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Transformers in Distribution System01:27

Transformers in Distribution System

470
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...
470
Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

503
Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
As the drone's propellers rotate, an upward force is generated that counteracts the force of gravity, enabling the drone to lift off from the ground. This initial movement of the drone is along a straight path, representing a form of translational motion. In this phase, every point on the...
503
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

490
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...
490
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

675
Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
675
Types Of Transformers01:16

Types Of Transformers

1.4K
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...
1.4K
The Ideal Transformer01:26

The Ideal Transformer

1.3K
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...
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Related Experiment Video

Updated: Jan 7, 2026

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
07:49

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

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Transformer-Based Soft Actor-Critic for UAV Path Planning in Precision Agriculture IoT Networks.

Guanting Ge1, Mingde Sun1, Yiyuan Xue1

  • 1College of Software, Shanxi Agricultural University, Taiyuan 030031, China.

Sensors (Basel, Switzerland)
|December 31, 2025
PubMed
Summary

A new algorithm, Multi-Agent Transformer-based Soft Actor-Critic (MATRS), enhances coordination for multiple Unmanned Aerial Vehicles (UAVs) in agricultural data collection. MATRS achieves faster task completion and autonomous area division for efficient, conflict-free operations.

Keywords:
UAVdata collectionmulti-agent deep reinforcement learningpath planningtransformer

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Area of Science:

  • Robotics and Artificial Intelligence
  • Agricultural Technology
  • Internet of Things (IoT)

Background:

  • Multi-agent path planning for Unmanned Aerial Vehicles (UAVs) faces challenges in efficiency and conflict avoidance, particularly in complex agricultural data collection.
  • Existing multi-agent reinforcement learning (MARL) algorithms struggle with high-dimensional state spaces, continuous actions, and inter-agent dependencies.

Purpose of the Study:

  • To propose a novel algorithm, Multi-Agent Transformer-based Soft Actor-Critic (MATRS), for safe and efficient collaborative data collection and trajectory optimization by UAVs.
  • To address the limitations of current MARL algorithms in handling complex multi-agent coordination tasks.

Main Methods:

  • Developed MATRS, utilizing the Centralized Training with Decentralized Execution (CTDE) paradigm.
  • Integrated a Transformer encoder with a self-attention mechanism into the critic network to model inter-agent relationships and improve joint action-value function evaluation.

Main Results:

  • MATRS demonstrated faster convergence and reduced task completion times compared to baseline algorithms (MADDPG, MATD3, MASAC) in simulations.
  • Scalability experiments revealed MATRS learned an effective 'task-space partitioning' strategy for autonomous, conflict-free area coverage by the UAV swarm.

Conclusions:

  • Combining attention-based architectures with Soft Actor-Critic learning provides a powerful and scalable solution for multi-UAV coordination in IoT data collection.
  • MATRS offers improved performance and autonomous operational strategies for complex agricultural data gathering missions.