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Electrical Energy01:10

Electrical Energy

1.3K
Using electric appliances for a longer period of time consumes more electrical energy and results in a higher electric bill. The energy produced by the transfer of electrons from one point to another is known as electrical energy. If power is delivered at a constant rate, the electrical energy can be defined as the product of power used by the device for a period of time. The energy unit on electric bills is the kilowatt-hour, where one kilowatt-hour is equivalent to 3.6 × 106 joules.
1.3K
Energy and Power Signals01:17

Energy and Power Signals

620
In an electrical system with a resistor, voltage and current signals facilitate the measurement of power and energy across the resistor. For a continuous-time signal, the total energy over a time interval is defined as the integral of the square of the signal's magnitude over that interval. Mathematically, this is expressed as:
620
Electrical Power01:07

Electrical Power

3.2K
Electric power is the product of current and voltage, represented in units of joules per second, or watts. For example, cars often have one or more auxiliary power outlets with which you can charge a cell phone or other electronic devices. These outlets may be rated at 20 amps and 12 volts, so that the circuit can deliver a maximum power of 240 watts. Consider a 25 Watt bulb and a 60 Watt bulb. The conversion of electrical energy produces heat and light, while the kinetic energy lost by the...
3.2K
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

185
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.
185
Energy Conservation and Bernoulli's Equation01:16

Energy Conservation and Bernoulli's Equation

9.4K
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...
9.4K
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

771
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...
771

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相关实验视频

Updated: Sep 18, 2025

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

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智能电网物联网框架用于预测使用联合学习同型加密的能源消耗.

Filip Jerkovic1, Nurul I Sarkar1, Jahan Ali1,2

  • 1Department of Computer and Information Sciences, School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand.

Sensors (Basel, Switzerland)
|June 27, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种安全的智能电网 (SG) 物联网 (IoT) 框架,使用联合学习 (FL) 和同态加密 (HE) 来预测能源消耗,同时保护用户隐私. 这种新的方法确保了智能电网系统中的数据安全.

关键词:
边缘计算是一种边缘计算.联合学习 (FL)物联网 (IoT) 的物联网 (IoT) 的物联网.互联网隐私和安全 互联网隐私和安全机器学习 (ML) 是指机器学习.智能电网 (SG) 是一个智能电网.

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科学领域:

  • 计算机科学 计算机科学
  • 电气工程 电气工程
  • 网络安全 网络安全

背景情况:

  • 智能电网 (SG) 技术需要为联合学习 (FL) 和物联网 (IoT) 应用程序增强数据隐私.
  • 现有的框架努力平衡能源消耗预测与强大的用户隐私保护.

研究的目的:

  • 提出一个新的SG物联网框架,集成FL,边缘计算和同态加密 (HE) 以安全地预测能源消耗.
  • 在智能电网环境中确保端到端机器学习工作负载安全性和用户隐私.

主要方法:

  • 开发了一个利用联合学习 (FL) 和边缘计算原则的框架.
  • 实现了Cheon-Kim-Kim-Song (CKKS) 完全同型加密 (HE) 在边缘设备之间进行点对点 (P2P) 数据交换.
  • 利用P2P网络在边缘设备之间进行安全通信.

主要成果:

  • 拟议的框架成功地预测了SG场景中的能源消耗.
  • 通过集成的HE和FL方法,证明了有效地保护用户隐私.
  • 验证了端到端机器学习工作负载执行的稳定性和安全性.

结论:

  • 新的SG物联网框架为安全的能源消耗预测提供了可行的解决方案.
  • 结果为研究人员和开发下一代SG物联网系统的工程师提供了宝贵的见解.
  • 结合FL,边缘计算和HE对于未来的安全智能电网进步至关重要.