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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 ofstatic 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.
We present the development and evaluation of a basic building block for a future wireless sensor network for agriculture monitoring in Argentina. The module consists of a compact battery-powered wireless sensor node capable of monitoring the ambient air parameters of temperature, humidity, gas and air pressure in the agriculture industry of Argentina's Pampa region. Further in-and outputs allow the system to be extended flexibly by adding more sensors. Throughout the development, a simple, low-cost and open-source-based approach together with a lightweight communication protocol was pursued. The sensor nodes cover ranges of over 400 metres and can be operated on two AAA alkaline batteries for several years. Detailed current consumption values, range limits and battery life estimates are presented.
Digital transformation in Higher Education and Science is a mission-critical demand to prepare educational institutions for their future competition on the international market. In many cases, the digitization goes along with the search for and acquisition of new software. For easily exchangeable software, wrong product decisions, in the worst case, lead to calculable financial losses. However, if a planned software requires a lot of technological adjustments and is to be applied as central component of a business- and/or security-critical environment, wrong decisions during the software acquisition process might lead to hardly calculable damage. Questions arising are how to decide for a product and how many resources should be invested for the acquisition process.
We planned to apply a commercial Business Support System, which should replace the currently used in-house developed software. Our goals were the increase of our university’s level of data security, to ease the interaction between stakeholders, to eliminate media discontinuities, to improve the process management and transparency, and to reduce the execution time of automated processes. Alongside with the introduction of the electronic case file, our agenda stipulates the digitization (and automation) of administrative university processes, especially, but not limited to, the student self-service and the administrative student life cycle. Usual tools and practices, commonly applied to (simple) software acquisition, failed in our scenario.
With the case study introduced in this paper, we address all persons, involved within software acquisition processes: From our experiences, we strongly recommend to place greater value on an exhaustively completed acquisition process, than on short-termed economic advantages.
The Learning Culture Survey (LCS) is a questionnaire-based research, investigating students’ perceptions of and expectations towards Higher Education (HE). The aim of this survey is to improve our understanding about the sources of cultural conflicts in educational scenarios. This understanding, shell help us to predict potential conflict situations and develop supportive measures.
After three years of development, the LCS was initialized in 2010 in South Korea and Germany. During the following years, the investigations were extended to further countries. The results, on the one hand, provided insights about the cultural context of HE in general and on the other hand, about specific (national / regional) characteristics of learners in HE. Most issues targeted with the questionnaire were directly linked to value systems. Thus, we expected from the beginning that the collected data would keep valid over longer periods of time. However, we had no evidence regarding the actual persistence of learning culture. For a study, designed to being implemented on a global scope and providing input for further applications, persistence is a basic condition to justify related investigations.
To answer the question on persistence, we repeated the LCS in our university every four years, between 2010 to 2018/19. Besides a small number of slight changes, explainable out of their situational context, the overall results kept consistent over the investigated years. In this paper, after an introduction of the LCS’ concept, setting and its general results from the past years, we present the insights from our most recently finalized longitudinal study on learning culture.
This work presents the preliminary research towards developing an adaptive tool for fault detection and diagnosis of distributed robotic systems, using explainable machine learning methods. Autonomous robots are complex systems that require high reliability in order to operate in different environments. Even more so, when considering distributed robotic systems, the task of fault detection and diagnosis becomes exponentially difficult.
To diagnose systems, models representing the behaviour under investigation need to be developed, and with distributed robotic systems generating large amount of data, machine learning becomes an attractive method of modelling especially because of its high performance. However, with current day methods such as artificial neural networks (ANNs), the issue of explainability arises where learnt models lack the ability to give explainable reasons behind their decisions.
This paper presents current trends in methods for data collection from distributed systems, inductive logic programming (ILP); an explainable machine learning method, and fault detection and diagnosis.
In the field of service robots, dealing with faults is crucial to promote user acceptance. In this context, this work focuses on some specific faults which arise from the interaction of a robot with its real world environment due to insufficient knowledge for action execution.
