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相关概念视频

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

93
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...
93
Feedback control systems01:26

Feedback control systems

259
Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
259
Reinforcement Schedules01:24

Reinforcement Schedules

120
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,...
120
Control Systems01:10

Control Systems

967
Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
At the heart...
967
Load-frequency control01:28

Load-frequency control

98
Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...
98
Root-Locus Method01:19

Root-Locus Method

116
A cruise control system in a car is designed to maintain a specified speed automatically by adjusting the gas pedal. The system continuously measures the vehicle's speed and makes fine adjustments to the pedal to achieve this goal. The root locus method is particularly useful for understanding how the cruise control system's behavior changes under varying conditions, such as when the car goes uphill, downhill, or faces strong wind resistance.
This system can be represented by a block...
116

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提高机场流量:基于VLC,重定向技术和自适应奖励学习的智能系统.

Manuela Vieira1,2,3, Manuel Augusto Vieira1,2, Gonçalo Galvão1,3

  • 1Electronics Telecommunication and Computer Department, Instituto Superior de Engenharia de Lisboa-Instituto Politécnico de Lisboa, 1949-014 Lisboa, Portugal.

Sensors (Basel, Switzerland)
|May 14, 2025
PubMed
概括

本研究介绍了一种使用可见光通信 (VLC) 和深度增强学习 (DRL) 来优化导航的AI驱动的机场交通系统. 该系统通过智能管理行人和自动驾驶车辆 (AGV) 交通流量来提高安全性和效率.

关键词:
适应性奖励机制 适应性奖励机制自主导向车辆 (AGVs)深度强化学习 (DRL) 是一种深度强化学习.室内局部化 室内局部化智能重定向技术是一种智能重定向技术.多代理系统是多代理系统.路线优化路线优化交通流量模拟 交通流量模拟可见光通信 (VLC) 是一种可见光通信.寻找途径的援助.

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科学领域:

  • 智能运输系统 智能运输系统
  • 在移动方面的人工智能.
  • 无线通信网络 无线通信网络

背景情况:

  • 机场需要有效地定位和管理行人和自动驾驶汽车 (AGV) 的交通.
  • 现有系统在优化复杂的机场流量和确保安全方面面临挑战.
  • 无需GPS的无室内导航对于运营效率至关重要.

研究的目的:

  • 开发一个人工智能驱动的机场交通管理系统,集成可见光通信 (VLC) 和深度强化学习 (DRL).
  • 为了优化交通流动,减少拥堵,并提高行人和农用车的安全性.
  • 为了实现准确的室内定位,并提高整体机场流动性.

主要方法:

  • 使用带开关调制的四色LED和VLC的SiC光学接收器实现混合网状网络.
  • 开发使用深度强化学习 (DRL) 和Q学习算法进行交通分析和决策的AI代理.
  • 整合重定向技术和适应性奖励机制,以实现动态交通负载平衡和避免瓶.

主要成果:

  • 实现了更平衡的绿色时间分配,减少了高达43%的车辆优先级阶段,以适应行人流动.
  • 改进了路线规划,减少了停车时间,加强了 AGV 和行人交通之间的协调.
  • 确认保留了交通流动的响应能力和稳定的关键清除阶段,尽管行人优先.

结论:

  • 拟议的人工智能驱动的VLC系统通过优化行人和AGV协调,显著提高了机场交通管理.
  • 整合DRL和适应性策略导致更安全,更有效,更以人为中心的机场流动.
  • 该系统提供了准确的室内定位,支持无操作,无需依赖GPS.