Refine
Departments, institutes and facilities
- Fachbereich Wirtschaftswissenschaften (91)
- Fachbereich Angewandte Naturwissenschaften (62)
- Fachbereich Informatik (55)
- Institut für Technik, Ressourcenschonung und Energieeffizienz (TREE) (41)
- Fachbereich Sozialpolitik und Soziale Sicherung (33)
- Fachbereich Ingenieurwissenschaften und Kommunikation (32)
- Institut für Verbraucherinformatik (IVI) (28)
- Internationales Zentrum für Nachhaltige Entwicklung (IZNE) (25)
- Präsidium (25)
- Institute of Visual Computing (IVC) (22)
Document Type
- Article (109)
- Part of a Book (94)
- Conference Object (93)
- Part of Periodical (26)
- Book (monograph, edited volume) (20)
- Report (13)
- Contribution to a Periodical (9)
- Working Paper (8)
- Doctoral Thesis (4)
- Preprint (3)
Year of publication
- 2018 (388) (remove)
Keywords
- Digitalisierung (5)
- ICT (5)
- Lehrbuch (4)
- Betriebswirtschaftslehre (3)
- Dementia (3)
- FPGA (3)
- Qualitätsmanagement (3)
- User Experience (3)
- drug release (3)
- lignin (3)
Der globalisierte Kapitalismus, Umweltkatastrophen, Überbevölkerung sowie zunehmende politisch und religiös motivierte Konflikte fordern ein Umdenken ökonomischer Konzepte. Allein mit den herkömmlichen Sozial- und Wirtschaftssystemen lassen sich existenzbedrohende Probleme wie Armut, Hunger, Krankheit, mangelnde Bildung und Betreuung nicht lösen. Es gilt dringend, neue, zukunftsweisende unternehmerische Konzepte zu entwickeln und zu fördern. Seit geraumer Zeit hat sich unter dem Begriff „Social Entrepreneurship“ eine Bewegung etabliert, die versucht, unter Beachtung klassischer Ökonomiegrundsätze gesellschaftliche Herausforderungen nachhaltig zu lösen.
Mit dem Anspruch, die Qualität in der medizinischen Rehabilitation weiterzuentwickeln, haben sich im Jahr 2007 13 Kliniken von acht Trägern zusammengeschlossen. Heute, ein gutes Jahrzehnt später, vereint der Qualitätsverbund Gesundheit rund 30 Reha-Kliniken von elf Trägern, darunter kommunale und kirchliche Institutionen, Privatunternehmen und die Rehazentren der Deutschen Rentenversicherung Baden-Württemberg.
Towards explaining deep learning networks to distinguish facial expressions of pain and emotions
(2018)
Deep learning networks are successfully used for object and face recognition in images and videos. In order to be able to apply such networks in practice, for example in hospitals as a pain recognition tool, the current procedures are only suitable to a limited extent. The advantage of deep learning methods is that they can learn complex non-linear relationships between raw data and target classes without limiting themselves to a set of hand-crafted features provided by humans. However, the disadvantage is that due to the complexity of these networks, it is not possible to interpret the knowledge that is stored inside the network. It is a black-box learning procedure. Explainable Artificial Intelligence (AI) approaches mitigate this problem by extracting explanations for decisions and representing them in a human-interpretable form. The aim of this paper is to investigate the explainable AI method Layer-wise Relevance Propagation (LRP) and apply it to explain how a deep learning network distinguishes facial expressions of pain from facial expressions of emotions such as happiness and disgust.
Background: Local injection of autologous conditioned serum (ACS) is a well-known therapy for inflammatory diseases (IDs). While patients’ blood is incubated to generate ACS (with subsequent centrifugation), immune cells produce high amounts of growth factors and cytokines. This include, amongst others, interleukin-1 receptor antagonist (IL-1ra), interleukins 6 and 10, tumour necrosis factor alpha (TNF-α) and transforming growth factor beta 1 (TGF-β1). The aim of this study was to analyse exosomes release into ACS as well as their cytokine cargo.