In our previous work [1], we have shown that such missing knowledge can be obtained through learning by experimentation. The combination of symbolic and geometric models allows us to represent action execution knowledge effectively. However we did not propose a suitable representation of the symbolic model.
In this work we investigate such symbolic representation and evaluate its learning capability. The experimental analysis is performed on four use cases using four different learning paradigms. As a result, the symbolic representation together with the most suitable learning paradigm are identified.
In Sensor-based Fault Detection and Diagnosis (SFDD) methods, spatial and temporal dependencies among the sensor signals can be modeled to detect faults in the sensors, if the defined dependencies change over time. In this work, we model Granger causal relationships between pairs of sensor data streams to detect changes in their dependencies. We compare the method on simulated signals with the Pearson correlation, and show that the method elegantly handles noise and lags in the signals and provides appreciable dependency detection. We further evaluate the method using sensor data from a mobile robot by injecting both internal and external faults during operation of the robot. The results show that the method is able to detect changes in the system when faults are injected, but is also prone to detecting false positives. This suggests that this method can be used as a weak detection of faults, but other methods, such as the use of a structural model, are required to reliably detect and diagnose faults.
Quantifying Interference in WiLD Networks using Topography Data and Realistic Antenna Patterns
(2019)
Synthesis of Substituted Hydroxyapatite for Application in Bone Tissue Engineering and Drug Delivery
(2019)
The paper presents the topological reduction method applied to gas transport networks, using contraction of series, parallel and tree-like subgraphs. The contraction operations are implemented for pipe elements, described by quadratic friction law. This allows significant reduction of the graphs and acceleration of solution procedure for stationary network problems. The algorithm has been tested on several realistic network examples. The possible extensions of the method to different friction laws and other elements are discussed.
Mobile navigation apps are among the most used mobile applications and are often used as a baseline to evaluate new mobile navigation technologies in field studies. As field studies often introduce external factors that are hard to control for, we investigate how pedestrian navigation methods can be evaluated in virtual reality (VR). We present a study comparing navigation methods in real life (RL) and VR to evaluate if VR environments are a viable alternative to RL environments when it comes to testing these. In a series of studies, participants navigated a real and a virtual environment using a paper map and a navigation app on a smartphone. We measured the differences in navigation performance, task load and spatial knowledge acquisition between RL and VR. From these we formulate guidelines for the improvement of pedestrian navigation systems in VR like improved legibility for small screen devices. We furthermore discuss appropriate low-cost and low-space VR-locomotion techniques and discuss more controllable locomotion techniques.
Die Nachfrage nach nachhaltigen und ökologischen Bau- und Dämmstoffen wächst, besonders im Hinblick auf den fortschreitenden Klimawandel und die knapper werdenden fossilen Ressourcen. Eine mögliche Alternative zu traditionellen Baustoffen stellen dabei nachwachsende Rohstoffe dar, denn diese sind im Vergleich zu fossilen Ressourcen CO2-neutral. [1,2]
Data-Driven Robot Fault Detection and Diagnosis Using Generative Models: A Modified SFDD Algorithm
(2019)
This paper presents a modification of the data-driven sensor-based fault detection and diagnosis (SFDD) algorithm for online robot monitoring. Our version of the algorithm uses a collection of generative models, in particular restricted Boltzmann machines, each of which represents the distribution of sliding window correlations between a pair of correlated measurements. We use such models in a residual generation scheme, where high residuals generate conflict sets that are then used in a subsequent diagnosis step. As a proof of concept, the framework is evaluated on a mobile logistics robot for the problem of recognising disconnected wheels, such that the evaluation demonstrates the feasibility of the framework (on the faulty data set, the models obtained 88.6% precision and 75.6% recall rates), but also shows that the monitoring results are influenced by the choice of distribution model and the model parameters as a whole.
