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相关概念视频

End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
342
Reducing Line Loss01:18

Reducing Line Loss

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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
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Classification of Signals01:30

Classification of Signals

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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
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Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Classification of Systems-I01:26

Classification of Systems-I

189
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
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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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相关实验视频

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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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在SDN中预测流量,以使用深度学习方法进行可解释的QoS.

Getahun Wassie1, Jianguo Ding2, Yihenew Wondie3

  • 1IP Networking and Mobile Internet, Addis Ababa University, Addis Ababa, Ethiopia. getahunws12@gmail.com.

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概括

这项研究开发了深度学习模型,以预测和识别大象流量,防止网络拥堵和提高服务质量 (QoS). 这些模型实现了近乎完美的准确性,突出显示了数据包和字节大小作为关键检测属性.

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科学领域:

  • 计算机科学 计算机科学
  • 网络工程 网络工程
  • 人工智能的人工智能

背景情况:

  • 越来越多的多媒体流量 (VOIP,视频) 要求提高服务质量 (QoS).
  • 大象流导致网络拥堵,导致数据包丢失和延迟.
  • 深度学习为实时网络流量管理提供了一个有前途的解决方案.

研究的目的:

  • 设计和开发先进的交通预测模型,以识别大象的流动.
  • 在软件定义网络 (SDN) 环境中主动防止网络拥堵.
  • 为使用可解释的人工智能 (XAI) 的网络管理员提供明确的模型解释.

主要方法:

  • 使用深度学习算法:H2O,深度自动编码器.
  • 使用的自动ML预测算法:XGBoost,梯度增强机 (GBM) 和梯度分布函数 (GDF).
  • 应用可解释的人工智能 (XAI) 用于模型解释性.

主要成果:

  • 实现了高的验证准确性:99.97% (XGBoost),99.99% (GBM) 和100% (GDF) 的验证准确性.
  • 报告的最小工程中错误:0.0003952 (XGBoost),0.001697 (GBM) 和0.00000408 (GDF) 在施工过程中发生的错误.
  • 确定了数据包大小和字节大小作为大象流检测的关键属性.

结论:

  • 开发的深度学习模型有效地预测大象流动,准确度非常高.
  • XAI提高了模型透明度,帮助SDN环境中的网络管理员.
  • 积极识别和管理大象流动对于维护网络QoS至关重要.