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Klausur-Coach Statistik
(2016)
Der "Klausur-Coach Statistik" ist das richtige Buch für alle Bachelor-Studenten, die sich auf eine Klausur im Fach Statistik vorbereiten möchten und besonders gut durch das Lösen vieler anwendungsorientierter Aufgaben lernen. Die Fragestellungen orientieren sich primär – aber nicht ausschließlich – an Themen aus dem Bereich der BWL und VWL sowie verwandter Fächer. Im ersten Teil finden sich thematisch geordnete Übungsaufgaben, welche am besten parallel zum Studium gelöst werden sollten. Der zweite Teil enthält Klausuren, wie sie in Prüfungen im Bachelor-Studium typischerweise vorkommen; hier gibt es immer eine Mischung aus Aufgaben zu allen Themengebieten der Statistik im Grundstudium. Ausführliche Lösungen zu allen Aufgaben im dritten Teil des Buches runden das Coaching ab … und Panik vor der Statistikklausur sollte damit der Vergangenheit angehören!
Significant improvements in module performance are possible via implementation of multi-wire electrodes. This is economically sound as long as the mechanical yield of the production is maintained. While flat ribbons have a relatively large contact area to exert forces onto the solar cell, wires with round cross section reduce this contact area considerably – in theory to an infinitively thin line. Therefore, the local stresses induced by the electrodes might increase to a point that mechanical production yields suffer unacceptably.
In this paper, we assess this issue by an analytical mechanical model as well as experiments with an encapsulant-free N.I.C.E. test setup. From these, we can derive estimations for the relationship between lay-up accuracy and expected breakage losses. This paves the way for cost-optimized choices of handling equipment in industrial N.I.C.E.-wire production lines.
In automotive parking scenario, where the curb shall be detected and classified to be traversable or not, radars play an important role. There are different approaches already proposed in other works to estimate the target height. This paper assesses and compares two methods. The first is based on Angle of Arrival (AoA) estimation of input signals of multiple antennas using the Multiple-Input-Multiple-Output (MIMO) principle. The second method uses the geometry in multipath propagation of the radar echo signal for one antenna input. In this work a modified method of calculation of the curb height based on the second method is proposed. The theory of approach is mathematically proved and effectiveness is demonstrated by evaluation of measurements with a 77 GHz Frequency Modulated Continuous Wave (FMCW) radar. In order to evaluate the performance of the introduced method the mean square error (MSE) is used in the proposed scenario. This method, using only one antenna input, produced up to 3.4 times better results for curb height detection in comparison with former methods.
Due to the increasing aging of the population, the number of elderly people requiring care is growing in most European countries. However, the number of caregivers working in nursing homes and on daily care services is declining in countries like Germany or Italy. This limits the time for interpersonal communication. Furthermore, as a result of the Covid-19 pandemic, social distancing during contact restrictions became more important, causing an additional reduction of personal interaction. This social isolation can strongly increase emotional stress. Robotic assistance could contribute to addressing this challenge on three levels: (1) supporting caregivers to respond individually to the needs of patients and residents in nursing homes; (2) observing patients’ health and emotional state; (3) complying with high hygiene standards and minimizing human contact if required. To further the research on emotional aspects and the acceptance of robotic assistance in care, we conducted two studies where elderly participants interacted with the social robot Misa. Facial expression and voice analysis were used to identify and measure the emotional state of the participants during the interaction. While interpersonal contact plays a major role in elderly care, the findings reveal that robotic assistance generates added value for both caregivers and patients and that they show emotions while interacting with them.
Memento mori!
(2022)
Das plötzliche Ende des romantischen Komponisten Felix Mendelssohn Bartholdy (1809–1847) gibt uns auch heute noch Rätsel auf. Einiges deutet auf ein rupturiertes zerebrales Aneurysma mit konsekutiver Subarachnoidalblutung hin. Das Quellenmaterial zu den Symptomen seiner Todeskrankheit wird in dieser Arbeit ausführlich vorgestellt und diskutiert. Eine mögliche familiäre Disposition im Sinne eines Ehlers-Danlos-Syndroms Typ IV wird erörtert.
Dieser Beitrag beschreibt, wie mit Campbells Schema der „Heldenreise“ personalisierte Narrative der obersten Führungsebene aufgebaut werden können, um für interne und externe Stakeholder eine Orientierung zu bieten und die Unternehmenskultur bewusst zu prägen und zu beeinflussen. Das Beispiel der Preisträgerportraits des Manager Magazins zeigt, dass diese Methode breite Anwendung findet und dabei auch unterschiedliche funktionale Zuschreibungen der Führungsrolle erfolgen können.
Journalist, Teil A
(2022)
Nach einem langen Vorlauf haben Bundestag und Bundesrat zum Ende der Legislatur 2021 einen Rechtsanspruch auf Ganztagsbildung in Grundschulen verabschiedet. Um aus diesem formalen Anspruch gute Angebote in der Rechtswirklichkeit schaffen zu können, bedarf es neben politischer und finanzieller Rahmenbedingungen auch eines gezielten Dialogs mit den relevanten Anspruchs- und Interessengruppen, weshalb dem Stakeholdermanagement vor allem der Akteure von Schulträgern und Schulen eine besondere Bedeutung zukommt.
Die fluktuierende Verfügbarkeit regenerativer Energiequellen stellt eine Herausforderung bei der Planung und Auslegung regenerativer Gebäudeenergiesysteme dar. Die in einem System benötigten Speicherkapazitäten hängen dabei sowohl von der eingesetzten Regelungsstrategie als auch von den temperaturabhängigen Wirkungsgraden der Anlagenkomponenten ab. Genauere Einblicke in das Betriebsverhalten eines Gesamtsystems können dynamische Simulationen liefern, die eine Analyse der Systemtemperaturen und von Teilenergiekennwerten ermöglichen.
Heat generation that is coupled with electricity usage, like combined heat and power generators or heat pumps, can provide operational flexibility to the electricity sector. In order to make use of this in an optimized way, the flexibility that can be provided by such plants needs to be properly quantified. This paper proposes a method for quantifying the flexibility provided through a cluster of such heat generators. It takes into account minimum operational time and minimum down-time of heat generating units. Flexibility is defined here as the time period over which plant operation can be either delayed or forced into operation, thus providing upward or downward regulation to the power system on demand. Results for one case study show that a cluster of several smaller heat generation units does not provide much more delayed operation flexibility than one large unit with the same power, while it more than doubles the forced operation flexibility. Considering minimum operational time and minimum down-time of the units considerably limits the available forced and delayed operation flexibility, especially in the case of one large unit.
Das TMKB-Modell beschreibt einen Weg, den persönlichen und unternehmerischen Erfolg im beruflichen Alltag effizient und nachhaltig zu erreichen. Hierbei steht TMKB für den Transfer von Methoden und Kompetenzen in den Beruf. Dieses Modell beachtet die Theorien der Kompetenzentwicklung im Kontext realer Problemstellungen in Unternehmen. Beispielhaft wird das TMKB-Modell in der ersten Stufe mit dem Schwerpunkt Lean- und Projektmanagement erläutert. Die Zielgruppe dieses Ansatzes lässt sich über die der Auszubildenden bis hin zu Studierenden und Berufserfahrenen erweitern.
This mature textbook brings the fundamentals of fluid mechanics in a concise and mathematically understandable presentation. In the current edition, a section on dissipation and viscous potential flows has been added. Exercises with solutions help to apply the material correctly and promote understanding.
