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相关概念视频

Network Function of a Circuit01:25

Network Function of a Circuit

599
Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
599
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

333
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
333
Van der Waals Interactions01:24

Van der Waals Interactions

70.0K
Atoms and molecules interact with each other through intermolecular forces. These electrostatic forces arise from attractive or repulsive interactions between particles with permanent, partial, or temporary charges. The intermolecular forces between neutral atoms and molecules are ion–dipole, dipole–dipole, and dispersion forces, collectively known as van der Waals forces.
70.0K
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

467
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...
467
Block Diagram Reduction01:22

Block Diagram Reduction

495
The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
The first step in this process is the identification and relocation of a branch point. A branch point, where a...
495
Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

723
The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
In this model, each generator is connected to a...
723

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相关实验视频

Updated: Jan 8, 2026

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

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基于区块链的安全MEC模型用于使用混合网络的VANET.

G Vijay Goud1, Rajesh Arunachalam2, Surendra Kumar Shukla3

  • 1Department of Electronics and Communication Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Thandalam, Chennai, Tamilnadu, 602105, India.

Scientific reports
|December 16, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种使用深度学习和区块链的安全车辆临时网络 (VANET) 模型. 这种创新方法提高了联网汽车的数据安全性和隐私.

关键词:
适应式和扩展式混合动力网络.同型与圆曲线密码学同型.服务质量服务的质量.随机数更新技能优化算法剩余的长期短期内存与封闭的反复单元.安全的多访问边缘计算边缘计算.车辆特设网络是车辆特设网络.

相关实验视频

Last Updated: Jan 8, 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

Published on: September 8, 2023

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科学领域:

  • 网络安全 网络安全
  • 网络工程 网络工程
  • 人工智能的人工智能

背景情况:

  • 车载临时网络 (VANET) 需要强大的安全性和低延迟.
  • 多访问边缘计算 (MEC) 提供基于近距离的计算和存储.
  • 将MEC与区块链集成,可以增强VANET的数据处理,安全和隐私.

研究的目的:

  • 开发一个创新的,安全的VANET模型,利用深度学习,MEC和区块链.
  • 增强数据隐私,防止欺诈,并确保VANET中可信的通信.
  • 创建一个由深度学习为VANET安全提供动力的区块链架构.

主要方法:

  • 一个三层网络架构:感知,边缘计算和服务.
  • 使用适应和扩展混合网络 (ADHyNet),包括Res-LSTM和GRU用于节点身份验证.
  • 使用随机数更新技能优化算法 (RNU-SOA) 进行超参数优化.
  • 实现同型加密与圆曲线加密 (HECC) 结合用于数据加密.

主要成果:

  • 拟议的框架有效地评估了车辆节点在区块链上的可靠性.
  • ADHyNet模型成功实现了节点身份验证.
  • 在HECC加密确保机密的用户信息被保护免受未经授权的访问.
  • 与现有方法相比,模拟表明数据安全性表现优越.

结论:

  • 集成的MEC和区块链模型显著提高了VANET数据安全性和隐私.
  • 基于深度学习的区块链架构为VANET安全提供了强大的解决方案.
  • 开发的系统为用户提供了更好的服务质量 (QoS) 和吞吐量.