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

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

954
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
954
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

217
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
217
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

426
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
426

You might also read

Related Articles

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

Sort by
Same author

Adaptive Learning Control of Uncertain Systems via Weight and Intrinsic Plasticity-Based Neural Networks.

IEEE transactions on neural networks and learning systems·2026
Same author

PID-Optimized Deep Learning for Adaptive Time-Frequency Forecasting in Dynamic Systems: Coal Calorific Value Prediction.

IEEE transactions on cybernetics·2026
Same author

Adaptive Sensor Fault-Tolerant Control for Distributed Parameter Systems.

IEEE transactions on cybernetics·2026
Same author

Prescribed-rate target tracking for time-delayed systems using output measurements.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

ADR-DMOEA: A Dynamic Multiobjective Optimization Evolutionary Algorithm Based on Adaptive Dynamic Response Strategy.

IEEE transactions on cybernetics·2026
Same author

DuaDiff: Dual-Conditional Diffusion Model for Guided Thermal Image Super-Resolution.

IEEE transactions on neural networks and learning systems·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 1, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

2.0K

Distributed Kalman Consensus Filter for Estimation With Moving Targets.

Bosen Lian, Yan Wan, Ya Zhang

    IEEE Transactions on Cybernetics
    |November 11, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel distributed Kalman consensus filter (DKCF) that improves convergence speed for mobile target estimation. The new filter enhances information flow topology, leading to more accurate consensus values among sensors.

    More Related Videos

    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

    15.2K
    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

    8.8K

    Related Experiment Videos

    Last Updated: Dec 1, 2025

    Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
    06:45

    Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

    Published on: October 28, 2022

    2.0K
    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

    15.2K
    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

    8.8K

    Area of Science:

    • Control Systems
    • Estimation Theory
    • Distributed Systems

    Background:

    • Consensus-based distributed Kalman filters are crucial for target estimation.
    • Existing average consensus methods suffer from slow convergence and limited consideration of sensing range and mobility impacts on information flow.
    • Target mobility and sensor limitations challenge traditional distributed estimation approaches.

    Purpose of the Study:

    • To design a novel distributed Kalman consensus filter (DKCF) with an information-weighted consensus structure.
    • To address limitations in convergence speed and information flow topology in existing distributed Kalman filters.
    • To enable accurate estimation of randomly mobile targets in continuous time.

    Main Methods:

    • Development of a novel distributed Kalman consensus filter (DKCF) employing an information-weighted consensus mechanism.
    • Creation of a new moving target information-flow topology considering sensor sensing ranges, target mobility, and local information-weighted neighbors.
    • Derivation of novel necessary and sufficient conditions for the convergence of the proposed DKCF.

    Main Results:

    • The proposed DKCF demonstrates improved convergence speed compared to traditional average consensus methods.
    • The developed information-flow topology effectively models the dynamic interactions between sensors and mobile targets.
    • Under derived conditions, estimates from all sensors converge to accurate consensus values.

    Conclusions:

    • The novel DKCF with an information-weighted consensus structure offers superior performance for mobile target estimation.
    • The developed convergence conditions ensure reliable and accurate distributed estimation in dynamic environments.
    • Simulation results validate the effectiveness and advantages of the proposed DKCF over existing methods.