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 -