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相关概念视频

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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Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
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车辆轨迹预测方法用于车辆边缘计算中的任务卸载.

Ruibin Yan1, Yijun Gu1, Zeyu Zhang1

  • 1College of Information and Cyber Security, People's Public Security University of China, Beijing 102600, China.

Sensors (Basel, Switzerland)
|September 28, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种使用频率模式的车辆轨迹预测方法,以提高车辆边缘计算 (VEC) 中的任务卸载效率. 这种新的方法提高了预测准确性和效率,优化了服务质量 (QoS).

关键词:
边缘计算是一种边缘计算.任务卸载 任务卸载轨迹的预测和预测.车辆轨迹的轨迹是什么

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相关实验视频

Last Updated: Jul 15, 2025

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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
  • 车辆计算系统 车辆计算系统

背景情况:

  • 车辆边缘计算 (VEC) 为车辆提供实时计算,但任务卸载效率影响服务质量 (QoS).
  • 在VEC中预测任务卸载面临由于资源限制和需要及时获得车辆轨迹数据的限制.
  • 在边缘服务器上部署机器学习模型用于轨迹预测具有重大挑战.

研究的目的:

  • 提出一种新的车辆轨迹预测方法,以提高VEC中的任务卸载.
  • 为了提高VEC任务卸载的轨迹预测的准确性和效率.
  • 设计任务卸载策略和优化算法,在时间限制内最大限度地降低能源消耗.

主要方法:

  • 使用历史车辆轨迹数据构建一个T模式预测树 (TPPT).
  • 确定车辆频繁项目集,在TPPT中提供最大的轨迹预测支持.
  • 实时更新TPPT,预测轨迹结果和设计节能任务卸载策略.

主要成果:

  • 与基线T模式方法相比,拟议的预测方法在准确度上显示了超过10%的改进.
  • 预测效率提高了6.5倍以上,明显超过了基线.
  • 在真实车辆和Capital Bikeshare数据集上的实验验证证证了该方法的有效性.

结论:

  • 基于频繁模式的车辆轨迹预测方法为VEC任务卸载提供了高精度和效率.
  • 这种方法有效地解决了在动态VEC环境中预测车辆轨迹的挑战.
  • 优化的任务卸载策略有助于减少能源消耗,同时满足时间限制.