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Types Of Transformers01:16

Types Of Transformers

1.0K
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...
1.0K
Transformers in Distribution System01:27

Transformers in Distribution System

141
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...
141
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

193
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...
193
The Ideal Transformer01:26

The Ideal Transformer

488
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...
488
Instrument Transformers01:23

Instrument Transformers

126
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...
126
Energy Losses in Transformers01:21

Energy Losses in Transformers

936
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...
936

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Related Experiment Video

Updated: Aug 19, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Asthma Detection Research Based on Voice Signal Processing and Machine Learning

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Transformers for Urban Sound Classification-A Comprehensive Performance Evaluation.

Ana Filipa Rodrigues Nogueira1, Hugo S Oliveira2, José J M Machado3

  • 1Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre 1021 1055, 4169-007 Porto, Portugal.

Sensors (Basel, Switzerland)
|November 26, 2022
PubMed
Summary
This summary is machine-generated.

A Transformer model with an Adam optimizer and audio transfer learning achieved high accuracy in classifying urban sound events. This robust sound classification approach is effective and prompt for real-world applications.

Keywords:
Adam optimizerconvolutional neural networkdata augmentationdeep learningurban sounds’ classification

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Area of Science:

  • Machine Learning
  • Acoustic Event Detection
  • Urban Sound Classification

Background:

  • Urban environments generate numerous relevant sound events requiring accurate and timely classification.
  • Existing models need improvement for effectiveness and promptness in identifying and duration-tracking sound events.
  • Robust sound classification models are crucial for monitoring and analyzing urban acoustic scenes.

Purpose of the Study:

  • To identify the best-performing model for classifying a wide range of urban sound events.
  • To analyze and model Transformer architectures for urban sound event classification.
  • To investigate the impact of pre-training, data augmentation, and complementary methods on model performance.

Main Methods:

  • Extensive analysis and modeling of Transformer models on public urban sound datasets.
  • Comparison of Transformer models against baseline and convolutional neural network (CNN) models.
  • Evaluation of pre-training strategies (image and sound domains) and data augmentation techniques.

Main Results:

  • A Transformer model utilizing a novel Adam optimizer with weight decay and AudioSet transfer learning achieved superior performance.
  • High accuracy scores were recorded: 89.8% on UrbanSound8K, 95.8% on ESC-50, and 99% on ESC-10.
  • Pre-training from the audio domain and data augmentation significantly improved model robustness.

Conclusions:

  • Transformer models, particularly when combined with optimized training and transfer learning, represent a highly promising approach for urban sound classification.
  • The proposed method offers effective and prompt identification of urban sound events, addressing key requirements for real-world applications.
  • Best practices include leveraging pre-trained models and data augmentation for enhanced sound classification system performance.