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

Discrete Fourier Transform01:15

Discrete Fourier Transform

945
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...
945
Vibrating Concrete01:19

Vibrating Concrete

415
Mechanical vibrators are instrumental in compacting newly poured concrete within formwork and around reinforcements. This process is essential to eliminate trapped air pockets and establish a dense concrete mass. One widely used method is vibrating by internal vibrators, often referred to as a poker vibrator or immersion vibrator. It is rapidly inserted through the full depth of the freshly laid concrete and slightly extends into the layer below it (which remains in a plastic state). Consistent...
415

You might also read

Related Articles

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

Sort by
Same author

An integrated immunopathological model of syphilis serofast: a systematic review and meta-analysis.

Frontiers in immunology·2026
Same author

Corrigendum to "Grazing practices affect phyllosphere and rhizosphere bacterial communities of Kobresia humilis by altering their network stability" [Sci. Total Environ. 900 (2023) 165814].

The Science of the total environment·2026
Same author

Risk prediction models for mortality in patients with multimorbidity: a systematic review and meta-analysis.

Frontiers in public health·2025
Same author

Electronically Mismatched α-Addition of Electron-Deficient Alkenes via Photoinduced Polarity Transduction.

Organic letters·2025
Same author

Identification of Nuclear Localization Sequence (NLS) Sites in R2R3-MYB Transcription Factor Involved in Anther Development.

Cells·2025
Same author

Risk and survival outcomes of secondary pelvic neoplasm after radiotherapy in female patients with genital neoplasms: A large Population-Based cohort study.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology·2024

Related Experiment Video

Updated: Feb 17, 2026

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.7K

A Data-Driven Response Virtual Sensor Technique with Partial Vibration Measurements Using Convolutional Neural

Shan-Bin Sun1, Yuan-Yuan He2,3,4, Si-Da Zhou5,6,7

  • 1School of Aerospace Engineering, Beijing Institute of Technology, Zhongguancun South Street 5, Beijing 100081, China. sunshanbin114@sina.com.

Sensors (Basel, Switzerland)
|December 13, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a virtual sensor model using convolutional neural networks and partial vibration data to accurately predict structural responses in aerospace engineering. This data-driven approach overcomes limitations of physical sensors in harsh space environments.

Keywords:
convolutional neural networkpartial vibration measurementsresponse transmissibilityvirtual sensor

More Related Videos

Author Spotlight: Enhancing Remote Rehabilitation with Virtual Reality and Electromyography
04:06

Author Spotlight: Enhancing Remote Rehabilitation with Virtual Reality and Electromyography

Published on: January 12, 2024

1.1K
Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
10:52

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

9.2K

Related Experiment Videos

Last Updated: Feb 17, 2026

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.7K
Author Spotlight: Enhancing Remote Rehabilitation with Virtual Reality and Electromyography
04:06

Author Spotlight: Enhancing Remote Rehabilitation with Virtual Reality and Electromyography

Published on: January 12, 2024

1.1K
Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
10:52

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

9.2K

Area of Science:

  • Aerospace Engineering
  • Structural Health Monitoring
  • Computational Mechanics

Background:

  • Accurate measurement of dynamic responses is crucial for structural health monitoring and damage detection.
  • Physical sensors face limitations in spacecraft due to the harsh space environment, necessitating alternative solutions.
  • Existing virtual sensor methods often rely on modal parameters, which can be complex to obtain.

Purpose of the Study:

  • To propose a novel virtual sensor model for predicting structural responses using limited vibration measurements.
  • To leverage convolutional neural networks (CNNs) and transmissibility functions for enhanced prediction accuracy.
  • To demonstrate the efficacy of the data-driven virtual sensor in aerospace applications.

Main Methods:

  • Development of a four-layer CNN model comprising convolutional, fully connected, and output layers.
  • Integration of transmissibility functions as prior knowledge within the CNN framework.
  • Validation through numerical simulations of two distinct structural dynamic systems and a simply supported beam experiment.

Main Results:

  • The proposed CNN-based virtual sensor accurately predicts structural responses using partial vibration data.
  • The data-driven approach demonstrates superior performance compared to a traditional modal-model-based virtual sensor.
  • High accuracy in predicting structural responses was consistently observed across numerical and experimental validations.

Conclusions:

  • The developed data-driven virtual sensor technique offers a highly accurate and robust method for structural response prediction.
  • This approach effectively addresses the challenges posed by sensor limitations in aerospace engineering and space exploration.
  • The CNN-based virtual sensor presents a promising advancement for structural health monitoring in demanding environments.