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相关概念视频

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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相关实验视频

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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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一种无监督的工业图像异常检测方法,使用基于视觉变压器的自动编码器.

Qiying Yang1, Rongzuo Guo1

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

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

本研究介绍了一种使用视觉变压器 (ViT) 的无监督异常检测模型,以改进工业图像分析. 新方法增强了特征表示和本地化,优于传统的卷积神经网络 (CNN).

科学领域:

  • 计算机视觉 计算机视觉
  • 机器学习 机器学习
  • 工业自动化 工业自动化

背景情况:

  • 目前的工业图像异常检测严重依赖于卷积神经网络 (CNN),它们经常在全球特征提取方面扎,并且可以误解具有类似像素值但具有不同的语义含义的异常.
  • 由于工业环境中异常样本稀缺,数据不平衡进一步使异常检测复杂化.
  • 现有的基于CNN的自动编码器仅限于局部特征,阻碍了它们同化全球背景的能力,并导致无效的异常检测.

研究的目的:

  • 提出一个无监督的异常检测模型,克服CNN在捕获全球特征信息和处理数据不平衡方面的局限性.
  • 为了提高工业图像中异常检测的准确性和定位精度.
  • 提高异常检测系统的概括性和特征表示能力.

主要方法:

  • 采用视觉变压器 (ViT) 架构,利用其变压器结构来理解图像块之间的全球上下文,并提取高级特征表示.
  • 集成了一个内存模块来存储正常样本特征,减轻异常重建问题并加强特征表示.
  • 整合了坐标注意 (CA) 机制,以关注空间和通道维度,最大限度地减少特征信息损失,以精确地识别和定位异常.

主要成果:

  • 与传统的CNN相比,拟议的基于ViT的模型在提取全球特征信息方面表现出卓越的表现.
  • 集成的内存模块和CA机制有效地解决了异常重建的挑战,并改善了特征表示.
关键词:
视觉变压器 视觉变压器检测异常检测异常检测注意力机制注意力机制记忆网络 记忆网络 记忆网络

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  • 对MVTec AD和BeanTech AD数据集的实验显示,与传统卷积编码器相比,图像层面的平均AUROC%大约有20%的改善.

    • 结论:

    • 使用视觉变压器架构的无监督异常检测模型比现有的基于CNN的工业图像分析方法提供了显著的进步.
    • 该模型能够捕捉全球上下文并增强特征表示的能力,导致更精确的异常识别和定位.
    • 拟议的方法有效地减轻了与工业异常检测中的局部特征限制和数据不平衡相关的问题.