| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Salmi, Ari
University of Helsinki
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (18/18 displayed)
- 2023Preventing Formation of Metal Dendrites During Electroplating Using External Ultrasonic Actuatorscitations
- 2023Evaluation of bone growth around bioactive glass S53P4 by scanning acoustic microscopy co-registered with optical interferometry and elemental analysiscitations
- 20234D Scanning Acoustic Microscopy
- 2023Ultrasound-based surface sampling in immersion for mass spectrometrycitations
- 2022Coupling Power Ultrasound into Industrial Pipe Walls
- 2022Preventing Formation of Metal Dendrites During Electroplating Using External Ultrasonic Actuatorscitations
- 2022FEM-based time-reversal technique for an ultrasonic cleaning applicationcitations
- 2022CESAM - Coded excitation scanning acoustic microscopecitations
- 2022Identifying Regions-of-Interest and Extracting Gold from PCBs Using MHz HIFUcitations
- 20224D Scanning Acoustic Microscopy
- 2021CESAM - Coded excitation scanning acoustic microscopecitations
- 2021FEM-based time-reversal enhanced ultrasonic cleaningcitations
- 2019Coded Acoustic Microscopy to Study Wood Mechanics and Developmentcitations
- 2019Digital Eyewearcitations
- 2018Detecting Industrial Fouling by Monotonicity during Ultrasonic Cleaningcitations
- 2013Cyclic impulsive compression loading along the radial and tangential wood directions causes localized fatiguecitations
- 2008Crystallization and shear modulus of a forming biopolymer film determined by in situ x-ray diffraction and ultrasound reflection methodscitations
- 2006Measuring in-plane mechanical properties of plate-like samples using phonographic pickupscitations
Places of action
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document
Detecting Industrial Fouling by Monotonicity during Ultrasonic Cleaning
Abstract
High power ultrasound permits non-invasive cleaning of industrial equipment, but to make such cleaning systems energy efficient, one needs to recognize when the structure has been sufficiently cleaned without using invasive diagnostic tools. This can be done using ultrasound reflections generated inside the structure. This inverse modeling problem cannot be solved by forward modeling for irregular and complex structures, and it is difficult to tackle also with machine learning since human-annotated labels are hard get. We provide a deep learning solution that relies on the physical properties of the cleaning process. We rely on the fact that the amount of fouling is reduced as we clean more. Using this monotonicity property as indirect supervision we develop a semi-supervised model for detecting when the equipment has been cleaned.