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Transformers with Off-Nominal Turns Ratios01:25

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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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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...
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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.
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Microbial communities are dynamic environments where cell lysis releases free DNA into the surroundings. Other cells can take up this extracellular DNA through a process known as transformation.When a cell incorporates this foreign DNA into its genome, resulting in genetic modification, the process is known as transformation. Cells capable of this process are termed competent. Competence can be natural, as observed in certain bacteria and archaea, or artificially induced in the...
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A position vector is a fundamental concept in mathematics that helps determine the position of one point with respect to another point in space. It is a vector that describes the direction and distance between two points. Position vectors are highly useful in the field of math and science, as they help represent spatial relationships and make calculations easier.
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Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
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MViT: A vision transformer with fractal path reordering and dynamic positional encoding.

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  • 1School of Design and Art, Shanghai Dianji University, Shanghai, China.

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The Moore-curve Vision Transformer (MViT) enhances image analysis by improving spatial continuity and adaptability. This novel framework offers better performance on classification tasks and robustness to image rotations.

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Vision Transformers (ViT) excel in image tasks but struggle with spatial continuity due to fixed partitioning and static positional encoding.
  • Limitations include difficulties in representing rotated structures and irregular boundaries, hindering performance on complex image layouts.

Purpose of the Study:

  • To introduce the Moore-curve Vision Transformer (MViT), a novel ViT framework designed to overcome spatial continuity limitations.
  • To enhance the representation of rotated structures and irregular boundaries in image analysis.

Main Methods:

  • The MViT framework utilizes a recursive Moore curve, incorporating multi-order fractal mapping for optimized patch reordering and enhanced spatial coherence.
  • A dynamic partitioning template and boundary compensation algorithm improve dense structural representation and resolution adaptability.
  • A period-aware positional encoding module aligns embeddings with fractal traversal patterns, integrating fractal parameters and convolutional features.

Main Results:

  • MViT achieved improved classification accuracy over ViT-B/16 by 0.52% on CIFAR-100 and 0.31% on ImageNet-21k.
  • Noticeable improvements in Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) were observed.
  • Experiments confirmed MViT's robustness to rotation and localized focus variations, demonstrating strong structural compatibility across different backbones and tasks.

Conclusions:

  • The MViT framework significantly enhances structural adaptability for complex image layouts.
  • MViT offers improved performance and robustness, making it a versatile tool for various computer vision applications.