Refine
Departments, institutes and facilities
- Fachbereich Informatik (86)
- Fachbereich Wirtschaftswissenschaften (69)
- Fachbereich Angewandte Naturwissenschaften (57)
- Fachbereich Ingenieurwissenschaften und Kommunikation (55)
- Institut für Technik, Ressourcenschonung und Energieeffizienz (TREE) (47)
- Fachbereich Sozialpolitik und Soziale Sicherung (40)
- Institut für Verbraucherinformatik (IVI) (27)
- Institut für funktionale Gen-Analytik (IFGA) (21)
- Institute of Visual Computing (IVC) (18)
- Institut für Cyber Security & Privacy (ICSP) (16)
- Graduierteninstitut (15)
- Internationales Zentrum für Nachhaltige Entwicklung (IZNE) (11)
- Institut für Medienentwicklung und -analyse (IMEA) (6)
- Institut für Sicherheitsforschung (ISF) (6)
- Zentrum für Innovation und Entwicklung in der Lehre (ZIEL) (5)
- Zentrum für Ethik und Verantwortung (ZEV) (4)
- Institut für Soziale Innovationen (ISI) (3)
- Stabsstelle Kommunikation und Marketing (3)
- Centrum für Entrepreneurship, Innovation und Mittelstand (CENTIM) (2)
- Präsidium (2)
- Institut für Detektionstechnologien (IDT) (1)
- Institut für IT-Service (ITS) (1)
- Sprachenzentrum (1)
- Verwaltung (1)
- Zentrum für Wissenschafts- und Technologietransfer (ZWT) (1)
Document Type
- Article (134)
- Conference Object (83)
- Part of a Book (44)
- Book (monograph, edited volume) (24)
- Preprint (18)
- Doctoral Thesis (16)
- Report (10)
- Contribution to a Periodical (6)
- Master's Thesis (6)
- Research Data (5)
Year of publication
- 2020 (361) (remove)
Keywords
- Digitalisierung (5)
- Inborn error of metabolism (3)
- Innovation (3)
- Lehrbuch (3)
- Organic aciduria (3)
- Quality diversity (3)
- Shared autonomous vehicles (3)
- Usable Privacy (3)
- Usable Security (3)
- post-buckling (3)
Striated muscle contraction is regulated by the translocation of troponin-tropomyosin strands over the thin filament surface. Relaxation relies partly on highly-favorable, conformation-dependent electrostatic contacts between actin and tropomyosin, which position tropomyosin such that it impedes actomyosin associations. Impaired relaxation and hypercontractile properties are hallmarks of various muscle disorders. The α-cardiac actin M305L hypertrophic cardiomyopathy-causing mutation lies near residues that help confine tropomyosin to an inhibitory position along thin filaments. Here, we investigate M305L actin in vivo, in vitro, and in silico to resolve emergent pathological properties and disease mechanisms. Our data suggest the mutation reduces actin flexibility and distorts the actin-tropomyosin electrostatic energy landscape that, in muscle, result in aberrant contractile inhibition and excessive force. Thus, actin flexibility may be required to establish and maintain interfacial contacts with tropomyosin as well as facilitate its movement over distinct actin surface features and is, therefore, likely necessary for proper regulation of contraction.
Mendelian diseases of dysregulated canonical NF-κB signaling: From immunodeficiency to inflammation
(2020)
This dissertation presents a probabilistic state estimation framework for integrating data-driven machine learning models and a deformable facial shape model in order to estimate continuous-valued intensities of 22 different facial muscle movements, known as Action Units (AU), defined in the Facial Action Coding System (FACS). A practical approach is proposed and validated for integrating class-wise probability scores from machine learning models within a Gaussian state estimation framework. Furthermore, driven mass-spring-damper models are applied for modelling the dynamics of facial muscle movements. Both facial shape and appearance information are used for estimating AU intensities, making it a hybrid approach. Several features are designed and explored to help the probabilistic framework to deal with multiple challenges involved in automatic AU detection. The proposed AU intensity estimation method and its features are evaluated quantitatively and qualitatively using three different datasets containing either spontaneous or acted facial expressions with AU annotations. The proposed method produced temporally smoother estimates that facilitate a fine-grained analysis of facial expressions. It also performed reasonably well, even though it simultaneously estimates intensities of 22 AUs, some of which are subtle in expression or resemble each other closely. The estimated AU intensities tended to the lower range of values, and were often accompanied by a small delay in onset. This shows that the proposed method is conservative. In order to further improve performance, state-of-the-art machine learning approaches for AU detection could be integrated within the proposed probabilistic AU intensity estimation framework.