In recent years, a variety of methods have been introduced to exploit the decrease in visual acuity of peripheral vision, known as foveated rendering. As more and more computationally involved shading is requested and display resolutions increase, maintaining low latencies is challenging when rendering in a virtual reality context. Here, foveated rendering is a promising approach for reducing the number of shaded samples. However, besides the reduction of the visual acuity, the eye is an optical system, filtering radiance through lenses. The lenses create depth-of-field (DoF) effects when accommodated to objects at varying distances. The central idea of this article is to exploit these effects as a filtering method to conceal rendering artifacts. To showcase the potential of such filters, we present a foveated rendering system, tightly integrated with a gaze-contingent DoF filter. Besides presenting benchmarks of the DoF and rendering pipeline, we carried out a perceptual study, showing that rendering quality is rated almost on par with full rendering when using DoF in our foveated mode, while shaded samples are reduced by more than 69%.
Untersuchungen zur Hydrophobierung von Miscanthus X giganteus für den Einsatz in Dämmstoffsystemen
(2018)
Friction effects impose a requirement for the supplementary amount of torque to be produced in actuators for a robot to move, which in turn increases energy consumption. We cannot eliminate friction, but we can optimize motions to make them more energy efficient, by considering friction effects in motion computations. Optimizing motions means computing efficient joint torques/accelerations based on different friction torques imposed in each joint. Existing friction forces can be used for supporting certain types of arm motions, e.g standing still.
Reducing energy consumption of robot's arms will provide many benefits, such as longer battery life of mobile robots, reducing heat in motor systems, etc.
The aim of this project is extending an already available constrained hybrid dynamic solver, by including static friction effects in the computations of energy optimal motions. When the algorithm is extended to account for static friction factors, a convex optimization (maximization) problem must be solved.
The author of this hybrid dynamic solver has briefly outlined the approach for including static friction forces in computations of motions, but without providing a detailed derivation of the approach and elaboration that will show its correctness. Additionally, the author has outlined the idea for improving the computational efficiency of the approach, but without providing its derivation.
In this project, the proposed approach for extending the originally formulated algorithm has been completely derived and evaluated in order to show its feasibility. The evaluation is conducted in simulation environment with one DOF robot arm, and it shows correct results from the computation of motions. Furthermore, this project presents the derivation of the outlined method for improving the computational efficiency of the extended solver.
From September 2016 to February 2017, I did an internship at the University of Cape Coast, Ghana (UCC) as part of my studies in Business Administration at Hochschule Bonn-Rhein-Sieg, University of Applied Sciences, Germany (H-BRS). At H-BRS, an internship of five or six months (or, alternatively, one exchange semester) is an obligatory part of the curriculum so students get hands-on experience even before they enter the job market. My internship was also part of the intercontinental partnership between UCC and H-BRS, which has resulted in many different projects.
Daryoush Daniel Vaziri illustrates that the use of mixed methods designs may support the induction of more subtle and complete theories about older adults’ use of technologies for the support of active and healthy aging. The results show that older adults’ social contexts and environments considerably affect their perspectives, practices and attitudes with respect to health, quality of life, well-being and technology use for active and healthy aging support. Results were collected with older adults aged 60+ as well as relevant secondary stakeholders like caregivers, policy makers or health insurance companies.
Speech understanding is a fundamental feature for many applications focused on human-robot interaction. Although many techniques and several services for speech recognition and natural language understanding have been developed in the last years, specific implementation and validation on domestic service robots have not been performed. In this paper, we describe the implementation and the results of a functional benchmark for speech understanding in service robotics that has been developed and tested in the context of different robot competitions: RoboCup@Home, RoCKIn@Home and within the European Robotics League on Service Robots. Different approaches used by the teams in the competitions are presented and the evaluation results obtained in the competitions are discussed.