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

Reinforcement01:23

Reinforcement

177
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:
177
Observational Learning01:12

Observational Learning

128
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...
128
Masking and Demasking Agents01:19

Masking and Demasking Agents

2.3K
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...
2.3K
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

1.7K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
1.7K
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

3.6K
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...
3.6K
Elastic Collisions: Case Study01:15

Elastic Collisions: Case Study

13.4K
Elastic collision of a system demands conservation of both momentum and kinetic energy. To solve problems involving one-dimensional elastic collisions between two objects, the equations for conservation of momentum and conservation of internal kinetic energy can be used. For the two objects, the sum of momentum before the collision equals the total momentum after the collision. An elastic collision conserves internal kinetic energy, and so the sum of kinetic energies before the collision equals...
13.4K

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

EPRS: Experience-Prioritized Reinforcement Scheduler in Edge Clusters.

Sensors (Basel, Switzerland)·2026
Same author

Graph Neural Networks for Fault Diagnosis in Photovoltaic-Integrated Distribution Networks with Weak Features.

Sensors (Basel, Switzerland)·2025
Same author

A Robust Method Based on Deep Learning for Compressive Spectrum Sensing.

Sensors (Basel, Switzerland)·2025
Same author

Task Assignment and Path Planning Mechanism Based on Grade-Matching Degree and Task Similarity in Participatory Crowdsensing.

Sensors (Basel, Switzerland)·2024
Same author

Arithmetic Optimization AOMDV Routing Protocol for FANETs.

Sensors (Basel, Switzerland)·2023
Same author

An Improved SAMP Algorithm for Sparse Channel Estimation in OFDM System.

Sensors (Basel, Switzerland)·2023

相关实验视频

Updated: Jun 3, 2025

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
07:49

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

8.0K

在无人机辅助边缘计算中使用LLM增强的多代理增强学习进行任务卸载.

Feifan Zhu1, Fei Huang2, Yantao Yu1

  • 1School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China.

Sensors (Basel, Switzerland)
|January 11, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新的多代理深度学习框架,用于优化边缘计算中的无人机 (UAV) 轨迹. 这种新的方法提高了任务完成率,并加快了无人机集群的趋同.

关键词:
法学士 (LLM) 是一个专业.无人机无人机无人机是什么?多代理深度学习多代理深度学习轨道规划 轨道规划 轨道规划

更多相关视频

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT
08:04

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT

Published on: April 23, 2020

6.7K
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

498

相关实验视频

Last Updated: Jun 3, 2025

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
07:49

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

8.0K
Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT
08:04

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT

Published on: April 23, 2020

6.7K
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

498

科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 机器人技术 机器人技术 机器人技术

背景情况:

  • 带有计算服务器的无人机 (UAV) 增强了远程用户设备 (UE) 的边缘计算.
  • 现有的价值分解算法在多无人机协调方面遇到了困难,导致任务完成率降低,融合时间延长.
  • 有效的无人机轨迹规划对于高效的边缘计算资源利用至关重要.

研究的目的:

  • 开发一个创新的多代理深度学习框架,以优化多无人机轨迹.
  • 解决当前算法的局限性,即在无人机集群中将本地观测与全球状态联系起来.
  • 提高无人机辅助边缘计算中的任务完成率和融合时间.

主要方法:

  • 概念化多无人机轨迹优化作为一个分散的部分可观测的马尔科夫决策过程 (Dec-POMDP).
  • 将QTRAN算法与区域分解的大型语言模型 (LLM) 集成.
  • 采用图形卷积网络 (GCN) 和自我注意机制来管理跨次区域关系.

主要成果:

  • 拟议的框架显著优于现有的深度强化学习方法.
  • 在趋同速度上有明显的改善,超过10%.
  • 与基线方法相比,实现了超过10%的任务完成率改善.

结论:

  • 开发的框架在边缘计算环境中推进无人机轨迹优化.
  • 在无人机辅助边缘计算中增强多代理系统的性能.
  • 提供了一个强大的解决方案,用于复杂的计算任务卸载使用无人机群.