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Transformers in Distribution System01:27

Transformers in Distribution System

105
Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
105
Types Of Transformers01:16

Types Of Transformers

987
Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
987
Classification of Systems-I01:26

Classification of Systems-I

191
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:
191
Instrument Transformers01:23

Instrument Transformers

90
Instrument transformers, comprising voltage transformers (VTs) and current transformers (CTs), play crucial roles in power substations by providing isolated replicas of current or voltage for measurement and protection purposes. Voltage transformers reduce the primary voltage to levels suitable for relay operation and measurement, while current transformers scale down the primary current. The primary winding of a current transformer often consists of a single turn, achieved by threading the...
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Survival Tree01:19

Survival Tree

88
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
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Transformers01:26

Transformers

1.1K
A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
The iron core has a substantial relative permeability. Therefore, the magnetic field lines generated due to the current in one winding are almost entirely confined within the core, such that the same magnetic flux permeates each turn of both...
1.1K

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相关实验视频

Updated: Jul 13, 2025

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

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注意力变压器深度学习算法用于对物联网系统的入侵检测,使用自动Xplainable功能选择.

Demóstenes Zegarra Rodríguez1, Ogobuchi Daniel Okey2, Siti Sarah Maidin3

  • 1Department of Computer Science, Federal University of Lavras, Minas Gerais, Brazil.

PloS one
|October 16, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了TabNet-IDS,这是一款用于物联网 (IoT) 的入侵检测系统,它使用专注的功能选择机制. 它在检测常见表格数据集上的网络威胁方面实现了高准确性.

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科学领域:

  • 网络安全 网络安全
  • 机器学习 机器学习
  • 物联网 (IoT) 安全 安全 物联网

背景情况:

  • 物联网 (IoT) 和工业物联网 (IIoT) 系统的普及增加了安全风险,特别是来自拒绝服务 (DoS) 和分布式拒绝服务 (DDoS) 攻击.
  • 现有的入侵检测系统 (IDS) 经常在机器学习任务中常见的表格数据格式中扎,在模型可解释性和特征选择方面面临挑战.

研究的目的:

  • 为物联网安全提出智能入侵检测系统 (IDS),以解决表格数据上的传统深度学习模型的局限性.
  • 开发一种模型,利用注意力机制自动选择突出特征,并提供可解释的结果.

主要方法:

  • 在PyTorch深度学习框架内使用TabNet算法实现TabNet-IDS模型.
  • 利用TabNet中的注意力机制进行自动特征选择和增强模型可解释性.
  • 评估模型在基准数据集上的表现:CIC-IDS2017,CSE-CICIDS2018,以及CIC-DDoS2019.这些数据集中的表现.

主要成果:

  • 在测试的数据集上,TabNet-IDS模型实现了高准确率:在CIC-IDS2017上达到了97%;在CSE-CICIDS2018上达到了95%;在CIC-DDoS2019上达到了98%
  • 使用表格数据展示了TabNet架构在物联网环境中用于入侵检测的有效性.
  • 该模型的注意力机制提供了可解释的特征选择,有助于更好地理解模型.

结论:

  • TabNet是一个可行的和有效的深度学习架构,用于对物联网安全中的表格数据集进行入侵检测.
  • 拟议的TabNet-IDS提供了一个强大的解决方案,通过精确检测网络威胁,提供可解释的见解来提高物联网安全性.
  • 未来的工作可以探索基于注意力的模型的进一步优化和应用,以应对先进的网络安全挑战.