排放模型有效地将服务器排放与CPU利用率联系起来.

结论:

  • CNN-BiLSTM模型对于预测云计算负载和随后的碳排放非常有效.
  • 拟议的动态预测模型为降低云数据中心碳排放提供了可行的策略.
  • 这项研究为优化云资源管理以实现环境可持续性提供了有价值的工具.