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

Neurogenesis and Regeneration of Nervous Tissue01:15

Neurogenesis and Regeneration of Nervous Tissue

728
In the CNS, neurogenesis, the birth of new neurons from stem cells, is limited to the hippocampus in adults. In other regions of the brain and spinal cord, neurogenesis is almost non-existent due to inhibitory influences from neuroglia, especially oligodendrocytes, and the absence of growth-stimulating cues. The myelin produced by oligodendrocytes in the CNS inhibits neuronal regeneration. Furthermore, astrocytes proliferate rapidly after neuronal damage, forming scar tissue that physically...
728

You might also read

Related Articles

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

Sort by
Same author

Beam Damage Assessment Using Natural Frequency Shift and Machine Learning.

Sensors (Basel, Switzerland)·2022
Same author

Damage Detection on a Beam with Multiple Cracks: A Simplified Method Based on Relative Frequency Shifts.

Sensors (Basel, Switzerland)·2021
Same author

Liquid identification by using a micro-electro-mechanical interdigital transducer.

The Analyst·2017
Same author

A model of TMS-induced I-waves in motor cortex.

Brain stimulation·2014
Same author

A new class of metrics for spike trains.

Neural computation·2013
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
Same journal

Three-Dimensional Modeling and Performance Analysis of Dynamic mmWave V2I Networks Based on Stochastic Geometry.

Sensors (Basel, Switzerland)·2026
See all related articles
  1. Home
  3. A Stacked Neural Network Model For Damage Localization.
  1. Home
  3. A Stacked Neural Network Model For Damage Localization.

Related Experiment Video

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.7K

A Stacked Neural Network Model for Damage Localization.

Catalin V Rusu1, Gilbert-Rainer Gillich2,3, Cristian Tufisi2,3

  • 1Department of Computer Science, Babeș-Bolyai University, Str. M. Kogălniceanu 1, 400084 Cluj-Napoca, Romania.

Sensors (Basel, Switzerland)
|November 9, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a stacked Artificial Neural Network (ANN) approach for accurate structural damage detection. The novel method utilizes Relative Frequency Shifts (RFSs) to pinpoint damage locations, outperforming traditional techniques.

Keywords:
ANNLSTMMLPdamage detectionmodel comparisonnatural frequencystacking techniques

More Related Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

471
A Drosophila In Vivo Injury Model for Studying Neuroregeneration in the Peripheral and Central Nervous System
09:55

A Drosophila In Vivo Injury Model for Studying Neuroregeneration in the Peripheral and Central Nervous System

Published on: May 5, 2018

9.7K

Related Experiment Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.7K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

471
A Drosophila In Vivo Injury Model for Studying Neuroregeneration in the Peripheral and Central Nervous System
09:55

A Drosophila In Vivo Injury Model for Studying Neuroregeneration in the Peripheral and Central Nervous System

Published on: May 5, 2018

9.7K

Area of Science:

  • Structural Health Monitoring
  • Artificial Intelligence in Engineering

Background:

  • Traditional vibration-based damage detection methods are often manual, leading to delays and errors.
  • Automated methods are needed to improve the efficiency and accuracy of structural damage assessment.

Purpose of the Study:

  • To develop an accurate and automated method for detecting structural damage locations using Artificial Neural Networks (ANNs).
  • To propose a novel stacked neural network architecture for enhanced damage prediction accuracy.

Main Methods:

  • Feature extraction using Relative Frequency Shifts (RFSs) from vibration modes.
  • Implementation of a stacked neural network approach using Multilayer Perceptron, Recurrent Neural Network, Long Short-term Memory, and Gated Recurrent Units.
  • Training individual networks on segmented beam data and comparing with a standard ANN.

Main Results:

  • The proposed stacked neural network approach demonstrated high accuracy in predicting damage locations.
  • A specific stacked model comprising 14 two-layer feedforward networks achieved the best performance.
  • The stacked approach outperformed a standard neural network trained on the entire structure.

Conclusions:

  • Stacked neural networks offer a powerful tool for accurate, automated structural damage detection.
  • The segmentation strategy and specific network configurations are crucial for optimizing performance.
  • This approach significantly reduces the time and potential for errors associated with traditional methods.