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Positioning mobile systems with high accuracy is a prerequisite for intelligent autonomous behavior, both in industrial environments and in field robotics. This paper describes the setup of a robotic platform and its use for the evaluation of simultaneous localization and mapping (SLAM) algorithms. A configuration using a mobile robot Husky A200, and a LiDAR (light detection and ranging) sensor was used to implement the setup. For verification of the proposed setup, different scan matching methods for odometry determination in indoor and outdoor environments are tested. An assessment of the accuracy of the baseline 3D-SLAM system and the selected evaluation system is presented by comparing different scenarios and test situations. It was shown that the hdl_graph_slam in combination with the LiDAR OS1 and the scan matching algorithms FAST_GICP and FAST_VGICP achieves good mapping results with accuracies up to 2 cm.
Seismic data has often missing traces due to technical acquisition or economical constraints. A compete dataset is crucial in several processing and inversion techniques. Deep learning algorithms, based on convolutional neural networks (CNNs), have shown alternative solutions that overcome limitation of traditional interpolation methods e.g. data regularity, linearity assumption, etc. There are two different paradigms of CNN methods for seismic interpolation. The first one, so-called deep prior interpolation (DPI), trains a CNN to map random noise to a complete seismic image using only the decimated image itself. The second one, referred as standard deep learning method, trains a CNN to map a decimated seismic image into a complete one using a dataset of complete and artificially decimated images. Within this research, we systematically compare the performance of both methods for different quantities of regular and irregular missing traces using 4 datasets. We evaluate the results of both methods using 5 well-known metrics. We found that DPI method performs better than the standard method if the percentage of missing traces is low (10%) and otherwise if the level of decimation is high (50%).
In this paper, the influence of the material hardening behavior on plasticity-induced fatigue crack closure is investigated for strain-controlled loading and fully plastic, large-scale yielding conditions by means of the finite element method. The strain amplitude and the strain ratio are varied for given Ramberg–Osgood material properties representing materials with different hardening behavior. The results show a pronounced influence of the hardening behavior on crack closure, while no significant effect is found from the considered strain amplitude and strain ratio. The effect of the hardening behavior on the crack opening stress cannot be described by existing crack opening stress equations.
The desire to connect more and more devices and to make them more intelligent and more reliable, is driving the needs for the Internet of Things more than ever. Such IoT edge systems require sound security measures against cyber-attacks, since they are interconnected, spatially distributed, and operational for an extended period of time. One of the most important requirements for the security in many industrial IoT applications is the authentication of the devices. In this paper, we present a mutual authentication protocol based on Physical Unclonable Functions, where challenge-response pairs are used for both device and server authentication. Moreover, a session key can be derived by the protocol in order to secure the communication channel. We show that our protocol is secure against machine learning, replay, man-in-the-middle, cloning, and physical attacks. Moreover, it is shown that the protocol benefits from a smaller computational, communication, storage, and hardware overhead, compared to similar works.
This paper presents a method for supporting the application of Additive Tooling (AT)-based validation environments in integrated product development. Based on a case study, relevant process steps, activities and possible barriers in the realisation of an injection-moulded product are identified and analysed. The aim of the method is to support the target-oriented application of Additive Tooling to obtain physical prototypes at an early stage and to shorten validation cycles.
Linear acceleration is a key performance determinant and major training component of many sports. Although extensive research about lower limb kinetics and kinematics is available, consistent definitions of distinctive key body positions, the underlying mechanisms and their related movement strategies are lacking. The aim of this ‘Method and Theoretical Perspective’ article is to introduce a conceptual framework which classifies the sagittal plane ‘shin roll’ motion during accelerated sprinting. By emphasising the importance of the shin segment’s orientation in space, four distinctive key positions are presented (‘shin block’, ‘touchdown’, ‘heel lock’ and ‘propulsion pose’), which are linked by a progressive ‘shin roll’ motion during swing-stance transition. The shin’s downward tilt is driven by three different movement strategies (‘shin alignment’, ‘horizontal ankle rocker’ and ‘shin drop’). The tilt’s optimal amount and timing will contribute to a mechanically efficient acceleration via timely staggered proximal-to-distal power output. Empirical data obtained from athletes of different performance levels and sporting backgrounds are required to verify the feasibility of this concept. The framework presented here should facilitate future biomechanical analyses and may enable coaches and practitioners to develop specific training programs and feedback strategies to provide athletes with a more efficient acceleration technique.
In recent years, the topic of embedded machine learning has become very popular in AI research. With the help of various compression techniques such as pruning, quantization and others compression techniques, it became possible to run neural networks on embedded devices. These techniques have opened up a whole new application area for machine learning. They range from smart products such as voice assistants to smart sensors that are needed in robotics. Despite the achievements in embedded machine learning, efficient algorithms for training neural networks in constrained domains are still lacking. Training on embedded devices will open up further fields of applications. Efficient training algorithms would enable federated learning on embedded devices, in which the data remains where it was collected, or retraining of neural networks in different domains. In this paper, we summarize techniques that make training on embedded devices possible. We first describe the need and requirements for such algorithms. Then we examine existing techniques that address training in resource-constrained environments as well as techniques that are also suitable for training on embedded devices, such as incremental learning. At the end, we also discuss which problems and open questions still need to be solved in these areas.