Intelligentes Data Governance und Data Management – Neue Chancen für die Kundendatenbewirtschaftung
(2022)
Mit der Digitalisierung haben sich für Marketing und Vertrieb vielzählige unterschiedliche neue Kanäle, Werbeformate und Zielgruppen eröffnet. Expertinnen und Experten schätzen, dass ca. 4.000 bis 10.000 Werbe- und Markenbotschaften täglich auf jede und jeden von uns einprasseln. Mögen diese Zahlen umstritten und eine Zahl zwischen 300 bis 500 Botschaften pro Tag realistischer sein, so ist dies noch immer eine Menge, die kaum noch verarbeitet und wahrgenommen werden kann. Als Reaktion auf diese Werbemasse und Reizüberflutung haben Konsumentinnen und Konsumenten zum Teil eine Art „Werbeblindheit“ entwickelt.
Für Unternehmen wird daher zunehmend herausfordernder, ihre Zielgruppe aktiv zu erreichen. Was können Unternehmen nun tun, um dennoch nachhaltig Aufmerksamkeit zu gewinnen, und wie kann Künstliche-Intelligenz-gestütztes Kundendatenmanagement dabei helfen?
Die Covid-19-Pandemie hat die Welt verändert. Alle Wirtschaftszweige wie etwa der Handel sahen sich von heute auf morgen mit einer veränderten Realität konfrontiert. Diese Entwicklung hat einerseits den schon vor der Pandemie wahrnehmbaren Digitalisierungsdruck, vor allem auf den stationären Handel, massiv erhöht. Und andererseits die Daten von Kundinnen und Kunden in das Zentrum der Aufmerksamkeit gerückt, da in der digitalen Welt der persönliche Kontakt zur Kundschaft fehlt. Dieser Beitrag beleuchtet die Bedeutung von Kundendaten, Datenqualität und Datenmanagement als wesentliche Erfolgsfaktoren für den Handel in dieser herausfordernden Situation. Er zeigt auf, wie Datenverantwortliche im Handel aus dem Wissen um die Daten mittels Identity Resolution klare Profile von Kundinnen und Kunden entwickeln und diese plattformbasiert ausrollen können. Hierzu wird das neue Konzept des Customer Digital Twins eingeführt. Die abschließenden Handlungsempfehlungen bieten eine ‚Arbeitsanweisung in fünf Schritten‘ für eine aktuelle, vollständige und verlässliche Datenbasis, als Grundlage für den datenorientierten stationären und Onlinehandel.
Wie in den grundsätzlichen Überlegungen zur Transformationsstrategie von Medienunternehmen in Abschn. 1.1 bereits deutlich wurde, ist es für Medienunternehmen und ihre Leitungen nicht einfach, einerseits neue Impulse nicht durch das überdimensionale Regelwerk einer auf Effizienz getrimmten Standardorganisation zu ersticken und andererseits aber auch die Fähigkeit zu entwickeln, die neuen Impulse nicht nur in eigenen Organisationseinheiten zu konservieren, sondern sie als Nukleus für Veränderungen und für eine Weiterentwicklung des Hauptunternehmens zuzulassen. Diese generelle Problematik zeigt sich auch in der Medienbranche beim Umgang mit Transformationsstrategien sehr deutlich, denn es ist letztlich immer wieder ein Austarieren und Nachsteuern, da es den einfachen, idealen Weg nicht gibt und wohl auch nicht geben kann.
In diesem Einführungskapitel geht es um die Frage, welche Transformationsstrategien in der Medienbranche in den vergangenen zwanzig Jahren anzutreffen waren und welche Risiken und Chancen sie in ihrer Anwendung für die einzelnen Unternehmen und Akteure beinhalten. Dabei wird sehr schnell deutlich, dass es „die Medienbranche“ schlechthin nicht gibt, auch nicht geben kann, sondern dass es sich um eine sehr komplexe, mitunter auch widersprüchliche Branche handelt. Wir werden in diesem Kapitel aufzeigen, dass es gerade die angrenzenden Bereiche sind, aus denen meist innovative Ansätze kommen, die einen disruptiven Effekt auf die Kernbranche mit ihrem traditionellen Kerngeschäft haben. Insofern wird in diesem Kapitel zwar eine strukturierte analytische Betrachtung angestrebt, dennoch ist es ein Themenfeld, in dem die Dynamik der Akteure und Märkte dafür sorgt, dass die Branche, die Teilsegmente, die Unternehmen, Produkte und Märkte ständig in Bewegung sind und in Bewegung bleiben.
Dieses Buch gibt einen fundierten Überblick über Change- und Corporate-Venture-Capital-Strategien im Mediensektor. Viele Medienunternehmen stehen vor der Herausforderung, das eigene (Kern-)Geschäft weiterzuentwickeln sowie eine deutlichere Orientierung am Kundennutzen zu verfolgen und dennoch die eigene kreative und publizistische Mission beizubehalten. Darüber hinaus gilt es, neue Geschäftsfelder aufzubauen und den Ankauf von Unternehmen oder die Beteiligung an Start-ups voranzutreiben. Der dabei erforderliche Spagat zwischen strategischen und finanziellen Zielen sowie die operative Umsetzung stellen eine beachtliche Herausforderung dar.
Die Branchenexperten analysieren in ihren Beiträgen die vielfältigen Potenziale sowie konkrete Maßnahmen und Best Cases. Dabei kommen Medieninsider und medienunabhängige Experten zur Sprache, die die Change-Strategien, -Maßnahmen und -Fallstudien beleuchten und neue Gestaltungsmöglichkeiten aufzeigen.
Dass die digitale Transformation als generelles Thema für alle Gesellschafts- und Wirtschaftsbereich von Bedeutung ist, und gerade für den Mittelstand und zahlreiche Hidden Champions eine besondere Herausforderung darstellt, wurde in den vorausgehenden Unterkapiteln bereits deutlich. In diesem Unterkapitel geht es um die konkrete Umsetzung der digitalen Transformation in diesen Branchen- und Unternehmenssegmenten, wo von den Geschäftsmodellen und den dazugehörigen Kundensegmenten bis hin zur Unternehmens- und Führungskultur Veränderungen erforderlich sind. Diese Felder der Transformation im Sinne von Entwicklung und Change gilt es für einen erfolgreichen Transformationsprozess zu identifizieren und aufeinander abzustimmen.
Neue konzeptionelle Entwicklungen wie Industrie 4.0 gelten in vielen Fällen als Domäne von jungen, kleinen Unternehmen, die im Sinne einer Start-Up-Kultur auf Innovationen fokussiert sind oder als Themenfeld von Großunternehmen, die mit Hilfe von Forschungs- und Entwicklungsabteilungen und eigenen Engineering-Ansätzen sowie einem großen Budget in der Lage sind, größere Projekte zu testen und eigene Konzepte zu entwickeln. Da die deutsche Wirtschaft und Gesellschaft stark von den erfolgreichen Entwicklungen des Mittelstands abhängt, der eine Schlüsselstellung für die diversifizierte Wirtschaft und eine robuste Entwicklung des Wohlstands besitzt, kommt der hier behandelten Frage, wie mittelständische Unternehmen mit der digitalen Transformation und der Herausforderung von Industrie 4.0 zurechtkommen, eine zentrale Rolle zu.
In diesem Kapitel geht es um die Frage, was unter dem Buzz-Word Industrie 4.0 zu verstehen ist und was nicht. Industrie 4.0 kann dabei unter gesellschaftlichen, kompetenzorientierten, produktionsorientierten oder verhaltensorientierten Gesichtspunkten unterschiedlich interpretiert werden. Die Umsetzung in Deutschland hat sich dabei vom politischen Konstrukt zur technisch-wirtschaftlichen Entwicklung im Rahmen der digitalen Transformation gewandelt.
Dieses Buch erläutert die allgemeinen Rahmenbedingungen von Industrie 4.0 und bietet innovativen KMUs im Industriesektor wertvolle Impulse.
