Refine
Year of publication
- 2018 (255) (remove)
Document Type
- Article (124)
- Conference Proceeding (77)
- Part of a Book (30)
- Book (12)
- Working Paper (3)
- Doctoral Thesis (2)
- Other (2)
- Patent (2)
- Part of Periodical (2)
- Report (1)
Keywords
- Datenschutz (2)
- Digitale Transformation (2)
- Energy efficiency (2)
- Engineering optimization (2)
- Literaturanalyse (2)
- MINLP (2)
- Pump System (2)
- Serious Game (2)
- Water (2)
- Agility (1)
- Antarctica (1)
- Awareness (1)
- Bahadur efficiency (1)
- Bioethanol (1)
- Biorefinery (1)
- Bladder (1)
- Booster Stations (1)
- Buffering Capacity (1)
- CDG (1)
- Chance Constraint (1)
- Chemical imaging (1)
- Cloud Computing (1)
- Coat protein (1)
- Competence Developing Game (1)
- Coverage probability (1)
- Cramér-von-Mises statistic (1)
- Datenschutzgrundverordnung (1)
- Datenschutzrecht (1)
- Dry surfaces (1)
- EBSCO Discovery Service (1)
- EU-DS-GVO (1)
- EUDSGVO (1)
- Engineering Application (1)
- Enterprise Architecture (1)
- Enzyme nanocarrier (1)
- Equivalence test (1)
- Field-effect device (1)
- Forschungsprozess (1)
- GOSSAMER-1 (1)
- Geschäftsprozessmanagement (1)
- Global optimization (1)
- Glucose biosensor (1)
- Glucose oxidase (1)
- Goodness-of-fit tests for uniformity (1)
- Growth modelling (1)
- IBM Watson Explorer (1)
- INODIS (1)
- IT-Sicherheit (1)
- Identitätsmanagement (1)
- Informationsgetriebene Geschäftsmodelle (1)
- Integrated empirical distribution (survival) function (1)
- Internet der Dinge (1)
- Jupiter (1)
- Kernel density estimator (1)
- Lab-on-Chip (1)
- Latin Hypercube Sampling (1)
- Length of confidence intervals (1)
- Light-addressable potentiometric sensor (1)
- Lignocellulose feedstook (1)
- Literatur-analyse-prozess (1)
- Literaturdaten (1)
- Literature review (1)
- MASCOT (1)
- Mars (1)
- Mechanical simulation (1)
- Microbial adhesion (1)
- Minimum dissipation (1)
- Mixed-integer nonlinear problem (1)
- Monetarisierung (1)
- Multi-criteria optimization (1)
- Muscle fibers (1)
- Network (1)
- Numerical inversion of Laplace transforms (1)
- Paper recycling (1)
- Passive stretching (1)
- Pelvic floor dysfunction (1)
- Pelvic muscle (1)
- Pitman efficiency (1)
- Planetary exploration (1)
- Player Types (1)
- Potentiometry (1)
- Process engineering (1)
- Projektbeispiele (1)
- Prozessautomatisierung (1)
- Qualitative Wertschöpfungsanalyse (1)
- RC frames (1)
- Reconstruction (1)
- Rehabilitation Technology and Prosthetics (1)
- Research process (1)
- Sampling methods (1)
- Softwareroboter (1)
- Stochastic Programming (1)
- Story (1)
- Surface microorganisms (1)
- Surgical Navigation and Robotics (1)
- Swabbing (1)
- Technische Schutzmaßnahmen (1)
- Text Analytics (1)
- Text Analytics (1)
- Text analytics (1)
- Text mining (1)
- Tobacco mosaic virus (TMV) (1)
- Transition (1)
- Turbulence (1)
- Uncertainty (1)
- Ureter (1)
- Video Game (1)
- Water Distribution (1)
- Water Supply Networks (1)
- Wilcoxon tests (1)
- Wissenstransfer (1)
- achilles tendon (1)
- agile (1)
- business simulation (1)
- design of technical systems (1)
- earthquakes (1)
- energy absorption (1)
- energy dissipation (1)
- frequency mixing (1)
- functional data (1)
- habitability (1)
- huge dimensional data (1)
- ice moons (1)
- icy moons (1)
- in-plane and out-of-plane failure (1)
- legal obligations (1)
- life detection (1)
- magnetic beads (1)
- magnetic sensing (1)
- mathematical optimization (1)
- mechanical buffer (1)
- multiple NEA rendezvous (1)
- optimization (1)
- product liability (1)
- remote sensing (1)
- resilience (1)
- separable Hilbert space (1)
- slum classification (1)
- small spacecraft (1)
- solar sail (1)
- space missions (1)
- stiffness (1)
- superparamagnetic nanoparticles (1)
- tablet game (1)
- underwater vehicle (1)
- water supply design (1)
Institute
- Fachbereich Medizintechnik und Technomathematik (67)
- Fachbereich Elektrotechnik und Informationstechnik (44)
- IfB - Institut für Bioengineering (41)
- INB - Institut für Nano- und Biotechnologien (25)
- Fachbereich Luft- und Raumfahrttechnik (24)
- Fachbereich Maschinenbau und Mechatronik (24)
- Fachbereich Energietechnik (22)
- Fachbereich Wirtschaftswissenschaften (21)
- Fachbereich Chemie und Biotechnologie (18)
- Fachbereich Bauingenieurwesen (16)
- Fachbereich Architektur (8)
- ECSM European Center for Sustainable Mobility (6)
- Solar-Institut Jülich (5)
- Fachbereich Gestaltung (4)
- Institut fuer Angewandte Polymerchemie (3)
- MASKOR Institut für Mobile Autonome Systeme und Kognitive Robotik (2)
- Nowum-Energy (2)
- ZHQ - Bereich Hochschuldidaktik und Evaluation (2)
- FH Aachen (1)
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.
