Refine
Year of publication
- 2021 (38) (remove)
Document Type
- Article (reviewed) (38) (remove)
Has Fulltext
- no (38) (remove)
Is part of the Bibliography
- yes (38) (remove)
Keywords
- Corporate Governance (2)
- Handprothese (2)
- Predictive maintenance (2)
- Surface acoustic waves (2)
- 3D CAD (1)
- 3D-CAD (1)
- Acoustical properties (1)
- Ancient Capua leg (1)
- Augmented Reality (1)
- Bacillus sp. (1)
Institute
- Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019) (16)
- Fakultät Maschinenbau und Verfahrenstechnik (M+V) (13)
- Fakultät Wirtschaft (W) (7)
- POIM - Peter Osypka Institute of Medical Engineering (7)
- ivESK - Institut für verlässliche Embedded Systems und Kommunikationselektronik (3)
- Fakultät Medien (M) (ab 22.04.2021) (2)
- IMLA - Institute for Machine Learning and Analytics (1)
- INES - Institut für nachhaltige Energiesysteme (1)
Open Access
- Closed Access (30)
- Open Access (8)
Significant progress in the development and commercialization of electrically conductive adhesives has been made. This makes shingling a very attractive approach for solar cell interconnection. In this study, we investigate the shading tolerance of two types of solar modules based on shingle interconnection: first, the already commercialized string approach, and second, the matrix technology where solar cells are intrinsically interconnected in parallel and in series. An experimentally validated LTspice model predicts major advantages for the power output of the matrix layout under partial shading. Diagonal as well as random shading of a 1.6-m2 solar module is examined. Power gains of up to 73.8 % for diagonal shading and up to 96.5 % for random shading are found for the matrix technology compared to the standard string approach. The key factor is an increased current extraction due to lateral current flows. Especially under minor shading, the matrix technology benefits from an increased fill factor as well. Under diagonal shading, we find the probability of parts of the matrix module being bypassed to be reduced by 40 % in comparison to the string module. In consequence, the overall risk of hotspot occurrence in matrix modules is decreased significantly.
A versatile liquid metal (LM) printing process enabling the fabrication of various fully printed devices such as intra- and interconnect wires, resistors, diodes, transistors, and basic circuit elements such as inverters which are process compatible with other digital printing and thin film structuring methods for integration is presented. For this, a glass capillary-based direct-write method for printing LMs such as eutectic gallium alloys, exploring the potential for fully printed LM-enabled devices is demonstrated. Examples for successful device fabrication include resistors, p–n diodes, and field effect transistors. The device functionality and easiness of one integrated fabrication flow shows that the potential of LM printing is far exceeding the use of interconnecting conventional electronic devices in printed electronics.
Objective: To quantify the effect of inhaled 5% carbon-dioxide/95% oxygen on EEG recordings from patients in non-convulsive status epilepticus (NCSE).
Methods: Five children of mixed aetiology in NCSE were given high flow of inhaled carbogen (5% carbon dioxide/95% oxygen) using a face mask for maximum 120s. EEG was recorded concurrently in all patients. The effects of inhaled carbogen on patient EEG recordings were investigated using band-power, functional connectivity and graph theory measures. Carbogen effect was quantified by measuring effect size (Cohen's d) between "before", "during" and "after" carbogen delivery states.
Results: Carbogen's apparent effect on EEG band-power and network metrics across all patients for "before-during" and "before-after" inhalation comparisons was inconsistent across the five patients.
Conclusion: The changes in different measures suggest a potentially non-homogeneous effect of carbogen on the patients' EEG. Different aetiology and duration of the inhalation may underlie these non-homogeneous effects. Tuning the carbogen parameters (such as ratio between CO2 and O2, duration of inhalation) on a personalised basis may improve seizure suppression in future.
The compliant nature of distal limb muscle-tendon units is traditionally considered suboptimal in explosive movements when positive joint work is required. However, during accelerative running, ankle joint net mechanical work is positive. Therefore, this study aims to investigate how plantar flexor muscle-tendon behavior is modulated during fast accelerations. Eleven female sprinters performed maximum sprint accelerations from starting blocks, while gastrocnemius muscle fascicle lengths were estimated using ultrasonography. We combined motion analysis and ground reaction force measurements to assess lower limb joint kinematics and kinetics, and to estimate gastrocnemius muscle-tendon unit length during the first two acceleration steps. Outcome variables were resampled to the stance phase and averaged across three to five trials. Relevant scalars were extracted and analyzed using one-sample and two-sample t-tests, and vector trajectories were compared using statistical parametric mapping. We found that an uncoupling of muscle fascicle behavior from muscle-tendon unit behavior is effectively used to produce net positive mechanical work at the joint during maximum sprint acceleration. Muscle fascicles shortened throughout the first and second steps, while shortening occurred earlier during the first step, where negative joint work was lower compared with the second step. Elastic strain energy may be stored during dorsiflexion after touchdown since fascicles did not lengthen at the same time to dissipate energy. Thus, net positive work generation is accommodated by the reuse of elastic strain energy along with positive gastrocnemius fascicle work. Our results show a mechanism of how muscles with high in-series compliance can contribute to net positive joint work.
