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Related Concept Videos

Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

184
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...
184
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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Equivalent Circuits for Practical Transformers01:28

Equivalent Circuits for Practical Transformers

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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...
489
Types Of Transformers01:16

Types Of Transformers

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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.
However, if this ratio is less than one, the transformer is said to be a step-down...
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Energy Losses in Transformers01:21

Energy Losses in Transformers

917
In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
There are four main reasons for energy losses in transformers.
The first cause can be  the high resistance of the...
917
Typical Model Studies01:30

Typical Model Studies

396
Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
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Related Experiment Video

Updated: Aug 4, 2025

Kinematic History of a Salient-recess Junction Explored through a Combined Approach of Field Data and Analog Sandbox Modeling
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XBound-Former: Toward Cross-Scale Boundary Modeling in Transformers.

Jiacheng Wang, Fei Chen, Yuxi Ma

    IEEE Transactions on Medical Imaging
    |April 5, 2023
    PubMed
    Summary

    This study introduces XBound-Former, a novel transformer model for skin lesion segmentation. It improves accuracy by effectively handling variations and ambiguous boundaries in dermoscopy images.

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    Area of Science:

    • Computer Vision
    • Medical Image Analysis
    • Artificial Intelligence

    Background:

    • Skin lesion segmentation from dermoscopy images is crucial for skin cancer analysis but challenging due to variations in size, shape, color, and ambiguous boundaries.
    • Existing vision transformers model global context but struggle with ambiguous boundaries by not integrating boundary knowledge.
    • A gap exists in effectively combining global context with detailed boundary information for precise skin lesion segmentation.

    Purpose of the Study:

    • To propose a novel cross-scale boundary-aware transformer, XBound-Former, for improved skin lesion segmentation.
    • To address both the inherent variations and ambiguous boundary issues in dermoscopy images.
    • To enhance the quantitative analysis of skin cancers through more accurate segmentation.

    Main Methods:

    • Developed XBound-Former, a purely attention-based network incorporating three boundary learners: implicit (im-Bound), explicit (ex-Bound), and cross-scale (X-Bound).
    • The implicit boundary learner focuses attention on areas with significant boundary variations.
    • The explicit and cross-scale boundary learners extract and utilize multi-scale boundary information to guide attention across different scales.

    Main Results:

    • XBound-Former consistently outperformed existing convolution- and transformer-based models on skin lesion and polyp lesion datasets.
    • The model demonstrated superior performance, particularly in boundary-wise metrics, indicating improved segmentation accuracy at lesion edges.
    • Evaluations on multiple datasets confirmed the effectiveness of the proposed boundary-aware approach.

    Conclusions:

    • XBound-Former effectively addresses the challenges of variation and ambiguous boundaries in skin lesion segmentation.
    • The proposed cross-scale boundary-aware mechanism significantly enhances segmentation performance, especially at lesion borders.
    • This approach offers a promising advancement for automated skin cancer analysis using dermoscopy images.