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Classification of Signals01:30

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When a ligand binds to a cell-surface receptor, the receptor's intracellular domain changes shape, which may either activate its enzyme function or allow its binding to other molecules. The initial signal is amplified by most signal transduction pathways. This means that a single ligand molecule can activate multiple molecules of a downstream target. Proteins that relay a signal are most commonly phosphorylated at one or more sites, activating or inactivating the protein. Kinases catalyze...
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Related Experiment Video

Updated: Sep 21, 2025

Cortical Bone Assessment Using Ultrasonic Guided Waves: A Reproducibility Study in a Healthy Population
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Cortical Bone Assessment Using Ultrasonic Guided Waves: A Reproducibility Study in a Healthy Population

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Diagnostic-Quality Guided Wave Signals Synthesized Using Generative Adversarial Neural Networks.

Mateusz Heesch1, Michał Dziendzikowski2, Krzysztof Mendrok1

  • 1Department of Robotics and Mechatronics, AGH University of Science and Technology, Al. A. Mickiewicza 30, 30-059 Krakow, Poland.

Sensors (Basel, Switzerland)
|May 28, 2022
PubMed
Summary

This study introduces a generative adversarial network to create synthetic guided wave signals, addressing data scarcity in structural health monitoring. The network effectively generates and reconstructs signals, enabling damage detection with minimal real-world data.

Keywords:
guided wavesneural networksstructural health monitoring

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Area of Science:

  • Engineering
  • Materials Science
  • Artificial Intelligence

Background:

  • Guided waves are crucial for structural health monitoring (SHM) due to complex signal characteristics.
  • Machine learning algorithms for SHM require extensive training data, which is often difficult and expensive to acquire, especially for damaged states.
  • Data scarcity is a significant challenge in guided wave analysis and SHM.

Purpose of the Study:

  • To develop a generative adversarial network (GAN) architecture for generating synthetic guided wave signals.
  • To address the data scarcity problem in training machine learning models for SHM.
  • To evaluate the effectiveness of GAN-generated data for damage detection tasks.

Main Methods:

  • A novel GAN architecture was designed and implemented for guided wave signal generation.
  • The GAN was trained and tested using pitch-catch experiment data from the OpenGuidedWaves database.
  • The generated synthetic data was used to train classifiers for a damage detection scenario.

Main Results:

  • The GAN successfully generated realistic random guided wave signals.
  • The network demonstrated the ability to accurately reconstruct unseen guided wave signals.
  • Classifiers trained solely on synthetic data achieved reliable performance in detecting damage when evaluated on real signals.
  • Signal compression of 98.44% was achieved while preserving critical damage information.

Conclusions:

  • The proposed GAN architecture effectively generates synthetic guided wave data, mitigating data scarcity issues in SHM.
  • Synthetic data generated by the GAN can be utilized to train effective damage detection models.
  • The GAN offers a promising solution for improving the efficiency and applicability of machine learning in structural health monitoring.