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Transformers in Distribution System01:27

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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.
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Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
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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...
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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Vision Transformers (ViT) excel in computer vision due to their self-attention mechanism.
    • Limited research exists on ViT explainability and the impact of cross-patch attention on performance.
    • Understanding ViT's attention mechanisms is crucial for unlocking its full potential.

    Purpose of the Study:

    • To develop a novel explainable visualization approach for analyzing ViT's patch interactions.
    • To quantify the impact of patch interactions on ViT performance.
    • To propose a new window-free transformer (WinfT) architecture based on patch responsiveness.

    Main Methods:

    • Introduced a quantification indicator to measure the impact of patch interactions.
    • Verified the quantification on attention window design and removal of non-discriminative patches.
    • Exploited the effective responsive field of each patch to design the WinfT architecture.

    Main Results:

    • The proposed quantitative method significantly facilitates ViT model learning.
    • Achieved a maximum improvement of 4.28% in top-1 accuracy on ImageNet.
    • Demonstrated the generalization of the approach on downstream fine-grained recognition tasks.

    Conclusions:

    • The developed explainable visualization and quantification methods enhance ViT performance.
    • The window-free transformer architecture offers a promising direction for future ViT development.
    • This work provides crucial insights into ViT's attention mechanisms and their impact on model efficacy.