Nowadays decarbonisation of the energy system is one of the main concerns for most governments. Renewable energy technologies, such as rooftop photovoltaic systems and home battery storage systems, are changing the energy system to be more decentralised. As a consequence, new ways of energy business models are emerging, e.g., peer-to-peer energy trading. This new concept provides an online marketplace where direct energy exchange can occur between its participants. The purpose of this study is to conduct a content analysis of the existing literature, ongoing research projects, and companies related to peer-to-peer energy trading. From this review, a summary of the most important aspects and journal papers is assessed, discussed, and classified. It was found that the different energy market types were named in various ways and a proposal for standard language for the several peer-to-peer market types and the different actors involved is suggested. Additionally, by grouping the most important attributes from peer-to-peer energy trading projects, an assessment of the entry barrier and scalability potential is performed by using a characterisation matrix.
The increase of the Internet of Things (IoT) calls for secure solutions for industrial applications. The security of IoT can be potentially improved by blockchain. However, blockchain technology suffers scalability issues which hinders integration with IoT. Solutions to blockchain’s scalability issues, such as minimizing the computational complexity of consensus algorithms or blockchain storage requirements, have received attention. However, to realize the full potential of blockchain in IoT, the inefficiencies of its inter-peer communication must also be addressed. For example, blockchain uses a flooding technique to share blocks, resulting in duplicates and inefficient bandwidth usage. Moreover, blockchain peers use a random neighbor selection (RNS) technique to decide on other peers with whom to exchange blockchain data. As a result, the peer-to-peer (P2P) topology formation limits the effective achievable throughput. This paper provides a survey on the state-of-the-art network structures and communication mechanisms used in blockchain and establishes the need for network-based optimization. Additionally, it discusses the blockchain architecture and its layers categorizes existing literature into the layers and provides a survey on the state-of-the-art optimization frameworks, analyzing their effectiveness and ability to scale. Finally, this paper presents recommendations for future work.
Voice user interfaces (VUIs) offer an intuitive, fast and convenient way for humans to interact with machines and computers. Yet, whether they’ll be truly successful and find widespread uptake in the near future depends on the user experience (UX) they offer. With this survey-based study (n = 108), we aim to identify the major annoyances German voice assistant users are facing in voice-driven human-computer interactions. The results of our questionnaire show that irritations appear in six categories: privacy issues, unwanted activation, comprehensibility, response quality, conversational design and voice characteristics. Our findings can help identify key areas of work to optimize voice user experience in order to achieve greater adaptation of the technology. In addition, they can provide valuable information for the further development and standardization of voice user experience (VUX) research.
Featherweight Generic Go (FGG) is a minimal core calculus modeling the essential features of the programming language Go. It includes support for overloaded methods, interface types, structural subtyping and generics. The most straightforward semantic description of the dynamic behavior of FGG programs is to resolve method calls based on runtime type information of the receiver.
This article shows a different approach by defining a type-directed translation from FGG to an untyped lambda-calculus. The translation of an FGG program provides evidence for the availability of methods as additional dictionary parameters, similar to the dictionary-passing approach known from Haskell type classes. Then, method calls can be resolved by a simple lookup of the method definition in the dictionary.
Every program in the image of the translation has the same dynamic semantics as its source FGG program. The proof of this result is based on a syntactic, step-indexed logical relation. The step-index ensures a well-founded definition of the relation in the presence of recursive interface types and recursive methods.
In dem Projekt BioMeth wurde der Ansatz der Membranbegasung zur Erhöhung der Verfügbarkeit von gelöstem Wasserstoff für die biologische Methanisierung im Sinn der Etablierung eines Power-to-Gas-Konzeptes zur Energiespeicherung verfolgt. Übergeordnetes Ziel war die Entwicklung eines skalierbaren Verfahrenskonzeptes, dass sich zur Nutzung CO2-haltiger Gasvolumenström eignet. Geplant war es, das Verfahren am Beispiel der Biogasanlage der Biokäserei Monte-Ziego in Teningen zu demonstrieren und dort das bestehende Konzept der parallelen Abwasseraufbereitung und Energieerzeugung zu erweitern. Die ursprüngliche Struktur des Arbeitspaketplanes ist in nachfolgender Abbildung gezeigt.
Subspace clustering aims to find all clusters in all subspaces of a high-dimensional data space. We present a massively data-parallel approach that can be run on graphics processing units. It extends a previous density-based method that scales well with the number of dimensions. Its main computational bottleneck consists of (sequentially) generating a large number of minimal cluster candidates in each dimension and using hash collisions in order to find matches of such candidates across multiple dimensions. Our approach parallelizes this process by removing previous interdependencies between consecutive steps in the sequential generation process and by applying a very efficient parallel hashing scheme optimized for GPUs. This massive parallelization gives up to 70x speedup for
the bottleneck computation when it is replaced by our approach and run on current GPU hardware. We note that depending on data size and choice of parameters, the parallelized part of the algorithm can take different percentages of the overall runtime of the clustering process, and thus, the overall clustering speedup may vary significantly between different cases. However, even
in our ”worst-case” test, a small dataset where the computation makes up only a small fraction of the overall clustering time, our parallel approach still yields a speedup of more than 3x for the complete run of the clustering process. Our method could also be combined with parallelization of other parts of the clustering algorithm, with an even higher potential gain in processing speed.
