| 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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Knaapila, Matti
Norwegian University of Science and Technology
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (21/21 displayed)
- 2024Classifying Tensile Loading History of Continuous Carbon Fiber Composites Using X‐Ray Scattering and Machine Learningcitations
- 2024Multi-scale correlation of impact-induced defects in carbon fiber composites using X-ray scattering and machine learning
- 2023Structural Study of Diketopyrrolopyrrole Derivative Thin Films: Influence of Deposition Method, Substrate Surface, and Aging
- 2023Structural Study of Diketopyrrolopyrrole Derivative Thin Films: Influence of Deposition Method, Substrate Surface, and Aging
- 2023Structural Study of Diketopyrrolopyrrole Derivative Thin Films: Influence of Deposition Method, Substrate Surface, and Aging
- 2022Local structure mapping of gel-spun ultrahigh-molecular-weight polyethylene fiberscitations
- 2022Classifying condition of ultra-high-molecular-weight polyethylene ropes with wide-angle X-ray scatteringcitations
- 2022Classifying condition of ultra-high-molecular-weight polyethylene ropes with wide-angle X-ray scatteringcitations
- 2021Early-stage growth observations of orientation-controlled vacuum-deposited naphthyl end-capped oligothiophenescitations
- 2021Early-stage growth observations of orientation-controlled vacuum-deposited naphthyl end-capped oligothiophenescitations
- 2021Early-stage growth observations of orientation-controlled vacuum-deposited naphthyl end-capped oligothiophenescitations
- 2021Structural effects of electrode proximity in vacuum deposited organic semiconductors studied by microfocused X-ray scatteringcitations
- 2021Structural effects of electrode proximity in vacuum deposited organic semiconductors studied by microfocused X-ray scatteringcitations
- 2020Surface-Controlled Crystal Alignment of Naphthyl End-Capped Oligothiophene on Graphene: Thin-Film Growth Studied by In Situ X-ray Diffractioncitations
- 2020Surface-Controlled Crystal Alignment of Naphthyl End-Capped Oligothiophene on Graphene: Thin-Film Growth Studied by in Situ X-ray Diffractioncitations
- 2016Incorporation of a Cationic Conjugated Polyelectrolyte CPE within an Aqueous Poly(vinyl alcohol) Solcitations
- 2016Self-assembled systems of water soluble metal 8-hydroxyquinolates with surfactants and conjugated polyelectrolytescitations
- 2015Solid State Structure of Poly(9,9-dinonylfluorene)citations
- 2014Transparency Enhancement for Photoinitiated Polymerization (UV-curing) through Magnetic Field Alignment in a Piezoresistive Metal/Polymer Compositecitations
- 2009Aqueous Solution Behavior of Anionic Fluorene-co-thiophene-Based Conjugated Polyelectrolytescitations
- 2001Self-organization of nitrogen-containing polymeric supramolecules in thin filmscitations
Places of action
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article
Classifying condition of ultra-high-molecular-weight polyethylene ropes with wide-angle X-ray scattering
Abstract
<p>Ropes of ultra-high-molecular-weight polyethylene (UHMWPE) are replacing steel wires in many applications and nondestructive testing to monitor their condition is of scientific and commercial interest. In this work, wide-angle X-ray scattering (WAXS) combined with linear discriminant analysis (LDA) is proposed as classification method to distinguish between healthy and damaged UHMWPE ropes. Healthy (as produced, after pre-stretching) and damaged (in-field use) ropes (⌀=22mm) have been analyzed using synchrotron radiation. Firstly, it is demonstrated that scans of healthy and damaged ropes can be distinguished with 100% cross-validated test classification accuracy using LDA; this is shown both with the input data consisting of pre-processed 1D WAXS data and with physical parameters retrieved by fitting the WAXS data. Secondly, it is demonstrated that the classification performance is similar using the two forms of input data and that the noise could be increased by a factor of three while maintaining 100% test classification accuracy across all the three cross-validation folds.</p>