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Related Experiment Video

Updated: Jan 10, 2026

Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline
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Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline

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Multi-Fluid Pipeline Leak Detection and Classification Using Savitzky-Golay Scalograms and Lightweight Vision

Niamat Ullah1, Zahoor Ahmad1, Jong-Myon Kim1,2

  • 1Department of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan 44610, Republic of Korea.

Sensors (Basel, Switzerland)
|November 27, 2025
PubMed
Summary

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During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
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This study introduces a new pipeline leak detection system using advanced deep learning and Savitzky-Golay scalograms. The framework accurately identifies leaks, improving pipeline safety and operational efficiency.

Area of Science:

  • Engineering
  • Signal Processing
  • Artificial Intelligence

Background:

  • Pipeline leaks pose significant risks, causing disruptions, environmental damage, and financial losses.
  • Acoustic emission (AE) signals from leaks are often obscured by noise and influenced by fluid type.
  • Existing methods struggle with the complexity and variability of leak-induced acoustic signals.

Purpose of the Study:

  • To develop a robust and fluid-independent pipeline leak diagnosis framework.
  • To enhance the detection of subtle acoustic emission patterns indicative of leaks.
  • To achieve high accuracy in identifying leak presence, severity, and absence.

Main Methods:

  • Acquisition of AE data under diverse operational conditions, including varying leak intensities.
Keywords:
Savitzky–Golay scalogramsacoustic emissionartificial neural networklightweight vision transformer

Related Experiment Videos

Last Updated: Jan 10, 2026

Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline
09:27

Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline

Published on: January 30, 2019

7.4K
  • Transformation of transient AE signals into detailed scalograms using Continuous Wavelet Transform (CWT).
  • Application of a Savitzky-Golay (SG) filter to refine scalograms (SG scalograms).
  • Training of a Convolutional Neural Network (CNN) and a lightweight Vision Transformer with streamlined self-attention (LViT-S) on SG scalograms.
  • Integration of local and global features learned by CNN and LViT-S.
  • Final classification using an Artificial Neural Network (ANN).

    • Main Results:

    • The proposed framework achieved a high classification accuracy of 98.6% across different pressures, fluid types (gas, water), and leak sizes.
    • The lightweight Vision Transformer with streamlined self-attention (LViT-S) demonstrated reduced computational cost while maintaining performance.
    • Comparative analysis showed superior accuracy against four state-of-the-art methods in diverse scenarios.

    Conclusions:

    • The novel framework effectively diagnoses pipeline leaks by combining advanced signal processing with efficient deep learning architectures.
    • The fluid-independent approach and refined feature extraction enhance detection reliability.
    • This method offers a promising solution for improving pipeline integrity and safety.