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

117
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...
117
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

4.5K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
4.5K
Second Order systems II01:18

Second Order systems II

204
In an underdamped second-order system, where the damping ratio ζ is between 0 and 1, a unit-step input results in a transfer function that, when transformed using the inverse Laplace method, reveals the output response. The output exhibits a damped sinusoidal oscillation, and the difference between the input and output is termed the error signal. This error signal also demonstrates damped oscillatory behavior. Eventually, as the system reaches a steady state, the error diminishes to zero.
204
Second Order systems I01:20

Second Order systems I

275
A servo system exemplifies a second-order system, featuring a proportional controller and load elements that ensure the output position aligns with the input position. The relationship between these components is described by a second-order differential equation. Applying the Laplace transform under zero initial conditions yields the transfer function, showing how inputs are converted to outputs in the system.
By reinterpreting the system, one can derive the closed-loop transfer function, which...
275
Determination of Multiple Dosing Parameters: Steady-State, Minimum and Maximum Concentrations01:15

Determination of Multiple Dosing Parameters: Steady-State, Minimum and Maximum Concentrations

15
Gentamicin, an aminoglycoside antibiotic, is commonly administered via intermittent intravenous infusion to treat severe infections. An intermittent one-hour infusion of gentamicin, administered at eight-hour intervals, allows for precise control of plasma drug concentrations, minimizing toxicity while ensuring therapeutic efficacy. Pharmacokinetic principles govern the dynamics of plasma concentrations and can be mathematically described using specific equations.The plasma drug concentration...
15
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

198
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...
198

You might also read

Related Articles

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

Sort by
Same author

Reply to: From traditional to artificial intelligence-driven approaches: Revolutionizing personalized and precision nutrition in inflammatory bowel disease.

Clinical nutrition ESPEN·2026
Same author

Accelerated Distributed Gradient Tracking for Constrained Aggregative Optimization Over Time-Varying Digraphs.

IEEE transactions on cybernetics·2026
Same author

Altered global longitudinal strain is a common finding in liver transplant recipients with mild cardiometabolic burden.

Internal and emergency medicine·2026
Same author

Stability Switching and Oscillation Regulation Strategies for Large-Scale Fractional-Order Neural Networks With Double Hubs and Multiple Delays.

IEEE transactions on cybernetics·2026
Same author

Comparative Analysis of Tumor Microbiota Identifies a Metastatic-Specific Bacterial Signature, Highlighting <i>Streptococcus</i> spp. As the Predominant Hub across Cancers.

Oncology research·2026
Same author

ABIGX: A Unified Framework for eXplainable Fault Detection and Classification.

IEEE transactions on pattern analysis and machine intelligence·2026

Related Experiment Video

Updated: Oct 11, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.1K

Distributed Time-Varying Optimization of Second-Order Multiagent Systems Under Limited Interaction Ranges.

Huifen Hong, Simone Baldi, Wenwu Yu

    IEEE Transactions on Cybernetics
    |November 30, 2021
    PubMed
    Summary

    This study addresses distributed optimization for multiagent systems (MASs) with limited communication. New methods ensure network connectivity and solve time-varying optimization problems efficiently.

    More Related Videos

    The HoneyComb Paradigm for Research on Collective Human Behavior
    06:48

    The HoneyComb Paradigm for Research on Collective Human Behavior

    Published on: January 19, 2019

    9.5K
    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

    731

    Related Experiment Videos

    Last Updated: Oct 11, 2025

    Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
    11:53

    Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

    Published on: December 9, 2012

    13.1K
    The HoneyComb Paradigm for Research on Collective Human Behavior
    06:48

    The HoneyComb Paradigm for Research on Collective Human Behavior

    Published on: January 19, 2019

    9.5K
    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

    731

    Area of Science:

    • Control Systems Engineering
    • Distributed Computing
    • Optimization Theory

    Background:

    • Multiagent systems (MASs) face challenges in distributed optimization due to limited communication ranges.
    • Local cost functions (CFs) are only accessible to individual agents, complicating global optimization.
    • Maintaining network connectivity is crucial for effective distributed decision-making in MASs.

    Purpose of the Study:

    • To investigate distributed time-varying optimization for second-order MASs with limited interaction ranges.
    • To develop algorithms that handle local, time-varying cost functions under communication constraints.
    • To ensure continuous network connectivity throughout the optimization process.

    Main Methods:

    • A novel continuous connectivity-preserving mechanism was introduced to maintain network cohesion.
    • Two distributed optimization algorithms were designed: one for time-varying CFs and one for time-invariant CFs.
    • Theoretical analysis and numerical simulations were employed for validation.

    Main Results:

    • The proposed connectivity-preserving mechanism effectively maintains network connectivity.
    • The developed distributed algorithms successfully solve the time-varying optimization problems.
    • Numerical examples demonstrate the practical effectiveness of the presented methods.

    Conclusions:

    • The study provides effective solutions for distributed optimization in MASs with limited communication.
    • The proposed methods enhance the robustness and efficiency of MASs operating under constrained environments.
    • This work contributes to advancing distributed control and optimization strategies for complex systems.