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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.
Emerging applications in soft robotics, wearables, smart consumer products or IoT-devices benefit from soft materials, flexible substrates in conjunction with electronic functionality. Due to high production costs and conformity restrictions, rigid silicon technologies do not meet application requirements in these new domains. However, whenever signal processing becomes too comprehensive, silicon technology must be used for the high-performance computing unit. At the same time, designing everything in flexible or printed electronics using conventional digital logic is not feasible yet due to the limitations of printed technologies in terms of performance, power and integration density. We propose to rather use the strengths of neuromorphic computing architectures consisting in their homogeneous topologies, few building blocks and analog signal processing to be mapped to an inkjet-printed hardware architecture. It has remained a challenge to demonstrate non-linear elements besides weighted aggregation. We demonstrate in this work printed hardware building blocks such as inverter-based comprehensive weight representation and resistive crossbars as well as printed transistor-based activation functions. In addition, we present a learning algorithm developed to train the proposed printed NCS architecture based on specific requirements and constraints of the technology.
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.
This work aimed to determine the influence of two hydrogels (alginate, alginate-di-aldehyde (ADA)/gelatin) on the mechanical strength of microporous ceramics, which have been loaded with these hydrogels. For this purpose, the compressive strength was determined using a Zwick Z005 universal testing machine. In addition, the degradation behavior according to ISO EN 10993-14 in TRIS buffer pH 5.0 and pH 7.4 over 60 days was determined, and its effects on the compressive strength were investigated. The loading was carried out by means of a flow-chamber. The weight of the samples (manufacturer: Robert Mathys Foundation (RMS) and Curasan) in TRIS solutions pH 5 and pH 7 increased within 4 h (mean 48 ± 32 mg) and then remained constant over the experimental period of 60 days. The determination surface roughness showed a decrease in the value for the ceramics incubated in TRIS compared to the untreated ceramics. In addition, an increase in protein concentration in solution was determined for ADA gelatin-loaded ceramics. The macroporous Curasan ceramic exhibited a maximum failure load of 29 ± 9.0 N, whereas the value for the microporous RMS ceramic was 931 ± 223 N. Filling the RMS ceramic with ADA gelatin increased the maximum failure load to 1114 ± 300 N. The Curasan ceramics were too fragile for loading. The maximum failure load decreased for the RMS ceramics to 686.55 ± 170 N by incubation in TRIS pH 7.4 and 651 ± 287 N at pH 5.0.
Introduction: The use of scaffolds in tissue engineering is becoming increasingly important as solutions need to be found to preserve human tissues such as bone or cartilage. Various factors, including cells, biomaterials, cell and tissue culture conditions, play a crucial role in tissue engineering. The in vivo environment of the cells exerts complex stimuli on the cells, thereby directly influencing cell behavior, including proliferation and differentiation. Therefore, to create suitable replacement or regeneration procedures for human tissues, the conditions of the cells’ natural environment should be well mimicked. Therefore, current research is trying to develop 3-dimensional scaffolds (scaffolds) that can elicit appropriate cellular responses and thus help the body regenerate or replace tissues. In this work, scaffolds were printed from the biomaterial polycaprolactone (PCL) on a 3D bioplotter. Biocompatibility testing was used to determine whether the printed scaffolds were suitable for use in tissue engineering.
Material and Methods: An Envisiontec 3D bioplotter was used to fabricate the scaffolds. For better cell-scaffold interaction, the printed polycaprolactone scaffolds were coated with type-I collagen. Three different cell types were then cultured on the scaffolds and various tests were used to investigate the biocompatibility of the scaffolds.
Results: Reproducible scaffolds could be printed from polycaprolactone. In addition, a coating process with collagen was developed, which significantly improved the cell-scaffold interaction. Biocompatibility tests showed that the PCL-collagen scaffolds are suitable for use with cells. The cells adhered to the surface of the scaffolds and as a result extensive cell growth was observed on the scaffolds. The inner part of the scaffolds, however, remained largely uninhabited. In the cytotoxicity studies, it was found that toxicity below 20% was present in some experimental runs. The determination of the compressive strength by means of the universal testing machine Z005 by ZWICK according to DIN EN ISO 604 of the scaffolds resulted in a value of 68.49 ± 0.47 MPa.
