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Related Concept Videos

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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Time-Series Graph00:54

Time-Series Graph

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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.
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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.
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Observational Learning01:12

Observational Learning

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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...
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Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

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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.
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Deep Learning for Time-Series Prediction in IIoT: Progress, Challenges, and Prospects.

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    Deep learning advances Industrial Internet of Things (IIoT) time-series prediction for better process control. This survey analyzes deep learning methods, challenges, and applications in IIoT, offering future research directions.

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    Area of Science:

    • Industrial Internet of Things (IIoT)
    • Artificial Intelligence
    • Data Science

    Background:

    • Time-series prediction is vital for intelligent control and management in the IIoT.
    • Traditional methods struggle with the increasing complexity of IIoT data.
    • Deep learning offers novel solutions for IIoT time-series prediction challenges.

    Purpose of the Study:

    • To survey deep learning-based time-series prediction methods for IIoT.
    • To identify and analyze key challenges in IIoT time-series prediction.
    • To propose a framework for state-of-the-art solutions and discuss practical applications.

    Main Methods:

    • Comprehensive literature review of deep learning techniques applied to IIoT time-series prediction.
    • Analysis of existing methods and identification of current challenges.
    • Framework proposal for advanced solutions and summary of real-world use cases.

    Main Results:

    • Deep learning methods show significant promise in addressing IIoT time-series prediction complexities.
    • Identified challenges include data heterogeneity, scalability, and interpretability.
    • A framework is proposed to guide the application of advanced solutions.

    Conclusions:

    • Deep learning is a powerful tool for enhancing IIoT time-series prediction.
    • Future research should focus on extensible knowledge mining for complex IIoT tasks.
    • The proposed framework aids in practical applications like predictive maintenance and supply chain management.