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Videos de Conceptos Relacionados

Maximum Power Transfer01:16

Maximum Power Transfer

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Numerous practical applications within engineering disciplines, such as telecommunications, necessitate optimizing power delivery to a connected load. This pursuit, however, entails inherent internal losses, which can either equal or exceed the power supplied to the load. The Thevenin equivalent circuit is helpful in finding the maximum power a linear circuit can deliver to a load. It is assumed in this context that the load resistance can be adjusted.
By substituting the entire circuit with...
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Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

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The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
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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.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
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Energy Conservation and Bernoulli's Equation01:16

Energy Conservation and Bernoulli's Equation

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Applying the conservation of energy principle or the work-energy theorem to an incompressible, inviscid fluid in laminar, steady, irrotational flow leads to Bernoulli's equation. It states that the sum of the fluid pressure, potential, and kinetic energy per unit volume is constant along a streamline.
All the terms in the equation have the dimension of energy per unit volume. The kinetic energy per unit volume is called the kinetic energy density, and the potential energy per unit volume is...
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Production Efficiency01:01

Production Efficiency

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Net production efficiency (NPE) is the efficiency at which organisms assimilate energy into biomass for the next trophic level. Due to low metabolic rates and less energy spent on thermoregulatory processes, the NPE of ectotherms (cold-blooded animals) is 10 times higher than endotherms (warm-blooded animals).
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Short-distance Transport of Resources02:12

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Short-distance transport refers to transport that occurs over a distance of just 2-3 cells, crossing the plasma membrane in the process. Small uncharged molecules, such as oxygen, carbon dioxide, and water, can diffuse across the plasma membrane on their own. In contrast, ions and larger molecules require the assistance of transport proteins due to their charge or size. Transport across membranes also occurs within individual cells, playing a variety of essential roles for the plant as a whole.
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Updated: Sep 10, 2025

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

657

Aprendizaje profundo basado en optimización híbrida para la asignación de recursos de eficiencia energética en redes

Mian Muhammad Kamal1, Ijaz Khan2, M A Al-Khasawneh3,4

  • 1School of Electronics and Communication Engineering, Quanzhou University of Information Engineering, Quanzhou, 362000, China. mianmuhammadkamal@qzuie.edu.cn.

Scientific reports
|August 27, 2025
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio introduce HGGO_XCovNet para optimizar la asignación de recursos en las redes inalámbricas 5G, mejorando la eficiencia energética y las tasas de datos para múltiples usuarios. El nuevo enfoque de aprendizaje profundo mejora el rendimiento y la confiabilidad del sistema.

Palabras clave:
Red 5GAprendizaje profundoEficiencia energéticaSistema de múltiples entradas y salidasAsignación de recursos

Videos de Experimentos Relacionados

Last Updated: Sep 10, 2025

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

657

Área de la Ciencia:

  • Comunicaciones inalámbricas
  • Inteligencia artificial
  • Algoritmos de optimización

Sus antecedentes:

  • La asignación de recursos en redes inalámbricas habilitadas para múltiples entradas y múltiples salidas (MIMO) es crucial para optimizar el rendimiento de la red y la eficiencia energética.
  • Los métodos existentes se enfrentan a desafíos para satisfacer las altas demandas de recursos de los usuarios de MIMO, que requieren técnicas avanzadas.
  • El aprendizaje profundo (DL) ofrece un potencial para mejorar la fiabilidad y la precisión en la asignación de recursos de la red 5G.

Objetivo del estudio:

  • Introducir una nueva técnica de optimización híbrida, HGGO_XCovNet, para una asignación eficiente de recursos en las redes inalámbricas habilitadas para MIMO.
  • Mejorar el rendimiento del sistema maximizando la eficiencia energética, la velocidad de datos y el rendimiento.
  • Aprovechar el aprendizaje profundo para una distribución precisa y confiable de los recursos.

Principales métodos:

  • Se consideró un escenario de estación base (BS) con múltiples usuarios para la asignación de recursos.
  • La red neuronal convolucional Xception (XCovNet) fue empleada para la asignación de recursos, entrenada por un algoritmo de optimización híbrido.
  • El algoritmo Hippo Graylag Goose Optimization (HGGO), que combina Greylag Goose Optimization (GGO) y Hippopotamus Optimization (HO), fue desarrollado para entrenar a XCovNet.

Principales resultados:

  • La técnica HGGO_XCovNet logró una eficiencia energética máxima de 74.943 kbits/joule.
  • El sistema demostró una velocidad de datos de suma de 269,93 Mbps.
  • Se registró un rendimiento máximo de 551.262 Mbps.

Conclusiones:

  • La técnica HGGO_XCovNet propuesta optimiza efectivamente la asignación de recursos en las redes MIMO 5G.
  • El enfoque de aprendizaje profundo híbrido mejora significativamente la eficiencia energética, la tasa de suma y el rendimiento.
  • Este método proporciona una solución confiable y precisa para la gestión de recursos en sistemas inalámbricos avanzados.