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相关概念视频

Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

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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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In fluid mechanics, buoyancy and stability are key concepts for understanding the behavior of submerged and floating bodies. When a stationary body is fully or partially submerged in a fluid, the fluid exerts a force on the body known as the buoyant force. This force acts vertically upward through a point called the center of buoyancy, which is the center of the displaced fluid volume. According to Archimedes' principle, the magnitude of the buoyant force is equal to the weight of the fluid...
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相关实验视频

Updated: Jul 15, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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使用智能优化算法和机器人协作优化水下无线传感器网络的节点部署优化.

Yangmei Zhang1, Zhouzhou Liu2, Yang Bi3

  • 1School of Electronic Engineering, Xi'an Aeronautical Institute, Xi'an, 710077, China. 201707019@xaau.edu.cn.

Scientific reports
|September 23, 2023
PubMed
概括
此摘要是机器生成的。

优化水下无线传感器网络 (UWSNs) 节点部署,使用增强的化学反应优化 (CRO) 算法和机器人协作显著提高覆盖率至95.66%. 这种方法改善了水下监测和海洋研究能力.

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科学领域:

  • 海洋技术 海洋技术
  • 网络工程 网络工程
  • 机器人技术 机器人技术 机器人技术

背景情况:

  • 水下无线传感器网络 (UWSNs) 在部署节点以实现有效覆盖方面面临挑战.
  • 优化UWSN部署对于环境监测和海洋资源勘探至关重要.
  • 现有的智能优化算法在解决UWSN部署复杂性方面存在局限性.

研究的目的:

  • 为了优化UWSNs中的节点部署,使用增强的化学反应优化 (CRO) 算法与机器人协作相结合.
  • 提高网络性能,覆盖范围和水下环境中的效率.
  • 为了证明增强的CRO算法的优越性,而不是UWSN节点部署的传统方法.

主要方法:

  • 使用化学反应优化 (CRO) 算法,整合遗传算法,模拟化和群算法的优势.
  • 通过结构校正函数来增强CRO算法,以实现最佳的节点放置.
  • 利用机器人的灵活性和自主性,实现高效的水下节点部署.
  • 进行各种智能优化算法的比较分析.

主要成果:

  • 改进的CRO算法实现了95.66%的平均网络覆盖率.
  • 拟议的方法在UWSN覆盖范围内显著优于传统的智能优化算法.
  • 机器人协作提高了在水下环境中部署节点的效率和适应性.

结论:

  • 增强的CRO算法和机器人协作提供了优化UWSN节点部署的有效策略.
  • 改进的节点部署显著提高了水下环境监测能力,资源利用率和数据准确性.
  • 这项研究为水下环境监测,海洋资源勘探和科学研究提供了宝贵的见解.