Enzyme und Biosensorik
(2018)
Enzymbasierte Biosensoren finden seit mehr als fünf Jahrzehnten einen prosperierenden Wachstumsmarkt und werden zunehmend auch in biotechnologischen Prozessen eingesetzt. In diesem Kapitel werden, ausgehend vom Sensorbegriff und typischen Kenngrößen für Biosensoren (Abschn. 18.1), elektrochemische Enzym-Biosensoren vorgestellt und deren typischen Einsatzgebiete diskutiert (Abschn. 18.2). Ein Blick über den „Tellerrand“ hinaus zeigt alternative Transduktorprinzipien (Abschn. 18.3) und führt abschließend in aktuelle Forschungstrends ein (Abschn. 18.4).
Highly competitive markets paired with tremendous production volumes demand particularly cost efficient products. The usage of common parts and modules across product families can potentially reduce production costs. Yet, increasing commonality typically results in overdesign of individual products. Multi domain virtual prototyping enables designers to evaluate costs and technical feasibility of different single product designs at reasonable computational effort in early design phases. However, savings by platform commonality are hard to quantify and require detailed knowledge of e.g. the production process and the supply chain. Therefore, we present and evaluate a multi-objective metamodel-based optimization algorithm which enables designers to explore the trade-off between high commonality and cost optimal design of single products.
Given industrial applications, the costs for the operation and maintenance of a pump system typically far exceed its purchase price. For finding an optimal pump configuration which minimizes not only investment, but life-cycle costs, methods like Technical Operations Research which is based on Mixed-Integer Programming can be applied. However, during the planning phase, the designer is often faced with uncertain input data, e.g. future load demands can only be estimated. In this work, we deal with this uncertainty by developing a chance-constrained two-stage (CCTS) stochastic program. The design and operation of a booster station working under uncertain load demand are optimized to minimize total cost including purchase price, operation cost incurred by energy consumption and penalty cost resulting from water shortage. We find optimized system layouts using a sample average approximation (SAA) algorithm, and analyze the results for different risk levels of water shortage. By adjusting the risk level, the costs and performance range of the system can be balanced, and thus the
system’s resilience can be engineered
The Kremer-Grest (KG) bead-spring model is a near standard in Molecular Dynamic simulations of generic polymer properties. It owes its popularity to its computational efficiency, rather than its ability to represent specific polymer species and conditions. Here we investigate how to adapt the model to match the universal properties of a wide range of chemical polymers species. For this purpose we vary a single parameter originally introduced by Faller and Müller-Plathe, the chain stiffness. Examples include polystyrene, polyethylene, polypropylene, cis-polyisoprene, polydimethylsiloxane, polyethyleneoxide and styrene-butadiene rubber. We do this by matching the number of Kuhn segments per chain and the number of Kuhn segments per cubic Kuhn volume for the polymer species and for the Kremer-Grest model. We also derive mapping relations for converting KG model units back to physical units, in particular we obtain the entanglement time for the KG model as function of stiffness allowing for a time mapping. To test these relations, we generate large equilibrated well entangled polymer melts, and measure the entanglement moduli using a static primitive-path analysis of the entangled melt structure as well as by simulations of step-strain deformation of the model melts. The obtained moduli for our model polymer melts are in good agreement with the experimentally expected moduli.
For fuel flexibility enhancement hydrogen represents a possible alternative gas turbine fuel within future low emission power generation, in case of hydrogen production by the use of renewable energy sources such as wind energy or biomass. Kawasaki Heavy Industries, Ltd. (KHI) has research and development projects for future hydrogen society; production of hydrogen gas, refinement and liquefaction for transportation and storage, and utilization with gas turbine / gas engine for the generation of electricity. In the development of hydrogen gas turbines, a key technology is the stable and low NOx hydrogen combustion, especially Dry Low Emission (DLE) or Dry Low NOx (DLN) hydrogen combustion. Due to the large difference in the physical properties of hydrogen compared to other fuels such as natural gas, well established gas turbine combustion systems cannot be directly applied for DLE hydrogen combustion. Thus, the development of DLE hydrogen combustion technologies is an essential and challenging task for the future of hydrogen fueled gas turbines. The DLE Micro-Mix combustion principle for hydrogen fuel has been in development for many years to significantly reduce NOx emissions. This combustion principle is based on cross-flow mixing of air and gaseous hydrogen which reacts in multiple miniaturized “diffusion-type” flames. The major advantages of this combustion principle are the inherent safety against flashback and the low NOx-emissions due to a very short residence time of the reactants in the flame region of the micro-flames.