Industrie 4.0 als Herzstück der digitalen Transformation vieler Produktionsbetriebe liefert ein sehr heterogenes Bild bezüglich der Umsetzung. Die Unterschiede in den organisatorischen Bedingungen liegen im Innovationsgrad der jeweiligen Organisation und lassen sich an der Nachhaltigkeit der Investitionen und dem damit verbundenen mittel- bis langfristigen Markterfolg festmachen.
Das Buch zeigt konkrete Umsetzungsbeispiele auf und beschreibt unter anderem Anwendungsszenarien von praxiserfahrenen und innovativen Ingenieur*innen im Maschinenbau und in der Medizintechnik. Zudem vermittelt es einen grundsätzlichen Überblick über Digitalstrategien und Anwendungsfelder von Industrie 4.0.
Es richtet sich gleichermaßen an Interessierte, Studierende sowie Praktiker aus den Themenfeldern Technologie, Ingenieurwesen und Management.
Dieses Fachbuch hat die Funktion einer fachlich fundierten und praxisnahen Überblicksdarstellung über die Möglichkeiten des Einsatzes von Social Media und Messenger-Diensten im kommunalen Bereich. Social Media sind insbesondere als Instrument für den Dialog mit der Bürgerschaft geeignet, was eine gezielte Analyse von Kommunikationsanlässen und Dialogbedürfnissen im kommunalen Sektor erfordert.
Um das volle Potenzial sozialer Medien ausschöpfen sowie zeitnah und dezentral kommunizieren zu können, müssen die daraus resultierenden Anforderungen an die Organisationskultur und Führungsstruktur berücksichtigt werden. Das Handbuch liefert den theoretischen Hintergrund und die dazugehörige praktische Umsetzung, um die notwendigen Handlungsfelder der kommunalen Kommunikation mithilfe von Social Media und Messenger-Diensten erschließen zu können.
This book, now in its third, completely revised and updated edition, offers a critical approach to the challenging interpretation of the latest research data obtained using functional neuroimaging in whiplash injury. Such a comprehensive guide to recent and current international research in the field is more necessary than ever, given that the confusion regarding the condition and the medicolegal discussions surrounding it have increased further despite the publication of much literature on the subject. In recent decades especially the functional imaging methods – such as single-photon emission tomography, positron emission tomography, functional MRI, and hybrid techniques – have demonstrated a variety of significant brain alterations. Functional Neuroimaging in Whiplash Injury - New Approaches covers all aspects, including the imaging tools themselves and the various methods of image analysis. Details on biomechanics, including the finite element method and facts on historical whiplash experiments and crash tests have now been added to this new edition. The book will continue to help physicians, patients and their relatives and friends, and others to understand this condition as a disease.
In previous work we [1] and other authors (e.g. [2]) have shown that agent-based systems are successful in optimizing delivery plans of single logistics companies and are meanwhile successfully productive in industry. In this paper we show that agent-based systems are particularly useful to also optimize transport across logistics companies. In intercompany optimization, privacy is of major importance between the otherwise competing companies. Some data has to be treated strictly private like the cost model or the constraint model. Other data like order information has to be shared. However, typically the amount of orders released to other companies has also to be limited. We show that our agent-based approach can be easily fine tuned to trade off privacy against the benefit of cooperation.
The transition from college to university can have a variety of psychological effects on students who need to cope with daily obligations by themselves in a new setting, which can result in loneliness and social isolation. Mobile technology, specifically mental health apps (MHapps), have been seen as promising solutions to assist university students who are facing these problems, however, there is little evidence around this topic. My research investigates how a mobile app can be designed to reduce social isolation and loneliness among university students. The Noneliness app is being developed to this end; it aims to create social opportunities through a quest-based gamified system in a secure and collaborative network of local users. Initial evaluations with the target audience provided evidence on how an app should be designed for this purpose. These results are presented and how they helped me to plan the further steps to reach my research goals. The paper is presented at MobileHCI 2020 Doctoral Consortium.
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.
Social Haptic Communication (SHC) is one of the many tactile modes of communication used by persons with deafblindness to access information about their surroundings. SHC usually involves an interpreter executing finger and hand signs on the back of a person with multi-sensory disabilities. Learning SHC, however, can become challenging and time-consuming, particularly to those who experience deafblindness later in life. In this work, we present PatRec: a mobile game for learning SHC concepts. PatRec is a multiple-choice quiz game connected to a chair interface that contains a 3x3 array of vibration motors emulating different SHC signs. Players collect scores and badges whenever they guess the right SHC vibration pattern, leading to continuous engagement and a better position on a leaderboard. The game is also meant for family members to learn SHC. We report the technical implementation of PatRec and the findings from a user evaluation.
Loneliness, an emotional distress caused by the lack of meaningful social connections, has been increasingly affecting university students who need to deal with everyday situations in a new setting, especially those who have come from abroad. Currently there is little work on digital solutions to reduce loneliness. Therefore, this work describes the general design considerations for mobile apps in this context and outlines a potential solution. The mobile app Noneliness is used to this end: it aims to reduce loneliness by creating social opportunities through a quest-based gamified system in a secure and collaborative network of local users. The results of initial evaluations with the target audience are described. The results informed a user interface redesign as well as a review of the features and the gamification principles adopted.
Das hier vorgestellte System verbindet das neue Konzept der Peer-to-Peer-Navigation mit dem Einsatz von Augmented Reality zur Unterstützung von bettseitig durchgeführten externen Ventrikeldrainagen. Das sehr kompakte und genaue Gesamtsystem beinhaltet einen Patiententracker mit integrierter Kamera, eine Augmented-Reality-Brille mit Kamera und eine Punktionsnadel bzw. einen Pointer mit zwei Trackern, mit dessen Hilfe die Anatomie des Patienten aufgenommen wird. Die exakte Position und Richtung der Punktionsnadel wird unter Zuhilfenahme der aufgenommenen Landmarken berechnet und über die Augmented-Reality-Brille für den Chirurgen sichtbar auf dem Patienten dargestellt. Die Methode zur Kalibrierung der statischen Transformationen zwischen Patiententracker und daran befestigter Kamera beziehungsweise zwischen den Trackern der Punktionsnadel sind für die Genauigkeit sehr wichtig und werden hier vorgestellt. Das Gesamtsystem konnte in vitro erfolgreich getestet werden und bestätigt den Nutzen eines Peer-to-Peer-Navigationssystems.
Wireless sensor networks have found their way into a wide range of applications, among which environmental monitoring systems have attracted increasing interests of researchers. Main challenges for these applications are scalability of the network size and energy efficiency of the spatially distributed nodes. Nodes are mostly battery-powered and spend most of their energy budget on the radio transceiver module. In normal operation modes most energy is spent waiting for incoming frames. A so-called Wake-On-Radio (WOR) technology helps to optimize trade-offs between energy consumption, communication range, complexity of the implementation and response time. We already proposed a new protocol called SmartMAC that makes use of such WOR technology. Furthermore, it gives the possibility to balance the energy consumption between sender and receiver nodes depending on the use case. Based on several calculations and simulations, it was predicted that the SmartMAC protocol was significantly more efficient than other schemes being proposed in recent publications, while preserving a certain backward compatibility with standard IEEE802.15.4 transceivers. To verify this prediction, we implemented the SmartMAC protocol for a given hardware platform. This paper compares the realtime performance of the SmartMAC protocol against simulation results, and proves the measured values are very close to the estimated values. Thus we believe that the proposed MAC algorithms outperforms all other Wake-on-Radio MACs.
