Refine
Year of publication
Document Type
- Article (99) (remove)
Language
- English (64)
- German (32)
- Multiple languages (3)
Is part of the Bibliography
- no (99)
Keywords
- Architektur (1)
- Automotive HMI (1)
- Computer Vision (1)
- Covid-19 (1)
- Digital Collaboration (1)
- Digital Transformation (1)
- Fahrerassistenzsystem (1)
- Higher Education (1)
- Highly Automated Vehicles (1)
- Human-Computer Interaction (1)
In this study, we looked at the competencies and changes in the competency spectrum required for global start-ups in the digital age. Specifically, we explored intergenerational collaboration as an intervention in which experienced business-people from senior adult groups support young entrepreneurs. We conducted a Delphi study with 20 experts from different disciplines, considering the study context. The results of this study shed light on understanding the necessary competencies of entrepreneurs for intergenerationally supported start-up innovation by providing 27 competencies categorized as follows: intergenerational safety facilitation, cultural awareness, virtues for growth, effectual creativity, technical expertise, responsive teamwork, values-based organization, and sustainable network development. In addition, the study results also reveal the competency priorities and the minimum requirements for each competency group based on the global innovation process and can be used to develop a readiness assessment for start-up entrepreneurs.
So far, researchers have used a wellbeing-centered approach to catalyze successful intergenerational collaboration (IGC) in innovative activities. However, due to the subject’s multidisciplinary nature, there is still a dearth of comprehensive research devoted to constructing the IGC system. Thus, the purpose of this study is to fill a research void by providing a conceptual framework for information technology (IT) system designers to use as a jumping-off point for designing an IGC system with a wellbeing-oriented design. A systematic literature study was conducted to identify relevant terms and develop a conceptual framework based on a review of 75 selected scientific papers. The result consists of prominent thematic linkages and a conceptual framework related to design technology for IGC systems. The conceptual framework provides a comprehensive overview of IGC systems in the innovation process by identifying five barrier dimensions and using six wellbeing determinants as IGC catalysts. Moreover, this study discusses future directions for research on IGC systems. This study offers a novel contribution by shifting the technology design process from an age-based design approach to wellbeing-driven IGC systems. Additional avenues for investigation were revealed through the analysis of the study’s findings.
Rapid digital transformation is taking place due to the COVID-19 pandemic, forcing organisations and higher educational institutions to change their working and learning culture. This study explores the challenges of rapid digital transformation arising during the pandemic in the higher education context. This research used the Q-methodology to understand the nine challenges that higher education encountered, perceived differently as four main patterns: (1) Digital-nomad enterprise; (2) Corporate-collectivism; (3) Well-being-oriented; and (4) Pluralistic. This study broadens the current understanding of digital transformation, especially in higher education. The nine challenges and four patterns of transformation actors serve as a starting point for organisations in supporting technological choice and strategic interventions, based on individual, group, and organisational behavioural levels. Moreover, five propositions, based on the competing concerns of these challenges, establish a framework for comprehending the ecosystem that enables rapid digital transformation. Strategies, prerequisites, and key factors during the (digital) technology development process benefit the cyber-society ecosystem. As a practical contribution, Q-methodology was used to investigate perspectives on digitalisation challenges during the pandemic.
The way we communicate with autonomous cars will fundamentally change as soon as manual input is no longer required as back-up for the autonomous system. Maneuver-based driving is a potential way to allow still the user to intervene with the autonomous car to communicate requests such as stopping at the next parking lot. In this work, we highlight different research questions that still need to be explored to gain insights into how such control can be realized in the future.
Human emotion detection in automated vehicles helps to improve comfort and safety. Research in the automotive domain focuses a lot on sensing drivers' drowsiness and aggression. We present a new form of implicit driver-vehicle cooperation, where emotion detection is integrated into an automated vehicle's decision-making process. Constant evaluation of the driver's reaction to vehicle behavior allows us to revise decisions and helps to increase the safety of future automated vehicles.
