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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

210
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
210
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

982
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
982
Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

619
Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
619
Production Efficiency01:01

Production Efficiency

17.9K
Net production efficiency (NPE) is the efficiency at which organisms assimilate energy into biomass for the next trophic level. Due to low metabolic rates and less energy spent on thermoregulatory processes, the NPE of ectotherms (cold-blooded animals) is 10 times higher than endotherms (warm-blooded animals).
17.9K
Optimal Foraging00:48

Optimal Foraging

13.1K
How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.
13.1K
Principle of Virtual Work: Problem Solving01:13

Principle of Virtual Work: Problem Solving

1.5K
The principle of virtual work is an essential concept in the field of mechanics and engineering. This is used to solve problems related to the equilibrium of a structure or system. It is based on the assumption that if a system is in equilibrium, the work done by all the forces during a virtual displacement is zero. This principle is applied by considering virtual displacements of the system and the corresponding work done by internal and external forces.
To apply the principle of virtual work,...
1.5K

You might also read

Related Articles

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

Sort by
Same author

Data-Driven Distributed Kalman Filter-Based Sensor Fault Isolation and Estimation for Large-Scale Interconnected Systems.

IEEE transactions on cybernetics·2025
Same author

Observer-Based Fault-Tolerant and Resilient Control Under Physical Faults and Integrity Cyberattacks.

IEEE transactions on cybernetics·2025
Same author

Integrating Deep Model-Based Learning With Modular State-Based Stackelberg Games for Self-Optimizing Distributed Production Systems.

IEEE transactions on cybernetics·2025
Same author

Resampling Multi-Resolution Signals Using the Bag of Functions Framework: Addressing Variable Sampling Rates in Time Series Data.

Sensors (Basel, Switzerland)·2025
Same author

Time series compression using quaternion valued neural networks and quaternion backpropagation.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

MSCSCC-Net: multi-scale contextual spatial-channel correlation network for forgery detection and localization of JPEG-compressed image.

Scientific reports·2025
Same journal

Relaxed Stability Conditions for Model Predictive Control of Hybrid Dynamical Systems Using Hybrid Recurrent Neural Networks.

IEEE transactions on cybernetics·2026
Same journal

An Evolutionary Algorithm Assisted by an Ensemble of Pareto-Optimal Surrogate Models.

IEEE transactions on cybernetics·2026
Same journal

A Quantum Self-Attention Neural Network Model on Quantum Circuits.

IEEE transactions on cybernetics·2026
Same journal

Semi-Explicit Solution of Some Discrete-Time Higher-Order-Cost Mean-Field-Type Control.

IEEE transactions on cybernetics·2026
Same journal

A Novel One-Step Small Object Detector for Autonomous Aerial Vehicles.

IEEE transactions on cybernetics·2026
Same journal

Online Data-Driven-Based Optimal Output Tracking Control Without Initial Stabilizing Policy.

IEEE transactions on cybernetics·2026
See all related articles

Related Experiment Video

Updated: Dec 13, 2025

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

12.1K

Distributed Self-Optimization of Modular Production Units: A State-Based Potential Game Approach.

Dorothea Schwung, Andreas Schwung, Steven X Ding

    IEEE Transactions on Cybernetics
    |July 30, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new distributed optimization method for production units using potential game (PG) theory and machine learning. The approach enables intelligent autonomous systems with plug-and-play functionality and fast adaptation to changing demands.

    More Related Videos

    Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology
    06:24

    Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology

    Published on: December 15, 2017

    10.5K
    Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
    05:30

    Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

    Published on: September 8, 2023

    977

    Related Experiment Videos

    Last Updated: Dec 13, 2025

    The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
    11:53

    The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

    Published on: October 14, 2017

    12.1K
    Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology
    06:24

    Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology

    Published on: December 15, 2017

    10.5K
    Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
    05:30

    Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

    Published on: September 8, 2023

    977

    Area of Science:

    • Engineering
    • Computer Science
    • Control Theory

    Background:

    • Industrial production units require efficient optimization strategies.
    • Distributed systems offer flexibility but pose coordination challenges.
    • Integrating artificial intelligence with control theory is crucial for modern manufacturing.

    Purpose of the Study:

    • To develop a novel distributed optimization approach for modular production units.
    • To leverage potential game (PG) theory and machine learning for autonomous system development.
    • To enhance the adaptability and efficiency of production processes.

    Main Methods:

    • Modeling the production environment as a state-based potential game (PG).
    • Assigning actuators as agents aiming to maximize utility through learned optimal behavior.
    • Developing a novel learning algorithm based on a global interpolation method.
    • Applying the approach to a laboratory-scale modular bulk good system.

    Main Results:

    • Demonstrated the effectiveness of state information in dynamic game environments.
    • Gained insights into the learning dynamics and process behavior.
    • Achieved encouraging results in optimizing a modular bulk good system.
    • Validated the potential for IEC 61131 conforming PLC implementation.

    Conclusions:

    • The proposed distributed optimization approach offers plug-and-play functionality and online capability.
    • The system demonstrates fast adaptation to changing production requirements.
    • Potential game theory combined with machine learning provides an intelligent solution for autonomous production systems.