Physically Unclonable Functions (PUFs) are hardware-based security primitives, which allow for inherent device fingerprinting. Therefore, intrinsic variation of imperfect manufactured systems is exploited to generate device-specific, unique identifiers. With printed electronics (PE) joining the internet of things (IoT), hardware-based security for novel PE-based systems is of increasing importance. Furthermore, PE offers the possibility for split-manufacturing, which mitigates the risk of PUF response readout by third parties, before commissioning. In this paper, we investigate a printed PUF core as intrinsic variation source for the generation of unique identifiers from a crossbar architecture. The printed crossbar PUF is verified by simulation of a 8×8-cells crossbar, which can be utilized to generate 32-bit wide identifiers. Further focus is on limiting factors regarding printed devices, such as increased parasitics, due to novel materials and required control logic specifications. The simulation results highlight, that the printed crossbar PUF is capable to generate close-to-ideal unique identifiers at the investigated feature size. As proof of concept a 2×2-cells printed crossbar PUF core is fabricated and electrically characterized.
Purpose: Participation and accessibility issues faced by gamers with multi-sensory disabilities are themes yet to be fully understood by accessible technology researchers. In this work, we examine the personal experiences and perceptions of individuals with deafblindness who play games despite their disability, as well as the reasons that lead some of them to stop playing games.
Materials and methods: We conducted 60 semi-structured interviews with individuals living with deafblindness in five European countries: United Kingdom, Germany, Netherlands, Greece and Sweden.
Results: Participants stated that reasons for playing games included them being a fun and entertaining hobby, for socialization and meeting others, or for occupying the mind. Reasons for stop playing games included essentially accessibility issues, followed by high cognitive demand, changes in gaming experience due their disability, financial reasons, or because the accessible version of a specific game was not considered as fun as the original one.
Conclusions: We identified that a considerable number of individuals with deafblindness enjoy playing casual mobile games such as Wordfeud and Sudoku as a pastime activity. Despite challenging accessibility issues, games provide meaningful social interactions to players with deafblindness. Finally, we introduce a set of user-driven recommendations for making digital games more accessible to players with a diverse combination of sensory abilities.
IMPLICATIONS FOR REHABILITATION
- Digital games were considered a fun and entertaining hobby by participants with deafblindness. Furthermore, participants play games for socialization and meeting others, or for occupying the mind.
- Digital games provide meaningful social interactions and past time to persons with deafblindness.
- On top of accessibility implications, our findings draw attention to the importance of the social element of gaming for persons with deafblindness.
- Based on interviews, we introduce a set of user-driven recommendations for making digital games more accessible to players with a diverse combination of sensory abilities.
Printed electronics (PE) offers flexible, extremely low-cost, and on-demand hardware due to its additive manufacturing process, enabling emerging ultra-low-cost applications, including machine learning applications. However, large feature sizes in PE limit the complexity of a machine learning classifier (e.g., a neural network (NN)) in PE. Stochastic computing Neural Networks (SC-NNs) can reduce area in silicon technologies, but still require complex designs due to unique implementation tradeoffs in PE. In this paper, we propose a printed mixed-signal system, which substitutes complex and power-hungry conventional stochastic computing (SC) components by printed analog designs. The printed mixed-signal SC consumes only 35% of power consumption and requires only 25% of area compared to a conventional 4-bit NN implementation. We also show that the proposed mixed-signal SC-NN provides good accuracy for popular neural network classification problems. We consider this work as an important step towards the realization of printed SC-NN hardware for near-sensor-processing.
Data Science
(2021)
Know-how für Data Scientists
• Übersichtliche und anwendungsbezogene Einführung
• Zahlreiche Anwendungsfälle und Praxisbeispiele aus unterschiedlichen Branchen
• Potenziale, aber auch mögliche Fallstricke werden aufgezeigt
Data Science steht derzeit wie kein anderer Begriff für die Auswertung großer Datenmengen mit analytischen Konzepten des Machine Learning oder der künstlichen Intelligenz. Nach der bewussten Wahrnehmung der Big Data und dabei insbesondere der Verfügbarmachung in Unternehmen sind Technologien und Methoden zur Auswertung dort gefordert, wo klassische Businss Intelligence an ihre Grenzen stößt.
Dieses Buch bietet eine umfassende Einführung in Data Science und deren praktische Relevanz für Unternehmen. Dabei wird auch die Integration von Data Science in ein bereits bestehendes Business-Intelligence-Ökosystem thematisiert. In verschiedenen Beiträgen werden sowohl Aufgabenfelder und Methoden als auch Rollen- und Organisationsmodelle erläutert, die im Zusammenspiel mit Konzepten und Architekturen auf Data Science wirken.
Diese 2., überarbeitete Auflage wurde um neue Themen wie Feature Selection und Deep Reinforcement Learning sowie eine neue Fallstudie erweitert.
Achieving Positive Hospitality Experiences through Technology: Findings from Singapore and Malaysia
(2021)
Customers’ experience is one of the most impactful factors in the tourism industry. Only by offering customers an excellent experience is it possible to build and ensure long-term customer loyalty. In today’s world, technology plays a key role in providing customers with an excellent customer experience. This study has the objective of analyzing how a positive customer experience can be achieved, and which technologies are necessary to ensure this. Results were collected through a literature review, and qualitative interviews with managers of selected hotels, as well as of attractions in Malaysia and Singapore. The analysis of these hotels and attractions is based on a set of criteria to determine the extent of the adoption of the new standards that contribute to positive online customer experiences. As a conclusion, different perspectives are compared, and positive and negative aspects of the use of modern technologies in the tourism industry are specified and discussed.
Strategische Analysetechniken ermöglichen langfristig eine strukturierte Erfassung unternehmensinterner Ressourcen in Ausrichtung auf den Markt. Die hier beschriebenen Basis-Techniken umfassen das Produkt-Lebenszyklusanalyse-Modell, verschiedene Typen der Portfolio-Analyse, die Wertketten-Analyse und die SWOT-Analyse. Diese Techniken unterstützen das Marketing-Controlling, Geschäftsfeld- und Marktanalysen für das Management zu erstellen und strategische Handlungsoptionen abzuleiten.
In diesem einführenden Kapitel geben die Autoren einen Überblick über die Entstehung des Marketing-Controllings, dessen Aufgaben, organisatorische Einbindung in das Unternehmen sowie dessen strategische und operative Ausprägungen. Zudem werden die einzelnen Beiträge dieses Handbuches im Zusammenhang vorgestellt.
Produkt-Controlling
(2021)
Zentraler Baustein des Marketing ist die „facettenreiche“ Produktpolitik. Im nachstehenden Beitrag wird zunächst die Einordnung der Produktpolitik in den Zielkatalog des Marketing und des Unternehmens skizziert. Das Produkt-Controlling wird verstanden als zielgerichtete Unterstützung der Managementaufgaben im Kontext der Produktpolitik mittels passender Instrumente – Instrumente, die der Phase der Produktentstehung wie der Marktzyklusphase zuzuordnen sind. Erkennbar wird: es gibt ein umfangreiches Set an Methoden, die das Marketing-Management unterstützen und die Sicherstellung der Marketing-Effektivität und Marketing-Effizienz gewährleisten. Die Komplexität des Produkt-Controllings bedingt sich auch durch den ausreichenden Einbezug preis-, qualitäts- und markenpolitischer Informationen in die Zielkontrolle.
Über zwei Jahrzehnte hat sich an der Hochschule Offenburg im Umfeld von Professor Elmar Bollin eine Forschungsgruppe etabliert, die die Bereiche Gebäudeautomation und nachhaltige Energietechnik zusammenführten. Anfänglich ging es darum die Potenziale der internetbasierten Wetterprognostik und modell-basierten Anlagensteuerung für die Verbesserung des Komforts und der Energieeffizienz im Gebäude zu nutzen. Im Rahmen von Forschungs- und Entwicklungsarbeiten mit Einsatz von dynamischen Gebäudesimulationen konnte schließlich ein Algorithmus gefunden werden, der es ermöglichte auf Basis von prognostizierter Außentemperatur und Sonneneinstrahlung den Energiebedarf eines Bürogebäudes für den Folgetag vorherzusagen. In Verbindung mit der Gebäudeautomation entstand so die adaptive und prädiktive TABS-Steuerung AMLR.
