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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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Distributed Loads01:19

Distributed Loads

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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...
555
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

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Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
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Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

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Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...
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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...
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Relation Between the Distributed Load and Shear01:23

Relation Between the Distributed Load and Shear

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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

Updated: Jul 18, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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针对分布式多访问边缘计算智能城市的动态适应攻击检测模型.

Nouf Saeed Alotaibi1, Hassan Ibrahim Ahmed2, Samah Osama M Kamel2

  • 1Computer Science Department, Shaqra University, Dawadmi City 11911, Saudi Arabia.

Sensors (Basel, Switzerland)
|August 26, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种智能自动化检测模型 (IADM),用于在智能城市中保护物联网 (IoT) 网络. IADM有效地检测和阻止恶意流量,增强智能城市的安全性.

关键词:
在 AdaBoost 中使用 AdaBoost.包装包装包装包装包装包装包装包装随机森林树木 随机森林树木深度强化学习的学习.智能流程自动化 (IPA) 是一种智能流程自动化.物联网的东西互联网.侵入检测系统 (IDS) 是一种入侵检测系统.k-最近的邻居多访问边缘计算边缘计算智慧城市是智慧城市.

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科学领域:

  • 网络安全 网络安全
  • 网络安全 网络安全
  • 物联网 (IoT) 的物联网 (IoT) 的物联网.

背景情况:

  • 物联网 (IoT) 网络容易受到破坏网络完整性和资源的攻击.
  • 智能城市依赖物联网提供各种服务,使其安全至关重要.

研究的目的:

  • 提出一个智能自动化检测模型 (IADM),用于检测和防止基于物联网的智能城市的恶意网络流量.
  • 在智能城市内的分布式多访问边缘计算环境中增强安全性.

主要方法:

  • IADM采用了两阶段的方法:用于初始检测的智能过程自动化 (IPA) 和用于动态适应的强化学习.
  • 第一阶段涉及数据集收集,检测,分析,规则定义和数据库管理的模块,使用诸如随机森林树 (RFT),k-Nearest Neighbor (K-NN),J48,AdaBoost和Bagging等分类器.
  • 第二阶段采用强化学习的一次性学习来适应新威胁并检测零日攻击.

主要成果:

  • 拟议的模型实现了高准确率 (约. 98.8%的人使用RFT,K-NN和AdaBoost分类器.
  • 像AdaBoost和Bagging这样的分类器表现出强的表现,AdaBoost在基于J48.8的基础上获得了98.9%的准确性.
  • 该模型表现出较低的错误率和高精度,回忆和F1测量得分,表明强大的检测能力.

结论:

  • IADM有效地检测和防止智能城市中的物联网网络中的恶意活动.
  • 该模型的自动适应性,使用强化学习,允许快速检测不断变化的威胁,并减少错误的阳性.
  • IADM通过优化内存和时间消耗来提高入侵检测系统 (IDS) 的性能,这对于资源有限的物联网设备至关重要.