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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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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 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:
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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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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Computational Offloading in Mobile Edge with Comprehensive and Energy Efficient Cost Function: A Deep Learning

Ziaul Haq Abbas1, Zaiwar Ali2, Ghulam Abbas3

  • 1Faculty of Electrical Engineering, GIK Institute of Engineering Sciences and Technology, Topi 23640, Pakistan.

Sensors (Basel, Switzerland)
|June 2, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning technique for mobile edge computing (MEC) to optimize task offloading. The method intelligently partitions tasks and selects offloading policies, significantly reducing energy consumption and service delay for user equipment (UE).

Keywords:
computational offloadingcost functiondeep learningenergy efficiencymobile edge computingremote execution

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Artificial Intelligence

Background:

  • Mobile edge computing (MEC) enables processing at the network edge, reducing latency and improving efficiency.
  • Partial computational offloading in MEC divides tasks between user equipment (UE) and mobile edge servers (MES) to manage resources.
  • Existing methods often overlook the complexities of task partitioning and its impact on offloading efficiency.

Purpose of the Study:

  • To develop an intelligent partial offloading technique for MEC that minimizes energy consumption and service delay.
  • To address the limitations of current approaches by considering task partitioning and offloading policy selection simultaneously.
  • To enhance the decision-making process for offloading in dynamic MEC environments.

Main Methods:

  • A supervised deep learning approach using a deep neural network (DNN) is proposed.
  • The DNN is trained on a comprehensive dataset generated from a complex mathematical model.
  • A novel cost function is introduced, incorporating various delays, energy consumption, radio, and computation resources, including task-division costs.

Main Results:

  • The proposed comprehensive and energy-efficient deep learning-based offloading technique (CEDOT) achieves over 70% accuracy in deciding offloading policies and task partitioning.
  • The trained DNN minimizes computational complexity, leading to faster decision-making and reduced energy consumption.
  • The technique demonstrates consistent accuracy and performance even with moving UEs, unaffected by mobility.

Conclusions:

  • The CEDOT technique effectively reduces service delay and energy consumption in MEC through intelligent partial offloading and task partitioning.
  • Deep learning provides an efficient method to optimize complex offloading decisions in MEC environments.
  • The proposed approach offers a significant advancement by holistically considering task division and offloading strategies.