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Related Concept Videos

Transformers01:26

Transformers

1.7K
A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
The iron core has a substantial relative permeability. Therefore, the magnetic field lines generated due to the current in one winding are almost entirely confined within the core, such that the same magnetic flux permeates each turn of both...
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Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

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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 System01:27

Transformers in Distribution System

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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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Related Experiment Video

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Improving Food Image Recognition with Noisy Vision Transformer.

Tonmoy Ghosh, Edward Sazonov

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
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    Summary
    This summary is machine-generated.

    Noisy Vision Transformers (NoisyViT) enhance food image recognition by introducing noise during training, improving accuracy. This computer vision technique shows promise for dietary assessment and healthcare applications.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Food image recognition is complex due to high variability.
    • Existing models struggle with diverse food imagery.

    Purpose of the Study:

    • Investigate Noisy Vision Transformers (NoisyViT) for improved food classification.
    • Evaluate NoisyViT's performance on benchmark food datasets.

    Main Methods:

    • Fine-tuned NoisyViT on Food2K, Food-101, and CNFOOD-241 datasets.
    • Introduced noise into the learning process to reduce task complexity and system entropy.

    Main Results:

    • Achieved Top-1 accuracies of 95% (Food2K), 99.5% (Food-101), and 96.6% (CNFOOD-241).
    • Significantly outperformed state-of-the-art food recognition models.

    Conclusions:

    • NoisyViT offers a promising approach for accurate food image recognition.
    • Potential applications include dietary assessment, nutritional monitoring, and healthcare.
    • Publicly available code facilitates further research in vision-based food computing.