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Related Concept Videos

Types Of Transformers01:16

Types Of Transformers

977
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...
977
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

157
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...
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Energy Losses in Transformers01:21

Energy Losses in Transformers

876
In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
There are four main reasons for energy losses in transformers.
The first cause can be  the high resistance of the...
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The Ideal Transformer01:26

The Ideal Transformer

395
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...
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Transformers in Distribution System01:27

Transformers in Distribution System

103
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...
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Three-Winding Transformers01:19

Three-Winding Transformers

228
Three identical single-phase transformers can be configured to form a three-phase transformer connection, which involves high-voltage and low-voltage windings. The high-voltage windings are denoted by capital letters A-B-C, while the low-voltage windings are labeled with lowercase letters a-b-c, representing their respective phases. This notation helps distinguish between the high and low voltage sides of the transformer.
In the per-unit equivalent circuit of a grounded Y-Y three-phase...
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Lightweight Transformer Model for Mobile Application Classification.

Minju Gwak1, Jeongwon Cha1, Hosun Yoon2

  • 1Department of Computer Engineering, Changwon National University, Changwon 51140, Republic of Korea.

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|January 23, 2024
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Summary
This summary is machine-generated.

A new lightweight transformer model accurately classifies applications using encrypted network traffic. This enables differentiated services for real-time applications like virtual reality (VR) and augmented reality (AR) with high accuracy.

Keywords:
application classificationdeep learningtransformer modelwireless LAN

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Area of Science:

  • Computer Science
  • Network Engineering
  • Machine Learning

Background:

  • Realistic services like virtual reality (VR) and augmented reality (AR) demand high-reliability, low-latency network transmission.
  • Current network infrastructure may not adequately support the deterministic requirements for these advanced applications.
  • Classifying applications based on encrypted data is crucial to protect user privacy while enabling differentiated service delivery.

Purpose of the Study:

  • To develop a robust method for classifying applications using only encrypted network payload data.
  • To propose and evaluate a lightweight transformer model for this classification task.
  • To compare the proposed model's performance against existing methods like 1D-CNN and ET-BERT.

Main Methods:

  • Collected and preprocessed network traffic data from four popular applications.
  • Extracted encrypted application data to serve as input for the machine learning model.
  • Designed and implemented a lightweight transformer model comprising an encoder, global average pooling, and a dense layer.
  • Optimized model hyperparameters through rigorous performance evaluations.
  • Benchmarked the transformer model against 1D-CNN and ET-BERT.

Main Results:

  • The proposed transformer model achieved a classification accuracy of 96% and an F1 score of 95%.
  • The model demonstrated superior classification performance compared to ET-BERT.
  • While having higher time complexity than 1D-CNN, the transformer model offered better application classification accuracy.
  • The transformer model exhibited lower time complexity than ET-BERT with enhanced classification performance.

Conclusions:

  • The lightweight transformer model effectively classifies applications based on encrypted network payloads, addressing privacy concerns.
  • This approach enables the provision of differentiated network services essential for demanding real-time applications.
  • The proposed model offers a promising solution for enhancing network performance and quality of service for emerging technologies.