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相关概念视频

Rolling Resistance: Problem Solving01:17

Rolling Resistance: Problem Solving

348
Rolling resistance, also known as rolling friction, is the force that resists the motion of a rolling object, such as a wheel, tire, or ball, when it moves over a surface. It is caused by the deformation of the object and the surface in contact with each other, as well as other factors like internal friction, hysteresis, and energy losses within the materials. Rolling resistance opposes the object's motion, requiring additional energy to overcome it and maintain movement. In practical...
348
Reinforcement01:23

Reinforcement

217
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:
217
Observational Learning01:12

Observational Learning

186
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...
186
Reducing Line Loss01:18

Reducing Line Loss

155
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
155
Reinforcement Schedules01:24

Reinforcement Schedules

156
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,...
156
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

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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...
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基于深度强化学习的V2V通信光束管理优化.

Junliang Ye1, Xiaohu Ge2

  • 1Huazhong University of Science and Technology, Wuhan, 430074, China.

Scientific reports
|November 22, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种深度强化学习 (DRL) 方法,用于5G NR FR2车辆对车辆 (V2V) 通信中的智能光束管理. DRL方法优化了光束对齐和跟踪,在关键性能指标上表现优于现有方法.

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科学领域:

  • 无线通信无线通信
  • 人工智能的人工智能
  • 智能运输系统 智能运输系统

背景情况:

  • 智能互联汽车对于智能城市和智能运输至关重要.
  • 车辆网络传输各种数据 (安全,传感,多媒体),需要高光谱效率,低延迟和可靠性.
  • 5G NR FR2 (24-71 GHz) 推用于V2X,但面临诸如高路径损失和频道波动等挑战.

研究的目的:

  • 提出基于深度强化学习 (DRL) 的智能光束管理方法,用于车辆对车辆 (V2V) 通信.
  • 解决5G NR FR2 V2X通信中频谱效率,延迟和可靠性之间的权衡问题.
  • 在复杂,动态的车辆环境中优化光束对齐和跟踪.

主要方法:

  • 为智能光束管理开发了一个深度强化学习 (DRL) 算法.
  • DRL方法的重点是对光束对齐和跟踪的最佳控制.
  • 进行了模拟,将拟议的方法与5G标准和扩展卡尔曼波器 (EKF) 方法进行比较.

主要成果:

  • 与5G标准相比,DRL辅助方法显著减少了通信延迟.
  • 拟议的方法在基于EKF的方法上显示出更高的可靠性和光谱效率.
  • 在具有挑战性的5G NR FR2 V2X场景中,DRL有效地管理了光束对齐和跟踪.

结论:

  • 深度强化学习为5G NR FR2 V2X通信中的智能光束管理提供了有效的解决方案.
  • 拟议的DRL方法成功地平衡了光谱效率,延迟和可靠性.
  • 这种方法可以提高车辆网络在复杂和动态环境中的性能.