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相关概念视频

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
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
Force Classification01:22

Force Classification

1.2K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.2K
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
Aggregates Classification01:29

Aggregates Classification

317
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
317
The Ideal Transformer01:26

The Ideal Transformer

381
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...
381

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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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双依赖注意力变压器用于细粒度视觉分类.

Shiyan Cui1,2,3,4, Bin Hui1,2,3

  • 1Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China.

Sensors (Basel, Switzerland)
|April 13, 2024
PubMed
概括

本研究介绍了一种用于细粒度视觉分类 (FGVC) 的双依赖性注意力转换器. 新模型实现了线性复杂性,提高了FGVC任务的性能.

关键词:
深度学习是一种深度学习.细粒度的视觉分类细粒度的视觉分类视觉变压器 视觉变压器

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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 视觉转换器 (ViT) 在像细粒度视觉分类 (FGVC) 这样的视觉任务中很普遍.
  • 在ViT中,核心的自我注意力机制会产生二次计算和内存成本.
  • 现有的稀疏注意力和局部注意力方法不足以满足FGVC对密集特征和全球依赖性建模的需求.

研究的目的:

  • 为视觉转换器开发一个有效的注意力机制,适合细粒度视觉分类.
  • 为了解决ViTs中标准自我注意的计算和内存复杂性限制.
  • 为了提高FGVC性能,增强歧视性线索的建模.

主要方法:

  • 提出一个双依赖的注意力转换器,将全球代币交互解.
  • 实施一个位置依赖的注意力路径,使用集体注意力.
  • 使用语义依赖注意路径与动态中心聚合.
  • 开发用于敏感提示跟踪的歧视性增强策略,使用基于知识的表示.

主要成果:

  • 双依赖性注意力变压器实现了线性计算复杂性.
  • 该模型证明适用于细粒度图像分类任务的适用性.
  • 对NABIRDS,CUB和DOGS数据集的实验结果验证了拟议的方法.

结论:

  • 拟议的双依赖性注意力转换器为细粒度视觉分类提供了有效的解决方案.
  • 这种方法在FGVC中平衡了计算效率与高性能.
  • 该方法增强了语义建模和歧视性提示跟踪,优于FGVC的现有方法.