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相关概念视频

Distributed Loads01:19

Distributed Loads

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...
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

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...
Relation Between the Distributed Load and Shear01:23

Relation Between the Distributed Load and Shear

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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相关实验视频

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Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
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基于自适应框架细分算法的车载以太网消息调度的研究.

Jiaoyue Chen1, Yujing Wu1, Yihu Xu1

  • 1College of Engineering, Yanbian University, Yanji 133002, China.

Sensors (Basel, Switzerland)
|April 26, 2025
PubMed
概括

一个新的自适应框架分割 (AFS) 算法通过优化时间敏感网络 (TSN) 调度,将车载以太网带宽利用率提高到94.16%. 这提高了智能驾驶系统的实时性能.

科学领域:

  • 汽车工程 汽车工程
  • 计算机网络 计算机网络
  • 实时系统 实时系统

背景情况:

  • 传统的车载网络 (LIN,CAN,FlexRay) 缺乏用于智能驾驶的带宽和速度.
  • 车载以太网对于下一代汽车通信至关重要,因为它具有高带宽和低延迟.
  • 现有的车载以太网调度方法在带宽利用方面存在局限性.

研究的目的:

  • 为车载以太网优化时间敏感网络 (TSN) 调度.
  • 为了提高带宽的利用率,并减少由防护带和先发制造造成的浪费.
  • 提高车载通信网络的实时性能和响应能力.

主要方法:

  • 开发基于TSN协议的创新自适应框架分割 (AFS) 算法.
  • 灵活的分割和高效的消息调度,以优化车载以太网性能.
  • 对AFS算法与现有调度方法进行实验性评估.

主要成果:

  • AFS算法实现了94.16%的平均本地带宽利用率.
  • 在AFS中,相对于Frame Preemption (4.35%),PAS (5.65%) 和改善的Qbv (30.48%) 进行了显著的改进.
  • 该算法在复杂的网络流量条件下被证明是稳定的和高效的.
关键词:
适应性框架细分的适应性框架细分.带宽利用率 带宽利用率车载以太网 在车载以太网消息编程 消息编程时间敏感的网络.

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结论:

  • AFS算法有效地减少了带宽浪费,并提高了车载以太网的实时功能.
  • 这项研究为智能互联汽车的高效通信提供了至关重要的技术支持.
  • 该研究促进了智能驾驶的车载以太网技术的开发和应用.