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.
Fifth-generation (5G) cellular mobile networks are expected to support mission-critical low latency applications in addition to mobile broadband services, where fourth-generation (4G) cellular networks are unable to support Ultra-Reliable Low Latency Communication (URLLC). However, it might be interesting to understand which latency requirements can be met with both 4G and 5G networks. In this paper, we discuss (1) the components contributing to the latency of cellular networks and (2) evaluate control-plane and user-plane latencies for current-generation narrowband cellular networks and point out the potential improvements to reduce the latency of these networks, (3) present, implement and evaluate latency reduction techniques for latency-critical applications. The two elements we detected, namely the short transmission time interval and the semi-persistent scheduling are very promising as they allow to shorten the delay to processing received information both into the control and data planes. We then analyze the potential of latency reduction techniques for URLLC applications. To this end, we develop these techniques into the long term evolution (LTE) module of ns-3 simulator and then evaluate the performance of the proposed techniques into two different application fields: industrial automation and intelligent transportation systems. Our detailed evaluation results from simulations indicate that LTE can satisfy the low-latency requirements for a large choice of use cases in each field.
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.
In recent years, physically unclonable functions (PUFs) have gained significant attraction in IoT security applications, such as cryptographic key generation and entity authentication. PUFs extract the uncontrollable production characteristics of different devices to generate unique fingerprints for security applications. When generating PUF-based secret keys, the reliability and entropy of the keys are vital factors. This study proposes a novel method for generating PUF-based keys from a set of measurements. Firstly, it formulates the group-based key generation problem as an optimization problem and solves it using integer linear programming (ILP), which guarantees finding the optimum solution. Then, a novel scheme for the extraction of keys from groups is proposed, which we call positioning syndrome coding (PSC). The use of ILP as well as the introduction of PSC facilitates the generation of high-entropy keys with low error correction costs. These new methods have been tested by applying them on the output of a capacitor network PUF. The results confirm the application of ILP and PSC in generating high-quality keys.
This article presents a study of cultural differences affecting the acceptance and design preferences of social robots. Based on a survey with 794 participants from Germany and the three Arab countries of Egypt, Jordan, and Saudi Arabia, we discuss how culture influences the preferences for certain attributes. We look at social roles, abilities and appearance, emotional awareness and interactivity of social robots, as well as the attitude toward automation. Preferences were found to differ not only across cultures, but also within countries with similar cultural backgrounds. Our findings also show a nuanced picture of the impact of previously identified culturally variable factors, such as attitudes toward traditions and innovations. While the participants’ perspectives toward traditions and innovations varied, these factors did not fully account for the cultural variations in their perceptions of social robots. In conclusion, we believe that more real-life practices emerging from the situated use of robots should be investigated. Besides focusing on the impact of broader cultural values such as those associated with religion and traditions, future studies should examine how users interact, or avoid interaction, with robots within specific contexts of use.
Evaluation of Deep Learning-Based Neural Network Methods for Cloud Detection and Segmentation
(2021)
This paper presents a systematic approach for accurate short-time cloud coverage prediction based on a machine learning (ML) approach. Based on a newly built omnidirectional ground-based sky camera system, local training and evaluation data sets were created. These were used to train several state-of-the-art deep neural networks for object detection and segmentation. For this purpose, the camera-generated a full hemispherical image every 30 min over two months in daylight conditions with a fish-eye lens. From this data set, a subset of images was selected for training and evaluation according to various criteria. Deep neural networks, based on the two-stage R-CNN architecture, were trained and compared with a U-net segmentation approach implemented by CloudSegNet. All chosen deep networks were then evaluated and compared according to the local situation.
