Refine
Year of publication
- 2024 (54) (remove)
Institute
- Fachbereich Medizintechnik und Technomathematik (23)
- IfB - Institut für Bioengineering (10)
- Fachbereich Elektrotechnik und Informationstechnik (8)
- Fachbereich Luft- und Raumfahrttechnik (7)
- INB - Institut für Nano- und Biotechnologien (7)
- Fachbereich Chemie und Biotechnologie (5)
- Fachbereich Wirtschaftswissenschaften (5)
- ECSM European Center for Sustainable Mobility (4)
- Nowum-Energy (2)
- Arbeitsstelle fuer Hochschuldidaktik und Studienberatung (1)
Language
- English (54) (remove)
Document Type
- Article (34)
- Conference Proceeding (12)
- Part of a Book (3)
- Book (2)
- Preprint (2)
- Working Paper (1)
Keywords
- Engineering education (2)
- Hot S-parameter (2)
- 1P hub loads (1)
- 3-D printing (1)
- 316L (1)
- ABE (1)
- Accuracy (1)
- Acid crash (1)
- Active humidity control (1)
- Acyl-amino acids (1)
- Acylation (1)
- Additive manufacturing (1)
- Aeroelasticity (1)
- Ageing (1)
- Agent-based modeling (1)
- Aminoacylase (1)
- Analytical models (1)
- Anatomy (1)
- Bio-inspired systems (1)
- Biocatalysis (1)
- Biological hydrogen (1)
- Biosurfactants (1)
- Boundary integral equations, (1)
- Butanol (1)
- C. acetobutylicum (1)
- Capacitive model (1)
- Carrier solvents (1)
- Centrifugal twisting moment (1)
- Chaperone (1)
- Circuit simulation (1)
- Coal (1)
- Collagen fibrils (1)
- Comparative simulation (1)
- Complex-valued eigenvalues (1)
- Connective tissues (1)
- Constitutive model (1)
- Control engineering (1)
- DAC (1)
- DC machines (1)
- Damage mechanics theory (1)
- Dark fermentation (1)
- Data analysis (1)
- Data visualization (1)
- Database (1)
- Discontinuous fractures (1)
- Dynamic modeling (1)
- E. coli detection (1)
- ESATAN-TMS (1)
- Eigenvalue trajectories (1)
- Electrochemical impedance spectroscopy (1)
- Electronic cigarettes (1)
- Electronic learning (1)
- Energy dispatch (1)
- Energy market (1)
- Enzyme coverage (1)
- Exponential Euler scheme, (1)
- Extension–twist coupling (1)
- Extracellular matrix (ECM) (1)
- Fault detection (1)
- Field-effect biosensor (1)
- Flutter (1)
- Focusing (1)
- Free-base nicotine (1)
- Fuel cell (1)
- Furnace (1)
- Fusion (1)
- German (1)
- Germany (1)
- H2 (1)
- Health management system (1)
- Hydrogenotrophic methanogens (1)
- Hydrolysis (1)
- Impedance analysis (1)
- Instruments (1)
- Interior transmission problem (1)
- LPBF (1)
- LQR (1)
- Li7La3Zr2O12 (1)
- LiGaO2 (1)
- Lifting propeller (1)
- Low field NMR (1)
- Low voltage (1)
- Lunar Surface (1)
- MOS (1)
- MPC (1)
- Manifolds (1)
- Master stamp (1)
- Matlab (1)
- Measurement (1)
- Mechanical stability (1)
- Metabolic shift (1)
- Methane (1)
- Methanogenesis (1)
- Mode converter (1)
- Modeling (1)
- Monitoring (1)
- Multianalyte detection (1)
- Muscle (1)
- Musculoskeletal system (1)
- Non-model-based Evaluation (1)
- Non-parallel fissures (1)
- Nonlinear eigenvalue problems (1)
- Obstacle avoidance (1)
- Online diagnostic (1)
- Online services (1)
- Open Data (1)
- Open source (1)
- Organic waste (1)
- P2G (1)
- PEM fuel cell (1)
- PPO (1)
- Parabolic SPDEs (1)
- Path planning (1)
- Penicillin (1)
- Photolithographic mimics (1)
- Physiology (1)
- Plasma (1)
- Plasma diagnostics (1)
- Power dissipation (1)
- Pre-culture (1)
- Pretreatment (1)
- Propeller whirl flutter (1)
- Quartz crystal microbalance (1)
- Raman spectroscopy (1)
- Refining (1)
- Reinforcement Learning (1)
- Relative air humidity (1)
- Renewable energy sources (1)
- Rotatory Inverted Pendulum (1)
- SLM (1)
- Simulation (1)
- Software packages (1)
- Surface imprinted polymer (1)
- Synchronous machines (1)
- Tapered ends (1)
- Thermal Model (1)
- Thermal analysis (1)
- Thermal conductivity (1)
- Throughput (1)
- Time-series (1)
- Training (1)
- Trapeze effect (1)
- Uniaxial compression test (1)
- Unmanned aerial vehicles (1)
- Unsteady aerodynamics (1)
- Weak organic acids (1)
- aquaculture (1)
- artificial olfactory image (1)
- biodegradable polymers (1)
- biomethane (1)
- bubble column (1)
- carbon dioxide removal (1)
- catalytic metal (1)
- chip-based sensor setup (1)
- climate neutrality (1)
- coupled Néel–Brownian relaxation dynamics (1)
- direct air capture (1)
- economics (1)
- electrical conductivity of liquids (1)
- electronic nose (1)
- field-effect structure (1)
- frequency mixing magnetic detection (1)
- ga-doping (1)
- garnet solid electrolyte (1)
- gas sensor (1)
- glycine (1)
- hydrogels (1)
- hydrogen peroxide (1)
- impedance spectroscopy (1)
- key performance indicators (1)
- large language models (1)
- light-addressing technologies (1)
- magnetic biosensing (1)