In pandemic times, the possibilities for conventional sports activities are severely limited; many sports facilities are closed or can only be used with restrictions. To counteract this lack of health activities and social exchange, people are increasingly adopting new digital sports solutions—a behavior change that had already started with the trend towards fitness apps and activity trackers. Existing research suggests that digital solutions increase the motivation to move and stay active. This work further investigates the potentials of digital sports incorporating the dimensions gender and preference for team sports versus individual sports. The study focuses on potential users, who were mostly younger professionals and academics. The results show that the SARS-CoV-19 pandemic had a significant negative impact on sports activity, particularly on persons preferring team sports. To compensate, most participants use more digital sports than before, and there is a positive correlation between the time spent physically active during the pandemic and the increase in motivation through digital sports. Nevertheless, there is still considerable skepticism regarding the potential of digital sports solutions to increase the motivation to do sports, increase performance, or raise a sense of team spirit when done in groups.
Deep learning models are intrinsically sensitive to distribution shifts in the input data. In particular, small, barely perceivable perturbations to the input data can force models to make wrong predictions with high confidence. An common defense mechanism is regularization through adversarial training which injects worst-case perturbations back into training to strengthen the decision boundaries, and to reduce overfitting. In this context, we perform an investigation of 3 × 3 convolution filters that form in adversarially- trained models. Filters are extracted from 71 public models of the ℓ ∞ -RobustBench CIFAR-10/100 and ImageNet1k leaderboard and compared to filters extracted from models built on the same architectures but trained without robust regularization. We observe that adversarially-robust models appear to form more diverse, less sparse, and more orthogonal convolution filters than their normal counterparts. The largest differences between robust and normal models are found in the deepest layers, and the very first convolution layer, which consistently and predominantly forms filters that can partially eliminate perturbations, irrespective of the architecture.
The significant market growth of stationary electrical energy storage systems both for private and commercial applications has raised the question of battery lifetime under practical operation conditions. Here, we present a study of two 8 kWh lithium-ion battery (LIB) systems, each equipped with 14 lithium iron phosphate/graphite (LFP) single cells in different cell configurations. One system was based on a standard configuration with cells connected in series, including a cell-balancing system and a 48 V inverter. The other system featured a novel configuration of two stacks with a parallel connection of seven cells each, no cell-balancing system, and a 4 V inverter. The two systems were operated as part of a microgrid both in continuous cycling mode between 30% and 100% state of charge, and in solar-storage mode with day–night cycling. The aging characteristics in terms of capacity loss and internal resistance change in the cells were determined by disassembling the systems for regular checkups and characterizing the individual cells under well-defined laboratory conditions. As a main result, the two systems showed cell-averaged capacity losses of 18.6% and 21.4% for the serial and parallel configurations, respectively, after 2.5 years of operation with 810 (serial operation) and 881 (parallel operation) cumulated equivalent full cycles. This is significantly higher than the aging of a reference single cell cycled under laboratory conditions at 20 °C, which showed a capacity loss of only 10% after 1000 continuous full cycles.
Many commonly well-performing convolutional neural network models have shown to be susceptible to input data perturbations, indicating a low model robustness. To reveal model weaknesses, adversarial attacks are specifically optimized to generate small, barely perceivable image perturbations that flip the model prediction. Robustness against attacks can be gained by using adversarial examples during training, which in most cases reduces the measurable model attackability. Unfortunately, this technique can lead to robust overfitting, which results in non-robust models. In this paper, we analyze adversarially trained, robust models in the context of a specific network operation, the downsampling layer, and provide evidence that robust models have learned to downsample more accurately and suffer significantly less from downsampling artifacts, aka. aliasing, than baseline models. In the case of robust overfitting, we observe a strong increase in aliasing and propose a novel early stopping approach based on the measurement of aliasing.
Many commonly well-performing convolutional neural network models have shown to be susceptible to input data perturbations, indicating a low model robustness. Adversarial attacks are thereby specifically optimized to reveal model weaknesses, by generating small, barely perceivable image perturbations that flip the model prediction. Robustness against attacks can be gained for example by using adversarial examples during training, which effectively reduces the measurable model attackability. In contrast, research on analyzing the source of a model’s vulnerability is scarce. In this paper, we analyze adversarially trained, robust models in the context of a specifically suspicious network operation, the downsampling layer, and provide evidence that robust models have learned to downsample more accurately and suffer significantly less from aliasing than baseline models.
