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

295
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...
295
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

421
Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
421
Randomized Experiments01:13

Randomized Experiments

8.6K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
8.6K
Masking and Demasking Agents01:19

Masking and Demasking Agents

3.2K
EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
3.2K
Multimachine Stability01:25

Multimachine Stability

389
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
389
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

1.2K
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
1.2K

You might also read

Related Articles

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

Sort by
Same author

Development of a dual-mode immunoassay kit for periostin detection in exhaled breath condensate using α-NaYF<sub>4</sub>@ZIF-67 core-shell nanostructures.

Biosensors & bioelectronics·2026
Same author

The Prognostic Value of Serum Inflammatory Markers in Nasopharyngeal Carcinoma Patients Treated With Immune Checkpoint Inhibitors: A Multicenter Study.

Cancer medicine·2026
Same author

Histone methylation machinery in gliomas: From enzymatic mechanisms to inhibitor development.

Cancer treatment and research communications·2026
Same author

Disturbance observer-based robust prescribed-time sliding mode tracking control for robotic manipulator systems: Theory and experiment.

ISA transactions·2026
Same author

Associations between urinary arsenic and vitamin D deficiency: a cross-sectional analysis of NHANES 2011-2018.

Journal of health, population, and nutrition·2026
Same author

Single-nucleus RNA sequencing reveals a spatiotemporal pattern of H<sub>2</sub>S signaling in Chinese cabbage.

Science China. Life sciences·2026
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 Videos

Consistency of Multiagent Distributed Generative Adversarial Networks.

Shuya Ke, Wenqi Liu

    IEEE Transactions on Cybernetics
    |October 20, 2020
    PubMed
    Summary
    This summary is machine-generated.

    Generative Adversarial Networks (GANs) achieve consensus through opinion dynamics, equating multiagent consistency with Nash equilibrium. This research introduces a novel Multi-Agent Distributed GAN (MADGAN) for improved large-scale network performance.

    Related Experiment Videos

    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Network Science

    Background:

    • Generative Adversarial Networks (GANs) are complex systems with emergent properties.
    • Understanding the mathematical underpinnings of GAN convergence is crucial for their application.
    • Multiagent system theory offers a novel perspective on GAN dynamics.

    Purpose of the Study:

    • To present the mathematical properties of GANs using opinion dynamics.
    • To establish a link between multiagent system consistency and GAN Nash equilibrium.
    • To propose a novel Multi-Agent Distributed GAN (MADGAN) for large-scale networks.

    Main Methods:

    • Modeling GANs as multiagent systems with generators and discriminators as agents.
    • Applying multiagent system consistency theory and algorithms.
    • Utilizing the DeGroot model to prove consensus on distribution functions.
    • Analyzing Markov chain stationary distributions for consensus conditions in MADGAN.

    Main Results:

    • Identified a novel sufficient and necessary condition for the existence of Nash equilibrium in GANs.
    • Demonstrated that GAN generators and discriminators can reach consensus on distribution functions.
    • Developed a Multi-Agent Distributed GAN (MADGAN) addressing cognitive consistency in distributed networks.
    • Validated theoretical findings through simulations on the MNIST dataset.

    Conclusions:

    • Opinion dynamics provide a robust framework for analyzing GAN mathematical properties.
    • MADGAN offers a scalable solution for distributed GANs by leveraging social group wisdom and network structure.
    • The study bridges game theory, multiagent systems, and deep learning for GAN advancements.