- magnetic nanoparticles (1)
- magnetic relaxation (1)
- metal-oxide-semiconductor structure (1)
- methanation (1)
- microfluidics (1)
- micromagnetic simulation (1)
- micronutrients (1)
- negative emissions (1)
- optical spore trapping (1)
- plug flow reactor (1)
- polyaspartic acid (1)
- rollout (1)
- scanned light pulse technique (1)
- semantic role labeling (1)
- solid-state battery (1)
- speaker attribution (1)
- sterilization conditions (1)
- superabsorbent polymers (1)
- swelling properties (1)
- temperature (1)
- thermometry (1)
- visualization (1)
Analyzing electroencephalographic (EEG) time series can be challenging, especially with deep neural networks, due to the large variability among human subjects and often small datasets. To address these challenges, various strategies, such as self-supervised learning, have been suggested, but they typically rely on extensive empirical datasets. Inspired by recent advances in computer vision, we propose a pretraining task termed "frequency pretraining" to pretrain a neural network for sleep staging by predicting the frequency content of randomly generated synthetic time series. Our experiments demonstrate that our method surpasses fully supervised learning in scenarios with limited data and few subjects, and matches its performance in regimes with many subjects. Furthermore, our results underline the relevance of frequency information for sleep stage scoring, while also demonstrating that deep neural networks utilize information beyond frequencies to enhance sleep staging performance, which is consistent with previous research. We anticipate that our approach will be advantageous across a broad spectrum of applications where EEG data is limited or derived from a small number of subjects, including the domain of brain-computer interfaces.
The growing body of political texts opens up new opportunities for rich insights into political dynamics and ideologies but also increases the workload for manual analysis. Automated speaker attribution, which detects who said what to whom in a speech event and is closely related to semantic role labeling, is an important processing step for computational text analysis. We study the potential of the large language model family Llama 2 to automate speaker attribution in German parliamentary debates from 2017-2021. We fine-tune Llama 2 with QLoRA, an efficient training strategy, and observe our approach to achieve competitive performance in the GermEval 2023 Shared Task On Speaker Attribution in German News Articles and Parliamentary Debates. Our results shed light on the capabilities of large language models in automating speaker attribution, revealing a promising avenue for computational analysis of political discourse and the development of semantic role labeling systems.
In this paper, the use of reinforcement learning (RL) in control systems is investigated using a rotatory inverted pendulum as an example. The control behavior of an RL controller is compared to that of traditional LQR and MPC controllers. This is done by evaluating their behavior under optimal conditions, their disturbance behavior, their robustness and their development process. All the investigated controllers are developed using MATLAB and the Simulink simulation environment and later deployed to a real pendulum model powered by a Raspberry Pi. The RL algorithm used is Proximal Policy Optimization (PPO). The LQR controller exhibits an easy development process, an average to good control behavior and average to good robustness. A linear MPC controller could show excellent results under optimal operating conditions. However, when subjected to disturbances or deviations from the equilibrium point, it showed poor performance and sometimes instable behavior. Employing a nonlinear MPC Controller in real time was not possible due to the high computational effort involved. The RL controller exhibits by far the most versatile and robust control behavior. When operated in the simulation environment, it achieved a high control accuracy. When employed in the real system, however, it only shows average accuracy and a significantly greater performance loss compared to the simulation than the traditional controllers. With MATLAB, it is not yet possible to directly post-train the RL controller on the Raspberry Pi, which is an obstacle to the practical application of RL in a prototyping or teaching setting. Nevertheless, RL in general proves to be a flexible and powerful control method, which is well suited for complex or nonlinear systems where traditional controllers struggle.
