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

Classification of Signals01:30

Classification of Signals

1.3K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.3K
Power Factor Correction01:20

Power Factor Correction

473
The power transmission to a factory involves the transfer of apparent power, a combination of active and reactive power. The power factor measures how effectively electrical power is converted into useful work output. The ratio of the real power (KW) that does the work to the apparent power (KVA) supplied to the circuit.
473
Classification of Systems-II01:31

Classification of Systems-II

447
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
447
Classification of Systems-I01:26

Classification of Systems-I

543
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
543
Power System Distribution01:25

Power System Distribution

1.0K
Power system distribution involves delivering electrical energy from power plants to consumers through a network of transmission and distribution systems. The process begins at power plants, where energy from coal, gas, nuclear, water, and wind is converted into electrical energy. These plants use three-phase generators, typically rated between 50 to 1300 MVA, with terminal voltages ranging from a few kV to 20 kV, depending on the size and age of the units.
The transmission system is designed...
1.0K
Aggregates Classification01:29

Aggregates Classification

956
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
956

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

SSDBFAN: Scalable and Secure Cluster-Based Data Aggregation with Blockchain for Flying Ad Hoc Networks.

Sensors (Basel, Switzerland)·2026
Same author

Efficient and Accurate Zero-Day Electricity Theft Detection from Smart Meter Sensor Data Using Prototype and Ensemble Learning.

Sensors (Basel, Switzerland)·2025
Same author

Hardware Acceleration-Based Privacy-Aware Authentication Scheme for Internet of Vehicles Using Physical Unclonable Function.

Sensors (Basel, Switzerland)·2025
Same author

Privacy-Preserving Detection of Tampered Radio-Frequency Transmissions Utilizing Federated Learning in LoRa Networks.

Sensors (Basel, Switzerland)·2024
Same author

Identifying Tampered Radio-Frequency Transmissions in LoRa Networks Using Machine Learning.

Sensors (Basel, Switzerland)·2024
Same author

Advanced 3D Face Reconstruction from Single 2D Images Using Enhanced Adversarial Neural Networks and Graph Neural Networks.

Sensors (Basel, Switzerland)·2024
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
Same journal

Three-Dimensional Modeling and Performance Analysis of Dynamic mmWave V2I Networks Based on Stochastic Geometry.

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

相关实验视频

Updated: Jan 10, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

999

强大的基于学习的联邦分类器,用于智能电网电力质量障碍.

Maazen Alsabaan1, Abdelrhman Elsayed2, Atef Bondok3

  • 1Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia.

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

联合学习 (FL) 使智能电网运营商能够在不共享敏感数据的情况下协同训练电源质量干扰 (PQD) 检测模型. 一个新的防御机制保护这些FL模型免受数据中毒攻击,增强电网安全.

关键词:
人工智能的人工智能是人工智能.联合学习的联合学习.机器学习是机器学习.毒害攻击 毒害攻击电力质量干扰 电力质量干扰智能电网是一个智能电网.

相关实验视频

Last Updated: Jan 10, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

999

科学领域:

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

背景情况:

  • 智能电网需要先进的方法来检测电力质量干扰 (PQD).
  • 由于可再生能源和非线性负载增加的PQD需要强大的检测.
  • 数据隐私问题限制了用于PQD分类的集中深度学习的使用.

研究的目的:

  • 开发和评估基于联邦学习 (FL) 的PQD检测分类器.
  • 在PQD分类中评估FL模型对数据中毒攻击的脆弱性.
  • 实施和验证FL对PQDs的中毒攻击的防御机制.

主要方法:

  • 开发基于FL的PQD检测分类器,并将其与集中模型进行比较.
  • 模拟了五个数据中毒攻击场景,以评估模型的稳定性.
  • 实现了一个检测机制来识别和隔离恶意客户端更新.

主要成果:

  • 与集中式模型相比,FL模型的性能略有下降 (97%至96%的准确性).
  • 数据中毒攻击显著降低了分类器的准确性.
  • 实施的防御机制有效地缓解了受毒更新影响的性能下降.

结论:

  • FL为智能电网中的PQD分类提供了一种保护隐私的方法.
  • FL模型易受数据中毒攻击的影响,影响分类准确性.
  • 拟议的防御机制增强了基于FL的PQD分类器对抗对方攻击的稳定性.