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相关概念视频

Prediction Intervals01:03

Prediction Intervals

2.3K
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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Time-Series Graph00:54

Time-Series Graph

4.4K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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Linear time-invariant Systems01:23

Linear time-invariant Systems

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A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
297
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...
386
Observational Learning01:12

Observational Learning

213
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
213
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

106
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
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Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
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在IIoT中进行时间序列预测的深度学习:进展,挑战和前景.

Lei Ren, Zidi Jia, Yuanjun Laili

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    此摘要是机器生成的。

    深度学习推进工业物联网 (IIoT) 时间序列预测,以更好地控制流程. 本调查分析了IIoT中的深度学习方法,挑战和应用,并提供了未来的研究方向.

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    科学领域:

    • 事物的工业互联网 (IIoT)
    • 人工智能的人工智能
    • 数据科学数据科学数据科学

    背景情况:

    • 时间序列预测对于IIoT的智能控制和管理至关重要.
    • 传统的方法与IIoT数据日益复杂的难度作斗争.
    • 深度学习为IIoT时间序列预测挑战提供了新的解决方案.

    研究的目的:

    • 调查IIoT的基于深度学习的时间序列预测方法.
    • 识别和分析 IIoT 时间序列预测中的关键挑战.
    • 为最先进的解决方案提出框架,并讨论实际应用.

    主要方法:

    • 对 IIoT 时间序列预测应用的深度学习技术的综合文献综述.
    • 分析现有的方法,并确定当前的挑战.
    • 关于先进解决方案的框架建议和现实世界使用案例的摘要.

    主要成果:

    • 深度学习方法在解决IIoT时间序列预测复杂性方面显示出重大前景.
    • 确定的挑战包括数据异质性,可扩展性和可解释性.
    • 提出了一个框架来指导先进解决方案的应用.

    结论:

    • 深度学习是提高IIoT时间序列预测的强大工具.
    • 未来的研究应该集中在复杂的IIoT任务的可扩展知识挖掘上.
    • 拟议的框架有助于实际应用,如预测性维护和供应链管理.