TY - JOUR A1 - Seynnes, O. R. A1 - Bojsen-Moller, J. A1 - Albracht, Kirsten A1 - Arndt, A. A1 - Cronin, N. J. A1 - Finni, T. A1 - Magnusson, S. P. T1 - Ultrasound-based testing of tendon mechanical properties: a critical evaluation JF - Journal of Applied Physiology Y1 - 2015 U6 - https://doi.org/10.1152/japplphysiol.00849.2014 SN - 8750-7587 VL - 118 IS - 2 SP - 133 EP - 141 ER - TY - CHAP A1 - Siebigteroth, Ines A1 - Kraft, Bodo A1 - Schmidts, Oliver A1 - Zündorf, Albert T1 - A Study on Improving Corpus Creation by Pair Annotation T2 - Proceedings of the Poster Session of the 2nd Conference on Language, Data and Knowledge (LDK-PS 2019) Y1 - 2019 SN - 1613-0073 SP - 40 EP - 44 ER - TY - CHAP A1 - Sildatke, Michael A1 - Karwanni, Hendrik A1 - Kraft, Bodo A1 - Schmidts, Oliver A1 - Zündorf, Albert T1 - Automated Software Quality Monitoring in Research Collaboration Projects T2 - ICSEW'20: Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops N2 - In collaborative research projects, both researchers and practitioners work together solving business-critical challenges. These projects often deal with ETL processes, in which humans extract information from non-machine-readable documents by hand. AI-based machine learning models can help to solve this problem. Since machine learning approaches are not deterministic, their quality of output may decrease over time. This fact leads to an overall quality loss of the application which embeds machine learning models. Hence, the software qualities in development and production may differ. Machine learning models are black boxes. That makes practitioners skeptical and increases the inhibition threshold for early productive use of research prototypes. Continuous monitoring of software quality in production offers an early response capability on quality loss and encourages the use of machine learning approaches. Furthermore, experts have to ensure that they integrate possible new inputs into the model training as quickly as possible. In this paper, we introduce an architecture pattern with a reference implementation that extends the concept of Metrics Driven Research Collaboration with an automated software quality monitoring in productive use and a possibility to auto-generate new test data coming from processed documents in production. Through automated monitoring of the software quality and auto-generated test data, this approach ensures that the software quality meets and keeps requested thresholds in productive use, even during further continuous deployment and changing input data. Y1 - 2020 U6 - https://doi.org/10.1145/3387940.3391478 N1 - ICSE '20: 42nd International Conference on Software Engineering, Seoul, Republic of Korea, 27 June 2020 - 19 July 2020 SP - 603 EP - 610 PB - IEEE CY - New York, NY ER - TY - JOUR A1 - Sildatke, Michael A1 - Karwanni, Hendrik A1 - Kraft, Bodo A1 - Zündorf, Albert T1 - A distributed microservice architecture pattern for the automated generation of information extraction pipelines JF - SN Computer Science N2 - Companies often build their businesses based on product information and therefore try to automate the process of information extraction (IE). Since the information source is usually heterogeneous and non-standardized, classic extract, transform, load techniques reach their limits. Hence, companies must implement the newest findings from research to tackle the challenges of process automation. They require a flexible and robust system that is extendable and ensures the optimal processing of the different document types. This paper provides a distributed microservice architecture pattern that enables the automated generation of IE pipelines. Since their optimal design is individual for each input document, the system ensures the ad-hoc generation of pipelines depending on specific document characteristics at runtime. Furthermore, it introduces the automated quality determination of each available pipeline