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Related Concept Videos

Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

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 rated...
Transformers in Distribution System01:27

Transformers in Distribution System

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...
Types Of Transformers01:16

Types Of Transformers

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...
Observational Learning01:12

Observational Learning

Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning because...
Associative Learning01:27

Associative Learning

Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...

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Related Experiment Videos

DOGT: Double-Order Graph Transformers With Adaptive Node-Group Learning.

Yupei Zhang, Xian Sheng, Mengfei Liu

    IEEE Transactions on Neural Networks and Learning Systems
    |June 10, 2026
    PubMed
    Summary
    This summary is machine-generated.

    Double-Order Graph Transformers (DOGT) dynamically integrate hyper-order features for graph representation learning. This novel framework improves graph classification and regression by adaptively learning node groups and fusing multi-order graph representations.

    Related Experiment Videos

    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Graph Representation Learning

    Background:

    • Graph Transformers (GTs) are popular for graph representation learning (GRL).
    • Existing methods often overlook hyper-order structures (implicit node groups) in graphs.
    • Current hypergraph construction methods are often heuristic and disconnected from optimization goals.

    Purpose of the Study:

    • Introduce Double-Order Graph Transformers (DOGT) for dynamic hyper-order feature incorporation.
    • Develop an Adaptive Node-Group (ANG) learning mechanism for inferring hyper-order edges.
    • Create a novel Double-Order Attention (DoA) mechanism for feature fusion.

    Main Methods:

    • DOGT employs a learnable mask matrix for ANG learning to identify hyper-order edges and construct hyper-order graphs (HOGs).
    • A dual-branch architecture processes both HOGs (via DoA and GNN) and raw-order graph features (via GNN).
    • A feature pyramid fuses representations from both branches for a comprehensive graph representation.

    Main Results:

    • DOGT demonstrates rapid convergence during training.
    • The framework significantly outperforms state-of-the-art methods on graph-level classification and regression tasks.
    • ANG and DoA mechanisms adaptively learn hyper-order structures, unlike traditional predefined rules.

    Conclusions:

    • DOGT offers a novel and effective approach to graph representation learning by integrating dynamic hyper-order structures.
    • The adaptive learning of node groups and attention mechanisms enhances performance on downstream tasks.
    • This framework advances GRL by bridging the gap between hypergraph construction and optimization objectives.