Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

632
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...
632
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

198
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
198

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Historical Text Image Enhancement Using Image Scaling and Generative Adversarial Networks.

Sensors (Basel, Switzerland)·2023
Same author

Enabling Artificial Intelligence of Things (AIoT) Healthcare Architectures and Listing Security Issues.

Computational intelligence and neuroscience·2023
Same author

Privacy in targeted advertising on mobile devices: a survey.

International journal of information security·2023
Same author

An Effective Self-Configurable Ransomware Prevention Technique for IoMT.

Sensors (Basel, Switzerland)·2022
Same author

A Machine Learning-Based Water Potability Prediction Model by Using Synthetic Minority Oversampling Technique and Explainable AI.

Computational intelligence and neuroscience·2022
Same author

AI-Based Prediction of Capital Structure: Performance Comparison of ANN SVM and LR Models.

Computational intelligence and neuroscience·2022
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
查看所有相关文章

相关实验视频

Updated: Jul 7, 2025

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

578

深度学习启发的IoT-IDS机制用于边缘计算环境.

Abdulaziz Aldaej1, Tariq Ahamed Ahanger2, Imdad Ullah3

  • 1College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia.

Sensors (Basel, Switzerland)
|December 23, 2023
PubMed
概括
此摘要是机器生成的。

这项研究介绍了一种新的深度学习 (DL) 侵入检测系统 (IDS) 用于物联网 (IoT). 基于DL的IDS有效地检测边缘设备上的网络攻击,在减少数据的情况下保持高准确度.

关键词:
在这里,我们可以看到AIAIAI.在IDS IDS中,您可以使用这就是为什么物联网是物联网物联网.边缘计算是一种边缘计算.安全的安全的安全的安全的安全.

更多相关视频

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.2K
Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring
06:32

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Published on: July 14, 2023

1.3K

相关实验视频

Last Updated: Jul 7, 2025

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

578
Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.2K
Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring
06:32

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Published on: July 14, 2023

1.3K

科学领域:

  • 网络安全 网络安全
  • 机器学习 机器学习
  • 网络入侵检测 网络入侵检测

背景情况:

  • 物联网 (IoT) 产生了大量数据,给网络安全带来了挑战.
  • 深度学习 (DL) 显示了在物联网环境中检测网络攻击的前景.
  • 当前的入侵检测系统 (IDS) 难以应对DL的规模和计算需求,以实现边缘部署.

研究的目的:

  • 提出基于边缘云的物联网IDS,利用DL进行高效的网络攻击检测.
  • 解决当前IDS在处理大量物联网数据量和计算要求方面的局限性.
  • 为了实现及时检测更接近物联网边缘设备的威胁,以保护关键基础设施.

主要方法:

  • 开发了一个分布式边缘云架构,用于物联网入侵检测.
  • 在时间序列物联网数据上实现了属性选择,以减少数据集大小.
  • 训练了一个DL模型,使用循环神经网络 (RNN) 和双向长期短期记忆 (Bi-LSTM) 进行攻击检测.
  • 在高维的BoT-IoT数据集上验证了模型.

主要成果:

  • 属性选择将数据集大小降低了85%,而不会影响检测能力.
  • DL模型实现了高性能指标:98.25%的回忆率,99.12%的F1得分,99.56%的准确率和99.45%的精度.
  • 在缩小数据集上训练的模型没有显示过少或过度装配.
  • 拟议的解决方案证明了对大量物联网数据的高效可扩展性.

结论:

  • 拟议的基于DL的物联网IDS对于边缘云部署是有效和可扩展的.
  • 它为物联网环境中的实时网络攻击检测提供了可行的解决方案.
  • 该方法成功地平衡了资源有限的边缘设备的性能和计算效率.