In the literature, many studies have described the 3D printing of ceramic-based scaffolds (e.g., printing with calcium phosphate cement) in the form of linear structures with layer rotations of 90°, although no right angles can be found in the human body. Therefore, this work focuses on the adaptation of biological shapes, including a layer rotation of only 1°. Sample shapes were printed with calcium phosphate cement using a 3D Bioplotter from EnvisionTec. Both straight and wavy spokes were printed in a round structure with 12 layers. Depending on the strand diameter (200 and 250 µm needle inner diameter) and strand arrangement, maximum failure loads of 444.86 ± 169.39 N for samples without subsequent setting in PBS up to 1280.88 ± 538.66 N after setting in PBS could be achieved.
Running footwear is continuously being modified and improved; however, running-related overuse injury rates remain high. Nevertheless, novel manufacturing processes enable the production of individualized running shoes that can fit the individual needs of runners, with the potential to reduce injury risk. For this reason, it is essential to investigate functional groups of runners, a collective of runners who respond similarly to a footwear intervention. Therefore, the objective of this study was to develop a framework to identify functional groups based on their individual footwear response regarding injury-specific running-related risk factors for Achilles tendinopathy, Tibial stress fractures, Medial tibial stress syndrome, and Patellofemoral pain syndrome. In this work, we quantified the footwear response patterns of 73 female and male participants when running in three different footwear conditions using unsupervised learning (k-means clustering). For each functional group, we identified the footwear conditions minimizing the injury-specific risk factors. We described differences in the functional groups regarding their running style, anthropometric, footwear perception, and demographics. The results implied that most functional groups showed a tendency for a single footwear condition to reduce most biomechanical risk factors for a specific overuse injury. Functional groups often differed in their hip and pelvis kinematics as well as their subjective rating of the footwear conditions. The footwear intervention only partially affected biomechanical risk factors attributed to more proximal joints. Due to its adaptive nature, the framework could be applied to other footwear interventions or performance-related biomechanical variables.
This paper has the objective of creating a framework for a different cultural dimension of corporate entrepreneurship leading to corporate entrepreneurial culture (CEC). The analysis of CEC is based on a review of existing concepts of organisational culture and entrepreneurship. They are combined to create a framework of CEC, including macro- and microlevels and examples of subcultures. Core ideas of the framework are validated by qualitative interviews with ten experts. The identified organisational category of the CEC framework is defined by the levels of micro-cultures or subcultures and includes the upper levels of the hierarchy, including the industry level. Geographic categories such as regional or national culture are also part of the system. The individual category of the CEC framework is characterised by competencies (including aspects such as motivation, creativity, mobilising others, coping with uncertainty, teamwork and social competencies) and entrepreneurial personalities. The results of the interviews show the importance of these individual competencies for a lively CEC. The different levels, such as national and professional cultures, as a dimension of the organisational category of the framework are also confirmed by the interviews. The findings indicate that the individual category of CEC could be used for job satisfaction or engagement and the degree of CEC of an organisation could be defined and developed by the organisational category. The identified framework contributes to an understanding of this complex topic and supports companies in the implementation of entrepreneurial ideas in different organisational contexts.
An Overview of Technologies for Improving Storage Efficiency in Blockchain-Based IIoT Applications
(2022)
Since the inception of blockchain-based cryptocurrencies, researchers have been fascinated with the idea of integrating blockchain technology into other fields, such as health and manufacturing. Despite the benefits of blockchain, which include immutability, transparency, and traceability, certain issues that limit its integration with IIoT still linger. One of these prominent problems is the storage inefficiency of the blockchain. Due to the append-only nature of the blockchain, the growth of the blockchain ledger inevitably leads to high storage requirements for blockchain peers. This poses a challenge for its integration with the IIoT, where high volumes of data are generated at a relatively faster rate than in applications such as financial systems. Therefore, there is a need for blockchain architectures that deal effectively with the rapid growth of the blockchain ledger. This paper discusses the problem of storage inefficiency in existing blockchain systems, how this affects their scalability, and the challenges that this poses to their integration with IIoT. This paper explores existing solutions for improving the storage efficiency of blockchain–IIoT systems, classifying these proposed solutions according to their approaches and providing insight into their effectiveness through a detailed comparative analysis and examination of their long-term sustainability. Potential directions for future research on the enhancement of storage efficiency in blockchain–IIoT systems are also discussed.
Solar energy plays a central role in the energy transition. Clouds generate locally large fluctuations in the generation output of photovoltaic systems, which is a major problem for energy systems such as microgrids, among others. For an optimal design of a power system, this work analyzed the variability using a spatially distributed sensor network at Stuttgart Airport. It has been shown that the spatial distribution partially reduces the variability of solar radiation. A tool was also developed to estimate the output power of photovoltaic systems using irradiation time series and assumptions about the photovoltaic sites. For days with high fluctuations of the estimated photovoltaic power, different energy system scenarios were investigated. It was found the approach can be used to have a more realistic representation of aggregated PV power taking spatial smoothing into account and that the resulting PV power generation profiles provide a good basis for energy system design considerations like battery sizing.
In each company Top Managers have the responsibility to take major decisions that supports the success of their company, Adopting TQM is one of these decisions, the decision to carry out companies’ operations and procedures within TQM frameworks. (ASQ , n.d.). Applying TQM, involves implementing practices that needs putting extra efforts, otherwise there will be no use of the practices and the execution. (Nicca Jirah F Campos1, 2022).
