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Related Concept Videos

Transformers in Distribution System01:27

Transformers in Distribution System

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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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Energy Losses in Transformers01:21

Energy Losses in Transformers

901
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...
901
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

176
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...
176
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
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...
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The Ideal Transformer01:26

The Ideal Transformer

423
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...
423

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Related Experiment Video

Updated: Jul 16, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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A Transformer-Optimized Deep Learning Network for Road Damage Detection and Tracking.

Niannian Wang1, Lihang Shang1, Xiaotian Song2

  • 1School of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou 450001, China.

Sensors (Basel, Switzerland)
|September 9, 2023
PubMed
Summary

This study introduces Road-TransTrack, an optimized transformer-based model for accurate road damage detection and tracking. It significantly improves upon existing methods for identifying potholes and cracks in road infrastructure.

Keywords:
object trackingroad damage detectionself-attention mechanismtransformer

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Road Infrastructure Monitoring

Background:

  • Existing road damage detection and tracking models suffer from low accuracy and false counts.
  • Automated monitoring of road conditions is crucial for timely maintenance and safety.

Purpose of the Study:

  • To develop an advanced tracking model, Road-TransTrack, for enhanced road damage object detection and tracking.
  • To improve the accuracy and reliability of automated road damage assessment.

Main Methods:

  • Utilized YOLOv5 for image classification, categorizing road damage into potholes and cracks.
  • Developed Road-TransTrack by integrating transformer optimization and a self-attention mechanism.
  • Trained and validated the model on a custom road damage dataset and real-world road videos.

Main Results:

  • Road-TransTrack achieved high detection accuracy: 91.60% for cracks and 98.59% for potholes.
  • The model demonstrated strong performance with F1 scores of 0.9417 for cracks and 0.9847 for potholes.
  • Outperformed conventional convolutional neural networks in both detection and counting accuracy.

Conclusions:

  • Road-TransTrack offers superior performance for road damage object detection and tracking.
  • The proposed transformer-based approach effectively addresses limitations of existing models.
  • This technology has significant potential for improving road maintenance and safety management.