How to Increase Automated Vehicles’ Acceptance through In-Vehicle Interaction Design: A Review
(2020)
Automated vehicles (AVs) are on the edge of being available on the mass market. Research often focuses on technical aspects of automation, such as computer vision, sensing, or artificial intelligence. Nevertheless, researchers also identified several challenges from a human perspective that need to be considered for a successful introduction of these technologies. In this paper, we first analyze human needs and system acceptance in the context of AVs. Then, based on a literature review, we provide a summary of current research on in-car driver-vehicle interaction and related human factor issues. This work helps researchers, designers, and practitioners to get an overview of the current state of the art.
The development of innovative measuring technology for process optimization in hot rolling mills becomes more and more relevant because of increasing demands on product quality. Measurement technology for high-resolution non-contact cross-sectional area measurement has shown that the variation in cross-sectional area contains information about the rolling process. This information can be used for the development of new measurement devices and analytical methods for process optimization. The harsh environmental conditions and strict safety regulations result in great effort when implementing a new sensor prototype in hot rolling mills. For this reason, this work presents a mechatronic test stand that can simulate the cross-sectional area variation under laboratory conditions realistically.
Technologie die beflügelt
(2016)
Das CameraFramework wurde entwickelt, um mittels Socket-Kommunikation [1] als Middleware zwischen verschiedenen Kamerainstanzen mit eigenen Kameratreibern und Clienten zu fungieren. Über diesen Kommunikationsweg ist es möglich Clienten nicht nur lokal, sondern auch über das Netzwerk mit Kameradaten zu versorgen. Um neue Kameras mit dem Framework nutzen zu können, muss die Implementierung gewissen Regeln folgen, was durch ein vorgegebenes Basis-Interface (abstrakte Basis-Klasse in C++ [2]) fast vollständig sichergestellt ist. Neue Kameras werden zur Laufzeit über dynamische Bibliotheken geladen. Parameter für Kameras sind über ein XML-File [3] einzustellen. Funktionen zur Übergabe von neuen Kameradaten sind implementiert und müssen durch den Entwickler der einzelnen Kamerainterfaces aufgerufen werden.
Die Zuordnung von Kameradaten zum passenden Nutzer übernimmt das Framework. Jeder Clienterhält seinen eigenen konfigurierbaren Ringbuffer [4] um unabhängig von anderen Nutzern und Kameras zu sein. Die Aufgaben des Frameworks sind auf verschiedene Module, wie in Abbildung 1 dargestellt, aufgeteilt.
Autonomous driving is one of the future visions in which many vehicle manufacturers are working with high pressure.
Nowadays, it is already supported partially by high-class vehicles. A completely autonomous journey is indeed the goal, but in cars for
the public road traffic still not available. Automatic lane keeping assistants, speed regulators as well as shield and obstacle detections
are parts or precursors on the way to completely autonomous driving.
The American vehicle manufacturer Tesla is not only known for its electric drive, but also for the fact that high-pressure work is carried out on the autonomous drive. Tesla is thus the only vehicle manufacturer to use its users as so-called beta testers for its assistance systems. The progress and the function of the currently available Model S in the field of assistance systems and autonomic driving is documented and described in this paper. It is shown how good or bad the test vehicle manages scenarios in normal road traffic situations
with the assistance systems, e.g. lane keeping assistant, speed control, lane change and distance assistant, and which scenarios can
not be managed by the vehicle itself.
Aktiv im Alter
(2016)
Die Prognosen für den demografischen Wandel sind eindeutig: In den kommenden Jahren wird es immer mehr Menschen über 65 Jahre geben. Damit verbunden sind große Herausforderungen für die Gesellschaft und ihre Sozialsysteme, aber auch für viele Angehörige, die ihre Verwandten im Alter pflegen. Doch nicht alle älteren Menschen leben im Kreise ihrer Familie oder können sich Fremdbetreuung durch Pflegedienste leisten. Häufig übernehmen Nachbarn oder Freunde aus der Umgebung diese Aufgabe. Für diese Menschen wird das Wohnquartier zum zentralen Gesundheitsstandort.
