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Related Concept Videos

Types Of Transformers01:16

Types Of Transformers

1.8K
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...
1.8K
Network Function of a Circuit01:25

Network Function of a Circuit

1.0K
Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
1.0K
The Ideal Transformer01:26

The Ideal Transformer

1.5K
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 tangential...
1.5K
Transformers in Distribution System01:27

Transformers in Distribution System

621
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...
621
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

676
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...
676
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

537
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...
537

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Related Experiment Video

Updated: Apr 8, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

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Graph transformer Q-network for collaborative governance and decentralized decision-making in multi-intersection

He Zhang1

  • 1School of Marxism, Suzhou Polytechnic University, Suzhou, 215000, Jiangsu, China. zhanghe6686@163.com.

Scientific Reports
|March 31, 2026
PubMed
Summary

The Graph Transformer Q-Network (GTQN) enhances urban traffic signal control by learning sparse interactions for better coordination, reducing vehicle stops and delays on large networks.

Keywords:
Corridor-level progressionDeep learningGraph neural networkIntelligent transportation systemsMulti-agent reinforcement learningTraffic signal controlTransformer

Related Experiment Videos

Last Updated: Apr 8, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.2K

Area of Science:

  • Intelligent Transportation Systems
  • Reinforcement Learning
  • Graph Neural Networks

Background:

  • Coordinated traffic signal control is vital for urban mobility but faces challenges in large-scale networks with partial observability and variable demand.
  • Existing multi-agent reinforcement learning (MARL) methods struggle with dense communication and local rewards, hindering corridor-level coordination and scalability.
  • Attention diffusion and unstable coordination are common issues as network size increases.

Purpose of the Study:

  • To develop a novel MARL controller, the Graph Transformer Q-Network (GTQN), for scalable and effective coordinated traffic signal control.
  • To enable decentralized controllers to form and sustain corridor progression through learned sparse interactions and centralized training governance.
  • To improve credit assignment and learning stability in traffic signal control systems.

Main Methods:

  • GTQN learns a state-dependent sparse interaction graph using a two-stage mechanism (peer gating and soft relevance weighting).
  • A unified graph-transformer backbone jointly encodes network topology and temporal history for capturing delayed effects.
  • A Collaborative Governance Graph (CTDE) framework utilizes a two-level reward coupling junction efficiency with corridor hindrance penalty.

Main Results:

  • GTQN significantly improves traffic progression quality, achieving lower Average Number of Stops (ANS) on synthetic and real-world networks.
  • The system effectively reduces vehicle waiting times and queue lengths while increasing traffic throughput.
  • Ablation studies confirm the necessity of learned sparsity, unified spatiotemporal encoding, and centralized governance for performance.

Conclusions:

  • GTQN offers a scalable and effective solution for coordinated traffic signal control, outperforming existing methods.
  • The learned sparse interaction graph and centralized governance are crucial for achieving stable and efficient corridor progression.
  • The proposed method demonstrates robustness to communication perturbations, making it suitable for real-world deployment.