Specifically in service sector, where the key to success and increased profit, comes directly through a satisfied customer. Therefor there is a need for both management and staff to have big tolerance and willingness to achieve the needed satisfaction, in order to attain the results that every company wants. (Charantimath, 2013)
In Germany in terms of customer care practices there is a famous stereotype ‘Customer is not the king’ A reputation That after DW investigated it, DW expressed it as a phenomenon where both expats and Germans tend to believe that service companies in Germany should do a better job of treating their consumers. (DW, 2016)
New concepts of business have emerged in the late century, for example strategy, leadership, marketing, entrepreneurship and others, these concepts spread internationally among most of the companies around the world. Many studies have been done reviewing these new business structures, some of them addressed the cultural differences within countries upon the applying them. But not many studies concentrated on taking into consideration how cultural differences affects the Implementation of TQM. (Lagrosen, 2002). It was concluded in general that although the comprehensive fundamentals of quality management are applicable and similar worldwide in all nations, but when coming to real practice accurate tunning must be made, it must be taken into account aligning different standards, due to different work cultures and traditions in Europe. (Krueger, 1999)
We consider the local group of agents for exchanging the time-series data value and computing the approximation of the mean value of all agents. An agent represented by a node knows all local neighbor nodes in the same group. The node has the contact information of other nodes in other groups. The nodes interact with each other in synchronous rounds to exchange the updated time-series data value using the random call communication model. The amount of data exchanged between agent-based sensors in the local group network affects the accuracy of the aggregation function results. At each time step, the agent-based sensor can update the input data value and send the updated data value to the group head node. The group head node sends the updated data value to all group members in the same group. Grouping nodes in peer-to-peer networks show an improvement in Mean Squared Error (MSE).
are you okay?
(2022)
Die Frage “Wie geht es dir?” ist in unserer Gesellschaft eine Höflichkeitsfloskel geworden. Die meisten Menschen antworten mit einem “Gut, und selbst?” und dann wird über etwas anderes gesprochen. Der Großteil redet nicht allzu oft darüber, was sie wirklich beschäftigt. Aber warum eigentlich nicht? Ist es Scham, Angst vor Ablehnung oder einfach nur „gutes“ Benehmen?
Besonders negative Gefühle, wie Wut und Trauer haben in unserem Leben wenig Platz. Sie werden als nicht gut betrachtet und alles was nicht “gut” ist, sollte laut der Gesellschaft lieber versteckt werden. Dieses Buch steht für mehr Gefühlsbewusstsein und soll einen offeneren Austausch mit sich selbst und anderen anstoßen. Denn auch negative Gefühle sollen einen Platz bekommen und können sanftmütig und bewusst gelebt werden.
Dieses Buch ist eine bunte Mischung aus ausgedachten aber realitätsnahen Geschichten vom Scheitern, Texten von Musikern und Bildern zum Träumen und Interpretieren. Es scheint zuerst ungewöhnlich, ausschließlich deutsche Rapper statt alte Philosophen zu zitieren. Doch die Hip-Hop-Kultur hat einiges zu sagen und bringt alltägliche Probleme und Ängste unverblümt auf den Punkt. Manchmal müssen wir nur etwas genauer hinhören.
Jeder redet heutzutage von Digitalisierung. Und dass Unternehmen unter Digitalisierungsdruck stehen, ist ein geflügeltes Wort. Aber was heißt das konkret? Welche Aufgaben stellen sich Führungskräften und welche Lösungsansätze gibt es dafür? Um diese Fragen zu beantworten, klärt das Buch zunächst die Begriffe „Digitalisierung“ und „Management“. Auf dieser begrifflichen Grundlage werden dann wichtige Aspekte des Managements der Digitalisierung analysiert. Experten aus Wissenschaft und Wirtschaft zeigen, wie sich der Kundennutzen optimieren lässt, wie maschinelles Lernen das Entscheiden unterstützt und wie man virtuelle Realität im Unternehmen praktisch einsetzen kann. Vorgestellt werden außerdem neue Entwicklungen in Regulatorik und Berichtswesen sowie Möglichkeiten der IT-gestützten Wirtschaftsförderung. Führungskräfte erhalten so eine Fülle wertvoller Anregungen, um ihre Unternehmen längerfristig noch erfolgreicher zu machen.