Im besten Fall können sie dort ihren Alltag noch lange selbstständig bewältigen und ihre sozialen Kontakte aufrechterhalten. Das soll bald eine App unterstützen. Sie ist Teil eines Trainingsprogramms, das die Hochschule für Gesundheit (hsg) im Verbund mit der Hochschule Ruhr West erarbeitet. Der Name des Projekts ist Programm: „Quartier agil – Aktiv vor Ort“. Mit Übungen zum kognitiven und körperlichen Training, Angeboten für Gruppenaktivitäten, Kommunikationsforen und Funktionen zur Selbstkontrolle wollen die Forscherinnen und Forscher
ältere Menschen fit halten.
For face recognition from video streams speed and accuracy are vital aspects. The first decision whether a preprocessed image region represents a human face or not is often made by a feed-forward neural network (NN), e.g. in the Viisage-FaceFINDER® video surveillance system. We describe the optimisation of such a NN by a hybrid algorithm combining evolutionary multi-objective optimisation (EMO) and gradient-based learning. The evolved solutions perform considerably faster than an expert-designed architecture without loss of accuracy. We compare an EMO and a single objective approach, both with online search strategy adaptation. It turns out that EMO is preferable to the single objective approach in several respects.
Das kEFIR‐Projekt untersucht die praktische Anwendung von thermographischen Verfahren zur Analyse der strukturellen Integrität von Windkraftrotorblättern. Das Projekt entstand in Zusammenarbeit der Hochschule Ruhr West (HRW) mit der IQbis Consulting GmbH im Rahmen eines ZIM‐Förderprojekts des Bundesministeriums für Wirtschaft und Energie (BMWi). Hintergrund ist die zunehmende Anzahl von Windkraftanlagen (WKA) und der somit steigende Wartungsaufwand. Um einen reibungslosen Betrieb dieser Anlagen zu gewährleisten und damit den besonderen Anforderungen an die Verfügbarkeit energieerzeugender Anlagen sicherzustellen, ist ein Bedarf an qualitativ hochwertigen Fehleranalysesystemen für im Betrieb befindlicher WKA von besonderer Bedeutung. Erfahrungsgemäß ist der Zeitaufwand für diese Inspektionen mit aktuellen Mitteln sehr groß und wird üblicherweise mit mehreren Arbeitstagen kalkuliert. Die Reproduzierbarkeit der gewonnenen Daten ist bei den derzeitigen Methoden meist nicht gewährleistet. Um frühzeitig auf Instabilitäten oder Schäden in den Rotorblättern einer WKA aufmerksam zu werden, ist die Entwicklung eines schnellen und qualitativ hoch wertigen Fehleranalysesystems von zentraler Bedeutung. Ein Forschungsschwerpunkt in diesem Zusammenhang ist die Entwicklung von geeigneten bildgebenden und berührungslosen Verfahren, welche bei den Inspektionen eingesetzt werden können. Beispielsweise erlaubt der Einsatz thermographischer Sensoren eine Analyse nicht nur der Rotorblattoberfläche, sondern auch ihrer inneren Struktur. Weiterhin ist aufgrund des schnell wachsenden Marktes bei unbemannten Luftfahrzeugen, wie beispielsweise positionsstabiler Quatrocoptersysteme, eine zusätzliche Möglichkeit gegeben, die Inspektion von Windenergieanlagen mit Hilfe mobiler, kompakter und fliegender Analysesysteme zu unterstützen.
