Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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...

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

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

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...

Introduction to Statistical Process Control

Introduction to Statistical Process Control

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...

Probability Histograms

Probability Histograms

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.

Estimation of the Physical Quantities

Estimation of the Physical Quantities

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...

Poisson Probability Distribution

Poisson Probability Distribution

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.
The...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Machine learning-based prediction of polyvinyl alcohol product viscosity and design of optimal process conditions.

Analytical sciences : the international journal of the Japan Society for Analytical Chemistry·2026

Same author

Data-Driven Design of Organic Semiconductors Exhibiting Low Reorganization Energy via Hierarchical Variational Autoencoders, Gaussian Mixture Regression, and Bayesian Optimization.

Journal of chemical information and modeling·2026

Same author

Generation of Molecules Near the Applicability Domain Boundaries of Property Prediction Models.

Journal of chemical information and modeling·2026

Same author

A general framework for extrapolation-aware prediction reliability in forward and inverse analyses of Gaussian mixture regression models.

Analytical sciences : the international journal of the Japan Society for Analytical Chemistry·2026

Same author

Robust machine learning and ensemble learning approach to predict variation in experimental data for multiple measurements and anomalies.

Analytical sciences : the international journal of the Japan Society for Analytical Chemistry·2026

Same author

Machine Learning Models Predicting Solubility and Polymerizability of Polyimides Considering Multiple Monomers for CO<sub>2</sub> Separation Membranes.

Molecular informatics·2026

Search research articles

Related Experiment Video

Updated: Jun 26, 2025

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

Estimation and visualization of process states using latent variable models based on Gaussian process.

Hiromasa Kaneko¹

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

Analytical Science Advances

|May 8, 2024

Summary

This summary is machine-generated.

This study introduces Gaussian process models for industrial process state estimation and visualization. Gaussian process dynamical models (GPDM) demonstrated superior accuracy in estimating process states, enabling simultaneous visualization.

Keywords:

Gaussian process dynamics latent variable machine learning process state estimation visualization

More Related Videos

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

Related Experiment Videos

Last Updated: Jun 26, 2025

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

Area of Science:

Chemical Engineering
Data Science
Control Systems

Background:

Accurate process state estimation and visualization are crucial for effective control in chemical and industrial plants.
Industrial processes often exhibit characteristics related to Gaussian distributions, making Gaussian process models theoretically relevant.

Purpose of the Study:

To propose novel methods for process state estimation and visualization using Gaussian process latent variable models.
To evaluate the performance of Bayesian Gaussian process latent variable model (BGPLVM), infinite warped mixture model (iWMM), and Gaussian process dynamical models (GPDM).

Main Methods:

Utilizing two latent variables based on BGPLVM, iWMM, and GPDM for process state estimation and visualization.
Analyzing the Tennessee Eastman process dataset to assess model performance.
Incorporating time-delayed process variables to account for process dynamics.

Main Results:

GPDM achieved the highest performance in process state estimation, followed by iWMM and BGPLVM.
The inclusion of time-delayed variables significantly improved estimation accuracy.
GPDM accurately estimated four process states with approximately 100% accuracy using only two latent variables.
GPDM achieved approximately 90% accuracy for estimating 10 process states.

Conclusions:

GPDM is highly effective for accurate process state estimation and simultaneous visualization in industrial settings.
The proposed methods, particularly GPDM with time-delayed variables, offer a robust solution for complex process monitoring.
Gaussian process latent variable models provide a powerful framework for understanding and controlling industrial processes.