Refine
Has Fulltext
- yes (30)
Is part of the Bibliography
- yes (30)
Year of publication
- 2020 (30) (remove)
Document Type
- Journal article (15)
- Doctoral Thesis (9)
- Working Paper (3)
- Report (2)
- Bachelor Thesis (1)
Keywords
- Crowdsourcing (3)
- Quality of Experience (3)
- Echtzeit (2)
- Ethernet (2)
- Latenz (2)
- Optical Character Recognition (2)
- QoE (2)
- Rechnernetz (2)
- endurance (2)
- jitter (2)
Institute
- Institut für Informatik (30) (remove)
Sonstige beteiligte Institutionen
EU-Project number / Contract (GA) number
- 320377 (1)
Das Thema dieser Dissertation lautet „Konzeption und Evaluation eines webbasierten Patienteninformationsprogrammes zur Überprüfung internistischer Verdachtsdiagnosen“. Zusammen mit dem Institut für Informatik wurde das wissensbasierte second-opinion-System SymptomCheck entwickelt. Das Programm dient zur Überprüfung von Verdachtsdiagnosen. Es wurden Wissensbasen erstellt, in denen Symptome, Befunde und Untersuchungen nach einem Bewertungsschema beurteilt werden. Folgend wurde eine online erreichbare Startseite erstellt, auf der Nutzer vornehmlich internistische Verdachtsdiagnosen überprüfen können. Das Programm wurde in zwei Studien bezüglich seiner Sensitivität und Spezifität sowie der Benutzerfreundlichkeit getestet. In der ersten Studie wurden die Verdachtsdiagnosen ambulanter Patienten mit den ärztlich gestellten Diagnosen verglichen, eine zweite an die Allgemeinbevölkerung gerichtete Onlinestudie galt vor allem der Bewertung der Benutzerfreundlichkeit. Soweit bekannt ist dies die erste Studie in der ein selbst entwickeltes Programm selbstständig an echten Patienten getestet wurde.
The DFG project “SDN-enabled Application-aware Network Control Architectures and their Performance Assessment” (DFG SDN-App) focused in phase 1 (Jan 2017 – Dec 2019) on software defined networking (SDN). Being a fundamental paradigm shift, SDN enables a remote control of networking devices made by different vendors from a logically centralized controller. In principle, this enables a more dynamic and flexible management of network resources compared to the traditional legacy networks. Phase 1 focused on multimedia applications and their users’ Quality of Experience (QoE).
This documents reports the achievements of the first phase (Jan 2017 – Dec 2019), which is jointly carried out by the Technical University of Munich, Technical University of Berlin, and University of Würzburg. The project started at the institutions in Munich and Würzburg in January 2017 and lasted until December 2019.
In Phase 1, the project targeted the development of fundamental control mechanisms for network-aware application control and application-aware network control in Software Defined Networks (SDN) so to enhance the user perceived quality (QoE). The idea is to leverage the QoE from multiple applications as control input parameter for application-and network control mechanisms. These mechanisms are implemented by an Application Control Plane (ACP) and a Network Control Plane (NCP). In order to obtain a global view of the current system state, applications and network parameters are monitored and communicated to the respective control plane interface. Network and application information and their demands are exchanged between the control planes so to derive appropriate control actions. To this end, a methodology is developed to assess the application performance and in particular the QoE. This requires an appropriate QoE modeling of the applications considered in the project as well as metrics like QoE fairness to be utilized within QoE management.
In summary, the application-network interaction can improve the QoE for multi-application scenarios. This is ensured by utilizing information from the application layer, which are mapped by appropriate QoS-QoE models to QoE within a network control plane. On the other hand, network information is monitored and communicated to the application control plane. Network and application information and their demands are exchanged between the control planes so to derive appropriate control actions.
Time-triggered communication is widely used throughout several industry do-
mains, primarily for reliable and real-time capable data transfers. However,
existing time-triggered technologies are designed for terrestrial usage and not
directly applicable to space applications due to the harsh environment. In-
stead, specific hardware must be developed to deal with thermal, mechanical,
and especially radiation effects.
SpaceWire, as an event-triggered communication technology, has been used
for years in a large number of space missions. Its moderate complexity, her-
itage, and transmission rates up to 400 MBits/s are one of the main ad-
vantages and often without alternatives for on-board computing systems of
spacecraft. At present, real-time data transfers are either achieved by prior-
itization inside SpaceWire routers or by applying a simplified time-triggered
approach. These solutions either imply problems if they are used inside dis-
tributed on-board computing systems or in case of networks with more than
a single router are required.
This work provides a solution for the real-time problem by developing
a novel clock synchronization approach. This approach is focused on being
compatible with distributed system structures and allows time-triggered data
transfers. A significant difference to existing technologies is the remote clock
estimation by the use of pulses. They are transferred over the network and
remove the need for latency accumulation, which allows the incorporation of
standardized SpaceWire equipment. Additionally, local clocks are controlled
decentralized and provide different correction capabilities in order to handle
oscillator induced uncertainties. All these functionalities are provided by a developed Network Controller (NC), able to isolate the attached network and
to control accesses.