Das kEFIR‐Projekt untersucht die praktische Anwendung von thermographischen Verfahren zur Analyse der strukturellen Integrität von Windkraftrotorblättern. Das Projekt entstand in Zusammenarbeit der Hochschule Ruhr West (HRW) mit der IQbis Consulting GmbH im Rahmen eines ZIM‐Förderprojekts des Bundesministeriums für Wirtschaft und Energie (BMWi). Hintergrund ist die zunehmende Anzahl von Windkraftanlagen (WKA) und der somit steigende Wartungsaufwand. Um einen reibungslosen Betrieb dieser Anlagen zu gewährleisten, und damit den besonderen Anforderungen an die Verfügbarkeit energieerzeugender Anlagen sicherzustellen, ist ein Bedarf an qualitativ hochwertigen Fehleranalysesystemen für im Betrieb befindlicher WKA von besonderer Bedeutung. Erfahrungsgemäß ist der Zeitaufwand für diese Inspektionen mit aktuellen Mitteln sehr groß und wird üblicherweise mit mehreren Arbeitstagen kalkuliert. Die Reproduzierbarkeit der gewonnenen Daten ist bei den derzeitigen Methoden meist nicht gewährleistet. Um frühzeitig auf Instabilitäten oder Schäden in den Rotorblättern einer WKA aufmerksam zu werden, ist die Entwicklung eines schnellen und qualitativ hochwertigen Fehleranalysesystems von zentraler Bedeutung. Ein Forschungsschwerpunkt in diesem Zusammenhang ist die Entwicklung von geeigneten bildgebenden und berührungslosen Verfahren, welche bei den Inspektionen eingesetzt werden können. Beispielsweise erlaubt der Einsatz thermographischer Sensoren eine Analyse nicht nur der Rotorblattoberfläche, sondern auch ihrer inneren Struktur. Weiterhin ist aufgrund des schnell wachsenden Marktes bei unbemannten Luftfahrzeugen, wie beispielsweise positionsstabiler Quatrocoptersysteme, eine zusätzliche Möglichkeit gegeben, die Inspektion von Windenergieanlagen mit Hilfe mobiler, kompakter und fliegender Analysesysteme zu unterstützen.
Es ist eine alltägliche Erfahrung, daß wir Urteile über gut oder schlecht, bzw. qualitativ hochwertig oder minderwertig eines Gegenstandes mit der Wahrnehmung des emittierten Geräuschschalls in Verbindung bringen. Der Geräuschlaut ist deshalb ein wichtiges Entscheidungskriterium bei der Auswahl eines Produktes, welches wahrnehmbaren Schall erzeugt. Die Fragestellung hinsichtlich der Geräuschqualität und des Geräuschdesigns stellt daher hohe Anforderungen an den Akustik-Ingenieur. Zum heutigen Zeitpunkt ist es jedoch nicht möglich, mit einer instrumentellen Meßtechnik Aussagen über die Eignung eines Geräuschschalls für ein Produkt zu machen. Es ist nicht möglich, kognitive Faktoren über eine instrumentelle Meßtechnik zu messen. Es reicht nicht aus, eine Geräuschgüte mit Bewertungsschemata wie dem A-bewerteten Schalldruckpegel
oder Lautheitsmodellen zu definieren. Diese lassen allein keine eindeutigen Aussagen über die Wahrnehmung von Geräuschen zu. Der vorliegende Beitrag ist als Ansatz für das Soundengineering von Fahrzeuginnengeräuschen zu sehen. Es wird anhand von Hörversuchen mit Fahrzeuginnengeräuschen ein objektiver Beschreibungskatalog ermittelt, der eine Aussage über die jeweilige Hörempfindung zuläßt.
Das vorliegende Paper gibt einen Überblick über das Verhalten von modernen, autonom navigierenden Fahrzeugen in Baustellen. Dabei werden besondere Herausforderungen für die autonome Navigation im Baustellenbereich benannt. Außerdem wird ein Überblick über die Sensorausstattung und die Fahrerassistenzsysteme von modernen Fahrzeugen gegeben und es werden Technologien vorgestellt, die für eine Verbesserung der autonomen Navigation durch Baustellen genutzt werden können. Es wird ein Versuch durchgeführt, der aufzeigt, wie zuverlässig moderne Fahrzeuge durch Baustellensituationen navigieren können. Dabei werden Schwachstellen, wie bspw. die mangelnde Verfügbarkeit von Fahrerassistenzsystemen bei niedrigen Geschwindigkeiten, aufgedeckt.
