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

Multi-input and Multi-variable systems

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...

You might also read

Related Articles

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

Sort by

Same author

Intelligent magnetic nanomaterials: a trinity framework of programmability, field-driven actuation, and data-guided intelligence.

Nanoscale·2026

Same author

Integrated single-cell and bulk transcriptomic analysis reveals shared pathogenesis and prognostic biomarkers in breast and thyroid cancers.

Frontiers in immunology·2026

Same author

Mertk-driven tunneling nanotube formation in macrophages preserves mitochondrial homeostasis during kidney Ischemia-Reperfusion Attack.

Biochemical pharmacology·2026

Same author

Application of single-cell deep learning in elucidating the mapping relationship between visceral and body surface inflammatory patterns.

Computational and structural biotechnology journal·2025

Same author

Ptgs2+ CPTC Function as a "Force-Immune Axis" by Responding to Acupuncture and Mediating M2 Macrophage Activation for Anti-Inflammatory Effects.

FASEB journal : official publication of the Federation of American Societies for Experimental Biology·2025

Same author

Unraveling neutrophil extracellular traps: A new frontier in kidney disease therapy.

Life sciences·2025

Same journal

Impact of an Artificial Albumin Corona on Surface Charge-Driven Nano-Bio Interactions and Cytotoxicity of Silver Nanoparticles.

ACS omega·2026

Same journal

Structural and Functional Disruption of Thiopurine S‑Methyltransferase by the A80P Variant: A Simulation and Genotyping Study.

ACS omega·2026

Same journal

CRISPR/Cas12a2-Mediated Ultrasensitive Assay for Rapid Detection of H1N1 Influenza Virus RNA.

ACS omega·2026

Same journal

Photocatalytic Treatment of Real Sugar Industry Wastewater Using Lignocellulosic Biomass-Derived Hydrochar/g-CN.

ACS omega·2026

Same journal

Electrochemical Dopamine Biosensor Based on Plant-Derived Peroxidase Immobilized on Titanate Nanowires.

ACS omega·2026

Same journal

Revealing the Effects of Process Parameters on Structural, Thermal, Mechanical, Biodegradation, and Biocompatibility Properties on the Electrospinning of Poly(vinyl alcohol)/Microbial Inulin Nanofibers.

ACS omega·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 23, 2026

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

Published on: November 24, 2015

Adaptive Temporal-Spatial Pyramid Variational Autoencoder Model for Multirate Dynamic Chemical Process Soft Sensing

Bingbing Shen¹, Zeyu Yang², Le Yao¹

¹School of Mathematics, Hangzhou Normal University, Hangzhou 311121, China.

|June 3, 2024

Summary

This summary is machine-generated.

This study introduces an adaptive temporal-spatial pyramid variational autoencoder (ATS-PVAE) for modeling complex, nonlinear multirate data in chemical processes. The novel approach enhances real-time prediction and quality control in industrial applications.

More Related Videos

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

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0

Published on: June 5, 2017

Related Experiment Videos

Last Updated: Jun 23, 2026

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

Published on: November 24, 2015

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

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0

Published on: June 5, 2017

Area of Science:

Chemical Engineering
Data Science
Process Control

Background:

Data-driven soft sensors are crucial for real-time quality prediction in industrial processes.
Modeling nonlinear dynamic multirate data presents significant challenges in chemical production.
Accurate modeling is essential for guiding production and improving product quality.

Purpose of the Study:

To propose an innovative temporal-spatial pyramid variational autoencoder (TS-PVAE) for extracting nonlinear temporal-spatial features from multirate data.
To develop an adaptive TS-PVAE (ATS-PVAE) model by integrating just-in-time (JIT) learning for real-time model fine-tuning.
To address the challenges of modeling complex nonlinear time-series data in industrial settings.

Main Methods:

Development of a temporal-spatial pyramid variational autoencoder (TS-PVAE) for multirate data feature extraction.
Integration of just-in-time (JIT) learning with the TS-PVAE to create an adaptive model (ATS-PVAE).
Utilizing historical data for real-time fine-tuning of the adaptive model.

Main Results:

The proposed TS-PVAE model effectively extracts nonlinear temporal-spatial features from multirate data.
The adaptive ATS-PVAE model demonstrates superior estimation performance through real-time fine-tuning.
Validation on a methanation furnace industrial case confirmed the model's effectiveness.

Conclusions:

The developed ATS-PVAE model offers a robust solution for nonlinear dynamic multirate data modeling in chemical processes.
The approach enhances the capability of data-driven soft sensors for real-time quality prediction and process optimization.
This work provides a significant advancement in handling complex industrial process data for improved control and quality assurance.