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相关概念视频

Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

609
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...
609
Distributed Loads01:19

Distributed Loads

491
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.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...
491
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

211
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
211
Elastic Curve from the Load Distribution01:16

Elastic Curve from the Load Distribution

151
The structural behavior of beams under distributed loads is critical for engineering analysis, which focuses on predicting how beams bend and react under such conditions. Different types of beams (e.g., cantilever, supported, or overhanging) behave differently under distributed load conditions.
For all beams, the analysis of the beam's reaction to distributed loads begins by understanding the relationship between a beam's load and the resulting shear forces and bending moments.
151
Reducing Line Loss01:18

Reducing Line Loss

140
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...
140
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

91
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.
91

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Updated: May 21, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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动态边缘负载平衡与边缘节点活动预测和加速模型融合

Wen Chen1, Sibin Liu1, Yuxiao Yang1

  • 1School of Information Science and Technology, Donghua University, Shanghai 201620, China.

Sensors (Basel, Switzerland)
|March 17, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了用于移动边缘计算负载平衡的深度强化学习方法. 该方法通过优化任务卸载和资源分配,有效地减少任务丢失和系统成本.

关键词:
深度强化学习的学习.负载平衡系统的负载平衡移动边缘计算移动边缘计算资源分配的资源分配.任务卸载 任务卸载

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Surrogate Model Development for Digital Experiments in Welding
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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 网络工程 网络工程

背景情况:

  • 移动边缘计算 (MEC) 网络在负载平衡方面面临挑战,原因是未知的边缘节点状态和动态负载变化.
  • 有效的负载平衡对于最小化多用户,多边节点环境中的任务处理延迟至关重要.
  • 现有的方法在与边缘节点负载的不确定性和动态作斗争.

研究的目的:

  • 提出基于深度强化学习 (DRL) 的方法,用于MEC网络中的任务卸载和资源分配.
  • 为了最大限度地降低长期的平均成本和平衡跨边缘节点的负载.
  • 为了应对先验未知的边缘节点负载状态的挑战.

主要方法:

  • 优化问题的分解成任务卸载和资源分配的子问题.
  • 利用Karush-Kuhn-Tucker (KKT) 条件来实现最佳的通信带宽和计算资源分配.
  • 长短期内存 (LSTM) 网络的应用,用于实时边缘节点活动预测.
  • 集成深度压缩技术,以加速模型的融合.

主要成果:

  • 与基线方案相比,任务放弃率减少了47%.
  • 总系统成本下降了14%.
  • 运行时间有7.6%的改进.

结论:

  • 拟议的基于DRL的方案有效地平衡了MEC网络的负载并降低了成本.
  • LSTM 网络和深度压缩提高了系统的效率和性能.
  • 该方法比现有的任务卸载和资源分配方法提供了显著的改进.