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相关概念视频

Transformers in Distribution System01:27

Transformers in Distribution System

498
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...
498
Types Of Transformers01:16

Types Of Transformers

1.4K
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...
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Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

523
In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
523
Classification of Signals01:30

Classification of Signals

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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...
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The Ideal Transformer01:26

The Ideal Transformer

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In single-phase two-winding transformers, two windings are coiled around a magnetic core characterized by cross-sectional area A and magnetic permeability μ. A phasor current i1 enters the left winding while i2 exits the right winding, establishing the fundamental working of the transformer through electromagnetic principles.
Ampere's Law forms the basis of understanding the magnetic field within the transformer. It states that the integral of the magnetic field intensity's tangential...
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Classification of Systems-I01:26

Classification of Systems-I

552
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:
552

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相关实验视频

基于变压器的代币化用于在各种网络环境中对物联网流量进行分类.

Firdaus Afifi1,2, Faiz Zaki2, Hazim Hanif2,3

  • 1Faculty of Computer Science and Mathematics, Universiti Malaysia Terengganu, Kuala Nerus, Terengganu, Malaysia.

PeerJ. Computer science
|September 24, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了MIND-IoT,这是物联网 (IoT) 流量分类的新框架. 在识别物联网流量方面,MIND-IoT实现了高准确度,优于现有的方法.

关键词:
这就是为什么物联网物联网物联网.模型的微调调节.网络流量分析 网络流量分析网络流量分类网络流量分类.准备培训 准备培训一个单一任务模型.转移学习转移学习变压器变压器变压器

相关实验视频

科学领域:

  • 计算机科学 计算机科学
  • 网络安全 网络安全
  • 机器学习 机器学习

背景情况:

  • 物联网 (IoT) 流量扩张需要对网络安全和效率进行准确的分类.
  • 现有的方法与一般化,数据依赖和动态场景作斗争.
  • 变压器模型看起来很有前途,但由于不规则的物联网流量和单一任务限制,它们存在局限性.

研究的目的:

  • 引入MIND-IoT,用于通用物联网流量分类的可扩展框架.
  • 解决当前处理各种物联网环境和数据的方法的局限性.
  • 为应对现实世界物联网网络挑战开发出强大而适应性的解决方案.

主要方法:

  • 一种混合架构,结合了变压器模型和卷积神经网络 (CNN).
  • IoT-Tokenize:一种定制的代币化管道,用于保护网络流的语义.
  • 两阶段操作:使用掩盖语言建模 (MLM) 的预训练和特定任务的微调.

主要成果:

  • 在各种数据集中达到高达98.14%的准确性和97.85%的F1分数.
  • 与传统方法相比,表现出卓越的性能,坚固性和适应性.
  • 展示了对新数据集进行分类和适应新出现的任务,最小微调的能力.

结论:

  • MIND-IoT为物联网流量分类提供了高效和可扩展的解决方案.
  • 该框架的混合架构和自定义代币化增强了概括性和效率.
  • 在保护和优化物联网网络方面,MIND-IoT代表了重大进步.