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

Optimization Problems01:26

Optimization Problems

8
Optimization problems often involve identifying maximum or minimum values under specific constraints. A well-known example is determining the longest horizontal pipe that can be moved around a right-angled corner, where a 3-meter-wide hallway meets a 2-meter-wide hallway. This scenario, common in architectural design and industrial transport, can be understood conceptually through geometric and trigonometric reasoning.To visualize the problem, consider the pipe as a straight line that touches...
8
Parallel Processing01:20

Parallel Processing

621
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...
621
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

1.1K
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...
1.1K
Improving Translational Accuracy02:07

Improving Translational Accuracy

3.5K
3.5K
Improving Translational Accuracy02:07

Improving Translational Accuracy

14.0K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
14.0K
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

724
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
724

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

Nano-Silver-Selenium Liquid Dressing Facilitates Treatment of Monkeypox and Prevention of Viral Transmission in a Surrogate Mouse Model.

Exploration (Beijing, China)·2026
Same author

DGOMapping: Real-Time Multi-Agent Mapping Based on 4D Gaussian Splatting.

Sensors (Basel, Switzerland)·2026
Same author

Associations of complement proteins and immunoglobulins with cognitive impairment in type 2 diabetes mellitus: a cross-sectional study.

BMC endocrine disorders·2026
Same author

Multimodal structure-guided diffusion model for Magnetic Particle Imaging reconstruction.

Medical image analysis·2026
Same author

Granulomas microenvironment-guided sono-immunotherapy to treat and prevent recurrence of tuberculosis.

Nature communications·2026
Same author

Targeting chemokine-driven metastasis in non-small cell lung cancer: Development and evaluation of chemokine nanosponges for therapy.

Materials today. Bio·2026
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
查看所有相关文章

相关实验视频

Updated: Jan 13, 2026

Operation of the Collaborative Composite Manufacturing CCM System
10:09

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

7.0K

RL-PMO:一种基于强化学习的优化算法,用于并行SFC迁移.

Hefei Hu1, Zining Liu1, Fan Wu1

  • 1School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.

Sensors (Basel, Switzerland)
|January 10, 2026
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种离线增强学习算法,用于边缘网络中多个虚拟网络函数 (VNF) 的并行迁移. RL-PMO方法实现了高成功率和更好的性能,特别是在高负载下.

关键词:
网络功能虚拟化 网络功能虚拟化线下强化学习是一种非线下强化学习.平行迁移是平行迁移的过程.服务功能链条 服务功能链条

相关实验视频

Last Updated: Jan 13, 2026

Operation of the Collaborative Composite Manufacturing CCM System
10:09

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

7.0K

科学领域:

  • 计算机科学 计算机科学
  • 网络工程 网络工程
  • 人工智能的人工智能

背景情况:

  • 边缘网络因硬件故障和资源限制而面临中断.
  • 服务功能链 (SFC) 易受节点故障的影响,需要有效的迁移策略.
  • 在有限的资源下同时迁移多个虚拟网络功能 (VNF) 是一个重大挑战.

研究的目的:

  • 提出一个高效可靠的算法,用于边缘网络中多个VNF的并行迁移.
  • 为了应对影响SFCs的资源限制和节点故障的挑战.
  • 优化VNF迁移流程以提高网络弹性.

主要方法:

  • 开发了一个基于线下强化学习的平行迁移优化算法 (RL-PMO).
  • 采用了两阶段的框架:用于生成迁移轨迹的启发式算法和用于政策网络培训的Decision Mamba模型.
  • 利用双关键架构和CQL规范化来缓解分布转移和Q值高估.

主要成果:

  • 在各种负载条件下,RL-PMO实现了大约95%的迁移成功率.
  • 与传统的线下RL算法相比,在低/中等负载下表现出~13%的性能改善,在高负载下高达17%.
  • 通过使用Decision Mamba模型,有效地捕捉了VNF和基础资源之间的依赖关系.

结论:

  • 在节点故障期间,RL-PMO为SFC迁移提供了一种高效,可靠和资源意识的解决方案.
  • 拟议的算法增强了边缘网络的稳定性和性能.
  • 线下强化学习与Decision Mamba等先进模型相结合,对复杂的网络管理任务具有前景.