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Related Concept Videos

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

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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.
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Reasoning is the action of thinking about something in a logical, sensible way. It is integral to problem-solving, decision-making, and critical thinking. Reasoning can be inductive or deductive. Reasoning involves transforming information into conclusions, which is essential for problem-solving, decision-making, and critical thinking.
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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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Related Experiment Video

Updated: Jan 9, 2026

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke
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Graph-Gated Relational Reasoning for Enhanced Coordination and Safety in Distributed Multi-Robot Systems: A

Tianshun Chang1, Yiping Ma1, Zhiqian Li1

  • 1Merchant Shipping Academy, Shanghai Maritime University, Shanghai 201306, China.

Sensors (Basel, Switzerland)
|December 11, 2025
PubMed
Summary
This summary is machine-generated.

We developed a Graph-Gated Transformer (GGT) to improve multi-robot coordination. This novel architecture enhances safety and efficiency in complex environments by focusing attention on critical agent relationships.

Keywords:
Unmanned Surface Vehicle (USV)collision avoidancecontextual reasoningcooperative controlmaritime roboticsmaritime safetymulti-agent reinforcement learningmulti-modal perceptiontransformer

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Multi-Agent Systems

Background:

  • Coordinating multi-robot systems in complex environments is challenging.
  • Current Multi-Agent Reinforcement Learning (MARL) methods struggle with dynamic agent-environment relationships.

Purpose of the Study:

  • Introduce the Graph-Gated Transformer (GGT) to enhance multi-robot coordination.
  • Improve reasoning about dynamic, causal relationships in multi-agent systems.

Main Methods:

  • Developed the Graph-Gated Transformer (GGT) architecture.
  • Dynamically construct a Tactical Relational Graph for explicit relational priors.
  • Integrate GGT into a Centralized Training with Decentralized Execution (CTDE) framework with QMIX.
  • Utilize a gated attention mechanism to focus Transformer reasoning.

Main Results:

  • GGT-based system achieved 95.3% coverage efficiency with 0.4 collisions/episode.
  • Standard QMIX achieved 60.3% coverage with 20.7 collisions/episode.
  • Demonstrated substantial improvements in high-fidelity simulations with dynamic obstacles and sensor noise.

Conclusions:

  • Explicitly constraining attention space with dynamic relational graphs enhances multi-robot coordination.
  • The structured, gated attention mechanism is key to robust collective autonomy.
  • GGT offers an effective architectural solution for safe and intelligent multi-robot systems.