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Hydrophile selbstschmierende Beschichtung für medizinische Interventionskatheter.

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2024.

Force controlled printing for material extrusion additive manufacturing.

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Schärer, Roman Pascal; Schumacher, Jürgen,

2024.

A hybrid 3D/2D model for performance predictions of organic flow battery cells [poster].

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The International Flow Battery Forum (IFBF), Glasgow, Scotland, 25-27 June 2024.

ZHAW Zürcher Hochschule für Angewandte Wissenschaften.

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Gopalakrishnan, Harish; Maddipati, Raj; Gruber, Andrea; Bothien, Mirko; Aditya, Konduri,

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A reactor-network framework to model performance and emissions of a longitudinally-staged combustion system for carbon-free fuels [paper].

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ASME Turbo Expo 2024, London, United Kingdom, 24-28 June 2024.

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Weng, Joanna; Denzel, Philipp; Reif, Monika Ulrike; Schilling, Frank-Peter; Billeter, Yann; Frischknecht-Gruber, Carmen; Brunner, Stefan; Chavarriaga, Ricardo; Repetto, Marco; Iranfar, Arman,

2024.

Certification scheme for artificial intelligence based systems [paper].

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34th European Safety and Reliability Conference (ESREL), Cracow, Poland, 23-27 June 2024.

ZHAW Zürcher Hochschule für Angewandte Wissenschaften.

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van de Laar, Piërre; Corvino, Rosilde; Mooij, Arjan J.; van Wezep, Hans; Rosmalen, Raymond,

2024.

Custom static analysis to enhance insight into the usage of in-house libraries.

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Deutschmann, Nicolas; Pelissier, Aurelien; Weber, Anna; Gao, Shuaijun; Bogojeska, Jasmina; Rodríguez Martínez, María,

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Do domain-specific protein language models outperform general models on immunology-related tasks?.

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Baumann, Joachim; Sapiezynski, Piotr; Heitz, Christoph; Hannák, Anikó,

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Fairness in online ad delivery [paper].

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Carroll, Daniel; Kiener, Luca; Baumgartner, Daniel; Nusser, Michaela,

2024.

Finite element modelling of locking plate fixation during humeral abduction cycle [poster].

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29th Congress of the European Society of Biomechanics (ESB), Edinburgh, Scotland, 30 June - 3 July 2024.

ZHAW Zürcher Hochschule für Angewandte Wissenschaften.

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HypIRskin : thermography-guided device for diagnosis and characterization of skin cancer lesions [paper].

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Brunner, Stefan; Reif, Monika Ulrike; Rejzek, Martin,

2024.

Improving resilience and robustness in artificial intelligence systems through adversarial training and verification [paper].

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34th European Safety and Reliability Conference (ESREL), Cracow, Poland, 23-27 June 2024.

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Polish Safety and Reliability Association.

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Dashti, Ali; Stadelmann, Thilo; Kohl, Thomas,

2024.

Machine learning for robust structural uncertainty quantification in fractured reservoirs.

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Sedding, Helmut A.,

2024.

Mixed-model moving assembly line material placement optimization for a shorter time-dependent worker walking time.

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Kittelmann, Florian; Sulimov, Pavel; Stockinger, Kurt,

2024.

QardEst : using quantum machine learning for cardinality estimation of join queries [paper].

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1st Workshop on Quantum Computing and Quantum-Inspired Technology for Data-Intensive Systems and Applications (Q-Data), ACM SIGMOD/PODS 2024, Santiago, Chile, 9 June 2024.

ZHAW Zürcher Hochschule für Angewandte Wissenschaften.

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Camenisch, Ursin; Michel, Clotaire,

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Screw configuration optimization in 4-part humeral fracture surgery : an algorithmic approach [poster].

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29th Congress of the European Society of Biomechanics (ESB), Edinburgh, Scotland, 30 June - 3 July 2024.

ZHAW Zürcher Hochschule für Angewandte Wissenschaften.

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Wulf, Jochen; Meierhofer, Jürg,

2024.

Utilizing large language models for automating technical customer support.

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Goren Huber, Lilach,

2024.

A data-centric-AI trick to clean your dirty data.

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Detecting unusual or abnormal patterns in data is one of the common tasks of AI algorithms in commercial applications. In some applications, such as fraud detection, defect detection or medical diagnostics, anomaly detection is the main objective. In other applications, detecting abnormal data points is part of the data cleaning and preparation pipeline. In all cases, the use of AI-based methods relies on having a training dataset which can represent the normal behaviour, and must therefore be free of anomalies. Problems arise when we realize that having an anomaly-free training dataset is not always possible in practice: most real-world datasets are contaminated with unknown anomalies or mislabeled data..

Tordoya Taquichiri, Carlos Rafael; Ganz, David,

2024.

AI acceleration for space applications.

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Embedded Computing Conference (ECC), Winterthur, Switzerland, 28 May 2024.

Rechsteiner, Ralph,

2024.

Development of a containerized embedded operating environment with GUIX.

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Embedded Computing Conference (ECC), Winterthur, Switzerland, 28. Mai 2024.