The design of self-driving cars is one of the most exciting and ambitious challenges of our days and every day, new research work is published. In order to give an orientation, this article will present an overview of various methods used to study the human side of autonomous driving. Simplifying roughly, you can distinguish between design science-oriented methods (such as Research through Design, Wizard of Oz or driving simulator ) and behavioral science methods (such as survey, interview, and observation). We show how these methods are adopted in the context of autonomous driving research and dis-cuss their strengths and weaknesses. Due to the complexity of the topic, we will show that mixed method approaches will be suitable to explore the impact of autonomous driving on different levels: the individual, the social interaction and society.
Die Globalisierung führt zu immer komplexeren, für die Einzelnen kaum nachvollziehbaren Wertschöpfungsketten in der Lebensmittelindustrie. Zugleich eröffnet die Digitalisierung neue Möglichkeiten, Informationen entlang der Kette zu sammeln, und so mehr Transparenz und Vertrauen für den Verbraucherbeziehungsweise die Verbraucherin zu schaffen. Jedoch finden Verbraucherinformations-Apps wie fTRACE bisher nur eine geringe Verbreitung. Daher haben wir in einer qualitativen Studie mit 16 Teilnehmer/-innen Bedürfnisse und Nutzungshürden von Verbraucher/-innen im Zusammenhang mit Verbraucherinformations-Apps analysiert. Es zeigt sich, dass das Vertrauen in die Informationen, sowie der einfache Zugang dazu für Verbraucher/-innen zentral sind. Durch die gut sichtbare Bereitstellung der Informationen am Point-of-Sale, sowie der automatisierten Informationsversorgung z. B. mittels digitaler Kassenzettel in Kombination mit weiteren Verbraucher-Services kann die Bekanntheit und Akzeptanz von Rückverfolgbarkeitssystemen weiter gesteigert werden.
Stakeholder-Analyse zum Einsatz IIoT-basierter Frischeinformationen in der Lebensmittelindustrie
(2019)
Eine Herausforderung bei der Implementierung des industriellen Internet of Things (IIoT) besteht darin, Mehrwerte in Wertschöpfungsketten zu identifizieren, um darauf aufbauend Lösungen nutzerzentriert zu gestalten. Dieser Beitrag stellt das Forschungsprojekt FreshIndex vor, bei dem diese Herausforderung durch eine Kombination aus Stakeholder-Analyse und User-Centered-Design-Methoden adressiert wurde. Ziel des Projekts ist es, eine IIoT-basierte Lösung zum Monitoring der Kühlkette in der Lebensmittelindustrie zu entwickeln. Hierzu ist es wichtig zu wissen, welche Nutzer/-innen mit den Daten in Berührung kommen und welche Erfahrungen, Fähigkeiten, Anforderungen und Wünsche sie mitbringen. Die Berücksichtigung dieser Aspekte ist relevant für den Erfolg der Konzeption, Implementierung und des Betriebs eines IIoT-Systems. So können nützliche und handhabbare Produktideen generiert und Anwendungen gestaltet werden, die von Mitarbeiter/-innen und Konsument/-innen angenommen werden. IIoT schließt somit die lokale Verwendbarkeit von Daten entlang der Wertschöpfungskette ein und beschränkt sich nicht auf zentrale Verfügbarkeit von Daten.
Trust is the lubricant of the sharing economy, especially in peer-to-peer carsharing where you leave a valuable good to a stranger in the hope of getting it back unscathed. Central mechanisms for handling this information gap nowadays are ratings and reviews of other users. The rising of connected car technology opens new possibilities to increase trust by collecting and providing e.g. driving behavior data. At the same time, this means an intrusion into the privacy of the user. Therefore, in this work we explore technological approaches that allow building trust without violating the privacy of individuals. We evaluate to what extent blockchain technology and smart contracts are suitable technologies to meet these challenges by setting up a prototype implementation of a blockchain-based carsharing approach. In this context, we present our research approach and evaluate the prototype in terms of trust and privacy.
