Guidetti, Xavier; Mingard, Nathan; Cruz-Olivera, Raul; Nagel, Yannick; Rueppel, Marvin; Rupenyan-Vasileva, Alisa; Balta, Efe C.; Lygeros, John,

2024.

Force controlled printing for material extrusion additive manufacturing.

Additive Manufacturing.

89(104297).

Verfügbar unter: https://doi.org/10.1016/j.addma.2024.104297

Schärer, Roman Pascal; Schumacher, Jürgen,

2024.

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

In:

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

ZHAW Zürcher Hochschule für Angewandte Wissenschaften.

Verfügbar unter: https://doi.org/10.21256/zhaw-31722

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

2024.

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

In:

Proceedings of ASME Turbo Expo 2024: Turbomachinery Technical Conference and Exposition: Volume 3B: Combustion, Fuels, and Emissions.

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

Verfügbar unter: https://doi.org/10.1115/GT2024-129095

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].

In:

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

ZHAW Zürcher Hochschule für Angewandte Wissenschaften.

Verfügbar unter: https://doi.org/10.21256/zhaw-30549

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.

Journal of Systems and Software.

212(112028).

Verfügbar unter: https://doi.org/10.1016/j.jss.2024.112028

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

2024.

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

Immunoinformatics.

142667-1190(100036).

Verfügbar unter: https://doi.org/10.1016/j.immuno.2024.100036

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

2024.

Fairness in online ad delivery [Paper].

In:

Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency.

7th ACM Conference on Fairness, Accountability, and Transparency (FAccT), Rio de Janeiro, Brazil, 3-6 June 2024.

Association for Computing Machinery.

S. 1418-1432.

Verfügbar unter: https://doi.org/10.1145/3630106.3658980

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

2024.

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

In:

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

ZHAW Zürcher Hochschule für Angewandte Wissenschaften.

Verfügbar unter: https://doi.org/10.21256/zhaw-31221

Verstockt, Jan; Clarys, Warre; Hillen, Michaël; Verspeek, Simon; Bonmarin, Mathias; Thiessen, Filip; Broche, Lieve; Steenackers, Gunther,

2024.

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

In:

2024 IEEE International Symposium on Medical Measurements and Applications (MeMeA).

2024 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Eindhoven, The Netherlands, 26-28 June 2024.

IEEE.

Verfügbar unter: https://doi.org/10.1109/MEMEA60663.2024.10596792

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

2024.

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

In:

Kołowrocki, Krzysztof; Dąbrowska, Ewa, Hrsg.,

Advances in Reliability, Safety and Security, Part 4.

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

Gdynia:

Polish Safety and Reliability Association.

S. 39-48.

Verfügbar unter: https://doi.org/10.21256/zhaw-30985

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

2024.

Machine learning for robust structural uncertainty quantification in fractured reservoirs.

Geothermics.

120(103012).

Verfügbar unter: https://doi.org/10.1016/j.geothermics.2024.103012

Sedding, Helmut A.,

2024.

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

Journal of Scheduling.

27(3), S. 257-275.

Verfügbar unter: https://doi.org/10.1007/s10951-023-00787-5

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

2024.

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

In:

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.

Verfügbar unter: https://doi.org/10.21256/zhaw-30917

Camenisch, Ursin; Michel, Clotaire,

2024.

Resilienz von Feuerwehren verbessern.

Management und Qualität.

2024(5-6), S. 41-43.

Verfügbar unter: https://doi.org/10.21256/zhaw-30910

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

2024.

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

In:

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

ZHAW Zürcher Hochschule für Angewandte Wissenschaften.

Verfügbar unter: https://doi.org/10.21256/zhaw-31222

Wulf, Jochen; Meierhofer, Jürg,

2024.

Utilizing large language models for automating technical customer support.

arXiv.

Verfügbar unter: https://doi.org/10.48550/arXiv.2406.01407

Goren Huber, Lilach,

2024.

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

In:

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.

In:

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

Rechsteiner, Ralph,

2024.

Development of a containerized embedded operating environment with GUIX.

In:

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

Lone, Omar; Doran, Hans Dermot,

2024.

Evolving certification testing for industrial communication protocols.

In:

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