TY  - CHAP
A1  - Serror, Martin
A1  - Henze, Martin
A1  - Hack, Sacha
A1  - Schuba, Marko
A1  - Wehrle, Klaus
T1  - Towards in-network security for smart homes
T2  - 13th International Conference on Availability, Reliability and Security, ARES 2018; Hamburg; Germany; 27 August 2018 through 30 August 2018
Y1  - 2018
SN  - 978-145036448-5
U6  - https://doi.org/10.1145/3230833.3232802
SP  - Article numer 3232802
ER  - 
TY  - CHAP
A1  - Schöning, Michael Josef
A1  - Wagner, Torsten
A1  - Poghossian, Arshak
A1  - Miyamoto, K.I.
A1  - Werner, C.F.
A1  - Krause, S.
A1  - Yoshinobu, T.
T1  - Light-addressable potentiometric sensors for (bio-)chemical sensing and imaging
T2  - Encyclopedia of Interfacial Chemistry: Surface Science and Electrochemistry. Vol. 7
Y1  - 2018
SN  - 9780128097397
SP  - 295
EP  - 308
PB  - Elsevier
CY  - Amsterdam
ER  - 
TY  - BOOK
A1  - Schöning, Michael Josef
A1  - Poghossian, Arshak
T1  - Label-free biosensing: advanced materials, devices and applications
Y1  - 2018
SN  - 978-3-319-75219-8
PB  - Springer
CY  - Cham
ER  - 
TY  - JOUR
A1  - Schwabedal, Justus T. C.
A1  - Sippel, Daniel
A1  - Brandt, Moritz D.
A1  - Bialonski, Stephan
T1  - Automated Classification of Sleep Stages and EEG Artifacts in Mice with Deep Learning
N2  - Sleep scoring is a necessary and time-consuming task in sleep studies. In animal models (such as mice) or in humans, automating this tedious process promises to facilitate long-term studies and to promote sleep biology as a data-driven f ield. We introduce a deep neural network model that is able to predict different states of consciousness (Wake, Non-REM, REM) in mice from EEG and EMG recordings with excellent scoring results for out-of-sample data. Predictions are made on epochs of 4 seconds length, and epochs are classified as artifactfree or not. The model architecture draws on recent advances in deep learning and in convolutional neural networks research. In contrast to previous approaches towards automated sleep scoring, our model does not rely on manually defined features of the data but learns predictive features automatically. We expect deep learning models like ours to become widely applied in different fields, automating many repetitive cognitive tasks that were previously difficult to tackle.
Y1  - 2018
U6  - https://doi.org/10.48550/arXiv.1809.08443
ER  - 
TY  - CHAP
A1  - Schulze, Sven
A1  - Mühleisen, M.
A1  - Feyerl, Günter
T1  - Adaptive energy management strategy for a heavy-duty truck with a P2-hybrid topology
T2  - 18. Internationales Stuttgarter Symposium. Proceedings
Y1  - 2018
U6  - https://doi.org/10.1007/978-3-658-21194-3
SP  - 75
EP  - 89
PB  - Springer Vieweg
CY  - Wiesbaden
ER  - 
TY  - CHAP
A1  - Schreiber, Marc
A1  - Kraft, Bodo
A1  - Zündorf, Albert
T1  - NLP Lean Programming Framework: Developing NLP Applications More Effectively
T2  - Proceedings of NAACL-HLT 2018: Demonstrations, New Orleans, Louisiana, June 2 - 4, 2018
N2  - This paper presents NLP Lean Programming
framework (NLPf), a new framework
for creating custom natural language processing
(NLP) models and pipelines by utilizing
common software development build systems.
This approach allows developers to train and
integrate domain-specific NLP pipelines into
their applications seamlessly. Additionally,
NLPf provides an annotation tool which improves
the annotation process significantly by
providing a well-designed GUI and sophisticated
way of using input devices. Due to
NLPf’s properties developers and domain experts
are able to build domain-specific NLP
applications more efficiently. NLPf is Opensource
software and available at https://
gitlab.com/schrieveslaach/NLPf.