It is only a matter of time until autonomous vehicles become ubiquitous; however, human driving supervision will remain a necessity for decades. To assess the drive's ability to take control over the vehicle in critical scenarios, driver distractions can be monitored using wearable sensors or sensors that are embedded in the vehicle, such as video cameras. The types of driving distractions that can be sensed with various sensors is an open research question that this study attempts to answer. This study compared data from physiological sensors (palm electrodermal activity (pEDA), heart rate and breathing rate) and visual sensors (eye tracking, pupil diameter, nasal EDA (nEDA), emotional activation and facial action units (AUs)) for the detection of four types of distractions. The dataset was collected in a previous driving simulation study. The statistical tests showed that the most informative feature/modality for detecting driver distraction depends on the type of distraction, with emotional activation and AUs being the most promising. The experimental comparison of seven classical machine learning (ML) and seven end-to-end deep learning (DL) methods, which were evaluated on a separate test set of 10 subjects, showed that when classifying windows into distracted or not distracted, the highest F1-score of 79%; was realized by the extreme gradient boosting (XGB) classifier using 60-second windows of AUs as input. When classifying complete driving sessions, XGB's F1-score was 94%. The best-performing DL model was a spectro-temporal ResNet, which realized an F1-score of 75%; when classifying segments and an F1-score of 87%; when classifying complete driving sessions. Finally, this study identified and discussed problems, such as label jitter, scenario overfitting and unsatisfactory generalization performance, that may adversely affect related ML approaches.
Towards an Interaction-Centered and Dynamically Constructed Episodic Memory for Social Robots
(2020)
This study advances the research and methodological approach to measuring and understanding national-level destination competitiveness, sustainability and governance, by creating a model that could be of use for both developing and developed destinations. The study gives a detailed overview of the research field of measuring destination competitiveness and sustainability. It also identifies major predictors of destination competitiveness and sustainability and thereby presents destination researchers and practitioners with a useful list of priority areas, both from a global perspective and from the perspective of other similar destinations. Finally, the study identifies two major types of destination governance with implications for research, policy and practice across the destination life-cycle. The research deals with the analysis of the secondary data from the World Economic Forum Travel and Tourism Index (WEF T&T). Major types of destination governance and predictors of belonging to either one of the types, as well as inside cluster predictors have been extracted through a two-step cluster analysis. The results support the notion that a meaningful model of national-level destination governance needs to take into account different development levels of different destinations. The main limitation of the study is its typology creation approach, as it inevitably leads to simplifications.
Towards a conceptual framework for sustainable business models in the food and beverage industry
(2020)
Alkaline methanol oxidation is an important electrochemical process in the design of efficient fuel cells. Typically, a system of ordinary differential equations is used to model the kinetics of this process. The fitting of the parameters of the underlying mathematical model is performed on the basis of different types of experiments, characterizing the fuel cell. In this paper, we describe generic methods for creation of a mathematical model of electrochemical kinetics from a given reaction network, as well as for identification of parameters of this model. We also describe methods for model reduction, based on a combination of steady-state and dynamical descriptions of the process. The methods are tested on a range of experiments, including different concentrations of the reagents and different voltage range.
The general method of topological reduction for the network problems is presented on example of gas transport networks. The method is based on a contraction of series, parallel and tree-like subgraphs for the element equations of quadratic, power law and general monotone dependencies. The method allows to reduce significantly the complexity of the graph and to accelerate the solution procedure for stationary network problems. The method has been tested on a large set of realistic network scenarios. Possible extensions of the method have been described, including triangulated element equations, continuation of the equations at infinity, providing uniqueness of solution, a choice of Newtonian stabilizer for nearly degenerated systems. The method is applicable for various sectors in the field of energetics, including gas networks, water networks, electric networks, as well as for coupling of different sectors.
Chancengerechte Online-Lehre
(2020)
Computers can help us to trigger our intuition about how to solve a problem. But how does a computer take into account what a user wants and update these triggers? User preferences are hard to model as they are by nature vague, depend on the user’s background and are not always deterministic, changing depending on the context and process under which they were established. We pose that the process of preference discovery should be the object of interest in computer aided design or ideation. The process should be transparent, informative, interactive and intuitive. We formulate Hyper-Pref, a cyclic co-creative process between human and computer, which triggers the user’s intuition about what is possible and is updated according to what the user wants based on their decisions. We combine quality diversity algorithms, a divergent optimization method that can produce many, diverse solutions, with variational autoencoders to both model that diversity as well as the user’s preferences, discovering the preference hypervolume within large search spaces.
