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

2024.

Machine learning for robust structural uncertainty quantification in fractured reservoirs.

Geothermics.

120(103012).

Available from: 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), pp. 257-275.

Available from: 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.

Available from: https://doi.org/10.21256/zhaw-30917

Camenisch, Ursin; Michel, Clotaire,

2024.

Resilienz von Feuerwehren verbessern.

Management und Qualität.

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

Available from: 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.

Available from: https://doi.org/10.21256/zhaw-31222

Wulf, Jochen; Meierhofer, Jürg,

2024.

Utilizing large language models for automating technical customer support.

arXiv.

Available from: 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..

Braunisch, Nico; Perez Olaya, Santiago Soler; Schmidt, Uwe; Ristin, Marko; Sadurski, Marcin; van de Venn, Hans Wernher; Wollschlaeger, Martin,

2024.

Digital twin in Industrie 4.0 for embedded systems [paper].

In:

2024 IEEE 20th International Conference on Factory Communication Systems (WFCS).

20th International Conference on Factory Communication Systems (WFCS), Toulouse, France, 17-19 April 2024.

IEEE.

Available from: https://doi.org/10.1109/wfcs60972.2024.10540968

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.

Veljanovska, Suzana; Doran, Hans Dermot,

2024.

Feasibility of utilizing symbolic aggregate approximation in IoT platforms.

In:

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

von Ritter-Zahony, Lorenzo; Cachin, Dominique,

2024.

High speed networking switch on ARM based SoC.

In:

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

Rosenthal, Matthias,

2024.

OMAHA : open measurement hardware for scanning probe microscopy.

In:

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

Tordoya Taquichiri, Carlos Rafael; Ganz, David; Buchheim, Klaus,

2024.

Reconfigurable hardware revisited : implementing an optimised AI pipeline on the HPDP processor.

In:

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

Doran, Hans Dermot,

2024.

Safe and dependable AI, state of the art and prospectives.

In:

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

Cellina, Francesca; Derboni, Marco; Giuffrida, Vincenzo; Tomic, Uros; Hoerler, Raphael,

2024.

Trust me if you can : practical challenges affecting the integration of carpooling in Mobility-as-a-Service platforms.

Travel Behaviour and Society.

2024(37), pp. 100832.

Available from: https://doi.org/10.1016/j.tbs.2024.100832

Schmitt-Koopmann, Felix; Huang, Elaine M.; Hutter, Hans-Peter; Stadelmann, Thilo; Darvishy, Alireza,

2024.

MathNet : a data-centric approach for printed mathematical expression recognition.

IEEE Access.

12, pp. 76963-76974.

Available from: https://doi.org/10.1109/ACCESS.2024.3404834

Stocker, David; Sommer, Christian; Gueng, Sarah; Stäuble, Jason; Özden, Ismail; Griessinger, Jennifer; Weyland, Mathias S.; Lutters, Gerd; Scheidegger, Stephan,

2024.

Probabilistic U-Net model observer for the DDC method in CT scan protocol optimization.

Physics in Medicine & Biology.

69(11), pp. 115026.

Available from: https://doi.org/10.1088/1361-6560/ad4302

Brunner, Kilian; Da Silva Miranda, Luis; Ostertag, Martin,

2024.

Timestamp accuracy of 10BASE-T1L for TSN applications [paper].

In:

2023 IEEE Third International Conference on Signal, Control and Communication (SCC).

3rd IEEE International Conference on Signal, Control and Communication (SCC), Hammamet, Tunisia, 1-3 December 2023.

IEEE.

Available from: https://doi.org/10.1109/SCC59637.2023.10527511