Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

149
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...
149
Electro-mechanical Systems01:19

Electro-mechanical Systems

1.2K
Electromechanical systems are intricate configurations that effectively combine electrical and mechanical elements to achieve a desired outcome. Central to many of these systems is the DC motor, a device that converts electrical energy into mechanical motion, enabling various applications ranging from simple fans to complex robotic mechanisms.
A key component of the DC motor is the armature, a rotating circuit positioned within a magnetic field. As an electric current passes through the...
1.2K
Control Systems01:10

Control Systems

1.4K
Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
At the heart...
1.4K
Mechanical Systems01:22

Mechanical Systems

289
Mechanical systems are analogous to to electrical networks where springs and masses play similar roles to inductors and capacitors, respectively. A viscous damper in mechanical systems functions similarly to a resistor in electrical networks, dissipating energy. The forces acting on a mass in such systems include an applied force in the direction of motion, counteracted by forces from the spring, a viscous damper, and the mass's acceleration. This interplay of forces is mathematically...
289
Control Systems: Applications01:25

Control Systems: Applications

739
Electrical engineering plays a pivotal role in our daily lives, with control systems at the heart of many applications, from home appliances to sophisticated space shuttles. Control systems manage and regulate the behavior of devices and processes, ensuring they function safely, correctly, and efficiently.
In modern vehicles, control systems manage various functions to enhance performance and safety. The steering wheel and accelerator are primary inputs in a car's control system. The...
739
Feedback control systems01:26

Feedback control systems

427
Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
427

You might also read

Related Articles

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

Sort by
Same author

Traumatic Facial Fractures in Children and Adolescents.

The Journal of craniofacial surgery·2018
Same author

Anatomical feasibility study of unilateral percutaneous kyphoplasty for lumbar through the conventional transpedicular approach: An observational study using 3D CT analysis.

Medicine·2018
Same author

SMAD4 promotes TGF-β-independent NK cell homeostasis and maturation and antitumor immunity.

The Journal of clinical investigation·2018
Same author

Constraining Dark Matter Models with a Light Mediator at the PandaX-II Experiment.

Physical review letters·2018
Same author

Islet transplantation improved penile tissue fibrosis in a rat model of type 1 diabetes.

BMC endocrine disorders·2018
Same author

Sensory Characteristics of Combinations of Phenolic Compounds Potentially Associated with Smoked Aroma in Foods.

Molecules (Basel, Switzerland)·2018
Same journal

Optimization of mode discerning control for nonlinear hybrid systems subject to unknown inputs with applications to active fault diagnosis.

ISA transactions·2026
Same journal

Convergence evaluation of optimization-based stochastic iterative learning control.

ISA transactions·2026
Same journal

Adaptive utility-aware event-triggered reinforcement learning for hybrid attack scheduling against remote state estimation.

ISA transactions·2026
Same journal

Hybrid vehicle state estimation using closed-form liquid neural networks and nonlinear Kalman filtering.

ISA transactions·2026
Same journal

Cross-coupled synchronization control strategy for rebar binding robots based on impedance control.

ISA transactions·2026
Same journal

Gas flow tracking for electronic pressure control system in gas chromatography under state constraints and hysteresis：An innovative fuzzy adaptive control approach.

ISA transactions·2026
See all related articles

Related Experiment Video

Updated: Sep 12, 2025

Data Acquisition Protocol for Determining Embedded Sensitivity Functions
07:46

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

6.2K

Mechanism and data fusion driven multi-indicator soft sensor framework for industrial processes.

Qingquan Xu1, Jie Dong2, Kaixiang Peng2

  • 1Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, PR China.

ISA Transactions
|August 7, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel soft sensor framework for industrial processes, fusing mechanistic and data-driven models to accurately predict quality indicators like strip flatness and crown in hot strip rolling.

Keywords:
Hot strip rolling processKolmogorov–Arnold networksMechanism and data fusion drivenMulti-indicator soft sensor

More Related Videos

Data Communication Based on MQTT in a Polymer Extrusion Process
08:15

Data Communication Based on MQTT in a Polymer Extrusion Process

Published on: July 15, 2022

3.6K
Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.4K

Related Experiment Videos

Last Updated: Sep 12, 2025

Data Acquisition Protocol for Determining Embedded Sensitivity Functions
07:46

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

6.2K
Data Communication Based on MQTT in a Polymer Extrusion Process
08:15

Data Communication Based on MQTT in a Polymer Extrusion Process

Published on: July 15, 2022

3.6K
Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.4K

Area of Science:

  • Industrial Process Control
  • Soft Sensor Technology
  • Mechanical Engineering

Background:

  • Traditional measurement methods have limitations in industrial quality control.
  • Complex mechanisms and time-varying delays in industrial processes challenge multi-indicator soft sensing.
  • Existing research lacks advanced fusion of mechanical and data-driven models for soft sensing.

Purpose of the Study:

  • To propose a mechanism and data fusion driven multi-indicator soft sensor framework.
  • To address challenges in multi-indicator soft sensing for complex industrial processes.
  • To improve product quality prediction in industrial settings.

Main Methods:

  • Mechanism analysis of the hot strip rolling process (HSRP) and identification of unknown parameters.
  • Fusion of data from mechanistic models with actual process data.
  • Development of Kolmogorov-Arnold Networks with an embedded time-series input layer (TS-KAN) to handle time-varying delays.

Main Results:

  • A novel soft sensor framework was developed and validated using HSRP production data.
  • The framework successfully addressed challenges posed by complex mechanisms and time-varying delays.
  • Simultaneous accurate prediction of strip flatness and crown was achieved.

Conclusions:

  • The proposed mechanism and data fusion framework offers a robust solution for multi-indicator soft sensing in industrial processes.
  • The integration of TS-KAN effectively manages time-varying delays, enhancing prediction accuracy.
  • This approach significantly improves quality indicator prediction in the hot strip rolling process.