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Related Concept Videos

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

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Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
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State Space Representation01:27

State Space Representation

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The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
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Space Trusses01:25

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A space truss is a three-dimensional counterpart of a planar truss. These structures consist of members connected at their ends, often utilizing ball-and-socket joints to create a stable and versatile framework. The space truss is widely used in various construction projects due to its adaptability and capacity to withstand complex loads.
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Transfer Function to State Space01:23

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State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an RLC...
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State Space to Transfer Function01:21

State Space to Transfer Function

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The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
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Space Trusses: Problem Solving01:29

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A space truss is a three-dimensional counterpart of a planar truss. These structures consist of members connected at their ends, often utilizing ball-and-socket joints to create a stable and versatile framework. Due to its adaptability and capacity to withstand complex loads, the space truss is widely used in various construction projects.
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Fragmenting Bulk Hydrogels and Processing into Granular Hydrogels for Biomedical Applications
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A Development of Granular Input Space in System Modeling.

Xiubin Zhu, Witold Pedrycz, Zhiwu Li

    IEEE Transactions on Cybernetics
    |March 21, 2019
    PubMed
    Summary

    This study introduces a new method for granular modeling by optimizing information granularity in input spaces. This approach enhances model specificity and data coverage for better insights and predictions.

    Area of Science:

    • Granular Computing
    • Data Analysis
    • Machine Learning

    Background:

    • Existing numeric models lack optimal input variable precision.
    • Granular modeling requires effective formation of granular input spaces.

    Purpose of the Study:

    • To present a novel design approach for granular input spaces and granular modeling.
    • To optimize the allocation of information granularity across input variables.

    Main Methods:

    • Formation of granular input space by allocating information granularity.
    • Optimization of granularity allocation based on specificity and coverage.
    • Utilizing differential evolution for optimization.

    Main Results:

    • Granular input space formation enhances input variable ranking by precision.

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  • Outputs of granular models become information granules (intervals, fuzzy sets, etc.).
  • Optimized granularity maximizes output specificity and data coverage.

    • Conclusions:

    • The proposed method provides a principled approach to granular input space construction.
    • Balancing specificity and coverage is key to quality granular models.
    • The approach is validated using synthetic and real-world datasets.