Wood juice, a liquid produced during wood processing, is a harmful waste that requires utilization. To achieve a circular economy, biowastes should be recycled, reducing fossil carbon usage. Therefore, the objective of this work was to examine the potential of wood juice as a feedstock for bioplastic synthesis by Bacillus sp. G8_19. Polyhydroxyalkanoate (PHA) syntheses using wood juice from Douglas fir trees and that from a mixture of spruce/fir trees were compared. It was found that the PHA content was higher after using wood juice from spruce/fir trees than that from Douglas fir trees (18.0% vs 6.1% of cell dry mass). Gas chromatography analysis showed that, with both wood juices, Bacillus sp. G8_19 accumulated poly(3-hydroxybutyrate-co-3-hydroxyvalerate). The content of 3-hydroxyvalerate (3HV) monomers was higher when spruce/fir wood juice was used (10.7% vs 1.9%). The C/N ratio did not have a statistically significant effect on the copolymer content in biomass, but it did significantly influence the 3HV content. The proposed concept may serve as an approach to wood waste valorization via production of biodegradable materials.
Human–robot collaborative applications have been receiving increasing attention in industrial applications. The efficiency of the applications is often quite low compared to traditional robotic applications without human interaction. Especially for applications that use speed and separation monitoring, there is potential to increase the efficiency with a cost-effective and easy to implement method. In this paper, we proposed to add human–machine differentiation to the speed and separation monitoring in human–robot collaborative applications. The formula for the protective separation distance was extended with a variable for the kind of object that approaches the robot. Different sensors for differentiation of human and non-human objects are presented. Thermal cameras are used to take measurements in a proof of concept. Through differentiation of human and non-human objects, it is possible to decrease the protective separation distance between the robot and the object and therefore increase the overall efficiency of the collaborative application.
A Review on Kinetic Energy Harvesting with Focus on 3D Printed Electromagnetic Vibration Harvesters
(2021)
The increasing amount of Internet of Things (IoT) devices and wearables require a reliable energy source. Energy harvesting can power these devices without changing batteries. Three-dimensional printing allows us to manufacture tailored harvesting devices in an easy and fast way. This paper presents the development of hybrid and non-hybrid 3D printed electromagnetic vibration energy harvesters. Various harvesting approaches, their utilised geometry, functional principle, power output and the applied printing processes are shown. The gathered harvesters are analysed, challenges examined and research gaps in the field identified. The advantages and challenges of 3D printing harvesters are discussed. Reported applications and strategies to improve the performance of printed harvesting devices are presented.
Interpreting seismic data requires the characterization of a number of key elements such as the position of faults and main reflections, presence of structural bodies, and clustering of areas exhibiting a similar amplitude versus angle response. Manual interpretation of geophysical data is often a difficult and time-consuming task, complicated by lack of resolution and presence of noise. In recent years, approaches based on convolutional neural networks have shown remarkable results in automating certain interpretative tasks. However, these state-of-the-art systems usually need to be trained in a supervised manner, and they suffer from a generalization problem. Hence, it is highly challenging to train a model that can yield accurate results on new real data obtained with different acquisition, processing, and geology than the data used for training. In this work, we introduce a novel method that combines generative neural networks with a segmentation task in order to decrease the gap between annotated training data and uninterpreted target data. We validate our approach on two applications: the detection of diffraction events and the picking of faults. We show that when transitioning from synthetic training data to real validation data, our workflow yields superior results compared to its counterpart without the generative network.
The work focuses on predictive capabilities of fundamental cyclic plasticity and fatigue life models, which can be calibrated using limited amount of experiments as specific ones needed for more advanced models are often absent. The analyses are conducted for the synthetic case of exhaust manifold made from cast iron. The thermal boundary conditions from the forced convection were obtained from the computational fluid dynamics considered as a conjugate heat transfer problem. Two rate-independent and temperature-dependent material models were calibrated for structural analyses. Both were validated with experiments on isothermal and anisothermal levels. Sequential thermal–mechanical finite element simulations were performed. Two fatigue life models were employed. The first was a temperature-dependent strain-based fatigue life criterion calibrated from uniaxial data. The second was a temperature-independent energy-based fatigue life criterion resulting in twice lower life than the strain-based criterion, while none of the plasticity models made a significant difference in that prediction.