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Transformers in Distribution System01:27

Transformers in Distribution System

102
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...
102
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

151
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...
151
Types Of Transformers01:16

Types Of Transformers

971
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...
971
The Ideal Transformer01:26

The Ideal Transformer

380
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...
380
Deconvolution01:20

Deconvolution

159
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
159
Energy Losses in Transformers01:21

Energy Losses in Transformers

864
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...
864

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Updated: Jun 27, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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An Unsupervised Method for Industrial Image Anomaly Detection with Vision Transformer-Based Autoencoder.

Qiying Yang1, Rongzuo Guo1

  • 1College of Computer Science, Sichuan Normal University, Chengdu 610101, China.

Sensors (Basel, Switzerland)
|April 27, 2024
PubMed
Summary

This study introduces an unsupervised anomaly detection model using Vision Transformer (ViT) to improve industrial image analysis. The new method enhances feature representation and localization, outperforming traditional convolutional neural networks (CNNs).

Keywords:
Vision Transformeranomaly detectionattention mechanismmemory network

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Industrial Automation

Background:

  • Current industrial image anomaly detection relies heavily on Convolutional Neural Networks (CNNs), which often struggle with global feature extraction and can misinterpret anomalies with similar pixel values but different semantic meanings.
  • Data imbalance due to the scarcity of abnormal samples in industrial settings further complicates anomaly detection.
  • Existing CNN-based autoencoders are limited to local features, hindering their ability to assimilate global context and leading to ineffective anomaly detection.

Purpose of the Study:

  • To propose an unsupervised anomaly detection model that overcomes the limitations of CNNs in capturing global feature information and handling data imbalance.
  • To enhance the accuracy and localization precision of anomaly detection in industrial images.
  • To improve the generalizability and feature representation capabilities of anomaly detection systems.

Main Methods:

  • Employed the Vision Transformer (ViT) architecture to leverage its Transformer structure for understanding global context between image blocks and extracting superior feature representations.
  • Integrated a memory module to store normal sample features, mitigating anomaly reconstruction issues and reinforcing feature representation.
  • Incorporated a coordinate attention (CA) mechanism to focus on spatial and channel dimensions, minimizing feature information loss for precise anomaly identification and localization.

Main Results:

  • The proposed ViT-based model demonstrated superior performance in extracting global feature information compared to traditional CNNs.
  • The integrated memory module and CA mechanism effectively addressed anomaly reconstruction challenges and improved feature representation.
  • Experiments on MVTec AD and BeanTech AD datasets showed an approximate 20% improvement in average AUROC% at the image level over traditional convolutional encoders.

Conclusions:

  • The unsupervised anomaly detection model utilizing Vision Transformer architecture offers a significant advancement over existing CNN-based methods for industrial image analysis.
  • The model's ability to capture global context and enhance feature representation leads to more precise anomaly identification and localization.
  • The proposed approach effectively mitigates issues related to local feature limitations and data imbalance in industrial anomaly detection.