Surface acoustic waves are propagated toward the edge of an anisotropic elastic medium (a silicon crystal), which supports leaky waves with a high degree of localization at the tip of the edge. At an angle of incidence corresponding to phase matching with this leaky wedge wave, a sharp peak in the reflection coefficient of the surface wave was found. This anomalous reflection is associated with efficient excitation of the leaky wedge wave. In laser ultrasound experiments, surface acoustic wave pulses were excited and their reflection from the edge of the sample and their partial conversion into leaky wedge wave pulses was observed by optical probe-beam deflection. The reflection scenario and the pulse shapes of the surface and wedge-localized guided waves, including the evolution of the acoustic pulse traveling along the edge, have been confirmed in detail by numerical simulations.
Properties of higher-order surface acoustic wave modes in Al(1-x)Sc(x)N / sapphire structures
(2021)
In this work, surface acoustic wave (SAW) modes and their dependence on propagation directions in epitaxial Al0.68Sc0.32N(0001) films on Al2O3(0001) substrates were studied using numerical and experimental methods. In order to find optimal propagation directions for higher-order SAW modes, phase velocity dispersion branches of Al0.68Sc0.32N on Al2O3 with Pt mass loading were computed for the propagation directions <11-20> and <1-100> with respect to the substrate. Experimental investigations of phase velocities and electromechanical coupling were performed for comparison with the numerical results. Simulations carried out with the finite element method (FEM) and with a Green function approach allowed identification of each wave type, including Rayleigh, Sezawa and shear horizontal wave modes. For the propagation direction <1-100>, significantly increased wave guidance of the Sezawa mode compared to other directions was observed, resulting in enhanced electromechanical coupling (k2eff = 1.6 %) and phase velocity (vphase = 6 km/s). We demonstrated, that selecting wave propagation in <1-100> with high mass density electrodes results in increased electromechanical coupling without significant reduction in phase velocities for the Sezawa wave mode. An improved combination of metallization, Sc concentration x, and SAW propagation direction is suggested which exhibits both high electromechanical coupling (k2eff > 6 %) and high velocity (vphase = 5.5 km/s) for the Sezawa mode.
It is important to minimize the unscheduled downtime of machines caused by outages of machine components in highly automated production lines. Considering machine tools such as, grinding machines, the bearing inside of spindles is one of the most critical components. In the last decade, research has increasingly focused on fault detection of bearings. In addition, the rise of machine learning concepts has also intensified interest in this area. However, up to date, there is no single one-fits-all solution for predictive maintenance of bearings. Most research so far has only looked at individual bearing types at a time.
This paper gives an overview of the most important approaches for bearing-fault analysis in grinding machines. There are two main parts of the analysis presented in this paper. The first part presents the classification of bearing faults, which includes the detection of unhealthy conditions, the position of the error (e.g. at the inner or at the outer ring of the bearing) and the severity, which detects the size of the fault. The second part presents the prediction of remaining useful life, which is important for estimating the productive use of a component before a potential failure, optimizing the replacement costs and minimizing downtime.
In the last decade, deep learning models for condition monitoring of mechanical systems increasingly gained importance. Most of the previous works use data of the same domain (e.g., bearing type) or of a large amount of (labeled) samples. This approach is not valid for many real-world scenarios from industrial use-cases where only a small amount of data, often unlabeled, is available.
In this paper, we propose, evaluate, and compare a novel technique based on an intermediate domain, which creates a new representation of the features in the data and abstracts the defects of rotating elements such as bearings. The results based on an intermediate domain related to characteristic frequencies show an improved accuracy of up to 32 % on small labeled datasets compared to the current state-of-the-art in the time-frequency domain.
Furthermore, a Convolutional Neural Network (CNN) architecture is proposed for transfer learning. We also propose and evaluate a new approach for transfer learning, which we call Layered Maximum Mean Discrepancy (LMMD). This approach is based on the Maximum Mean Discrepancy (MMD) but extends it by considering the special characteristics of the proposed intermediate domain. The presented approach outperforms the traditional combination of Hilbert–Huang Transform (HHT) and S-Transform with MMD on all datasets for unsupervised as well as for semi-supervised learning. In most of our test cases, it also outperforms other state-of-the-art techniques.
This approach is capable of using different types of bearings in the source and target domain under a wide variation of the rotation speed.
Mit zunehmender Datenverfügbarkeit wird der Einsatz Maschinellen Lernens zur Steuerung und Optimierung von Supply Chains attraktiver, da die Qualität der Datenauswertung erhöht und gleichzeitig der Aufwand gesenkt werden kann. Anhand des SCOR-Modells werden exemplarische Ansätze als Orientierungshilfe eingeordnet und dazu passende Verfahren des Maschinellen Lernens vorgestellt.
IoT-Plattformen stellen ein zentrales Element für die Vernetzung von physischen Objekten und die Bereitstellung deren Daten für digitale Zwillinge dar. Der Markt für solche Plattformen ist in den vergangenen Jahren stark gewachsen. Bei inzwischen über 600 Anbietern ist die Wahl der „richtigen“ Plattform für das eigene Unternehmen keine triviale Aufgabe mehr. Dieser Beitrag soll Unternehmen im Auswahlprozess unterstützen, indem gängige Funktionen von IoT-Plattformen und Kriterien für die Auswahl von IoT-Plattformen aufgezeigt werden.