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

202
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...
202
Electro-mechanical Systems01:19

Electro-mechanical Systems

1.4K
Electromechanical systems are intricate configurations that effectively combine electrical and mechanical elements to achieve a desired outcome. Central to many of these systems is the DC motor, a device that converts electrical energy into mechanical motion, enabling various applications ranging from simple fans to complex robotic mechanisms.
A key component of the DC motor is the armature, a rotating circuit positioned within a magnetic field. As an electric current passes through the...
1.4K
Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

622
The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
In this model, each generator is connected to a...
622
Motional Emf01:22

Motional Emf

3.8K
Magnetic flux depends on three factors: the strength of the magnetic field, the area through which the field lines pass, and the field's orientation with respect to the surface area. If any of these quantities vary, a corresponding variation in magnetic flux occurs. If the area through which the magnetic field lines are passing changes, then the magnetic flux also changes. This change in the area can be of two types: the flux through the rectangular loop increases as it moves into the...
3.8K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

177
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
177
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

430
In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
430

You might also read

Related Articles

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

Sort by
Same author

Machine learning assisted analysis: inorganic catalyzed hydrothermal carbonization to enhance biomass carbon stability.

Bioresource technology·2025
Same author

A Necrotrophic Phytopathogen-Derived GPI-Anchored Protein Functions as an Elicitor to Activate Plant Immunity and Enhance Resistance.

Molecular plant pathology·2025
Same author

TRMT10A regulates tRNA-ArgCCT m<sup>1</sup>G9 modification to generate tRNA-derived fragments influencing vasculogenic mimicry formation in glioblastoma.

Cell death & disease·2025
Same author

Metabolomics analysis of acute lung injury induced by aortic dissection in mice.

Annals of medicine and surgery (2012)·2025
Same author

Effectiveness of wearable activity trackers on physical activity among adolescents in school-based settings: a systematic review and meta-analysis.

BMC public health·2025
Same author

A quantitative analysis method based on network evolution for risk factors of safety production in chemical enterprises.

Scientific reports·2025
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Dec 10, 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

Structural Model Identification Using a Modified Electromagnetism-Like Mechanism Algorithm.

Zhouquan Feng1,2,3, Zhengtao Ye1, Wenzan Wang1

  • 1Key Laboratory of Wind and Bridge Engineering of Hunan Province, College of Civil Engineering, Hunan University, Changsha 410082, China.

Sensors (Basel, Switzerland)
|August 29, 2020
PubMed
Summary
This summary is machine-generated.

A modified electromagnetism-like mechanism (EM) algorithm improves structural model parameter identification. This enhanced algorithm shows superior accuracy and convergence, even with noisy data, making it effective for complex engineering problems.

Keywords:
electromagnetism-like mechanismmodal dataoptimizationstructural model identification

More Related Videos

Electric and Magnetic Field Devices for Stimulation of Biological Tissues
13:29

Electric and Magnetic Field Devices for Stimulation of Biological Tissues

Published on: May 15, 2021

5.6K
Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment
04:35

Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment

Published on: July 5, 2024

2.2K

Related Experiment Videos

Last Updated: Dec 10, 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
Electric and Magnetic Field Devices for Stimulation of Biological Tissues
13:29

Electric and Magnetic Field Devices for Stimulation of Biological Tissues

Published on: May 15, 2021

5.6K
Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment
04:35

Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment

Published on: July 5, 2024

2.2K

Area of Science:

  • Structural engineering
  • Computational mechanics
  • Optimization algorithms

Background:

  • Accurate structural model parameter identification is crucial for performance assessment and design.
  • Traditional methods may struggle with noisy data and limited measurements.
  • The electromagnetism-like mechanism (EM) algorithm offers a heuristic approach to optimization.

Purpose of the Study:

  • To propose a modified electromagnetism-like mechanism (EM) algorithm for enhanced structural model parameter identification.
  • To improve the accuracy and convergence rate of the original EM algorithm.
  • To validate the modified algorithm's effectiveness on numerical and experimental structural models.

Main Methods:

  • Development of a modified electromagnetism-like mechanism (EM) algorithm.
  • Incorporation of new local search strategies, charge/force calculations, and particle update rules.
  • Testing on benchmark functions and application to numerical truss and experimental shear-building models.

Main Results:

  • The modified EM algorithm demonstrated superior accuracy and faster convergence compared to the original EM and particle swarm optimization (PSO) algorithms on benchmark functions.
  • Successful parameter identification was achieved for both numerical and experimental structural models.
  • The approach proved robust even with significant noise and limited measurement data.

Conclusions:

  • The modified EM algorithm is a highly effective tool for structural model parameter identification.
  • The enhanced algorithm offers significant improvements in accuracy and convergence speed.
  • This method shows broad applicability for optimization problems in structural engineering and beyond.