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相关概念视频

Per-Unit Sequence Models01:26

Per-Unit Sequence Models

98
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
98
Equivalent Circuits for Practical Transformers01:28

Equivalent Circuits for Practical Transformers

478
The practical equivalent circuits of single-phase two-winding transformers exhibit significant deviations from their idealized versions due to the inherent properties of winding resistance and finite core permeability. These properties result in real and reactive power losses, affecting the transformer's performance. Understanding these deviations is crucial for designing more efficient transformers.
In a practical transformer, each winding exhibits resistance and leakage reactance. The...
478
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 with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

182
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...
182
Reducing Line Loss01:18

Reducing Line Loss

176
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
176
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

125
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
125

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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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一个罗变压器网络为零射击古老硬币分类.

Zhongliang Guo1, Ognjen Arandjelović1, David Reid1

  • 1School of Computer Science, University of St Andrews, Scotland KY16 9AJ, UK.

Journal of imaging
|June 27, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的双语ViT模型用于古代硬币识别,通过使用对对匹配而不是分类来提高准确性. 这种新方法有效地处理稀有硬币发行,并超越了以前的基准标准.

关键词:
姆神经网络的神经网络计算机视觉 计算机视觉深度学习是一种深度学习.低射学习的低射学习机器学习是机器学习.匹配的匹配匹配的匹配

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科学领域:

  • 计算机视觉 计算机视觉
  • 机器学习 机器学习
  • 古代的数字学 古代的数字学

背景情况:

  • 古代硬币的识别是数字学中的一个具有挑战性的问题.
  • 现有的方法,主要是以分类为基础的,与罕见的硬币问题作斗争,并需要对新数据进行重新训练.
  • 传统的计算机视觉方法对此任务的效果不如深度学习.

研究的目的:

  • 开发一种更强大的古老硬币归因方法,克服分类任务的局限性.
  • 为了利用深度学习,特别是变压器,在硬币分析中改进特征提取.
  • 创建一个模型,不需要训练特定类的示例.

主要方法:

  • 采用一对硬币匹配范式,而不是传统的分类.
  • 实现一个语神经网络架构,特别是双语视觉变压器 (ViT).
  • 在一个数据集上利用转移学习,该数据集包括14,820个古老硬币图像,跨越7605个发行量,以及一个由542个图像组成的小型训练集.

主要成果:

  • 双姆人ViT模型的整体精度达到了81%,明显超过了当前的技术水平.
  • 该模型在处理很少或没有示例的问题时表现出有效性.
  • 分析表明,大多数错误是由于数据质量问题而不是算法限制.

结论:

  • 采用双米ViT的拟议的双对匹配方法对于古代硬币的归因是非常有效的.
  • 基于变压器的模型与非局部注意力机制显示出分析复杂的视觉数据的前景,例如古老的硬币.
  • 数据预处理和质量检查对于最大限度地提高自动化数字分析系统的性能至关重要.