43 lines
2.0 KiB
Markdown
43 lines
2.0 KiB
Markdown
---
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layout: single
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title: "Detection and localization of leakages in water networks."
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categories: projects
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excerpt: "We researched the possibilities of leakage detection in real-world water networks in Munich’s suburbs."
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tags: acoustic anomaly-detection
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header:
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teaser: assets/images/projects/pipe_leak.png
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---
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{: .align-left style="padding:0.1em; width:5em"}
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In cooperation with the Munich City Services ([Stadtwerke München (SWM)](https://www.swm.de/)), we researched the possibilities of leakage detection in real-world water networks.
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Therefore, we installed contact microphones directly on the infrastructure in one of Munich's suburbs. {% cite muller2021acoustic%}
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<center>
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<figure class="half" style="max-width: 70%; text-align:center;">
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<img src="\assets\images\projects\pipe_leak\1fe8265e-ff21-4e9c-8a2a-2ebcaec41728.jpeg" style="margin-bottom: 0em;">
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<img src="\assets\images\projects\pipe_leak\8d2364f1-1b03-480d-9ed3-09d548f47383.jpeg" style="margin-bottom: 0em;">
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</figure>
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</center><br>
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The feasibility study revealed the technical limitations of this undertaking. Nonetheless, we gain valuable insights. Through audio-to-mel spectrogram transformations and the power of machine learning, deep neural networks (conv. and recurrent) are more capable of extracting important features for the given task than classic ML approaches.
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In this context, our team was able to produce further publications. {% cite elsner2019deep illium2020surgical muller2020analysis illium2022empirical%}
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<center>
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<figure class="half" style="max-width: 70%; text-align:center;">
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<img src="\assets\images\projects\pipe_leak\5ea8f0ee-6c61-4835-944c-b77683d397ca.jpeg" style="margin-bottom: 0em;">
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<img src="\assets\images\projects\pipe_leak\cc01cc58-d3f6-4220-b4f4-c7ea26b3a116.jpeg" style="margin-bottom: 0em;">
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</figure>
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</center><br>
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This project ran from late 2018 until the early months of 2020.
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{% include reference.html %}
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