@article{SeynnesBojsenMollerAlbrachtetal.2015, author = {Seynnes, O. R. and Bojsen-Moller, J. and Albracht, Kirsten and Arndt, A. and Cronin, N. J. and Finni, T. and Magnusson, S. P.}, title = {Ultrasound-based testing of tendon mechanical properties: a critical evaluation}, series = {Journal of Applied Physiology}, volume = {118}, journal = {Journal of Applied Physiology}, number = {2}, issn = {8750-7587}, doi = {10.1152/japplphysiol.00849.2014}, pages = {133 -- 141}, year = {2015}, language = {en} } @inproceedings{SiebigterothKraftSchmidtsetal.2019, author = {Siebigteroth, Ines and Kraft, Bodo and Schmidts, Oliver and Z{\"u}ndorf, Albert}, title = {A Study on Improving Corpus Creation by Pair Annotation}, series = {Proceedings of the Poster Session of the 2nd Conference on Language, Data and Knowledge (LDK-PS 2019)}, booktitle = {Proceedings of the Poster Session of the 2nd Conference on Language, Data and Knowledge (LDK-PS 2019)}, issn = {1613-0073}, pages = {40 -- 44}, year = {2019}, language = {en} } @inproceedings{SildatkeKarwanniKraftetal.2020, author = {Sildatke, Michael and Karwanni, Hendrik and Kraft, Bodo and Schmidts, Oliver and Z{\"u}ndorf, Albert}, title = {Automated Software Quality Monitoring in Research Collaboration Projects}, series = {ICSEW'20: Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops}, booktitle = {ICSEW'20: Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops}, publisher = {IEEE}, address = {New York, NY}, doi = {10.1145/3387940.3391478}, pages = {603 -- 610}, year = {2020}, abstract = {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.}, language = {en} } @article{SildatkeKarwanniKraftetal.2023, author = {Sildatke, Michael and Karwanni, Hendrik and Kraft, Bodo and Z{\"u}ndorf, Albert}, title = {A distributed microservice architecture pattern for the automated generation of information extraction pipelines}, series = {SN Computer Science}, journal = {SN Computer Science}, number = {4, Article number: 833}, publisher = {Springer Singapore}, address = {Singapore}, issn = {2661-8907}, doi = {10.1007/s42979-023-02256-4}, pages = {19 Seiten}, year = {2023}, abstract = {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.}, language = {en} } @article{SimonisDawgulLuethetal.2005, author = {Simonis, A. and Dawgul, M. and L{\"u}th, H. and Sch{\"o}ning, Michael Josef}, title = {Miniaturised reference electrodes for field-effect sensors compatible to silicon chip technology}, series = {Electrochimica Acta. 51 (2005), H. 5}, journal = {Electrochimica Acta. 51 (2005), H. 5}, isbn = {0013-4686}, doi = {10.1016/j.electacta.2005.04.063}, pages = {930 -- 937}, year = {2005}, language = {en} } @article{SimonisKringsLuethetal.2001, author = {Simonis, A. and Krings, T. and L{\"u}th, H. and Wang, J. and Sch{\"o}ning, Michael Josef}, title = {A „hybrid" thin-film pH sensor with integrated thick-film reference}, series = {Sensors. 1 (2001), H. 6}, journal = {Sensors. 1 (2001), H. 6}, isbn = {1424-8220}, pages = {183 -- 192}, year = {2001}, language = {en} } @article{SimonisLuethWangetal.2003, author = {Simonis, A. and L{\"u}th, H. and Wang, J. and Sch{\"o}ning, Michael Josef}, title = {Strategies of miniaturised reference electrodes integrated in a silicon-based „one chip" pH sensor}, series = {Sensors. 3 (2003), H. 9}, journal = {Sensors. 3 (2003), H. 9}, isbn = {1424-8220}, pages = {330 -- 339}, year = {2003}, language = {en} } @article{SimonisLuethWangetal.2004, author = {Simonis, A. and L{\"u}th, H. and Wang, J. and Sch{\"o}ning, Michael Josef}, title = {New concepts of miniaturised reference electrodes in silicon technology for potentiometric sensor systems}, series = {Sensors and Actuators B. 103 (2004), H. 1-2}, journal = {Sensors and Actuators B. 103 (2004), H. 1-2}, isbn = {0925-4005}, pages = {429 -- 435}, year = {2004}, language = {en} } @article{SimonisRugeMuellerVeggianetal.2003, author = {Simonis, A. and Ruge, C. and M{\"u}ller-Veggian, Mattea and L{\"u}th, H. and Sch{\"o}ning, Michael Josef}, title = {A long-term stable macroporoustype EIS structure for electrochemical sensor applications}, series = {Sensors and Actuators B. 91 (2003), H. 1-3}, journal = {Sensors and Actuators B. 91 (2003), H. 1-3}, isbn = {0925-4005}, pages = {21 -- 25}, year = {2003}, language = {en} } @inproceedings{SimsekKrauseEngelmann2024, author = {Simsek, Beril and Krause, Hans-Joachim and Engelmann, Ulrich M.}, title = {Magnetic biosensing with magnetic nanoparticles: Simulative approach to predict signal intensity in frequency mixing magnetic detection}, series = {YRA MedTech Symposium (2024)}, booktitle = {YRA MedTech Symposium (2024)}, editor = {Digel, Ilya and Staat, Manfred and Trzewik, J{\"u}rgen and Sielemann, Stefanie and Erni, Daniel and Zylka, Waldemar}, publisher = {Universit{\"a}t Duisburg-Essen}, address = {Duisburg}, organization = {MedTech Symposium}, isbn = {978-3-940402-65-3}, doi = {10.17185/duepublico/81475}, pages = {27 -- 28}, year = {2024}, abstract = {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.}, language = {en} }