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Reducing Line Loss01:18

Reducing Line Loss

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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
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Distributed Loads: Problem Solving01:21

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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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Principle of Linear Impulse and Momentum for a Single Particle: Problem Solving01:23

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Consider a wooden box and a cylinder of known masses m1 and m2, respectively,  hanging from a ceiling with the help of a massless pulley system.
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Conservation of Linear Momentum for a System of Particles01:28

Conservation of Linear Momentum for a System of Particles

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In the dynamic realm of billiards, a fascinating interplay of forces governs the motion of cue balls and stationary balls. When the cue ball collides with a stationary ball, linear momentum is exchanged. The cue ball imparts a fraction of its linear momentum to the stationary ball, causing the cue ball to decelerate while initiating the motion of the stationary ball.
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Laminar Flow: Problem Solving01:24

Laminar Flow: Problem Solving

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Laminar flow occurs when a fluid moves smoothly in parallel layers with minimal mixing and turbulence. In fluid mechanics, ensuring laminar flow within a pipe is essential for precise control of flow characteristics, especially in engineering applications. The key factor in determining whether flow remains laminar is the Reynolds number, a dimensionless quantity that depends on the fluid's velocity, density, viscosity, and the pipe's diameter. A Reynolds number of 2100 or lower...
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Associative Learning01:27

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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.
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Related Experiment Video

Updated: Nov 20, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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FedPSO: Federated Learning Using Particle Swarm Optimization to Reduce Communication Costs.

Sunghwan Park1, Yeryoung Suh1, Jaewoo Lee2

  • 1The Department of Security Convergence Science, Chung-Ang University, Seoul 06974, Korea.

Sensors (Basel, Switzerland)
|January 20, 2021
PubMed
Summary
This summary is machine-generated.

Federated Particle Swarm Optimization (FedPSO) enhances federated learning by transmitting scores instead of weights, improving accuracy and reducing data usage in unstable networks.

Keywords:
aggregationconvolutional neural network (CNN)federated learningparticle swarm optimization

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Area of Science:

  • Machine Learning
  • Distributed Systems
  • Network Communications

Background:

  • Federated learning ensures data privacy by aggregating models on a central server.
  • Clients in federated learning often face limited bandwidth and unstable network conditions.
  • Existing aggregation methods like FedAvg suffer accuracy degradation due to large weight transmissions in unstable environments.

Purpose of the Study:

  • To propose a novel federated learning algorithm, Federated Particle Swarm Optimization (FedPSO).
  • To enhance the robustness and communication efficiency of federated learning in unstable network environments.
  • To improve the accuracy and reduce communication overhead compared to traditional methods.

Main Methods:

  • Replaced the standard FedAvg aggregation with particle swarm optimization.
  • Developed FedPSO to transmit score values instead of large model weights between clients and servers.
  • Evaluated FedPSO performance in simulated unstable network conditions.

Main Results:

  • FedPSO significantly reduced the amount of data transmitted over the network.
  • The proposed FedPSO algorithm improved global model accuracy by an average of 9.47%.
  • FedPSO demonstrated an approximate 4% improvement in accuracy loss under unstable network conditions.

Conclusions:

  • FedPSO offers a robust alternative to FedAvg for federated learning in challenging network environments.
  • Transmitting scores instead of weights effectively mitigates accuracy loss due to network instability.
  • FedPSO enhances both communication efficiency and model performance in federated learning.