This book constitutes the refereed proceedings of the 21st International TRIZ Future Conference on Automated Invention for Smart Industries, TFC 2021, held virtually in September 2021 and sponsored by IFIP WG 5.4.
The 28 full papers and 8 short papers presented were carefully reviewed and selected from 48 submissions. They are organized in the following thematic sections: inventiveness and TRIZ for sustainable development; TRIZ, intellectual property and smart technologies; TRIZ: expansion in breadth and depth; TRIZ, data processing and artificial intelligence; and TRIZ use and divulgation for engineering design and beyond.
Chapter ‘Domain Analysis with TRIZ to Define an Effective “Design for Excellence’ is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Sustainable chemical processes should be designed to combine the technological advantages and progress with lower safety risks and minimization of environmental impact such as, for example, reduction of raw materials, energy and water consumption, and avoidance of hazardous waste and pollution with toxic chemical agents. A number of novel eco-friendly chemical technologies have been developed in the recent decades with the help of the eco-innovations approaches and methods such as Life Cycle Analysis, Green Process Engineering, Process Intensification, Process Design for Sustainability, and others. An emerging approach to the sustainable process design in process engineering builds on the innovative solutions inspired from nature. However, the implementation of the eco-friendly technologies often faces secondary ecological problems. The study postulates that the eco-inventive principles identified in natural systems allow to avoid secondary eco-problems and proposes to apply these principles for sustainable design in chemical process engineering. The research work critically examines how this approach differs from the biomimetics, as it is commonly used for copying natural systems. The application of nature-inspired eco-design principles is illustrated with an example of a sustainable technology for extraction of nickel from pyrophyllite.
The proposed method includes identification and documentation of the elementary TRIZ inventive principles from the TRIZ body of knowledge, extension and enhancement of inventive principles by patents and technologies analysis, avoiding overlapping and redundant principles, classification and adaptation of principles to at least following categories such as working medium, target object, useful action, harmful effect, environment, information, field, substance, time, and space, assignment of the elementary inventive principles to the at least following underlying engineering domains such as universal, design, mechanical, acoustic, thermal, chemical, electromagnetic, intermolecular, biological, and data processing. The method includes classification of abstraction level of the elementary principles, definition of the statistical ranking of principles for different problem types, and specific engineering or non-technical domains, definition of strategies for selection of principles sets with high solution potential for predefined problems, automated semantic transformation of the elementary inventive principles into solution ideas, evaluation of automatically generated ideas and transformation of ideas to innovation or inventive concepts.
Energiemanagement im Betrieb
(2021)
Über zwei Jahrzehnte hat sich an der Hochschule Offenburg eine Forschungsgruppe etabliert, die die beiden Bereiche Gebäudeautomation und nachhaltige Energietechnik zusammenführte. Anfangs ging es darum, Potentiale der internetbasierten Wetterprognostik und modell-basierten Anlagensteuerung für die Verbesserung des Komforts und der Energieeffizienz im Gebäude zu nutzen. Im Rahmen von Forschungs- und Entwicklungsarbeiten mit Einsatz von dynamischen Gebäudesimulationen konnte ein Algorithmus gefunden werden, der es ermöglichte auf Basis von prognostizierter Außentemperatur und Sonneneinstrahlung den Energiebedarf eines Bürogebäudes für den Folgetag vorherzusagen. In Verbindung mit der Gebäudeautomation entstand so die adaptive und prädiktive TABS-Steuerung AMLR.
Dieses Fachbuch gibt einen vertieften Einblick in das dynamische Verhalten von thermoaktiven Bauteilsystemen. Es wird eine neu entwickelte und vielfach erprobte, selbstlernende und vorausschauende TABS-Steuerung vorgestellt. Dazu wird auf die Erfordernisse einer effektiven TABS-Steuerung eingegangen und die Grundlagen und Funktionsweise der neu entwickelten AMLR-Steuerung erläutert. Anhand mehrerer Anwendungsbeispiele wird die Umsetzung in die bauliche Praxis erläutert und mit Hilfe von umfangreichen Messergebnissen die Funktion der neuen AMLR-Steuerung nachgewiesen. Abschließend werden Empfehlungen für die Anwendung von AMLR in der baulichen TABS-Praxis hinsichtlich Anlagenhydraulik und Umsetzung in der Gebäudeautomation gegeben.
The paper describes the implementation of practical laboratory settings in a virtual environment. With the entry of VR glasses into the mass market, there is a chance to establish educational and training applications for displaying some teaching materials and practical works. Therefore our project focuses on the realization of virtual experiments and environments, which gives users a deep insight into selected subfields of Optics and Photonics. Our goal is not to substitute the hand on experiments rather to extend them. By means of VR glasses, the user is offered the possibility to view the experiment from several angles and to make changes through interactive control functions. During the VR application, additional context-related information is displayed. By using object recognition, the specific graphics and texts for the respective object are loaded and supplemented at the appropriate place. Thus, complex facts are supported in an informative way. The prototype is developed using the Unity Engine and can thus be exported to different platforms and end devices. Another major advantage of virtual simulations to the real situation is the high degree of controllability as well as the easy repeatability. With slight modifications, entire experiments can be reused. Our research aims to acquire new knowledge in the field of e-learning in association with VR technology. Here we try to answer a core question of the compatibility of the individual media components.
Increasing power density causes increased self-generation of harmonics and intermodulation. As this leads to violations of the strict linearity requirements, especially for carrier aggregation (CA), the nonlinearity must be considered in the design process of RF devices. This raises the demand of accurate simulation models. Linear and nonlinear P-Matrix/COM models are used during the design due to their fast simulation times and accurate results. However, the finite element method (FEM) is useful to get a deeper insight in the device's nonlinearities, as the total field distributions can be visualized. The FE method requires complete sets of material tensors, which are unknown for most relevant materials in nonlinear micro-acoustics. In this work, we perform nonlinear FEM simulations, which allow the calculation of nonlinear field distributions of a lithium tantalate based layered SAW system up to third order. We aim at achieving good correspondence to measured data and determine the contributions of each material layer to the nonlinear signals. Therefore, we use approximations circumventing the issue of limited higher order tensor data. Experimental data for the third order nonlinearity is shown to validate the presented approach.
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.
Due to its potential in improving the efficiency of energy supply, smart energy metering (SEM) has become an area of interest with the surge in Internet of Things (IoT). SEM entails remote monitoring and control of the sensors and actuators associated with the energy supply system. This provides a flexible platform to conceive and implement new data driven Demand Side Management (DSM) mechanisms. The IoT enablement allows the data to be gathered and analyzed at requisite granularity. In addition to efficient use of energy resources and provisioning of power, developing countries face an additional challenge of temporal mismatch in generation capacity and load factors. This leads to widespread deployment of inefficient and expensive Uninterruptible Power Supply (UPS) solutions for limited power provisioning during resulting blackouts. Our proposed “Soft-UPS” allows dynamic matching of load and generation through a combination of managed curtailment. This eliminates inefficiencies in the energy and power value chain and allows a data-driven approach to solving a widespread problem in developing countries, simultaneously reducing both upfront and running costs of conventional UPS and storage. A scalable and modular platform is proposed and implemented in this paper. The architecture employs “WiMODino” using LoRaWAN with a “Lite Gateway” and SQLite repository for data storage. Role based access to the system through an android application has also been demonstrated for monitoring and control.