Young female handball players represent a high-risk population for anterior cruciate ligament (ACL) injuries. While the external knee abduction moment (KAM) is known to be a risk factor, it is unclear how cutting technique affects KAMs in sport-specific cutting maneuvers. Further, the effect of added game specificity (e.g., catching a ball or faking defenders) on KAMs and cutting technique remains unknown. Therefore, this study aimed: (i) to test if athletes grouped into different clusters of peak KAMs produced during three sport-specific fake-and-cut tasks of different complexities differ in cutting technique, and (ii) to test whether technique variables change with task complexity. Fifty-one female handball players (67.0 ± 7.7 kg, 1.70 ± 0.06 m, 19.2 ± 3.4 years) were recruited. Athletes performed at least five successful handball-specific sidestep cuts of three different complexities ranging from simple pre-planned fake-and-cut maneuvers to catching a ball and performing an unanticipated fake-and-cut maneuver with dynamic defenders. A k-means cluster algorithm with squared Euclidean distance metric was applied to the KAMs of all three tasks. The optimal cluster number of koptimal = 2 was calculated using the average silhouette width. Statistical differences in technique variables between the two clusters and the tasks were analyzed using repeated-measures ANOVAs (task complexity) with nested groupings (clusters). KAMs differed by 64.5%, on average, between clusters. When pooling all tasks, athletes with high KAMs showed 3.4° more knee valgus, 16.9% higher downward and 8.4% higher resultant velocity at initial ground contact, and 20.5% higher vertical ground reaction forces at peak KAM. Unlike most other variables, knee valgus angle was not affected by task complexity, likely due to it being part of inherent movement strategies and partly determined by anatomy. Since the high KAM cluster showed higher vertical center of mass excursions and knee valgus angles in all tasks, it is likely that this is part of an automated motor program developed over the players' careers. Based on these results, reducing knee valgus and downward velocity bears the potential to mitigate knee joint loading and therefore ACL injury risk.
As a university it is more and more difficult to reach all target groups equally. Common problems like information overload, numerous institutions with same focuses or multi-channel-communication make it hard to gain the attention of the target group. This paper is four-fold: we present an overview of the state of art and the importance of the study (I), based on which we highlight the approach to user experience analysis. First, we identified the irritations in the course of an expert evaluation (II) and verified them within the test, including the target groups (III). Finally, based on the results, we were able to pro-vide recommendations for action to improve the UX and to be used for the conception of an intranet (IV).
Die vorliegende Arbeit, welche im Rahmen des Forschungsprojektes LaDiVo an der Hochschule Offenburg angefertigt wurde, beschäftigt sich mit Aushärten keramischer Farben auf Einscheiben- und Verbundsicherheitsglas mittels eines CO2-Lasers. Zur Erreichung dieses Ziels soll experimentell ein Verfahren erarbeitet werden.
Nach einer ausführlichen Analyse des Stands der Technik und einer Klärung des zur Verfügung stehenden Versuchsaufbaus werden verschiedene Versuche in einer Planungsphase konzeptioniert. Dazu gehören, neben den reinen Aushärteversuchen mittels Laser, ebenso eine mikroskopische Untersuchung der Ergebnisse mittels Licht- und Rasterelektronenmikroskop als auch der Aufbau einer Simulation zur qualitativen Validierung der Versuchsergebnisse.
Durch die Versuche zeigt sich, dass es möglich ist, keramische Farbe auf Einscheibensicherheitsglas mittels Laser auszuhärten, ohne dass es zu einer Schädigung des Glases kommt. Die mikroskopische Untersuchung unterstützt dieses Ergebnis. Beim Verbundsicherheitsglas konnte kein Verfahren zur schädigungsfreien Aushärtung der keramischen Farbe erarbeitet werden.
Auswirkung eines Importstopps russischer Energieträger auf die Klimaschutzziele in Deutschland
(2022)
Ein Importstopp russischer Energieträger nach Deutschland wird derzeit vermehrt diskutiert. Wir wollen die Diskussion unterstützen, indem wir einen Weg zeigen, wie das Elektrizitätssystem in Deutschland kurzfristig mit geringen Energieimporten auskommt und welche Maßnahmen notwendig sind, um die Klimaschutzziele trotzdem einzuhalten. Die Ergebnisse eines solchen Energiewendeszenarios mit reduzierter Importabhängigkeit werden mit dem Energiesystemmodell MyPyPSA-Ger berechnet. Die wichtigsten Erkenntnisse sind, dass ein zügiger Ausbau Erneuerbarer Energien und von Speichertechnologien • die Abhängigkeit des deutschen Elektrizitätssystems von Energieimporten deutlich reduziert. • auch langfristig keine wesentlichen Importe der Energieträger Erdgas, Steinkohle und Mineralöl nach sich zieht. • über die Klimaziele der Bundesregierung hinaus das 1,5-Grad-Ziel im Elektrizitätssystem erreicht wird.
Um ein neues System zur Korrektur des Tool Center Points des Roboterwerkzeugs zu finden, wurde diese Bachelorarbeit von der Firma Badische Staal Enginering angeboten. Das Ziel ist es, die Position und den Winkel des TCP1 des an den Roboter angebrachten Tools zu korrigieren, basierend auf dem Messergebnis des TCP. Für diese Arbeit wurde eine Roboterstation bereitgestellt, die auch mit einer Triangulationskamera ausgestattet war.
Nach einer Analyse und Entwicklung des Systems wurde ein Programm erstellt, das Bewegungen, Messungen und Berechnungen kombiniert. Sobald dieses Korrektursystem entwickelt ist, wird eine Testbasis an die Projektbedingungen angepasst, um seine Zuverlässigkeit und Wiederholbarkeit unter realen Bedingungen zu testen. Diese Arbeit wird in der Testumgebung der Halle der BSW2 Anlagenbau und Ausbildung GmbH durchgeführt.