In this review, we describe current Machine Learning approaches to hand gesture recognition with depth data from time-of-flight sensors. In particular, we summarise the achievements on a line of research at the Computational Neuroscience laboratory at the Ruhr West University of Applied Sciences. Relating our results to the work of others in this field, we confirm that Convolutional Neural Networks and Long Short-Term Memory yield most reliable results. We investigated several sensor data fusion techniques in a deep learning framework and performed user studies to evaluate our system in practice. During our course of research, we gathered and published our data in a novel benchmark dataset (REHAP), containing over a million unique three-dimensional hand posture samples.
With the introduction of Apple’s iPhone, gesture control became pop-
ular and was perceived as an intuitive means of interaction. Contact-
less gestures received broad attention with the X-Box Kinect.
Current technology is limited to a small number of uses, mainly
in entertainment systems. The target of this project is to increase the
range of possible applications, e.g. to the field of automotive,
industrial applications (manufacturing plants), assisted living in con-
texts ranging from private households to hospitals (interaction for
people with disabilities) and many more.
With a rapidly ageing population, it is increasingly important to de-
velop devices for elderly and disabled people that can support and aid
them in their daily lives, helping them to live at home as long as pos-
sible. The goal of this project is to implement a human-machine inter-
action and assistance system that can offer personalised health sup-
port for elderly people, or for those who have special needs in the
home environment.
One of the technical building blocks of Cloud Computing infrastructures are Web Services. With respect to mobile devices their role as Web Service consumers is widely accepted and today a large number of mobile applications already consume Web Services in order to fulfill their task. Still, not much research is conducted, as yet, to allow deploying Web Services on mobile devices and thus uses these kinds of devices as Web Service providers. This paper presents an analysis of one already implemented approach for provisioning mobile Web Services with respect to energy/battery consumption. Here, after shortly presenting the implementation for the provisioning of mobile Web Services an evaluation of the battery consumption that results in using the approach is presented. Last but not least, an improvement with respect to the battery consumption is presented. The performance test shows that the improved approach provides a reasonable way to introduce Web Service provisioning for mobile devices.
The term “Cloud Computing” does not primarily specify new types of core technologies but rather addresses features to do with integration, inter-operability and accessibility. Although not new, virtualization and automation are cor features that characterize Cloud Computing. In this paper, we intend to explore the possibility of integrating cloud services with educational scenarios without re-defining neither the technology nor the usage scenarios from scratch. Our suggestion is based on certain solutions that have already been implemented and tested for specific cases.
Design and Evaluation of a Platform Independent Application for Mobile Access of Moodle Quizzes
(2013)
One of the latest hypes in IT is the well-known Cloud
Computing paradigm. This paradigm that showed up in recent years
is a paradigm for the dynamic usage of computational power, memory and other computational resources. With respect to hypes, the author strongly believes that the
Cloud Computing paradigm has the potential to survive the hype and to become a usual technology used for the provision of IT based services. Therefore, it will be necessary to deploy Cloud Computing based infrastructures in a professional, stable and reliable way. This would lead to the idea that the Cloud Computing paradigm needs to be concerned with respect to IT Service Management, since cloud based infrastructures have to be managed differently in comparison to a usual infrastructure. This paper discusses, based on the IT Infrastructure Library (ITIL), as the de-facto standard for IT Service Management, whether this de-facto standard might also be able to manage Cloud Computing based infrastructures, how the according processes might change and whether ITIL supports a division of labor between the customer and the service provider
of a Cloud Computing based infrastructure.
The role of mobile devices as Web Service consumers is widely accepted and a large number of mobile applications already consumes Web Services in order to fullfill their task. Nevertheless, the growing number of powerful mobile devices, e.g. mobile phones, tablets even raise the question whether these devices can not only be used as Web Service consumers but at the same time also as Web Service providers. Therefore, this paper presents an approach that allows to deploy Web Services on mobile devices by the usage of the well-known protocols and standards, e.g. SOAP/REST and WSDL.