Cryptographic protection of messages requires frequent updates of the symmetric cipher key used for encryption and decryption, respectively. Protocols of legacy IT security, like TLS, SSH, or MACsec implement rekeying under the assumption that, first, application data exchange is allowed to stall occasionally and, second, dedicated control messages to orchestrate the process can be exchanged. In real-time automation applications, the first is generally prohibitive, while the second may induce problematic traffic patterns on the network. We present a novel seamless rekeying approach, which can be embedded into cyclic application data exchanges. Although, being agnostic to the underlying real-time communication system, we developed a demonstrator emulating the widespread industrial Ethernet system PROFINET IO and successfully use this rekeying mechanism.
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.
This paper presents an extended version of a previously published Bayesian algorithm for the automatic correction of the positions of the equipment on the map with simultaneous mobile object trajectory localization (SLAM) in underground mine environment represented by undirected graph. The proposed extended SLAM algorithm requires much less preliminary data on possible equipment positions and uses an additional resample move algorithm to significantly improve the overall performance.
Towards a Formal Verification of Seamless Cryptographic Rekeying in Real-Time Communication Systems
(2022)
This paper makes two contributions to the verification of communication protocols by transition systems. Firstly, the paper presents a modeling of a cyclic communication protocol using a synchronized network of transition systems. This protocol enables seamless cryptographic rekeying embedded into cyclic messages. Secondly, we test the protocol using the model checking verification technique.
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.
It seems to be a widespread impression that the use of strong cryptography inevitably imposes a prohibitive burden on industrial communication systems, at least inasmuch as real-time requirements in cyclic fieldbus communications are concerned. AES-GCM is a leading cryptographic algorithm for authenticated encryption, which protects data against disclosure and manipulations. We study the use of both hardware and software-based implementations of AES-GCM. By simulations as well as measurements on an FPGA-based prototype setup we gain and substantiate an important insight: for devices with a 100 Mbps full-duplex link, a single low-footprint AES-GCM hardware engine can deterministically cope with the worst-case computational load, i.e., even if the device maintains a maximum number of cyclic communication relations with individual cryptographic keys. Our results show that hardware support for AES-GCM in industrial fieldbus components may actually be very lightweight.
For the past few years Low Power Wide Area Networks (LPWAN) have emerged as key technologies for the connectivity of many applications in the Internet of Things (IoT) combining low-data rates with strict cost and energy restrictions. Especially LoRa/LoRaWAN enjoys a high visibility on today’s markets, because of its good performance and its open community. Originally LoRa was designed for operation within the Sub-GHz ISM bands for Industrial, Scientific and Medical applications. However, at the end of 2018, a LoRa-based solution in the 2.4GHz ISM-band was presented promising higher bandwidths and higher data rates. Furthermore, it overcomes the limited duty-cycle prescribed by the regulations in the ISM-bands and therefore also opens doors to many novel application fields. Also, due to higher bandwidths and shorter transmission times, the use of alternative MAC layer protocols becomes very interesting, i.e. for TDMA based-approaches. Within this paper, we propose a system architecture with 2.4GHz LoRa components combining two aspects. On the one hand, we present a design and an implementation of a 2.4GHz based LoRaWAN solution that can be seamlessly integrated into existing LoRaWAN back-hauls. On the other hand, we describe deterministic setup using a Time Slotted Channel Hopping (TSCH) approach as defined in the IEEE802.15.4-2015 standard for industrial applications. Finally, measurements show the performance of the system.
Lernziele:
Die Leser
• kennen die Bestandteile und Aufgaben der Produktion;
• wissen, welche Ansätze die Vision einer 100 % nachhaltigen Produktion hinsichtlich Material- und Energieverwendung beinhalten;
• verfügen über den Einblick, welche Stufen auf dem Weg zur nachhaltigen Produktion zu nehmen sind;
• wissen auch anhand von Beispielen, welche Ansätze zur Umsetzung einer nachhaltigen Produktion bestehen;
• kennen im Überblick die Ansätze zur Messung von Nachhaltigkeit in der Produktion.
Autonomous driving is disrupting the automotive industry as we know it today. For this, fail-operational behavior is essential in the sense, plan, and act stages of the automation chain in order to handle safety-critical situations on its own, which currently is not reached with state-of-the-art approaches.The European ECSEL research project PRYSTINE realizes Fail-operational Urban Surround perceptION (FUSION) based on robust Radar and LiDAR sensor fusion and control functions in order to enable safe automated driving in urban and rural environments. This paper showcases some of the key exploitable results (e.g., novel Radar sensors, innovative embedded control and E/E architectures, pioneering sensor fusion approaches, AI-controlled vehicle demonstrators) achieved until its final year 3.
Für Bildungseinrichtungen steht das Jahr 2020 für Schulschließungen und coronabedingte Zwangsdigitalisierung. Binnen weniger Wochen wurde notgedrungen auf Fernunterricht und Lernmanagementsysteme (LMS), Schulcloud und Videokonferenzen umgestellt. Zu denken geben sollte weniger die der Pandemie geschuldete Umstellung von Präsenz- auf Distanzunterricht, sondern dessen beabsichtigte Verstetigung samt Forderung nach zunehmend automatisierten Beschulungssystemen. Werden Bildungseinrichtungen Teil der Daten-Ökonomie oder gelten weiterhin pädagogische Prämissen?
We describe a prototype for power line communi- cation for grid monitoring. The PLC receiver is used to gain information about the PLC channel and the current state of the power grid. The PLC receiver uses the communication signal to obtain an accurate estimate of the current channel and provides information which can be used as a basis for further processing with the aim to detect partial discharges and other anomalies in the grid. This monitoring of the power grid takes advantage of existing PLC infrastructure and uses the data signals, which are transmitted anyway to obtain a real-time measurement of the channel transfer function and the received noise signal. Since this signal is sampled at a high sampling rate compared to simpler measurement sensors, it contains valuable information about possible degradations in the grid which need to be addressed. While channel measurements are based on a received PLC signal, information about partial discharges or other sources of interference can be gathered by a PLC receiver in the absence of a transmit signal. A prototype based on Software Defined Radio has been developed, which implements the simultaneous communication and sensing for a power grid.
The following describes a new method for estimating the parameters of an interior permanent magnet synchronous machine (IPMSM). For the estimation of the parameters the current slopes caused by the switching of the inverter are used to determine the unknowns of the system equations of the electrical machine. The angle and current dependence of the machine parameters are linearized within a PWM cycle. By considering the different switching states of the inverter, several system equations can be derived and a solution can be found within one PWM cycle. The use of test signals and filter-based approaches is avoided. The derived algorithm is explained and validated with measurements on a test bench.
This study aims to investigate the individual response concerning BRFs for AT when the mid-sole hardness underneath the rearfoot was systematically altered. We first identified FGs based on the footwear condition that minimised the risk for AT across BRFs. We then tested the FGs for differences in anthropometrics, footwear comfort, and running characteristics.
Onlineshops in Deutschland verschenken sehr viel Potenzial im Registrierungs- und Bestellprozess. Dabei lässt sich mit wenigen gezielten Verbesserungen der Checkout barrierefrei und smart gestalten. Zu diesem Ergebnis kommt eine heuristische Untersuchung der Top 100 Onlineshops von Uniserv gemeinsam mit der Hochschule Offenburg. Die Eingabe und Qualität von Adressdaten spielen dabei eine besondere Rolle.
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.
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.
