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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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Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
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A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
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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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Relation Between the Distributed Load and Shear01:23

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Understanding the relationship between the distributed load and shear force in structural analysis is crucial for analyzing beams subjected to various loading conditions. Consider the case of a beam experiencing a distributed load, two concentrated loads, and a couple moment.
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Multi-User Computation Offloading and Resource Allocation Algorithm in a Vehicular Edge Network.

Xiangyan Liu1, Jianhong Zheng1, Meng Zhang2

  • 1School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.

Sensors (Basel, Switzerland)
|April 13, 2024
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Summary
This summary is machine-generated.

This study introduces a Deep Deterministic Policy Gradient (DDPG) algorithm to optimize computation offloading and resource allocation in Vehicular Edge Computing Networks (VECN). The proposed method enhances Quality of Service (QoS) and reduces system delay by 24-29%.

Keywords:
Vehicular Edge Computing Network (VECN)computation offloadingdeep reinforcement learningresource allocation

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Area of Science:

  • * Vehicular Edge Computing Networks (VECN)
  • * Mobile Computing
  • * Network Resource Allocation

Background:

  • * Vehicle mobility in VECN introduces channel state information uncertainty.
  • * This uncertainty challenges the Quality of Service (QoS) for computation offloading and resource allocation in Vehicular Edge Computing Servers (VECS).

Purpose of the Study:

  • * To address the challenges of uncertain channel state information in VECN.
  • * To develop an optimized model for multi-user computation offloading and resource allocation.
  • * To minimize total system delay and ensure QoS for task execution.

Main Methods:

  • * Modeled the problem as a Mixed Integer Nonlinear Programming (MINLP) problem.
  • * Proposed a Deep Deterministic Policy Gradient (DDPG) based algorithm to handle large state spaces and mixed discrete/continuous action spaces.
  • * Compared the DDPG-based scheme against three benchmark algorithms.

Main Results:

  • * The DDPG-based scheme effectively selects task offloading modes and allocates VECS resources.
  • * Ensured QoS for task execution with demonstrated stability and scalability.
  • * Achieved a 24-29% reduction in total completion time compared to existing techniques.

Conclusions:

  • * The proposed DDPG algorithm offers an effective solution for computation offloading and resource allocation in VECN.
  • * The method ensures QoS and reduces system delay, outperforming baseline approaches.
  • * Demonstrates significant improvements in efficiency and performance for vehicular edge computing scenarios.