In diesem Artikel wird eine flexible Architektur vorgestellt, mit deren Hilfe eine modulare Lösung von Fahrerassistenzaufgaben in Kraftfahrzeugen gezeigt werden kann. Es wird eine Objektbezogene Analyse von Sensordaten, eine Verhaltensbasierte Szeneninterpretation und eine Verhaltensplanung vorgestellt. Eine globale Wissensbasis, auf der jedes einzelne Modul arbeitet, beinhaltet die Beschreibung physikalischer Zusammenhänge, Verhaltensregeln für den Straßenverkehr, sowie Objekt- und Szenenwissen. Externes Wissen (z.B. GPS - Global Positioning System) kann ebenfalls in die Wissensbasis eingebunden werden. Als Anwendungsbeispiel der Verhaltensplanung wird ein intelligenter Tempomat vorgestellt.
Systems for automated image analysis are useful for a variety of tasks. Their importance is still growing due to technological advances and increased social acceptance. Especially driver assistance systems have reached a high level of sophistication. Fully or partly autonomously guided vehicles, particularly for road traffic, require highly reliable algorithms due to the conditions imposed by natural environments. At the Institut fur Neuroinformatik, methods for analyzing driving relevant scenes by computer vision are developed in cooperation with several partners from the automobile industry. We present a system extracting important information from an image taken by a CCD camera installed at the rear-view mirror in a car. The approach is divided into a sequential and a parallel phase of sensor and information processing. Three main tasks, namely initial segmentation (object detection), object tracking and object classification are realized by integration in the sequential phase and by fusion in the parallel phase. The main advantage of this approach is integrative coupling of different algorithms providing partly redundant information. q 2000 Elsevier Science B.V. All rights reserved.
Group-centered framework towards a positive design of digital collaboration in global settings
(2017)
Globally distributed groups require collaborative systems to support their work. Besides being able to support the teamwork, these systems also should promote well-being and maximize the human potential that leads to an engaging system and joyful experience. Designing such system is a significant challenge and requires a thorough understanding of group work. We used the field theory as a lens to view the essential aspects of group motivation and then utilized collaboration personas to analyze the elements of group work. We integrated well-being determinants as engagement factors to develop a group-centered framework for digital collaboration in a global setting. Based on the outcomes, we proposed a conceptual framework to design an engaging collaborative system and recommend system values that can be used to evaluate the system further.
Women are still underrepresented at the highest management levels. The think-manager-think-male phenomenon suggests that leadership is associated with male rather than female attributes. Although styling has been shown to influence the evaluation of women's leadership abilities, the relevant specific features have been left remarkably unaddressed. In a 2 × 2 × 2 × 2 (skirt/pants, with/without jewelry, loose hair/braid, with/without makeup) between-subjects design, 354 participants evaluated a woman in a photograph. Women with makeup, pants, or with jewelry were rated as more competent than women without makeup, with skirts, or without jewelry. A combination of loose hair and no makeup was perceived as warmest, and women with loose hair were more likely to be hired than those with braids. In sum, even subtle changes in styling have a strong impact on how women's leadership abilities are evaluated.
The Bitcoin whitepaper states that security of the system is guaranteed as long as honest miners control more than half of the current total computational power. The whitepaper assumes a static difficulty, thus it is equally hard to solve a cryptographic proof-of-work puzzle for any given moment of the system history. However, the real Bitcoin network is using an adaptive difficulty adjustment mechanism. In this paper we introduce and analyze a new kind of attack on a mining difficulty retargeting function used in Bitcoin. A malicious miner is increasing his mining profits from the attack, named coin-hopping attack, and, as a side effect, an average delay between blocks is increasing. We propose an alternative difficulty adjustment algorithm in order to reduce an incentive to perform coin-hopping, and also to improve stability of inter-block delays. Finally, we evaluate the presented approach and show that the novel algorithm performs better than the original algorithm of Bitcoin.
In this paper we present an approach for contextual big data analytics in social networks, particularly in Twitter. The combination of a Rich Context Model (RCM) with machine learning is used in order to improve the quality of the data mining techniques. We propose the algorithm and architecture of our approach for real-time contextual analysis of tweets. The proposed approach can be used to enrich and empower the predictive analytics or to provide relevant context-aware recommendations.