Bach, Gas, Strom und Wasser
(2022)
In this paper, the Bauschinger effect and latent hardening of single crystals are assessed in finite element calculations using a single crystal plasticity model with kinematic hardening. To this end, results of cyclic micro-bending experiments on single crystal Alloy 718 in different crystal orientations (single slip and multi slip) with respect to the loading direction are used to determine the slip system related material properties of the single crystal plasticity model. Two kinematic hardening laws are considered: a kinematic hardening law describing latent hardening and a kinematic hardening law without latent hardening. For the determination of material properties for both hardening laws, a gradient-based optimization method is used. The results show that the different strength levels observed for micro-bending tests on different crystal orientations can only be described with latent kinematic hardening well, whereas the pronounced Bauschinger effect is described well by both kinematic hardening laws. It is concluded that cyclic micro-bending experiments on single crystals using different crystal orientations give an appropriate data base for the determination of the slip system related material properties of the single crystal plasticity model with latent kinematic hardening.
In this paper, we study the runtime performance of symmetric cryptographic algorithms on an embedded ARM Cortex-M4 platform. Symmetric cryptographic algorithms can serve to protect the integrity and optionally, if supported by the algorithm, the confidentiality of data. A broad range of well-established algorithms exists, where the different algorithms typically have different properties and come with different computational complexity. On deeply embedded systems, the overhead imposed by cryptographic operations may be significant. We execute the algorithms AES-GCM, ChaCha20-Poly1305, HMAC-SHA256, KMAC, and SipHash on an STM32 embedded microcontroller and benchmark the execution times of the algorithms as a function of the input lengths.
Beuys-Gespräch
(2022)
Bildnisverwertungsklauseln
(2022)
Biodegradable metals have entered the implant market in recent years, but still do not show fully satisfactory degradation behaviour and mechanical properties. In contrast, it has been shown that pure molybdenum has an excellent combination of the required properties in this respect. We report on PM based screen printing of thin-walled molybdenum tubes as a processing step for medical stent manufacture. We also present data on the in vivo degradation and biocompatibility of molybdenum. The degradation of molybdenum wires implanted in the aorta of rats was evaluated by SEM and EDX. Biocompatibility was assessed by histological investigation of organs and analysis of molybdenum levels in tissue extracts and body fluids. Degradation rates of up to 13.5 μm/y were observed after 12 months. No histological changes or elevated molybdenum levels in organ tissues were observed. In summary, the results further underline that molybdenum is a highly promising biodegradable metallic material.
As society continues to age, the implementation of hip stems increases every year. However, there are a variety of different hip stem designs.
The aim of this project is to analyze which hip stem design implemented in a femur is most effective under different static loading conditions such as gait and sideways falling. In addition, a four-point-bending test was carried out. Therefore, the tech-niques were simulated in silico by FEA with Abaqus/CAE 2019.
A short stem, a straight stem and an anatomical stem were tested. All prosthesis are cementless press-fit stems. Each hip stem was examined in physiological and osteoporotic bone applying all three tests. To compare the stems the tests were also applied on a native bone model as a reference. Boundary conditions were used in order to simulate the tests.
The average von Mises stress, tension, compression and the risk of fracture were extracted and compared.
Biomechanical results show that the straight stem induces higher von Mises stresses compared to the anatomical stem. The risk of fracture is higher for osteo-porotic bone than for physiological bone. However, there is no risk of fracture as all the results are below the risk of value.
Biomechanical Risk Factors of Injury-Related Single-Leg Movements in Male Elite Youth Soccer Players
(2022)
Altered movement patterns during single-leg movements in soccer increase the risk of lower-extremity non-contact injuries. The identification of biomechanical parameters associated with lower-extremity injuries can enrich knowledge of injury risks and facilitate injury prevention. Fifty-six elite youth soccer players performed a single-leg drop landing task and an unanticipated side-step cutting task. Three-dimensional ankle, knee and hip kinematic and kinetic data were obtained, and non-contact lower-extremity injuries were documented throughout the season. Risk profiling was assessed using a multivariate approach utilising a decision tree model (classification and regression tree method). The decision tree model indicated peak knee frontal plane angle, peak vertical ground reaction force, ankle frontal plane moment and knee transverse plane angle at initial contact (in this hierarchical order) for the single-leg landing task as important biomechanical parameters to discriminate between injured and non-injured players. Hip sagittal plane angle at initial contact, peak ankle transverse plane angle and hip sagittal plane moment (in this hierarchical order) were indicated as risk factors for the unanticipated cutting task. Ankle, knee and hip kinematics, as well as ankle and hip kinetics, during single-leg high-risk movements can provide a good indication of injury risk in elite youth soccer players.
Einleitung: Nach einer Verletzung oder Operation ist die Rehabilitation und das eigenständige Training entscheidend für die Wiederherstellung der Funktion und Unabhängigkeit. Für eine höhere Trainingsadhärenz wurde der auf Inertialsensoren basierende digitale Trainingsassistent re.flex (Kineto Tech Rehab SRL, Bukarest, Rumänien) entwickelt, der Gelenkwinkel messen und visuell darstellen kann. Eine Smartphone App dient als Therapieanleitung. Durch die Kombination des re.flexSystems mit einer Knieorthese, wurde die Genudyn® OA smart (Sporlastic GmbH, Nürtingen, Deutschland) entwickelt, ein digitales Behandlungskonzept bei Gonarthrose. Damit haben Patient:innen die Möglichkeit eigenständig ein 12-wöchiges individuelles Trainingsprogramm mit integriertem Echtzeit-Biofeedback zu absolvieren. In der vorliegenden Arbeit soll die Messgenauigkeit des re.flexSystems evaluiert und mögliche Einflussgrößen erfasst werden.
