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Maximum Power Transfer01:16

Maximum Power Transfer

322
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...
322
Network Function of a Circuit01:25

Network Function of a Circuit

341
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.
341
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

155
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.
155
Short-distance Transport of Resources02:12

Short-distance Transport of Resources

16.3K
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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Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

254
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
254
Reducing Line Loss01:18

Reducing Line Loss

184
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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Related Experiment Video

Updated: Aug 10, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Energy-Efficient Resource Allocation Based on Deep Q-Network in V2V Communications.

Donghee Han1, Jaewoo So1

  • 1Department of Electronic Engineering, Sogang University, Seoul 04107, Republic of Korea.

Sensors (Basel, Switzerland)
|February 11, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a deep reinforcement learning approach for efficient radio resource management in vehicle-to-everything (V2X) communications. The method optimizes resource allocation to reduce power consumption and latency in vehicle-to-vehicle (V2V) links without impacting overall network performance.

Keywords:
deep Q-networkdeep reinforcement learningenergy efficiencyresource allocationvehicular communications

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Area of Science:

  • * Wireless Communication Networks
  • * Artificial Intelligence in Telecommunications
  • * Autonomous Driving Systems

Background:

  • * Vehicle-to-Everything (V2X) communication is crucial for autonomous driving safety and efficiency.
  • * Increasing vehicle density necessitates effective radio resource management for Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) links.
  • * Existing resource allocation schemes face challenges in balancing low latency, high reliability, and energy efficiency.

Purpose of the Study:

  • * To propose a decentralized resource allocation scheme for V2X networks using deep reinforcement learning (DRL).
  • * To optimize resource blocks and transmit power for V2V links to enhance V2I and V2V communication performance.
  • * To ensure energy-efficient transmissions that meet V2V latency constraints while minimizing interference.

Main Methods:

  • * Implementation of a deep Q-network (DQN) for decentralized decision-making in resource allocation.
  • * Utilization of channel state information (CSI) and signal-to-interference-plus-noise ratio (SINR) for optimal allocation.
  • * Simulation of the proposed scheme in a Manhattan-based V2X communication scenario (3GPP TR 36.885).

Main Results:

  • * The DRL-based scheme effectively reduces the transmit power of V2V links.
  • * The proposed method maintains or improves the sum rate of the V2X network.
  • * Average power consumption of V2V links is significantly reduced, and outage probability is managed effectively.

Conclusions:

  • * The proposed DQN-based resource allocation scheme offers an energy-efficient solution for V2X communication networks.
  • * The DRL approach successfully balances competing demands of latency, reliability, and power consumption in V2V communications.
  • * This scheme provides a viable strategy for efficient radio resource management in future autonomous driving systems.