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

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

106
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
106
Reinforcement01:23

Reinforcement

212
Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
212
Observational Learning01:12

Observational Learning

181
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
181
Reinforcement Schedules01:24

Reinforcement Schedules

149
Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
149
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

4.2K
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...
4.2K
Masking and Demasking Agents01:19

Masking and Demasking Agents

2.4K
EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
2.4K

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

Power Control in Wireless Body Area Networks: A Review of Mechanisms, Challenges, and Future Directions.

Sensors (Basel, Switzerland)·2026
Same author

CPVPD-2024: A Chinese photovoltaic plant dataset derived via a topography-enhanced deep learning framework.

Scientific data·2025
Same author

5G High-Precision Positioning in GNSS-Denied Environments Using a Positional Encoding-Enhanced Deep Residual Network.

Sensors (Basel, Switzerland)·2025
Same author

ConvFormer-KDE: A Long-Term Point-Interval Prediction Framework for PM<sub>2.5</sub> Based on Multi-Source Spatial and Temporal Data.

Toxics·2024
Same author

HRP-OG: Online Learning with Generative Feature Replay for Hypertension Risk Prediction in a Nonstationary Environment.

Sensors (Basel, Switzerland)·2024
Same author

Heterogeneous bimetallic selenides encapsulated within graphene aerogel as advanced anodes for sodium ion batteries.

Journal of colloid and interface science·2024

相关实验视频

Updated: Jul 9, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.0K

基于多代理深度强化学习的空对空通信系统的多目标优化.

Shaofu Lin1, Yingying Chen1, Shuopeng Li1

  • 1Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.

Sensors (Basel, Switzerland)
|December 9, 2023
PubMed
概括
此摘要是机器生成的。

这项研究介绍了无人机的新型空对空通信系统,集成移动边缘计算和无线电力传输. 该系统优化了能源,并减少了扩展无人机任务的计算延迟.

关键词:
移动边缘计算 (MEC) 是指移动边缘计算.多代理深度强化学习 (MADRL) 的应用.多目标优化 (MOO) 是指多目标优化.无人驾驶飞行器 (UAV) 是一种无人驾驶飞行器.无线电力传输 (WPT) 是一种无线电力传输.

更多相关视频

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

592
A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents
06:25

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents

Published on: May 16, 2025

161

相关实验视频

Last Updated: Jul 9, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.0K
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

592
A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents
06:25

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents

Published on: May 16, 2025

161

科学领域:

  • *专注于空中通信系统和移动边缘计算.
  • * 集成无线电源传输,以提高无人机耐力.

背景情况:

  • *无人驾驶飞行器 (UAV) 系统由于能源限制而面临飞行时间和计算能力的限制.
  • * 现有的系统难以平衡实时数据处理和持续的空中操作.

研究的目的:

  • *为无人机开发一个全双重的空对空通信系统 (A2ACS).
  • *为了减少无人机的计算延迟和能源消耗,同时确保任务的连续性.
  • *为了优化系统吞吐量,最大限度地减少低功耗警报,并高效地管理能量传输.

主要方法:

  • * 提出了一种结合移动边缘计算和无线电力传输的新系统.
  • * 开发了一个多目标优化框架,以平衡系统吞吐量,无人机功率状态,能源接收和能源消耗.
  • *使用多代理深度决定性政策梯度 (MADDPG) 算法来优化AEES位置和能量传输功率.
  • *使用K-means集群,以便在空边能源服务器 (AEES) 和无人机之间进行公平的关联.

主要成果:

  • * 拟议的多目标DDPG (MODDPG) 算法与基线方法相比显示出更高的性能.
  • * 该系统有效地减少了无人机的计算延迟和能源消耗.
  • * 实现了系统吞吐量,无人机能量水平和能源服务器效率之间的平衡.

结论:

  • * 集成的A2ACS模型提高了无人机的操作效率和耐久性.
  • *基于MADDPG的优化为动态的空中环境提供了有效的决策.
  • *K-means算法确保了公平的资源分配,提高了整体系统的公平性.