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相关概念视频

Equivalent Circuits for Practical Transformers01:28

Equivalent Circuits for Practical Transformers

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 winding...
Three-Winding Transformers01:19

Three-Winding Transformers

Three identical single-phase transformers can be configured to form a three-phase transformer connection, which involves high-voltage and low-voltage windings. The high-voltage windings are denoted by capital letters A-B-C, while the low-voltage windings are labeled with lowercase letters a-b-c, representing their respective phases. This notation helps distinguish between the high and low voltage sides of the transformer.
In the per-unit equivalent circuit of a grounded Y-Y three-phase...
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

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 rated...

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在视觉变压器模型中使用TAVAC量化解释可复制性.

Yue Zhao1, Dylan Agyemang2, Yang Liu1

  • 1The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.

bioRxiv : the preprint server for biology
|February 8, 2024
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概括

一个新的指标,训练注意力和验证注意力一致性 (TAVAC),评估视觉变压器 (ViT) 模型中的过拟合数字病理学. TAVAC量化了解释的可重现性,确保从生物医学图像中可靠地提取诊断特征.

科学领域:

  • 人工智能的人工智能
  • 生物医学成像技术 生物医学成像技术

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  • 数字病理学数字病理学
  • 背景情况:

    • 深度学习,特别是视觉转换器 (ViT) 模型,显示出从生物医学图像中提取诊断特征的前景,在图像分类和解释性方面超过卷积神经网络 (CNN).
    • 然而,有限的注释数据集可能会导致ViT模型过度适应,导致不可靠的预测和受损的模型解释,特别是在数字病理学应用中.

    结论:

    • TAVAC建立了一个新的标准来评估深度学习模型的解释性能,特别是生物医学图像.
    • 该指标增强了对像素分辨率的解释性可重现性的监测,有助于基础研究和疾病机制发现.
    • TAVAC有助于确定注意力地图区域的意义,这对于诊断和研究中透明和可靠的AI至关重要.