Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

646
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...
646
Distributed Loads01:19

Distributed Loads

538
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...
538
Prediction Intervals01:03

Prediction Intervals

2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.3K
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

116
The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
116
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

327
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
327
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

1.5K
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).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
1.5K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

A unified post-quantum zero-trust architecture with AI-driven orchestration for secure healthcare fog networks.

Scientific reports·2026
Same author

Harris Hawks-tuned severity-aware YOLOv8 instance segmentation framework for vehicle damage assessment.

Scientific reports·2026
Same author

Meta-Gamofy: Automated Metaverse Gaming for Healthcare Conditions.

Clinical anatomy (New York, N.Y.)·2026
Same author

Design of an integrated evidence-driven few-shot meta-learning for zero-day malware detection and forensic attributions.

Scientific reports·2026
Same author

Text encryption using Sosemanuk and Harris Hawks optimization by laser communication.

Scientific reports·2026
Same author

Automated knee MRI segmentation with KneeSeg-U for cartilage and bone extraction using an unsupervised deep learning framework.

BMC musculoskeletal disorders·2026

相关实验视频

Updated: Jul 5, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.0K

一种混合云负载平衡和主机利用率预测方法,采用深度学习和优化技术.

Sarita Simaiya1, Umesh Kumar Lilhore2, Yogesh Kumar Sharma3

  • 1Department of Computer Science and Engineering, Chandigarh University, Gharuan, Mohali, Punjab, 140413, India. saritasimaiya@gmail.com.

Scientific reports
|January 16, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种新的DPSO-GA混合模型,用于云计算中的动态工作负载配置,改善资源分配和负载平衡的准确性.

更多相关视频

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.1K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K

相关实验视频

Last Updated: Jul 5, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.0K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.1K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K

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:

  • 虚拟机集成优化了云数据中心负载平衡,但在成本,服务质量和资源利用方面面临挑战.
  • 现有的云负载平衡深度学习方法因有限的资源配置而与杂的工作负载波动作斗争.
  • 长短期内存 (LSTM) 模型对于预测服务器负载和工作负载配置至关重要.

研究的目的:

  • 提出一种新的混合深度学习模型,DPSO-GA,用于云计算中的动态工作负载配置.
  • 解决云负载平衡中成本,服务质量,性能和资源利用方面的权衡问题.
  • 克服预测噪音工作负载波动的局限性,改善资源层面的配置.

主要方法:

  • 一个双相混合模型:第一阶段使用粒子群智能 (PSO) 和基因算法 (GA) 进行超参数调整. 第二阶段使用卷积神经网络 (CNN) 和LSTM (CNN-LSTM) 来预测资源消耗,并使用PSO-GA方法进行训练.
  • 使用一维CNN从VM工作负载统计中提取特征,LSTM模拟时间信息以预测即将到来的VM工作负载.
  • 整合多种资源的利用,以解决负载平衡和过度供应问题.

主要成果:

  • 拟议的DPSO-GA模型在云环境中展示了增强的精度,准确性和负载分配.
  • 使用谷歌集群跟踪基准的模拟验证了模型在资源分配和负载平衡方面的效率.
  • 混合方法有效地克服了预测杂工作负载波动的局限性,并改善了资源配置.

结论:

  • DPSO-GA混合模型在为云计算提供动态工作负载配置方面取得了重大进展.
  • 它有效地平衡成本,服务质量和资源利用,减轻过度提供问题.
  • 该模型为优化云数据中心负载平衡和资源分配提供了强大的解决方案.