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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)
Ein Prüf- und Zertifizierungsstandard zur gesellschaftlichen Verantwortung von Organisationen
(2019)
Nach einem breiten Konsens in Gesellschaft, Politik und Wissenschaft lässt sich die gegenwärtige Art zu wirtschaften so nicht fortsetzen. Ein weltweit gültiger Zertifizierungsstandard, der einen einheitlichen und politisch neutralen Ordnungsrahmen zur gesellschaftlichen Verantwortung von Organisationen gegenüber Mensch und Natur zum Ziel hat, kann helfen, die sich dynamisch verändernden und jeweils notwendigen Anforderungen zu messen, deren Einhaltung (auch komparativ) nachzuweisen und ggf. notwendige Korrekturen zu definieren. Die internationale Norm DIN EN ISO/IEC 26000:2010 bietet einen grundsätzlich geeigneten Orientierungsrahmen zu gesellschaftlich verantwortlichem Handeln für Organisationen.
BWL-Klausuren für Dummies
(2019)
Synthesis of Substituted Hydroxyapatite for Application in Bone Tissue Engineering and Drug Delivery
(2019)
Gas Chromatography
(2019)
Gas chromatography (GC) is one of the most important types of chromatography used in analytical chemistry for separating and analyzing chemical organic compounds. Today, gas chromatography is one of the most widespread investigation methods of instrumental analysis. This technique is used in the laboratories of chemical, petrochemical, and pharmaceutical industries, in research institutes, and also in clinical, environmental, and food and beverage analysis. This book is the outcome of contributions by experts in the field of gas chromatography and includes a short history of gas chromatography, an overview of derivatization methods and sample preparation techniques, a comprehensive study on pyrazole mass spectrometric fragmentation, and a GC/MS/MS method for the determination and quantification of pesticide residues in grape samples.
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.
Wir präsentieren einen interdisziplinären rechtskonformen Ansatz zur Verwaltung, Verteilung und Durchsetzung von Privatsphäre-Einstellungen: PRIVACY-AVARE soll es Anwendern ermöglichen, ihre Datenschutzpräferenzen zentral an einer Stelle festzulegen und dann global auf verschiedenen Geräten (Mobiltelefonen, Tablets, Smart Homes, Autos,....) anzuwenden.
Die Präsentation wird die technische Funktionsweise von PRIVACY-AVARE erörtern. Dabei wird aufgezeigt, wie mit Hilfe verschiedener eigener Komponenten und bestehender Open Source Komponenten Android-Anwendungen innerhalb je einer AVARE-Sandbox betrieben und so deren Datenzugriff kontrolliert werden kann. PRIVACY-AVARE wird live am Beispiel eines Messengers gezeigt. Es können nicht nur Zugriffe auf bestimmte Ressourcen blockiert werden. Mit PRIVACY-AVARE ist es möglich, dass der Messenger nur Zugriff auf bestimmte ausgewählte Datenfelder wie bspw. Name und Mobilfunknummer von explizit freigegebenen Kontakten erhält. Die Funktionsweise dieser vertikalen und horizontalen Filterung wird erörtert. Darüber hinaus bietet PRIVACY-AVARE die Möglichkeit, passende Ersatzdaten bereitzustellen, falls der Messenger ohne die Freigabe des Zugriffs auf bestimmte Daten nicht funktioniert.
Der Quellcode von PRIVACY-AVARE wird zur FrOSCon unter der Apache Lizenz 2.0 veröffentlicht (https://github.com/fzi-forschungszentrum-informatik/PRIVACY-AVARE). Die FrOSCon kann so für weitere Entwickler zum Ausgangspunkt werden, sich an der Weiterentwicklung zu beteiligen.
Survival of patients with pediatric acute lymphoblastic leukemia (ALL) after allogeneic hematopoietic stem cell transplantation (allo-SCT) is mainly compromised by leukemia relapse, carrying dismal prognosis. As novel individualized therapeutic approaches are urgently needed, we performed whole-exome sequencing of leukemic blasts of 10 children with post–allo-SCT relapses with the aim of thoroughly characterizing the mutational landscape and identifying druggable mutations. We found that post–allo-SCT ALL relapses display highly diverse and mostly patient-individual genetic lesions. Moreover, mutational cluster analysis showed substantial clonal dynamics during leukemia progression from initial diagnosis to relapse after allo-SCT. Only very few alterations stayed constant over time. This dynamic clonality was exemplified by the detection of thiopurine resistance-mediating mutations in the nucleotidase NT5C2 in 3 patients’ first relapses, which disappeared in the post–allo-SCT relapses on relief of selective pressure of maintenance chemotherapy. Moreover, we identified TP53 mutations in 4 of 10 patients after allo-SCT, reflecting acquired chemoresistance associated with selective pressure of prior antineoplastic treatment. Finally, in 9 of 10 children’s post–allo-SCT relapse, we found alterations in genes for which targeted therapies with novel agents are readily available. We could show efficient targeting of leukemic blasts by APR-246 in 2 patients carrying TP53 mutations. Our findings shed light on the genetic basis of post–allo-SCT relapse and may pave the way for unraveling novel therapeutic strategies in this challenging situation.
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]
Dysregulation of IL12 Signaling As a Novel Cause of an Autoimmune Lymphoproliferative like Syndrome
(2014)
The Concept of Purpose, Travelling, and Connectivity: Three Pillars of Organization and Leadership
(2020)
The three pillars of organization and leadership are discussed as being fundamental because they enable organizations to continuously synchronize with business needs and opportunities and enable their employees to interact flexibly and successfully. The building blocks or pillars are interconnected with agile practices such as iterative procedures and minimum viable solutions. They also challenge our learning abilities beyond enriching our inner maps, i.e., adapting them and even managing their adaption.
Application developers constitute an important part of a digital platform’s ecosystem. Knowledge about psychological processes that drive developer behavior in platform ecosystems is scarce. We build on the lead userness construct which comprises two dimensions, trend leadership and high expected benefits from a solution, to explain how developers’ innovative work behavior (IWB) is stimulated. We employ an efficiencyoriented and a social-political perspective to investigate the relationship between lead userness and IWB. The efficiency-oriented view resonates well with the expected benefit dimension of lead userness, while the social-political view might be interpreted as a reflection of trend leadership. Using structural equation modeling, we test our model with a sample of over 400 developers from three platform ecosystems. We find that lead userness is indirectly associated with IWB and the performance-enhancing view to be the stronger predictor of IWB. Finally, we unravel differences between paid and unpaid app developers in platform ecosystems.
