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

636
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...
636
Distributed Loads01:19

Distributed Loads

524
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...
524
Three-Dimensional Force System:Problem Solving01:30

Three-Dimensional Force System:Problem Solving

657
A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
To solve a three-dimensional force system, first resolve each force into its respective scalar components. Do this using...
657
Dynamic Equilibrium02:20

Dynamic Equilibrium

51.0K
A reversible chemical reaction represents a chemical process that proceeds in both forward (left to right) and reverse (right to left) directions. When the rates of the forward and reverse reactions are equal, the concentrations of the reactant and product species remain constant over time and the system is at equilibrium. A special double arrow is used to emphasize the reversible nature of the reaction. The relative concentrations of reactants and products in equilibrium systems vary greatly;...
51.0K
Two-Dimensional Force System: Problem Solving01:29

Two-Dimensional Force System: Problem Solving

552
Solving problems related to two-dimensional force systems is an essential aspect of mechanics and engineering. By applying the principles of vector analysis and force equilibrium, one can determine the effect of multiple forces acting on an object in a two-dimensional space.
The first step to solving a two-dimensional force system problem is to draw a free-body diagram of the object under consideration. This diagram helps identify all the external forces acting on the object, including their...
552
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

3.7K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
3.7K

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

Two-Tier Efficient QoE Optimization for Partitioning and Resource Allocation in UAV-Assisted MEC.

Sensors (Basel, Switzerland)·2024
Same author

A Multi-Agent RL Algorithm for Dynamic Task Offloading in D2D-MEC Network with Energy Harvesting.

Sensors (Basel, Switzerland)·2024
Same author

Differential gene expression in ovaries of Qira black sheep and Hetian sheep using RNA-Seq technique.

PloS one·2015
Same author

[Effect of anisodamine on myocardial connexin 43 expression in pig after resuscitation from cardiac arrest].

Zhonghua wei zhong bing ji jiu yi xue·2015
Same author

Corpus callosum atrophy associated with the degree of cognitive decline in patients with Alzheimer's dementia or mild cognitive impairment: a meta-analysis of the region of interest structural imaging studies.

Journal of psychiatric research·2015
Same author

Evaluation of rosuvastatin as an organic anion transporting polypeptide (OATP) probe substrate: in vitro transport and in vivo disposition in cynomolgus monkeys.

The Journal of pharmacology and experimental therapeutics·2015
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: Jun 14, 2025

Investigating Motor Skill Learning Processes with a Robotic Manipulandum
07:52

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Published on: February 12, 2017

8.7K

多代理深度增强学习基于设备对设备移动边缘计算网络中的动态任务卸载,以最大限度地减少与截止日期限制的平均任务延迟.

Huaiwen He1, Xiangdong Yang1,2, Xin Mi1,2

  • 1School of Computer, Zhongshan Institute, University of Electronic Science and Technology of China, Zhongshan 528400, China.

Sensors (Basel, Switzerland)
|August 29, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种新的多代理深度强化学习算法,用于设备对设备移动边缘计算系统中的动态任务卸载. 它大大减少了对延迟敏感应用程序的任务延迟和丢弃的任务.

关键词:
D2D D2D D2D D2D D2D D2D D2D D2D D2D延迟约束的限制动态匹配匹配的动态匹配.移动边缘计算移动边缘计算多种代理强化学习的多种代理强化学习

更多相关视频

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

503
An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents
07:42

An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents

Published on: August 2, 2018

13.5K

相关实验视频

Last Updated: Jun 14, 2025

Investigating Motor Skill Learning Processes with a Robotic Manipulandum
07:52

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Published on: February 12, 2017

8.7K
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

503
An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents
07:42

An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents

Published on: August 2, 2018

13.5K

科学领域:

  • 计算机科学 计算机科学
  • 电气工程 电气工程
  • 人工智能的人工智能

背景情况:

  • 设备对设备 (D2D) 通信可以在移动设备 (MD) 之间直接卸载任务,优化资源利用.
  • 移动边缘计算 (MEC) 系统利用D2D来有效处理延迟敏感任务.
  • 现有的研究不足以解决D2D-MEC中的动态分区和任务卸载,使用随机到达和多时段执行.

研究的目的:

  • 为D2D-MEC系统中动态任务卸载提出一种新的算法.
  • 根据截止日期限制,尽量减少延迟敏感任务的长期平均延迟.
  • 在D2D-MEC中引入活跃和置设备的动态分区方案.

主要方法:

  • 制定了任务卸载作为一个基于队列的优化问题.
  • 以马尔科夫决策过程 (MDP) 为模型.
  • 应用多代理深度强化学习 (DRL) 使用多代理近接政策优化 (MAPPO) 与分散执行 (CTDE) 框架的集中培训.

主要成果:

  • 拟议的多代理DRL算法证明了效率和快速融合.
  • 与单剂DRL相比,平均任务完成延迟减少了11.0%.
  • 减少了17%的丢弃任务的比率.

结论:

  • 这种新的算法有效地减少了D2D-MEC系统中的任务延迟和丢弃任务.
  • 动态分区方案通过考虑随机任务到达和多时段执行来提高性能.
  • 这种方法对于传感器网络和对延迟敏感的应用程序具有高度相关性,这些应用程序需要高质量体验 (QoE) 和遵守服务级别协议 (SLA).