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相关概念视频

Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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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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Optimal Foraging00:48

Optimal Foraging

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How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.
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Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

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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?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of the...
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Mechanical Efficiency of Real Machines01:14

Mechanical Efficiency of Real Machines

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The mechanical efficiency of a machine is a fundamental concept that describes how effectively a machine can convert input work into output work. According to this concept, the efficiency of a machine is equal to the ratio of the output work to the input work. An ideal machine, meaning a machine that has no energy losses, has an efficiency of one. This implies that the input work and the output work are equal.
However, in reality, no machine can be truly ideal, and all of them experience some...
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Maximum Power Transfer01:16

Maximum Power Transfer

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Numerous practical applications within engineering disciplines, such as telecommunications, necessitate optimizing power delivery to a connected load. This pursuit, however, entails inherent internal losses, which can either equal or exceed the power supplied to the load. The Thevenin equivalent circuit is helpful in finding the maximum power a linear circuit can deliver to a load. It is assumed in this context that the load resistance can be adjusted.
By substituting the entire circuit with...
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相关实验视频

Updated: Jan 15, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

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在工业物联网中使用量子知情的人工蜂鸟优化算法进行节能集群.

S Rajkumar1, R Gopalakrishnan2

  • 1Department of Computer Science and Engineering, K. S. Rangasamy College of Technology, Tiruchengode, 637215, Tamil Nadu, India. rajkumars@ksrct.ac.in.

Scientific reports
|October 10, 2025
PubMed
概括
此摘要是机器生成的。

一个新的能源效率高的量子信息化人工蜂鸟优化算法 (EEQIAHBOA) 增强了工业物联网 (IIoT) 网络. 这种方法提高了能源效率32.12%,网络寿命提高了35.62%.

关键词:
集群头 (CHs) 是一个集群头.集群集成是指集群集成.能源效率 能源效率是指能源的使用效率.物联网 (IoT) 的物联网 (IoT) 的物联网.现场物联网操作节点的实时物联网操作节点.量子信息化的人工蜂鸟优化算法

相关实验视频

Last Updated: Jan 15, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

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科学领域:

  • 计算机科学 计算机科学
  • 工程 工程师 工程师 工程师
  • 人工智能的人工智能

背景情况:

  • 工业物联网 (IIoT) 网络面临着复杂性,实时处理和大量数据量的挑战.
  • 在IIoT中,聚类对于组织设备和数据至关重要,以改善资源利用和网络管理.
  • 采用集群智能优化算法来选择最佳的集群头 (CHs) 并确保可靠的路由以提高性能.

研究的目的:

  • 为IIoT网络提出一个能源效率高的量子信息化人工蜂鸟优化算法 (EEQIAHBOA).
  • 为解决IIoT系统中节能这一关键问题.
  • 通过优化集群和路由来最大限度地提高工业物联网应用程序的性能和可靠性.

主要方法:

  • 开发EEQIAHBOA,整合量子知情原理与人工蜂鸟优化.
  • 实现EEQIAHBOA作为路由算法,确定信息启发式因素和有效的编码方案.
  • 应用EEQIAHBOA作为一个集群算法,考虑CH选择的剩余能量,节点距离和能耗.

主要成果:

  • 在EEQIAHBOA,能源效率提高了32.12%.
  • 与基线方法相比,实现了35.62%的网络寿命增长.
  • 该算法在收集信息并将其传送到基站进行决策方面被证明是有效的.

结论:

  • EEQIAHBOA为IIoT提供了能源效率和网络寿命的显著提升.
  • 拟议的算法为复杂的工业环境中的路由和集群提供了可靠的解决方案.
  • 通过优化资源管理和节能,EEQIAHBOA有助于实现更具成本效益和可靠的工业应用.