Object Detection and Mapping with Unmanned Aerial Vehicles Using Convolutional Neural Networks
(2021)
Significant progress has been made in the field of deep learning through intensive research over the last decade. So-called convolutional neural networks are an essential component of this research. In this type of neural network, the mathematical convolution operator is used to extract characteristics or anomalies. The purpose of this work is to investigate the extent to which it is possible in certain initial settings to input aerial recordings and flight data of Unmanned Aerial Vehicles (UAVs) in the architecture of a neural network and to detect and map an object. Using the calculated contours or dimensions of the so-called bounding boxes, the position of the objects can be determined relative to the current UAV location.
The applicability of characteristics of local magnetic fields for more precise determination of localization of subjects and/or objects in indoor environments, such as railway stations, airports, exhibition halls, showrooms, or shopping centers, is considered. An investigation has been carried out to find out whether and how low-cost magnetic field sensors and mobile robot platforms can be used to create maps that improve the accuracy and robustness of later navigation with smartphones or other devices.
The aim of this work is the application and evaluation of a method to visually detect markers at a distance of up to five meters and determine their real-world position. Combinations of cameras and lenses with different parameters were studied to determine the optimal configuration. Based on this configuration, camera images were taken after proper calibration. These images are then transformed into a bird's eye view using a homography matrix. The homography matrix is calculated with four-point pairs as well as with coordinate transformations. The obtained images show the ground plane un distorted, making it possible to convert a pixel position into a real-world position with a conversion factor. The proposed approach helps to effectively create data sets for training neural networks for navigation purposes.
For some years now, additive manufacturing (AM) has offered an alternative to conventional manufacturing processes. The strengths of AM are primarily the rapid implementation of ideas into a usable product and the ability to produce geometrically complex shapes. It has also significantly advanced the lightweight design of products made of plastic. So far, the strength of printed components made of polymers is previously very limited.
Recently, new AM processes have become available that allow the embedding of short and also long fibers in polymer matrix. Thus, the manufacturing of components that provide a significant increase in strength becomes possible. In this way, both complex geometries and sophisticated applications can be implemented. This paper therefore investigates how this new technology can be implemented in product development, focusing on sports equipment. An extensive literature research shows that lightweight design plays a decisive role in sports equipment. In addition, the advantages of AM in terms of individualized products and low quantities can be fully exploited.
An example of this approach is the steering system for a seat sled used by paraplegic athletes in the Olympic discipline of Nordic paraskiing. A particular challenge here is the placement and alignment of the long carbon fibers within the polymer matrix and the verification of the strength by means of Finite-Element-Analysis (FEA). In addition, findings from bionics are used to optimize the lightweight design of the steering system. Using this example, it can be shown that the weight of the steering system can be drastically reduced compared to conventional manufacturing. At the same time, a number of parts can be saved through function integration and thus the manufacturing and assembly effort can be reduced significantly.
Today, Additive Manufacturing (AM) is an important part of teaching for the education of future engineers. Therefore, a variety of approaches have been developed in recent years on how to bring the design for additive manufacturing (DfAM) into university teaching. In a detailed literature review, the advantages and disadvantages of the previous approaches are considered and analysed. Based on this, an extended approach is presented in which students analyse and optimize a given product with respect to additive manufacturing. In doing so, the students have to solve challenging tasks in optimization in product development with the help of methodical approaches and practically implement their developed solutions with state-of-the-art additive processes. To work on this task, the students have two different 3D printers at their disposal, which work with different processes and materials. Thus, the students learn to adapt the design to different manufacturing processes and to consider the restrictions of different materials. The assessment of the results from this course is done through feedback and a written survey.
This paper presents the development of an energy harvesting solution for a driven tool holder. The tool holder environment was analysed, a test stand built and the designed electromagnetic rotation harvester was evaluated. The reported harvester is based on low cost off-the-shelf components and 3D printed parts. The utilisation of SMD coils allows easy adaptation to changing parameters of the integration area. Energy harvesting in tool holders enables predictive maintenance or condition monitoring in the industrial production. These capabilities are mandatory nowadays in regards of IIoT. A reliable energy source is key for continuous monitoring. Changing batteries becomes obsolete. The results provide useful insight for future harvesters.
Correlation Clustering, also called the minimum cost Multicut problem, is the process of grouping data by pairwise similarities. It has proven to be effective on clustering problems, where the number of classes is unknown. However, not only is the Multicut problem NP-hard, an undirected graph G with n vertices representing single images has at most edges, thus making it challenging to implement correlation clustering for large datasets. In this work, we propose Multi-Stage Multicuts (MSM) as a scalable approach for image clustering. Specifically, we solve minimum cost Multicut problems across multiple distributed compute units. Our approach not only allows to solve problem instances which are too large to fit into the shared memory of a single compute node, but it also achieves significant speedups while preserving the clustering accuracy at the same time. We evaluate our proposed method on the CIFAR10 …
Transformer models have recently attracted much interest from computer vision researchers and have since been successfully employed for several problems traditionally addressed with convolutional neural networks. At the same time, image synthesis using generative adversarial networks (GANs) has drastically improved over the last few years. The recently proposed TransGAN is the first GAN using only transformer-based architectures and achieves competitive results when compared to convolutional GANs. However, since transformers are data-hungry architectures, TransGAN requires data augmentation, an auxiliary super-resolution task during training, and a masking prior to guide the self-attention mechanism. In this paper, we study the combination of a transformer-based generator and convolutional discriminator and successfully remove the need of the aforementioned required design choices. We evaluate our approach by conducting a benchmark of well-known CNN discriminators, ablate the size of the transformer-based generator, and show that combining both architectural elements into a hybrid model leads to better results. Furthermore, we investigate the frequency spectrum properties of generated images and observe that our model retains the benefits of an attention based generator.
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.
Despite the success of convolutional neural networks (CNNs) in many computer vision and image analysis tasks, they remain vulnerable against so-called adversarial attacks: Small, crafted perturbations in the input images can lead to false predictions. A possible defense is to detect adversarial examples. In this work, we show how analysis in the Fourier domain of input images and feature maps can be used to distinguish benign test samples from adversarial images. We propose two novel detection methods: Our first method employs the magnitude spectrum of the input images to detect an adversarial attack. This simple and robust classifier can successfully detect adversarial perturbations of three commonly used attack methods. The second method builds upon the first and additionally extracts the phase of Fourier coefficients of feature-maps at different layers of the network. With this extension, we are able to improve adversarial detection rates compared to state-of-the-art detectors on five different attack methods. The code for the methods proposed in the paper is available at github.com/paulaharder/SpectralAdversarialDefense
In this work, we evaluate two different image clustering objectives, k-means clustering and correlation clustering, in the context of Triplet Loss induced feature space embeddings. Specifically, we train a convolutional neural network to learn discriminative features by optimizing two popular versions of the Triplet Loss in order to study their clustering properties under the assumption of noisy labels. Additionally, we propose a new, simple Triplet Loss formulation, which shows desirable properties with respect to formal clustering objectives and outperforms the existing methods. We evaluate all three Triplet loss formulations for K-means and correlation clustering on the CIFAR-10 image classification dataset.
The term “attribute transfer” refers to the tasks of altering images in such a way, that the semantic interpretation of a given input image is shifted towards an intended direction, which is quantified by semantic attributes. Prominent example applications are photo realistic changes of facial features and expressions, like changing the hair color, adding a smile, enlarging the nose or altering the entire context of a scene, like transforming a summer landscape into a winter panorama. Recent advances in attribute transfer are mostly based on generative deep neural networks, using various techniques to manipulate images in the latent space of the generator. In this paper, we present a novel method for the common sub-task of local attribute transfers, where only parts of a face have to be altered in order to achieve semantic changes (e.g. removing a mustache). In contrast to previous methods, where such local changes have been implemented by generating new (global) images, we propose to formulate local attribute transfers as an inpainting problem. Removing and regenerating only parts of images, our “Attribute Transfer Inpainting Generative Adversarial Network” (ATI-GAN) is able to utilize local context information to focus on the attributes while keeping the background unmodified resulting in visually sound results.