Y1  - 2018
U6  - https://doi.org/10.18653/v1/N18-5001 
ER  - 
TY  - CHAP
A1  - Scholl, Ingrid
A1  - Suder, Sebastian
A1  - Schiffer, Stefan
T1  - Direct Volume Rendering in Virtual Reality
T2  - Bildverarbeitung für die Medizin 2018
Y1  - 2018
SN  - 978-3-662-56537-7
U6  - https://doi.org/10.1007/978-3-662-56537-7_79
SP  - 297
EP  - 302
PB  - Springer Vieweg
CY  - Berlin
ER  - 
TY  - CHAP
A1  - Schmitt, Timo
A1  - Rosin, Julia
A1  - Butenweg, Christoph
T1  - Seismic Impact And Design Of Buried Pipelines
T2  - 16th European Conference on Earthquake Engineering, Thessaloniki, 18-21 June, 2018
N2  - Seismic design of buried pipeline systems for energy and water supply is not only important for plant and operational safety but also for the maintenance of the supply infrastructure after an earthquake. The present paper shows special issues of the seismic wave impacts on buried pipelines, describes calculation methods, proposes approaches and gives calculation examples. This paper regards the effects of transient displacement differences and resulting tensions within the pipeline due to the wave propagation of the earthquake. However, the presented model can also be used to calculate fault rupture induced displacements. Based on a three-dimensional Finite Element Model parameter studies are performed to show the influence of several parameters such as incoming wave angle, wave velocity, backfill height and synthetic displacement time histories. The interaction between the pipeline and the surrounding soil is modeled with non-linear soil springs and the propagating wave is simulated affecting the pipeline punctually, independently in time and space. Special attention is given to long-distance heat pipeline systems. Here, in regular distances expansion bends are arranged to ensure movements of the pipeline due to high temperature. Such expansion bends are usually designed with small bending radii, which during the earthquake lead to high bending stresses in the cross-section of the pipeline. Finally, an interpretation of the results and recommendations are given for the most critical parameters.
Y1  - 2018
N1  - Paper No 10600
SP  - 1
EP  - 12
ER  - 
TY  - CHAP
A1  - Schmidts, Oliver
A1  - Kraft, Bodo
A1  - Schreiber, Marc
A1  - Zündorf, Albert
T1  - Continuously evaluated research projects in collaborative decoupled environments
T2  - 2018 ACM/IEEE 5th International Workshop on Software Engineering Research and Industrial PracticePractice, May 29, 2018, Gothenburg, Sweden : SER&IP' 18
N2  - Often, research results from collaboration projects are not transferred into productive environments even though approaches are proven to work in demonstration prototypes. These demonstration prototypes are usually too fragile and error-prone to be transferred
easily into productive environments. A lot of additional work is required.
Inspired by the idea of an incremental delivery process, we introduce an architecture pattern, which combines the approach of Metrics Driven Research Collaboration with microservices for the ease of integration. It enables keeping track of project goals over the course of the collaboration while every party may focus on their expert skills: researchers may focus on complex algorithms,
practitioners may focus on their business goals.
Through the simplified integration (intermediate) research results can be introduced into a productive environment which enables
getting an early user feedback and allows for the early evaluation of different approaches. The practitioners’ business model benefits throughout the full project duration.
Y1  - 2018
SP  - 1
EP  - 9
PB  - ACM
CY  - New York, NY
ER  - 
TY  - JOUR
A1  - Schirra, Julian
A1  - Bissonnette, William
A1  - Bramesfeld, Götz
T1  - Wake-model effects on induced drag prediction of staggered boxwings
JF  - Aerospace
Y1  - 2018
U6  - https://doi.org/10.3390/aerospace5010014
SN  - 2226-4310
VL  - 5
IS  - 1
ER  -