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

Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

301
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
301
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

153
Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
Consider the example of control of motor torque. Initially, a positive...
153
Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

322
The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
322
Discrete Fourier Transform01:15

Discrete Fourier Transform

350
The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
350
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

110
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,...
110
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

270
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
270

You might also read

Related Articles

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

Sort by
Same author

Investigation of Frequency-Domain Dimension Reduction for A<sup>2</sup>M-Based Bridge Damage Detection Using Accelerations of Moving Vehicles.

Materials (Basel, Switzerland)·2023
Same author

Fatigue Life Prediction of Notched Details Using SWT Model and LEFM-Based Approach.

Materials (Basel, Switzerland)·2023
Same author

Experimental Study on Seismic Performance of Precast Columns Repaired with CFRP Fabrics.

Materials (Basel, Switzerland)·2022
Same journal

Correction: Yang et al. Microstructural Characteristics of High-Pressure Die Casting with High Strength-Ductility Synergy Properties: A Review. <i>Materials</i> 2023, <i>16</i>, 1954.

Materials (Basel, Switzerland)·2026
Same journal

Effect of La and Ce Microalloying on the Corrosion Resistance of 0.4Sb Low-Alloy Steel in a Harsh Marine Atmospheric Environment.

Materials (Basel, Switzerland)·2026
Same journal

High-Temperature Properties of Magnesium Ammonium Phosphate Cement Modified with Gold Tailings.

Materials (Basel, Switzerland)·2026
Same journal

A Study on the Evolution of Intermetallic Phase Microstructure and High-Temperature Creep Behavior in Mg-8.0Al-1.0Nd-1.5Gd-Mn Alloys.

Materials (Basel, Switzerland)·2026
Same journal

Material-Driven Clinical Complications in Mechanical Circulatory Support: From Blood-Material Interactions to Device-Related Adverse Events.

Materials (Basel, Switzerland)·2026
Same journal

Influence of Final Irrigation on Calcium Silicate-Based Sealer Dentinal Tubular Penetration: A Systematic Review.

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

Related Experiment Video

Updated: Aug 3, 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

9.3K

A Time-Domain Signal Processing Algorithm for Data-Driven Drive-by Inspection Methods: An Experimental Study.

Yifu Lan1, Zhenkun Li1, Weiwei Lin1

  • 1Department of Civil Engineering, Aalto University, 02150 Espoo, Finland.

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

A new signal processing algorithm enhances bridge damage detection using vehicle data. This method improves accuracy and efficiency for machine learning models in drive-by inspection, overcoming common data challenges.

Keywords:
drive-by bridge inspectionmachine learningsignal processingsliding windowstructural health monitoringvehicle-bridge interaction

More Related Videos

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
11:41

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Published on: February 1, 2020

20.5K
Probing Structural and Dynamic Properties of Trafficking Subcellular Nanostructures by Spatiotemporal Fluctuation Spectroscopy
08:17

Probing Structural and Dynamic Properties of Trafficking Subcellular Nanostructures by Spatiotemporal Fluctuation Spectroscopy

Published on: August 16, 2021

1.9K

Related Experiment Videos

Last Updated: Aug 3, 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

9.3K
Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
11:41

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Published on: February 1, 2020

20.5K
Probing Structural and Dynamic Properties of Trafficking Subcellular Nanostructures by Spatiotemporal Fluctuation Spectroscopy
08:17

Probing Structural and Dynamic Properties of Trafficking Subcellular Nanostructures by Spatiotemporal Fluctuation Spectroscopy

Published on: August 16, 2021

1.9K

Area of Science:

  • Structural Health Monitoring
  • Mechanical Engineering
  • Data Science

Background:

  • Bridge structure deterioration affects dynamic characteristics, detectable via vehicle-bridge interaction (VBI).
  • Existing data-driven drive-by methods for damage inspection show promise but require performance enhancement.
  • Challenges include measurement noise, speed variations, and large data volumes in VBI analysis.

Purpose of the Study:

  • To propose a novel time-domain signal processing algorithm for raw vehicle acceleration data in data-driven drive-by bridge inspection.
  • To optimize the algorithm's parameters for peak data processing performance.
  • To improve the accuracy and efficiency of Machine Learning (ML) models for vehicle-based bridge damage detection.

Main Methods:

  • A time-domain signal processing algorithm involving filtering (denoising), pooling (data reduction), and optimization (parameter tuning).
  • An optimizing strategy to automatically search for optimal algorithm parameters.
  • Laboratory experiments using a scale truck model and a steel beam to validate the algorithm.

Main Results:

  • The proposed algorithm significantly increases the average accuracy (12.2-15.0%) and efficiency (35.7-96.7%) compared to using raw data.
  • Performance improvements were observed across different damaged cases and ML models.
  • The study investigated the impact of filtering, pooling, window function parameters, and sensor locations.

Conclusions:

  • The developed signal processing algorithm effectively addresses challenges in drive-by bridge inspection.
  • It offers a substantial improvement in accuracy and efficiency for ML-based damage detection.
  • This method enhances the capability to detect bridge damage from material deterioration or structural changes.