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

Weighted Mean00:57

Weighted Mean

6.0K
While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
6.0K
Mean Absolute Deviation01:13

Mean Absolute Deviation

3.1K
The mean absolute deviation is also a measure of the variability of data in a sample. It is the absolute value of the average difference between the data values and the mean.
Let us consider a dataset containing the number of unsold cupcakes in five shops: 10, 15, 8, 7, and 10. Initially, calculate the sample mean. Then calculate the deviation, or the difference, between each data value and the mean. Next, the absolute values of these deviations are added and divided by the sample size to...
3.1K
Trimmed Mean01:10

Trimmed Mean

3.1K
While measuring the mean of a data set, care needs to be taken when associating the mean to its central tendency. The same goes for the arithmetic mean, the geometric mean, or the harmonic mean. This is because the presence of a single outlier data value can significantly affect the mean. That is, the mean is sensitive to fluctuations in the data set.
Although certain measures of central tendency are not sensitive to outliers, there are alternative versions of the mean that get around the...
3.1K
Regression Toward the Mean01:52

Regression Toward the Mean

6.6K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.6K
Root Mean Square00:57

Root Mean Square

3.5K
If in an experiment, data values have a probability of being both positive and negative, neither the arithmetic mean, the geometric mean, nor the harmonic mean can be used to calculate the central tendency of the data set. In particular, if the positive and negative values are equally likely, the arithmetic mean is close to zero.
For example, consider the velocity of gas molecules in a container. The gas molecules are moving in different directions, which might impart positive and negative...
3.5K
Multimachine Stability01:25

Multimachine Stability

286
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:
286

You might also read

Related Articles

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

Sort by
Same author

Adaptive Task-Space Control for Hydraulic Excavators Based on the High-Order Fully Actuated System Approach.

IEEE transactions on cybernetics·2026
Same author

Trained immunity-primed DLL3-targeted CAR macrophages for the eradication of small cell lung cancer.

Journal of advanced research·2026
Same author

SAND: Spectral-Attention Neural Decoding of Hand Kinematics from Low-Frequency EEG Dynamics.

IEEE transactions on bio-medical engineering·2026
Same author

A self-powered chloroplast-driven nanophotosystem for treating skin photoaging through rejuvenating mitochondria and revitalizing senescent fibroblasts.

Journal of nanobiotechnology·2026
Same author

A New Arbitrary-Time Guaranteed-Performance Adaptive Tracking Control Scheme Design for Uncertain Nonlinear Systems.

IEEE transactions on cybernetics·2026
Same author

Corrections to "Learning From M-Tuple One-vs-All Confidence Comparison Data".

IEEE transactions on neural networks and learning systems·2026

Related Experiment Video

Updated: Nov 12, 2025

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

9.6K

Delay and Packet-Drop Tolerant Multistage Distributed Average Tracking in Mean Square.

Fei Chen, Changjiang Chen, Ge Guo

    IEEE Transactions on Cybernetics
    |March 17, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This study presents a robust distributed average tracking (DAT) algorithm for multiagent systems facing delays and packet drops. The new method achieves accurate tracking while managing uncertainties, improving upon existing approaches.

    More Related Videos

    A Method for Tracking the Time Evolution of Steady-State Evoked Potentials
    12:03

    A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

    Published on: May 25, 2019

    8.7K
    A Protocol for Real-time 3D Single Particle Tracking
    10:16

    A Protocol for Real-time 3D Single Particle Tracking

    Published on: January 3, 2018

    15.1K

    Related Experiment Videos

    Last Updated: Nov 12, 2025

    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

    9.6K
    A Method for Tracking the Time Evolution of Steady-State Evoked Potentials
    12:03

    A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

    Published on: May 25, 2019

    8.7K
    A Protocol for Real-time 3D Single Particle Tracking
    10:16

    A Protocol for Real-time 3D Single Particle Tracking

    Published on: January 3, 2018

    15.1K

    Area of Science:

    • Control Systems Engineering
    • Networked Multiagent Systems
    • Signal Processing

    Background:

    • Distributed Average Tracking (DAT) is crucial for coordinating multiagent systems.
    • Existing DAT algorithms often fail under realistic conditions like input delays, packet drops, and noise.
    • There's a need for more robust and practically attainable DAT solutions.

    Purpose of the Study:

    • To develop a novel distributed average tracking algorithm for discrete-time linear time-invariant multiagent networks.
    • To design an algorithm tolerant to input delays, random packet drops, and reference noise.
    • To determine the upper bound of tracking error in uncertain environments.

    Main Methods:

    • Integration of Kalman filtering, multistage consensus filtering, and predictive control techniques.
    • Development of a delay and packet-drop-tolerant algorithm.
    • Analysis of convergence properties based on network topology and parameter selection.

    Main Results:

    • A simple, compelling DAT algorithm robust to initialization errors is proposed.
    • The algorithm allows a trade-off between communication/computation costs and tracking error.
    • Convergence analysis reveals parameter constraints dependent on network degree and convergence speed on network topology's spectral properties.

    Conclusions:

    • The developed DAT algorithm offers improved practical attainability by addressing common network uncertainties.
    • Theoretical findings on parameter bounds and convergence speed are validated.
    • The approach provides a foundation for more reliable distributed control in complex networks.