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

Problem Solving: Energy in Simple Harmonic Motion01:17

Problem Solving: Energy in Simple Harmonic Motion

1.4K
Simple harmonic motion (SHM) is a type of periodic motion in time and position, in which an object oscillates back and forth around an equilibrium position with a constant amplitude and frequency. In SHM, there is a continuous exchange between the potential and kinetic energy, which results in the oscillation of the object.
Consider the spring in a shock absorber of a car. The spring attached to the wheel executes simple harmonic motion while the car is moving on a bumpy road. The force on the...
1.4K
Routh-Hurwitz Criterion I01:15

Routh-Hurwitz Criterion I

296
Consider an electrical power grid, where stability is essential to prevent blackouts. The Routh-Hurwitz criterion is a valuable tool for assessing system stability under varying load conditions or faults. By analyzing the closed-loop transfer function, the Routh-Hurwitz criterion helps determine whether the system remains stable.
To apply the Routh-Hurwitz criterion, a Routh table is constructed. The table's rows are labeled with powers of the complex frequency variable s, starting from the...
296
State Space Representation01:27

State Space Representation

251
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...
251
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

611
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...
611
Multimachine Stability01:25

Multimachine Stability

208
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:
208
The Power Flow Problem and Solution01:26

The Power Flow Problem and Solution

278
Power flow problem analysis is fundamental for determining real and reactive power flows in network components, such as transmission lines, transformers, and loads. The power system's single-line diagram provides data on the bus, transmission line, and transformer. Each bus k in the system is characterized by four key variables: voltage magnitude Vk​, phase angle δk​, real power Pk​, and reactive power Qk​. Two of these four variables are inputs, while the...
278

You might also read

Related Articles

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

Sort by
Same author

Quantitative Damage Detection and Evolution in Composite Structures Using Digital Image Correlation, Machine Learning, and Peridynamics.

Materials (Basel, Switzerland)·2026
Same author

Modeling and Evaluation of Customizable Immobilization Masks for Precision Radiotherapy.

Polymers·2026
Same author

From Pixels to Predictions: Integrating Machine Learning and Digital Image Correlation for Damage Identification in Engineering Materials.

Materials (Basel, Switzerland)·2026
Same author

Mechanical Properties of Fully Recyclable 3D-Printable Materials Used for Application in Patient-Specific Devices in Radiotherapy.

Polymers·2025
Same author

Modeling of LCF Behaviour on AISI316L Steel Applying the Armstrong-Frederick Kinematic Hardening Model.

Materials (Basel, Switzerland)·2024
Same author

Digital Image Correlation and Ultrasonic Lamb Waves for the Detection and Prediction of Crack-Type Damage in Fiber-Reinforced Polymer Composite Laminates.

Polymers·2024

Related Experiment Video

Updated: Aug 2, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.0K

A Data-Driven System Identification Method for Random Eigenvalue Problem Using Synchrosqueezed Energy and Phase

Swarup Mahato1, Arunasis Chakraborty2, Paulius Griškevičius1

  • 1Department of Mechanical Engineering, Kaunas University of Technology, 51424 Kaunas, Lithuania.

Sensors (Basel, Switzerland)
|April 13, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an automated algorithm using synchrosqueezing and K-means clustering to accurately estimate modal parameters in structures with random parameters. The method effectively quantifies uncertainties in random eigenvalue problems for improved structural analysis.

Keywords:
asymptotic integralk-means clusteringmodal identificationrandom eigenvaluesynchrosqueezed transformwavelet transform

More Related Videos

Experimental and Data Analysis Workflow for Soft Matter Nanoindentation
13:04

Experimental and Data Analysis Workflow for Soft Matter Nanoindentation

Published on: January 18, 2022

4.0K
Experimental Methods to Study Human Postural Control
08:12

Experimental Methods to Study Human Postural Control

Published on: September 11, 2019

9.5K

Related Experiment Videos

Last Updated: Aug 2, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.0K
Experimental and Data Analysis Workflow for Soft Matter Nanoindentation
13:04

Experimental and Data Analysis Workflow for Soft Matter Nanoindentation

Published on: January 18, 2022

4.0K
Experimental Methods to Study Human Postural Control
08:12

Experimental Methods to Study Human Postural Control

Published on: September 11, 2019

9.5K

Area of Science:

  • Structural Dynamics and Vibration Analysis
  • Computational Mechanics
  • Signal Processing

Background:

  • Modal parameter estimation is crucial for understanding structural behavior.
  • Random variations in structural parameters introduce uncertainties and errors in traditional identification methods.
  • Existing techniques struggle with the complexities of random eigenvalue problems.

Purpose of the Study:

  • To develop and validate an automated modal identification algorithm for structures with random parameters.
  • To quantify the uncertainty associated with modal parameter estimation in inverse dynamic problems.
  • To enhance the efficiency and accuracy of modal identification for random eigenvalue problems.

Main Methods:

  • Utilized an advanced synchrosqueezing wavelet transform for high-resolution time-frequency analysis.
  • Employed unsupervised K-means clustering for automated quantification of modal parameters.
  • Applied an ensemble of measurements to statistically determine random eigenvalues.

Main Results:

  • The proposed automated algorithm demonstrated high efficiency and accuracy in simulated and experimental tests.
  • Synchrosqueezing provided superior resolution for identifying modal features in random systems.
  • K-means clustering effectively quantified modal parameters from time-frequency representations.

Conclusions:

  • The developed data-driven, output-only scheme is effective for identifying modal parameters in random eigenvalue problems.
  • The methodology offers a robust solution for handling uncertainties in structural dynamics.
  • This approach advances automated modal identification for complex, real-world structures.