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Related Concept Videos

Transformers in Distribution System01:27

Transformers in Distribution System

133
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...
133
Types Of Transformers01:16

Types Of Transformers

1.0K
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...
1.0K
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

183
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...
183
Equivalent Circuits for Practical Transformers01:28

Equivalent Circuits for Practical Transformers

488
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...
488
The Ideal Transformer01:26

The Ideal Transformer

447
In single-phase two-winding transformers, two windings are coiled around a magnetic core characterized by cross-sectional area A and magnetic permeability μ. A phasor current i1 enters the left winding while i2 exits the right winding, establishing the fundamental working of the transformer through electromagnetic principles.
Ampere's Law forms the basis of understanding the magnetic field within the transformer. It states that the integral of the magnetic field intensity's...
447
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

104
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
104

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Updated: Aug 2, 2025

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans
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RelTR: Relation Transformer for Scene Graph Generation.

Yuren Cong, Michael Ying Yang, Bodo Rosenhahn

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |April 19, 2023
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    Summary
    This summary is machine-generated.

    We introduce Relation Transformer (RelTR), a novel model for scene graph generation. RelTR efficiently predicts relationships between objects using a set prediction approach, improving accuracy and speed.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Understanding relationships between objects in a scene is crucial for AI.
    • Existing methods for scene graph generation can be complex and computationally intensive.

    Purpose of the Study:

    • To propose an end-to-end scene graph generation model called Relation Transformer (RelTR).
    • To treat scene graph generation as a set prediction problem, inspired by object detection advancements.

    Main Methods:

    • Utilizing an encoder-decoder architecture for visual feature context reasoning.
    • Employing attention mechanisms with coupled subject and object queries for triplet inference.
    • Implementing a set prediction loss for end-to-end training and matching ground truth with predicted triplets.

    Main Results:

    • RelTR achieves superior performance on benchmark datasets like Visual Genome, Open Images V6, and VRD.
    • The model demonstrates fast inference speeds compared to existing methods.
    • RelTR is a one-stage method that directly predicts sparse scene graphs using visual appearance.

    Conclusions:

    • Relation Transformer (RelTR) offers an effective and efficient approach to scene graph generation.
    • The set prediction framework simplifies the scene graph generation process.
    • RelTR advances the state-of-the-art in understanding complex visual scenes.