TY - CHAP AU - Hoppe, Julia Amelie AU - Johansson-Pajala, Rose-Marie AU - Gustafsson, Christine AU - Melkas, Helinä AU - Tusku, Outi AU - Pekkarinen, Satu AU - Hennala, Lea AU - Thommes, Kirsten ED - Haltaufderheide, Joschka ED - Hovemann, Johanna ED - Vollmann, Jochen ID - 17367 T2 - Aging between Participation and Simulation - Ethical Dimensions of Socially Assistive Technologies in elderly care TI - Assistive robots in care: Expectations and perceptions of older people ER - TY - JOUR AU - Chantakit, Teanchai AU - Schlickriede, Christian AU - Sain, Basudeb AU - Meyer, Fabian AU - Weiss, Thomas AU - Chattham, Nattaporn AU - Zentgraf, Thomas ID - 17390 IS - 9 JF - Photonics Research SN - 2327-9125 TI - All-dielectric silicon metalens for two-dimensional particle manipulation in optical tweezers VL - 8 ER - TY - JOUR AB - The development of renewable energies and smart mobility has profoundly impacted the future of the distribution grid. An increasing bidirectional energy flow stresses the assets of the distribution grid, especially medium voltage switchgear. This calls for improved maintenance strategies to prevent critical failures. Predictive maintenance, a maintenance strategy relying on current condition data of assets, serves as a guideline. Novel sensors covering thermal, mechanical, and partial discharge aspects of switchgear, enable continuous condition monitoring of some of the most critical assets of the distribution grid. Combined with machine learning algorithms, the demands put on the distribution grid by the energy and mobility revolutions can be handled. In this paper, we review the current state-of-the-art of all aspects of condition monitoring for medium voltage switchgear. Furthermore, we present an approach to develop a predictive maintenance system based on novel sensors and machine learning. We show how the existing medium voltage grid infrastructure can adapt these new needs on an economic scale. AU - Hoffmann, Martin W. AU - Wildermuth, Stephan AU - Gitzel, Ralf AU - Boyaci, Aydin AU - Gebhardt, Jörg AU - Kaul, Holger AU - Amihai, Ido AU - Forg, Bodo AU - Suriyah, Michael AU - Leibfried, Thomas AU - Stich, Volker AU - Hicking, Jan AU - Bremer, Martin AU - Kaminski, Lars AU - Beverungen, Daniel AU - zur Heiden, Philipp AU - Tornede, Tanja ID - 17426 JF - Sensors SN - 1424-8220 TI - Integration of Novel Sensors and Machine Learning for Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and Mobility Revolutions ER - TY - GEN AB - Syntactic annotation of corpora in the form of part-of-speech (POS) tags is a key requirement for both linguistic research and subsequent automated natural language processing (NLP) tasks. This problem is commonly tackled using machine learning methods, i.e., by training a POS tagger on a sufficiently large corpus of labeled data. While the problem of POS tagging can essentially be considered as solved for modern languages, historical corpora turn out to be much more difficult, especially due to the lack of native speakers and sparsity of training data. Moreover, most texts have no sentences as we know them today, nor a common orthography. These irregularities render the task of automated POS tagging more difficult and error-prone. Under these circumstances, instead of forcing the POS tagger to predict and commit to a single tag, it should be enabled to express its uncertainty. In this paper, we consider POS tagging within the framework of set-valued prediction, which allows the POS tagger to express its uncertainty via predicting a set of candidate POS tags instead of guessing a single one. The goal is to guarantee a high confidence that the correct POS tag is included while keeping the number of candidates small. In our experimental study, we find that extending state-of-the-art POS taggers to set-valued prediction yields more precise and robust taggings, especially for unknown words, i.e., words not occurring in the training data. AU - Heid, Stefan Helmut AU - Wever, Marcel Dominik AU - Hüllermeier, Eyke ID - 17605 T2 - Journal of Data Mining and Digital Humanities TI - Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction ER - TY - CONF AU - Haeb-Umbach, Reinhold ED - Böck, Ronald ED - Siegert, Ingo ED - Wendemuth, Andreas ID - 17763 KW - Poster SN - 978-3-959081-93-1 T2 - Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2020 TI - Sprachtechnologien für Digitale Assistenten ER - TY - JOUR AB - We numerically simulate multiple light scattering in discrete disordered media represented by large clusters of irregular non-absorbing particles. The packing density of clusters is 0.5. With such conditions diffuse scattering is significantly reduced and light transport follows propagation channels that are determined by the particle size and topology of the medium. This kind of localization produces coherent backscattering intensity surge and enhanced negative polarization branch if compared to lower density samples. AU - Grynko, Yevgen AU - Shkuratov, Yuriy AU - Förstner, Jens ID - 17803 JF - Journal of Quantitative Spectroscopy and Radiative Transfer KW - tet_topic_scattering SN - 0022-4073 TI - Light backscattering from large clusters of densely packed irregular particles VL - 255 ER - TY - CONF AU - Reinhold, Jannik AU - Frank, Maximilian AU - Koldewey, Christian AU - Dumitrescu, Roman AU - Buss, Eugen ID - 18876 T2 - Proceedings of the ISPIM Connects Bangkok – Partnering for an Innovative Community TI - In-depth Analysis of the Effects of Smart Services on Value Creation ER - TY - JOUR AU - Eke, Norbert Otto ID - 19153 IS - 1 JF - German Life and Letters (Special Issue: Herta Müller and the Currents of European History) TI - Der ‚Eigene Kalender‘ des Erinnerns: Die Wahrheit der erfundenen Erinnerung in Herta Müllers Romanen, Erzählungen und Essays VL - 73 ER - TY - CHAP AU - Reinhold, Jannik AU - Koldewey, Christian AU - Dumitrescu, Roman ED - Buchholz, Birgit ED - Bürger, Matthias ID - 20568 T2 - Der Geschäftsmodell-Toolguide TI - GEMINI-Modellierungssprache für Wertschöpfungssysteme ER - TY - CHAP AU - Koldewey, Christian AU - Reinhold, Jannik AU - Dumitrescu, Roman ED - Buchholz, Birgit ED - Bürger, Matthias ID - 20570 T2 - Der Geschäftsmodell-Toolguide TI - GEMINI-Geschäftsmodellmuster-Kartenset ER - TY - CHAP AU - Koldewey, Christian AU - Reinhold, Jannik AU - Dumitrescu, Roman ED - Buchholz, Birgit ED - Bürger, Matthias ID - 20571 T2 - Der Geschäftsmodell-Toolguide TI - Geschäftsmodellvalidierung ER - TY - CHAP AU - Koldewey, Christian AU - Reinhold, Jannik AU - Dumitrescu, Roman ED - Buchholz, Birgit ED - Bürger, Matthias ID - 20573 T2 - Der Geschäftsmodell-Toolguide TI - Geschäftsmodell-Roadmapping ER - TY - CONF AU - Boeddeker, Christoph AU - Nakatani, Tomohiro AU - Kinoshita, Keisuke AU - Haeb-Umbach, Reinhold ID - 20695 SN - 9781509066315 T2 - ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) TI - Jointly Optimal Dereverberation and Beamforming ER - TY - CONF AU - Boeddeker, Christoph AU - Cord-Landwehr, Tobias AU - Heitkaemper, Jens AU - Zorila, Catalin AU - Hayakawa, Daichi AU - Li, Mohan AU - Liu, Min AU - Doddipatla, Rama AU - Haeb-Umbach, Reinhold ID - 20700 T2 - Proc. CHiME 2020 Workshop on Speech Processing in Everyday Environments TI - Towards a speaker diarization system for the CHiME 2020 dinner party transcription ER - TY - BOOK ED - Herzig, Bardo ED - Klar, Tilman-Mathies ED - Martin, Alexander ED - Meister, Dorothee ID - 20705 SN - 1424-3636 TI - Orientierungen in der digitalen Welt ER - TY - CONF AU - Camberg, Alan Adam AU - Tröster, Thomas ID - 20854 TI - A simplified method for the evaluation of the layer compression test using one 3D digital image correlation system and considering the material anisotropy by the equibiaxial Lankford parameter ER - TY - CONF AU - Camberg, Alan Adam AU - Erhart, Tobias AU - Tröster, Thomas ID - 20856 TI - Predicting fracture at non-isothermal forming conditions: A temperature dependent extension of the LS-DYNA GISSMO fracture indicator framework ER - TY - CONF AB - Augmented Reality (AR) has recently found high attention in mobile shopping apps such as in domains like furniture or decoration. Here, the developers of the apps focus on the positioning of atomic 3D objects in the physical environment. With this focus, they neglect the configuration of multi-faceted 3D object composition according to the user needs and environmental constraints. To tackle these challenges, we present a model-based approach to support AR-assisted product con-figuration based on the concept of Dynamic Software Product Lines. Our approach splits products (e.g. table) into parts (e.g. tabletop, ta-ble legs, funnier) with their 3D objects and additional information (e.g. name, price). The possible products, which can be configured out of these parts, are stored in a feature model. At runtime, this feature model can be used to configure 3D object compositions out of the product parts and adapt to user needs and environmental constraints. The benefits of this approach are demonstrated by a case study of configuring modular kitchens with the help of a prototypical mobile-based implementation. AU - Gottschalk, Sebastian AU - Yigitbas, Enes AU - Schmidt, Eugen AU - Engels, Gregor ED - Bernhaupt, Regina ED - Ardito, Carmelo ED - Sauer, Stefan ID - 18249 KW - Product Configuration KW - Augmented Reality KW - Runtime Adaptation KW - Dynamic Software Product Lines T2 - Human-Centered Software Engineering. HCSE 2020 TI - Model-based Product Configuration in Augmented Reality Applications VL - 12481 ER - TY - GEN AU - Schürmann, Patrick ID - 18637 TI - A Group Signature Scheme from Flexible Public Key Signatures and Structure-Preserving Signatures on Equivalence Classes ER - TY - JOUR AB - Many dimensionality and model reduction techniques rely on estimating dominant eigenfunctions of associated dynamical operators from data. Important examples include the Koopman operator and its generator, but also the Schrödinger operator. We propose a kernel-based method for the approximation of differential operators in reproducing kernel Hilbert spaces and show how eigenfunctions can be estimated by solving auxiliary matrix eigenvalue problems. The resulting algorithms are applied to molecular dynamics and quantum chemistry examples. Furthermore, we exploit that, under certain conditions, the Schrödinger operator can be transformed into a Kolmogorov backward operator corresponding to a drift-diffusion process and vice versa. This allows us to apply methods developed for the analysis of high-dimensional stochastic differential equations to quantum mechanical systems. AU - Klus, Stefan AU - Nüske, Feliks AU - Hamzi, Boumediene ID - 21819 JF - Entropy SN - 1099-4300 TI - Kernel-Based Approximation of the Koopman Generator and Schrödinger Operator ER -