and controls the integration of new microservices based on their impact on the business value. The introduced system enables fast prototyping of the newest approaches from research and supports companies in automating their IE processes. Based on the automated quality determination, it ensures that the generated pipelines always meet defined business requirements when they come into productive use. KW - Architectural design KW - Model-driven software engineering KW - Software and systems modeling KW - Enterprise information systems KW - Information extraction Y1 - 2023 U6 - https://doi.org/10.1007/s42979-023-02256-4 SN - 2661-8907 N1 - Corresponding authors: Michael Sildatke, Hendrik Karwanni IS - 4, Article number: 833 PB - Springer Singapore CY - Singapore ER - TY - JOUR A1 - Simonis, A. A1 - Dawgul, M. A1 - Lüth, H. A1 - Schöning, Michael Josef T1 - Miniaturised reference electrodes for field-effect sensors compatible to silicon chip technology JF - Electrochimica Acta. 51 (2005), H. 5 Y1 - 2005 SN - 0013-4686 U6 - https://doi.org/10.1016/j.electacta.2005.04.063 SP - 930 EP - 937 ER - TY - JOUR A1 - Simonis, A. A1 - Krings, T. A1 - Lüth, H. A1 - Wang, J. A1 - Schöning, Michael Josef T1 - A „hybrid“ thin-film pH sensor with integrated thick-film reference JF - Sensors. 1 (2001), H. 6 Y1 - 2001 SN - 1424-8220 SP - 183 EP - 192 ER - TY - JOUR A1 - Simonis, A. A1 - Lüth, H. A1 - Wang, J. A1 - Schöning, Michael Josef T1 - Strategies of miniaturised reference electrodes integrated in a silicon-based „one chip“ pH sensor JF - Sensors. 3 (2003), H. 9 Y1 - 2003 SN - 1424-8220 SP - 330 EP - 339 ER - TY - JOUR A1 - Simonis, A. A1 - Lüth, H. A1 - Wang, J. A1 - Schöning, Michael Josef T1 - New concepts of miniaturised reference electrodes in silicon technology for potentiometric sensor systems JF - Sensors and Actuators B. 103 (2004), H. 1-2 Y1 - 2004 SN - 0925-4005 SP - 429 EP - 435 ER - TY - JOUR A1 - Simonis, A. A1 - Ruge, C. A1 - Müller-Veggian, Mattea A1 - Lüth, H. A1 - Schöning, Michael Josef T1 - A long-term stable macroporoustype EIS structure for electrochemical sensor applications JF - Sensors and Actuators B. 91 (2003), H. 1-3 Y1 - 2003 SN - 0925-4005 SP - 21 EP - 25 ER - TY - CHAP A1 - Simsek, Beril A1 - Krause, Hans-Joachim A1 - Engelmann, Ulrich M. ED - Digel, Ilya ED - Staat, Manfred ED - Trzewik, Jürgen ED - Sielemann, Stefanie ED - Erni, Daniel ED - Zylka, Waldemar T1 - Magnetic biosensing with magnetic nanoparticles: Simulative approach to predict signal intensity in frequency mixing magnetic detection T2 - YRA MedTech Symposium (2024) N2 - Magnetic nanoparticles (MNP) are investigated with great interest for biomedical applications in diagnostics (e.g. imaging: magnetic particle imaging (MPI)), therapeutics (e.g. hyperthermia: magnetic fluid hyperthermia (MFH)) and multi-purpose biosensing (e.g. magnetic immunoassays (MIA)). What all of these applications have in common is that they are based on the unique magnetic relaxation mechanisms of MNP in an alternating magnetic field (AMF). While MFH and MPI are currently the most prominent examples of biomedical applications, here we present results on the relatively new biosensing application of frequency mixing magnetic detection (FMMD) from a simulation perspective. In general, we ask how the key parameters of MNP (core size and magnetic anisotropy) affect the FMMD signal: by varying the core size, we investigate the effect of the magnetic volume per MNP; and by changing the effective magnetic anisotropy, we study the MNPs’ flexibility to leave its preferred magnetization direction. From this, we predict the most effective combination of MNP core size and magnetic anisotropy for maximum signal generation. Y1 - 2024 SN - 978-3-940402-65-3 U6 - https://doi.org/10.17185/duepublico/81475 N1 - 4th YRA MedTech Symposium, February 1, 2024. FH Aachen, Campus Jülich SP - 27 EP - 28 PB - Universität Duisburg-Essen CY - Duisburg ER -