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Reinforcement Schedules01:24

Reinforcement Schedules

438
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,...
438
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

1.1K
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...
1.1K
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

578
The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
578
Energy Budgets00:51

Energy Budgets

10.5K
Organisms must balance energy intake with the energy required for growth, maintenance and reproduction. These trade-offs result in a variety of survivorship and reproductive strategies, including semelparity and iteroparity. Semelparous species, like annual plants, have only one reproductive episode in their lifetimes and consequently have short lifespans. Iteroparous species, by contrast, have many reproductive events during their lifetimes but have relatively few offspring. These two...
10.5K
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

1.1K
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of the...
1.1K
Energy Conservation and Bernoulli's Equation01:16

Energy Conservation and Bernoulli's Equation

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Applying the conservation of energy principle or the work-energy theorem to an incompressible, inviscid fluid in laminar, steady, irrotational flow leads to Bernoulli's equation. It states that the sum of the fluid pressure, potential, and kinetic energy per unit volume is constant along a streamline.
All the terms in the equation have the dimension of energy per unit volume. The kinetic energy per unit volume is called the kinetic energy density, and the potential energy per unit volume is...
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相关实验视频

Updated: Jan 10, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

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基于强化学习的多目标任务安排,以实现节能和成本效益高的云端计算.

Wenfan Zhang1, Haijiao Ou2

  • 1Information Center , Xiangya Hospital Central South University , Hunan, 410008, Changsha, China.

Scientific reports
|November 25, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了基于强化学习的多目标任务安排 (RL-MOTS),用于在混合云边缘系统中有效地分配资源. RL-MOTS显著降低了能源消耗和成本,同时提高了对延迟敏感应用程序的性能.

关键词:
云计算是一种云计算.云端计算是云端的计算.优化成本,优化成本.深度 Q 网络动态的工作负载适应.能源效率 能源效率是指能源的使用效率.多目标优化优化多目标优化服务质量服务的质量.强化学习是一种强化学习.任务安排 任务安排

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 分布式系统 分布式系统

背景情况:

  • 物联网 (IoT) 设备的扩散需要在混合云边缘环境中进行高效的任务安排.
  • 传统的调度算法与动态的工作负载和性能,能源和成本等冲突目标作斗争.
  • 对于延迟敏感的应用程序,需要优化资源配置,以便及时执行.

研究的目的:

  • 引入基于强化学习的多目标任务安排 (RL-MOTS) 以实现智能资源分配.
  • 开发一个平衡任务延迟,能源消耗和运营成本的框架.
  • 在异质云边缘系统中增强任务调度的适应性和可扩展性.

主要方法:

  • 制定任务安排作为马尔科夫决策过程.
  • 使用深度Q网络 (DQN) 进行自适应性资源分配.
  • 实施基于优先级的动态排队机制和多目标奖励功能.
  • 在异质节点之间使用状态奖励张量进行实时决策.

主要成果:

  • RL-MOTS实现了高达28%的能源消耗降低和20%的成本效益提高.
  • 与基线策略相比,观察到的Makepan和截止日期违反的显著减少.
  • 该框架在不同的工作负载条件下保持了严格的服务质量 (QoS) 要求.
  • 证明了适应先发制约和非先发制约调度场景的适应性.

结论:

  • RL-MOTS为下一代分布式计算提供了一个可持续的,成本高效的,以性能为导向的解决方案.
  • 该框架为混合云边缘环境提供智能和适应性资源配置.
  • 未来的工作将探索转移和联合学习,以提高分散系统中的可扩展性和隐私.