Dürrenmatt in der Schule
(2020)
Short summary
This dataset accompanies our paper
A. Mitrevski, P. G. Plöger, and G. Lakemeyer, "Representation and Experience-Based Learning of Explainable Models for Robot Action Execution," in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020.
Contents
There are three zip archives included, each of them a dump of a MongoDB database corresponding to one of the three experiments in the paper:
Grasping a drawer handle (handle_drawer_logs.zip)
Grasping a fridge handle (handle_fridge_logs.zip)
Pulling an object (pull_logs.zip)
All three experiments were performed with a Toyota HSR. Only the data necessary for learning the models used in our experiments are included here.
Usage
After unzipping the archives, each database can be restored with the command
mongorestore [directory_name]
This will create a MongoDB database with the name of the directory (handle_drawer_logs, handle_fridge_logs, and pull_logs).
Code for processing the data and model learning can be found in our <a href="https://github.com/alex-mitrevski/explainable-robot-execution-models">GitHub repository.
This dataset contains data from two measurement campaigns in autumn 2018 and summer 2019 that were part of the BMWi project "MetPVNet", and serve as a supplement to the paper "Dynamic model of photovoltaic module temperature as a function of atmospheric conditions", published in the special edition of "Advances in Science and Research", the proceedings of the 19th EMS Annual Meeting: European Conference for Applied Meteorology and Climatology 2019.
Data are resampled to one minute, and include:
PV module temperature
Ambient temperature
Plane-of-array irradiance
Windspeed
Atmospheric thermal emission
The data were used for the dynamic temperature model, as presented in the paper
Modern Monte-Carlo-based rendering systems still suffer from the computational complexity involved in the generation of noise-free images, making it challenging to synthesize interactive previews. We present a framework suited for rendering such previews of static scenes using a caching technique that builds upon a linkless octree. Our approach allows for memory-efficient storage and constant-time lookup to cache diffuse illumination at multiple hitpoints along the traced paths. Non-diffuse surfaces are dealt with in a hybrid way in order to reconstruct view-dependent illumination while maintaining interactive frame rates. By evaluating the visual fidelity against ground truth sequences and by benchmarking, we show that our approach compares well to low-noise path traced results, but with a greatly reduced computational complexity allowing for interactive frame rates. This way, our caching technique provides a useful tool for global illumination previews and multi-view rendering.
Kollaborative Industrieroboter werden für produzierende Unternehmen immer kosteneffizienter. Während diese Systeme für den menschlichen Mitarbeiter eine große Hilfe sein können, stellen sie gleichzeitig ein ernstes Gesundheitsrisiko dar, wenn die zwingend notwendigen Sicherheitsmaßnahmen nur unzureichend umgesetzt werden. Herkömmliche Sicherheitseinrichtungen wie Zäune oder Lichtvorhänge bieten einen guten Schutz, aber solch statische Schutzvorrichtungen sind in neuen, hochdynamischen Arbeitsszenarien problematisch.
Im Forschungsprojekt BeyondSPAI wurde ein Funktionsmuster eines Multisensorsystems zur Absicherung solcher dynamischer Arbeitsszenarien entworfen, implementiert und im Feld getestet. Kern des Systems ist eine robuste optische Materialklassifikation, die mit Hilfe eines intelligenten InGaAs-Kamerasystems Haut von anderen typischen Werkstückoberflächen (z.B. Holz, Metalle od. Kunststoffe) unterscheiden kann. Diese einzigartige Eigenschaft wird genutzt, um menschliche Mitarbeiter zuverlässig zu erkennen, so dass ein konventioneller Roboter in Folge als personenbewusster Cobot arbeiten kann.
Das System ist modular und kann leicht mit weiteren Sensoren verschiedenster Art erweitert werden. Es kann an verschiedene Marken von Industrierobotern angepasst werden und lässt sich schnell an bestehenden Robotersystemen integrieren. Die vier vom System bereitgestellten Sicherheitsausgänge können dazu verwendet werden - abhängig von der durchdrungenen Überwachungszone - entweder eine Warnung auszugeben, die Bewegung des Roboters auf eine sichere Geschwindigkeit zu verlangsamen, oder den Roboter sicher anzuhalten. Sobald alle Zonen wieder als „eindeutig frei von Personen“ identifiziert sind, kann der Roboter wieder beschleunigen, seine ursprüngliche Bewegung wiederaufnehmen und die Arbeit fortsetzen.