Methoden: Die Messergebnisse des re.flex-Systems wurden mit einem 3D MotionCapture-System von Vicon (Vicon, Oxford, England) als Referenz verglichen. In vier Durchgängen wurden jeweils fünf therapeutische Übungen durchgeführt, bei denen Knie- und Hüftwinkel gemessen wurden. Für die Erfassung möglicher Einflussgrößen wurden Messungen nur mit den Sensoren am Bein, mit Knieorthese, mit 1cm und 3cm senkrechter Verschiebung eines Sensors durchgeführt und kleinere mit größeren Oberschenkelumfängen verglichen. Für die statistische Analyse wurde der RMSE und ICC berechnet und Bland-Altman-Plots erstellt.
Ergebnisse: Der ICC aus der ROM lieferte Werte von 0,26 - 0,82, aus den Maxima 0,01 – 0,82. Der RMSE reichte von 6,53° - 15,39°. Kniewinkel und Hüftflexion lieferten die besten Ergebnisse, Hüftabduktion mittelmäßige und Hüftextension die schlechtesten. Die Bland-Altman-Plots zeigten wenige Ausreißer. Die Knieorthese und das Weichteilgewebe zeigten einen negativen Einfluss auf die Messgenauigkeit. Die Verschiebung des Sensors hatte keinen Einfluss.
Schlussfolgerung: Da besonders die Kniegelenkwinkelmessungen, auf denen der Fokus für das Behandlungskonzept bei Kniearthrose liegt, hohe Genauigkeiten erzielten, konnte der digitale Trainingsassistent re.flex in der vorliegenden Arbeit als Therapiehilfsmittel überzeugen.
The integration of Internet of Things devices onto the Blockchain implies an increase in the transactions that occur on the Blockchain, thus increasing the storage requirements.
A solution approach is to leverage cloud resources for storing blocks within the chain. The paper, therefore, proposes two solutions to this problem. The first being an improved hybrid architecture design which uses containerization to create a side chain on a fog node for the devices connected to it and an Advanced Time‑variant Multi‑objective Particle Swarm Optimization Algorithm (AT‑MOPSO) for determining the optimal number of blocks that should be transferred to the cloud for storage. This algorithm uses time‑variant weights for the velocity of the particle swarm optimization and the non‑dominated sorting and mutation schemes from NSGA‑III. The proposed algorithm was compared with results from the original MOPSO algorithm, the Strength Pareto Evolutionary Algorithm (SPEA‑II), and the Pareto Envelope‑based Selection Algorithm with region‑based selection (PESA‑II), and NSGA‑III. The proposed AT‑MOPSO showed better results than the aforementioned MOPSO algorithms in cloud storage cost and query probability optimization. Importantly, AT‑MOPSO achieved 52% energy efficiency compared to NSGA‑III.
To show how this algorithm can be applied to a real‑world Blockchain system, the BISS industrial Blockchain architecture was adapted and modified to show how the AT‑MOPSO can be used with existing Blockchain systems and the benefits it provides.
Die thermischen Wirkungsgrade von Kraftwerken zur Stromerzeugung sind relativ gering. Beispielsweise erreichen moderne Kohlekraftwerke heute bis etwa 45 %, Gasturbinen maximal 40 % sowie Diesel-und Gas-Motoren bis ca. 50 %. Kombinations-Kraftwerke, d. h. Gas- und Dampfturbinen-Prozesse, können über 60 % thermischen Wirkungsgrad bei der Umwandlung der zugeführten Wärme in mechanische bzw. elektrische Energie erzielen. Ein ähnlich hoher Wert wird in Zukunft von den Brennstoffzellen erwartet. Der nicht in Arbeit umgewandelte Anteil der zugeführten Wärme fällt als Abwärme an und geht ungenutzt in die Umgebung. Ein Teil dieser Abwärme lässt sich durch entsprechende Installationen bei allen Kraftwerksprozessen zur Wassererwärmung oder zur Dampferzeugung für industrielle Zwecke nutzen. Für Heiz- und Prozesswärme genügt eine Temperatur der Abwärme von 60 bis 80 °C, während die Erzeugung von Industriedampf deutlich höhere Temperaturen voraussetzt.
Die neuen Realitäten digitalwirtschaftlicher Geschäftsmodelle stellen die Verfügbarkeit und Verwendung großer Datenmengen in den Mittelpunkt unternehmerischer Aktivitäten. Das Risikomanagement, das bereits intensiv stochastische Methoden anwendet, sollte an dieser Entwicklung teilhaben. Im vorliegenden Beitrag geht es um die angemessene Rahmung und Einordnung von Analytics-Projekten.