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

Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.4K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.4K
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

1.1K
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...
1.1K
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
State Space Representation01:27

State Space Representation

359
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
359
Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

6.8K
On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
6.8K
Uncertainty: Overview00:59

Uncertainty: Overview

1.3K
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
1.3K

You might also read

Related Articles

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

Sort by
Same author

Structure-aware fatigue modeling in foot deformities: A digital health framework for tissue-specific running injury risk prediction using multi-modal data.

PLOS digital health·2026
Same author

Spatiotemporal inequities in early-life ecological liveability and sleep health in preschool children.

Environmental pollution (Barking, Essex : 1987)·2026
Same author

Understanding the "how" and "why": A mixed methods process evaluation for the PRO-HIIT intervention.

PloS one·2026
Same author

Interlimb differences in knee joint loading and stress distribution following anterior cruciate ligament reconstruction during stair descent.

Clinical biomechanics (Bristol, Avon)·2026
Same author

CAFE: Cross-View Adaptive Fusion and Cluster Center Enhancement for Robust Multi-View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Wavelet spectral-aware Kolmogorov-Arnold Network for organ and tumor segmentation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026

Related Experiment Video

Updated: Nov 12, 2025

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

915

Distributed Fusion Estimation for Stochastic Uncertain Systems With Network-Induced Complexity and Multiple Noise.

Li Liu, Wenju Zhou, Minrui Fei

    IEEE Transactions on Cybernetics
    |March 17, 2021
    PubMed
    Summary

    This study introduces a new event-triggered signal selection method and delay compensation strategy for distributed fusion estimation, improving target tracking performance despite network issues and uncertainties.

    Related Experiment Videos

    Last Updated: Nov 12, 2025

    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

    915

    Area of Science:

    • Control Systems Engineering
    • Networked Systems
    • Estimation Theory

    Background:

    • Network-induced complexity and stochastic parameter uncertainties challenge distributed fusion estimation.
    • Packet dropouts and disorders from random delays degrade system performance.

    Purpose of the Study:

    • To develop a robust distributed fusion estimation algorithm for networked systems.
    • To address network-induced packet dropouts, disorders, and parameter uncertainties.
    • To enhance system performance in target tracking applications.

    Main Methods:

    • An event-triggered signal selection method is proposed to manage network-induced issues.
    • H2/H∞ performance analysis is conducted under various noise conditions.
    • A linear delay compensation strategy is implemented to mitigate network complexity.
    • A weighted fusion scheme integrates multiple data sources using an error cross-covariance matrix.

    Main Results:

    • The novel event-triggered method effectively handles packet dropouts and disorders.
    • The linear delay compensation strategy improves system robustness against network-induced problems.
    • The weighted fusion scheme successfully integrates distributed information.
    • Validated case studies confirm satisfactory system performance in target tracking.

    Conclusions:

    • The proposed distributed fusion estimation algorithm demonstrates effectiveness in complex networked environments.
    • The integration of event-triggered control and delay compensation enhances estimation accuracy and robustness.
    • The method offers a viable solution for reliable target tracking with uncertain and complex network conditions.