Soziale Roboter, die mit uns kommunizieren und menschliche Verhaltensmuster imitieren, sind ein wichtiges Zukunftsthema. Während viele Arbeiten ihr Design und ihre Akzeptanz erforschen, gibt es bislang nur wenige Untersuchungen zu ihrer Marktfähigkeit. Der Schwerpunkt dieser Arbeit liegt auf dem Einsatz sozialer Roboter in den Bereichen Gesundheit und Pflege, wo die zukünftige Integration sozialer Roboter ein enormes Potenzial hat. Eine Studie mit 197 Personen aus Italien und Deutschland untersucht gewünschte Funktionalitäten und Kaufpräferenzen und berücksichtigt hierbei kulturelle Unterschiede. Dabei bestätigte sich die Wichtigkeit mehrerer Dimensionen des ALMERE-Modells (z. B. wahrgenommene Freude, Nützlichkeit und Vertrauenswürdigkeit). Die Akzeptanz korreliert stark mit der Investitionsbereitschaft. Viele ältere Personen betrachten soziale Roboter als „assistierende technische Geräte“ und erwarten, dass diese von Versicherungen und der öffentlichen Hand bezuschusst werden. Um ihren zukünftigen Einsatz zu erleichtern, sollten soziale Roboter in die Datenbanken medizinischer Hilfsmittel integriert werden.
The Go programming language is an increasingly popular language but some of its features lack a formal investigation. This article explains Go's resolution mechanism for overloaded methods and its support for structural subtyping by means of translation from Featherweight Go to a simple target language. The translation employs a form of dictionary passing known from type classes in Haskell and preserves the dynamic behavior of Featherweight Go programs.
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.
Im Rahmen des EU-Forschungsprojektes ACA-Modes (Advanced Control Algorithms for Management of Decentralised Energy Systems) werden reale Labore der Projektpartner primärenergetisch, ökonomisch und die Emissionen betreffend bewertet. Vier Projektpartner liefern Datensätze aus Messreihen typischer Bereitstellungsszenarien. Die verschiedenen Systeme bestehen unter anderem aus einer KWK-Anlage mit Erdgas-Verbrennungsmotor, einer KWKK-Anlage mit Adsorptionskältemaschine, einer Photovoltaik-Anlage mit Batteriespeicher und Wärmepumpe und einer Solarthermieanlage mit Adsorptionskältemaschine.
Drawing off the technical flexibility of building polygeneration systems to support a rapidly expanding renewable electricity grid requires the application of advanced controllers like model predictive control (MPC) that can handle multiple inputs and outputs, uncertainties in forecast data, and plant constraints amongst other features. In this original work, an economic-MPC-based optimal scheduling of a real-world building energy system is demonstrated and its performance is evaluated against a conventional controller. The demonstration includes the steps to integrate an optimisation-based supervisory controller into a standard building automation and control system with off-the-shelf HVAC components and usage of state-of-art algorithms for solving complex nonlinear mixed integer optimal control problems. With the MPC, quantitative benefits in terms of 6–12% demand-cost savings and qualitative benefits in terms of better controller adaptability and hardware-friendly operation are identified. Further research potential for improving the MPC framework in terms of field-level stability, minimising constraint violations, and inter-system communication for its deployment in a prosumer-network is also identified.
The increasing use of artificial intelligence (AI) technologies across application domains has prompted our society to pay closer attention to AI’s trustworthiness, fairness, interpretability, and accountability. In order to foster trust in AI, it is important to consider the potential of interactive visualization, and how such visualizations help build trust in AI systems. This manifesto discusses the relevance of interactive visualizations and makes the following four claims: i) trust is not a technical problem, ii) trust is dynamic, iii) visualization cannot address all aspects of trust, and iv) visualization is crucial for human agency in AI.
Due to higher combustion chamber temperatures and pressures in efficient combustion engines, both the high-cycle and thermomechanical fatigue loads on service life-critical components, such as the cylinder head, are increasing. Material comparisons and analysis of damage behavior are very expensive and time-consuming using component tests. This study therefore develops a test method for cylinder head materials that takes into account the combined loading conditions from the above-mentioned loads and allows realistic temperature transients and gradients on near-component samples. The near-component cylinder head sample represents the failure-critical exhaust valve crosspiece and is tested in a test rig specially designed with the aid of conjugate heat transfer simulations. In the test rig, the sample is subjected to thermal stress by a hot gas burner and to mechanical stress by a high-frequency pulsator. Optical crack detection allows permanent observation of fatigue crack growth and crack closure during the test. Fractographic and metallo-graphic examinations of the fracture areas as well as analyses of the damage patterns show that loads close to engine operation can be set in this way and their influences on the damage can be monitored.
The aim of this review was to determine whether smartphone applications are reliable and valid to measure range of motion (RoM) in lower extremity joints. A literature search was performed up to October 2020 in the databases PubMed and Cochrane Library. Studies that reported reliability or validity of smartphone applications for RoM measurements were included. The study quality was assessed with the QUADAS-2 tool and baseline information, validity and reliability were extracted. Twenty-five studies were included in the review. Eighteen studies examined knee RoM, whereof two apps were analysed as having good to excellent reliability and validity for knee flexion (“DrGoniometer”, “Angle”) and one app showed good results for knee extension (“DrGoniometer”). Eight studies analysed ankle RoM. One of these apps showed good intra-rater reliability and excellent validity for dorsiflexion RoM (“iHandy level”), another app showed excellent reliability and moderate validity for plantarflexion RoM (“Coach’s Eye”). All other apps concerning lower extremity RoM had either insufficient results, lacked study quality or were no longer available. Some apps are reliable and valid to measure RoM in the knee and ankle joint. No app can be recommended for hip RoM measurement without restrictions.
Uphill training is applied to induce specific overload on the musculoskeletal system to improve sprinting mechanics. This study aimed to identify unique kinematic features of uphill sprinting at different slopes and to suggest practical implications based on comparisons we early stance phase. At take-off, steeper slopes induced significantly more extended joint angles and higher ROMs during the late stance phase. Compared with moderate slopes, more anti-phase coordination patterns were detected at steeper slopes. Thus, uphill sprinting at steeper slopes shares essential kinematic features with the early acceleration phase of level sprinting. Moderate inclinations induce biomechanical adaptations similar to those in the late acceleration phase of level sprinting. Hence, the specific transfer of uphill sprinting to acceleration depends on the slope inclinations.
Quantifying the midsole material characteristics of athletic footwear is a standard task in footwear research and development. Current material testing protocols primarily focus on the determination of cushioning properties of the heel region or the quantification of the midsole properties as one assembly. However, midsoles possess different spatial material properties that have not been quantified from previous methodologies. Therefore, new material testing methods are required to quantify the local material response of athletic footwear. We developed a cyclical force-controlled material testing protocol for the determination of non-homogeneously distributed material stiffness with a high spatial resolution. In five prototype shoes varying in their stiffness distribution, we found that the material properties can be reliably measured across the midsole. Furthermore, we observed a characteristic non-linear material response regardless of the midsole location. We found that the material stiffness increased with an increase of the applied force and that this effect is further intensified by higher testing cycles. Additionally, the obtained midsole stiffness depends on the geometry of the midsole. We explored different approaches to reduce the measurement time of the testing protocol and found that the number of measurements can be reduced by 70% using 2 D-interpolation procedures. Determining the spatial material properties of midsoles needs to be considered to understand foot-shoe interactions. Furthermore, this measurement protocol can be used for quality control within the footwear and can be adapted for considering the effects of different running styles or speeds on ground force application characteristics.