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相关概念视频

One-Degree-of-Freedom System01:24

One-Degree-of-Freedom System

519
In mechanical engineering, one-degree-of-freedom systems form the basis of a wide range of electrical and mechanical components. Using these models, engineers can predict the behavior of various parts in a larger system, which gives them insight into how different forces interact with each other.
A one-degree-of-freedom system is defined by an independent variable that determines its state and behavior. One example of a one-degree-of-freedom system is a simple harmonic oscillator, such as a...
519
Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

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Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
As the drone's propellers rotate, an upward force is generated that counteracts the force of gravity, enabling the drone to lift off from the ground. This initial movement of the drone is along a straight path, representing a form of translational motion. In this phase, every point on the...
243
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

424
Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
424
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

125
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
125
Planar Rigid-Body Motion01:22

Planar Rigid-Body Motion

481
Understanding the movement of a rigid body in planar motion involves recognizing that every particle within this body is traversing a path that maintains a consistent distance from a specific plane. This concept is fundamental in the study of physics and mechanical engineering, and it allows us to comprehend better how objects move in space.
Planar motion is typically divided into three distinct categories. The first is rectilinear translation, demonstrated by a subway train that moves along...
481
Reinforcement Schedules01:24

Reinforcement Schedules

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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.
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深度强化学习用于联合轨迹规划,传输调度和无人机辅助无线传感器网络的访问控制.

Xiaoling Luo1,2, Che Chen3,4, Chunnian Zeng1

  • 1School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China.

Sensors (Basel, Switzerland)
|July 11, 2023
PubMed
概括

优化无人飞行器 (UAV) 路径和访问控制可以提高无线传感器网络的能源效率. 多代理深度强化学习提高了数据收集和传输性能.

关键词:
无人机无人机无人机是什么?访问控制 访问控制 访问控制多代理的深度强化学习学习.轨道规划 轨道规划 轨道规划

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科学领域:

  • 无线传感器网络 无线传感器网络
  • 机器人技术 机器人技术 机器人技术
  • 人工智能的人工智能

背景情况:

  • 无人机可以通过将传感信息和计算任务转发到远程基站 (RBS) 来支持地面用户.
  • 优化无人机轨迹,调度和访问控制对于在地面无线传感器网络中节能收集和传输数据至关重要.
  • 由于GU分布和流量需求的动态性,无人机辅助网络面临着挑战.

研究的目的:

  • 提高无人机辅助无线传感器网络中传感数据收集和传输的能源效率.
  • 调查无人机访问控制和轨迹规划之间的权衡,在一个时间区分的框架结构中.
  • 为动态网络环境开发一个高效的学习框架.

主要方法:

  • 采用多代理深度强化学习方法来优化无人机轨迹,调度和访问控制策略.
  • 设计了一个层次化的学习框架,以减少动作和状态空间,提高学习效率.
  • 这项研究考虑了一种有时间槽的框架结构,其中包含了飞行,传感和信息转发的子槽.

主要成果:

  • 无人机轨迹规划与通道控制相结合,可以显著提高无人机的能源效率.
  • 层次学习方法表现出增强的学习稳定性和卓越的传感性能.
  • 模拟结果验证了在动态网络条件下提议的优化策略的有效性.

结论:

  • 优化无人机轨迹和访问控制是提高无线传感器网络能源效率的关键.
  • 层次化的多代理深度增强学习为无人机辅助传感网络提供了稳定有效的解决方案.
  • 拟议的框架有效地应对动态环境和不确定的用户需求所带来的挑战.