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相关概念视频

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
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One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
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Introduction to Statistical Process Control01:15

Introduction to Statistical Process Control

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Statistical Process Control (SPC) is a method used to monitor and control quality within processes, particularly in manufacturing and service delivery, by employing statistical methods. SPC aims to distinguish between natural (common cause) variation and variation due to specific changes or events (special cause), allowing for timely improvements and sustained quality. The control chart, a pivotal tool in SPC, visually displays data over time alongside a central line of upper and lower control...
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Probability Histograms01:17

Probability Histograms

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A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
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Estimation of the Physical Quantities01:05

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On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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Poisson Probability Distribution01:09

Poisson Probability Distribution

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A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
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使用基于高斯过程的潜在变量模型估计和可视化过程状态.

Hiromasa Kaneko1

  • 1Department of Applied Chemistry, School of Science and Technology Meiji University Kanagawa Japan.

Analytical science advances
|May 8, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了工业过程状态估计和可视化的高斯过程模型. 高斯过程动态模型 (GPDM) 在估计过程状态方面表现出卓越的准确性,使同时可视化成为可能.

关键词:
斯过程是高斯过程.动力学 动力学 动力学隐性变量是一个隐性变量.机器学习是机器学习.过程状态估计过程状态估计视觉化的可视化

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科学领域:

  • 化学工程是化学工程的重要组成部分.
  • 数据科学数据科学数据科学
  • 控制系统 控制系统

背景情况:

  • 准确的工艺状态估计和可视化对于化学和工业工厂的有效控制至关重要.
  • 工业过程经常表现出与高斯分布相关的特征,使得高斯过程模型在理论上具有相关性.

研究的目的:

  • 提出使用高斯过程潜变量模型进行过程状态估计和可视化的新方法.
  • 评估贝叶斯高斯过程隐性变量模型 (BGPLVM),无限扭曲混合模型 (iWMM) 和高斯过程动态模型 (GPDM) 的性能.

主要方法:

  • 使用基于BGPLVM,iWMM和GPDM的两个潜在变量进行过程状态估计和可视化.
  • 分析田纳西东曼过程数据集以评估模型性能.
  • 整合时间延迟的过程变量来解释过程动态.

主要成果:

  • 在过程状态估计中,GPDM获得了最高的性能,其次是iWMM和BGPLVM.
  • 包含时间延迟变量显著提高了估计准确性.
  • 通过GPDM准确地估计了四个过程状态,准确度约为100%,仅使用两个潜在变量.
  • 在估计10个工艺状态时,GPDM的准确度约为90%.

结论:

  • 在工业环境中,GPDM对于准确的工艺状态估计和同时可视化非常有效.
  • 提出的方法,特别是具有时间延迟变量的GPDM,为复杂的过程监控提供了可靠的解决方案.
  • 高斯过程潜变量模型为了解和控制工业过程提供了强大的框架.