Muscle function is compromised by gravitational unloading in space affecting overall musculoskeletal health. Astronauts perform daily exercise programmes to mitigate these effects but knowing which muscles to target would optimise effectiveness. Accurate inflight assessment to inform exercise programmes is critical due to lack of technologies suitable for spaceflight. Changes in mechanical properties indicate muscle health status and can be measured rapidly and non-invasively using novel technology. A hand-held MyotonPRO device enabled monitoring of muscle health for the first time in spaceflight (> 180 days). Greater/maintained stiffness indicated countermeasures were effective. Tissue stiffness was preserved in the majority of muscles (neck, shoulder, back, thigh) but Tibialis Anterior (foot lever muscle) stiffness decreased inflight vs. preflight (p < 0.0001; mean difference 149 N/m) in all 12 crewmembers. The calf muscles showed opposing effects, Gastrocnemius increasing in stiffness Soleus decreasing. Selective stiffness decrements indicate lack of preservation despite daily inflight countermeasures. This calls for more targeted exercises for lower leg muscles with vital roles as ankle joint stabilizers and in gait. Muscle stiffness is a digital biomarker for risk monitoring during future planetary explorations (Moon, Mars), for healthcare management in challenging environments or clinical disorders in people on Earth, to enable effective tailored exercise programmes.
This easy-to-understand introduction to SAP S/4HANA guides you through the central processes in sales, purchasing and procurement, finance, production, and warehouse management using the model company Global Bike. Familiarize yourself with the basics of business administration, the relevant organizational data, master data, and transactional data, as well as a selection of core business processes in SAP. Using practical examples and tutorials, you will soon become an SAP S/4HANA professional!
Tutorials and exercises for beginners, advanced users, and experts make it easy for you to practice your new knowledge. The prerequisite for this book is access to an SAP S/4HANA client with Global Bike version 4.1.
- Business fundamentals and processes in the SAP system
- Sales, purchasing and procurement, production, finance, and warehouse management
- Tutorials at different qualification levels, exercises, and recap of case studies
- Includes extensive download material for students, lecturers, and professors
This study presents the concept of AstroBioLab, an autonomous astrobiological field laboratory tailored for the exploration of (sub)glacial habitats. AstroBioLab is an integral component of the TRIPLE (Technologies for Rapid Ice Penetration and subglacial Lake Exploration) DLR-funded project, aimed at advancing astrobiology research through the development and deployment of innovative technologies. AstroBioLab integrates diverse measurement techniques such as fluorescence microscopy, DNA sequencing and fluorescence spectrometry, while leveraging microfluidics for efficient sample delivery and preparation.
Humic substances possess distinctive chemical features enabling their use in many advanced applications, including biomedical fields. No chemicals in nature have the same combination of specific chemical and biological properties as humic substances. Traditional medicine and modern research have demonstrated that humic substances from different sources possess immunomodulatory and anti-inflammatory properties, which makes them suitable for the prevention and treatment of chronic dermatoses, allergic rhinitis, atopic dermatitis, and other conditions characterized by inflammatory and allergic responses [1-4]. The use of humic compounds as agentswith antifungal and antiviral properties shows great potential [5-7].
Pulmonary arterial cannulation is a common and effective method for percutaneous mechanical circulatory support for concurrent right heart and respiratory failure [1]. However, limited data exists to what effect the positioning of the cannula has on the oxygen perfusion throughout the pulmonary artery (PA). This study aims to evaluate, using computational fluid dynamics (CFD), the effect of different cannula positions in the PA with respect to the oxygenation of the different branching vessels in order for an optimal cannula position to be determined. The four chosen different positions (see Fig. 1) of the cannulas are, in the lower part of the main pulmonary artery (MPA), in the MPA at the junction between the right pulmonary artery (RPA) and the left pulmonary artery (LPA), in the RPA at the first branch of the RPA and in the LPA at the first branch of the LPA.
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.