Nowadays, employees have to work with applications, technical services, and systems every day for hours. Hence, performance degradation of such systems might be perceived negatively by the employees, increase frustration, and might also have a negative effect on their productivity. The assessment of the application's performance in order to provide a smooth operation of the application is part of the application management. Within this process it is not sufficient to assess the system performance solely on technical performance parameters, e.g., response or loading times. These values have to be set into relation to the perceived performance quality on the user's side - the quality of experience (QoE).
This dissertation focuses on the monitoring and estimation of the QoE of enterprise applications. As building models to estimate the QoE requires quality ratings from the users as ground truth, one part of this work addresses methods to collect such ratings. Besides the evaluation of approaches to improve the quality of results of tasks and studies completed on crowdsourcing platforms, a general concept for monitoring and estimating QoE in enterprise environments is presented. Here, relevant design dimension of subjective studies are identified and their impact of the QoE is evaluated and discussed. By considering the findings, a methodology for collecting quality ratings from employees during their regular work is developed. The method is realized by implementing a tool to conduct short surveys and deployed in a cooperating company.
As a foundation for learning QoE estimation models, this work investigates the relationship between user-provided ratings and technical performance parameters. This analysis is based on a data set collected in a user study in a cooperating company during a time span of 1.5 years. Finally, two QoE estimation models are introduced and their performance is evaluated.
Evaluating the Quality of Experience (QoE) of video streaming and its influence factors has become paramount for streaming providers, as they want to maintain high satisfaction for their customers. In this context, crowdsourced user studies became a valuable tool to evaluate different factors which can affect the perceived user experience on a large scale. In general, most of these crowdsourcing studies either use, what we refer to, as an in vivo or an in vitro interface design. In vivo design means that the study participant has to rate the QoE of a video that is embedded in an application similar to a real streaming service, e.g., YouTube or Netflix. In vitro design refers to a setting, in which the video stream is separated from a specific service and thus, the video plays on a plain background. Although these interface designs vary widely, the results are often compared and generalized. In this work, we use a crowdsourcing study to investigate the influence of three interface design alternatives, an in vitro and two in vivo designs with different levels of interactiveness, on the perceived video QoE. Contrary to our expectations, the results indicate that there is no significant influence of the study’s interface design in general on the video experience. Furthermore, we found that the in vivo design does not reduce the test takers’ attentiveness. However, we observed that participants who interacted with the test interface reported a higher video QoE than other groups.
The rating of perceived exertion (RPE) is a subjective load marker and may assist in individualizing training prescription, particularly by adjusting running intensity. Unfortunately, RPE has shortcomings (e.g., underreporting) and cannot be monitored continuously and automatically throughout a training sessions. In this pilot study, we aimed to predict two classes of RPE (≤15 “Somewhat hard to hard” on Borg’s 6–20 scale vs. RPE >15 in runners by analyzing data recorded by a commercially-available smartwatch with machine learning algorithms. Twelve trained and untrained runners performed long-continuous runs at a constant self-selected pace to volitional exhaustion. Untrained runners reported their RPE each kilometer, whereas trained runners reported every five kilometers. The kinetics of heart rate, step cadence, and running velocity were recorded continuously ( 1 Hz ) with a commercially-available smartwatch (Polar V800). We trained different machine learning algorithms to estimate the two classes of RPE based on the time series sensor data derived from the smartwatch. Predictions were analyzed in different settings: accuracy overall and per runner type; i.e., accuracy for trained and untrained runners independently. We achieved top accuracies of 84.8 % for the whole dataset, 81.8 % for the trained runners, and 86.1 % for the untrained runners. We predict two classes of RPE with high accuracy using machine learning and smartwatch data. This approach might aid in individualizing training prescriptions.
Von technischen Systemen wird in der heutigen Zeit erwartet, dass diese stets fehlerfrei funktionieren, um einen reibungslosen Ablauf des Alltags zu gewährleisten. Technische Systeme jedoch können Defekte aufweisen, die deren Funktionsweise einschränken oder zu deren Totalausfall führen können. Grundsätzlich zeigen sich Defekte durch eine Veränderung im Verhalten von einzelnen Komponenten. Diese Abweichungen vom Nominalverhalten nehmen dabei an Intensität zu, je näher die entsprechende Komponente an einem Totalausfall ist. Aus diesem Grund sollte das Fehlverhalten von Komponenten rechtzeitig erkannt werden, um permanenten Schaden zu verhindern. Von besonderer Bedeutung ist dies für die Luft- und Raumfahrt. Bei einem Satelliten kann keine Wartung seiner Komponenten durchgeführt werden, wenn er sich bereits im Orbit befindet. Der Defekt einer einzelnen Komponente, wie der Batterie der Energieversorgung, kann hierbei den Verlust der gesamten Mission bedeuten. Grundsätzlich lässt sich Fehlererkennung manuell durchführen, wie es im Satellitenbetrieb oft üblich ist. Hierfür muss ein menschlicher Experte, ein sogenannter Operator, das System überwachen. Diese Form der Überwachung ist allerdings stark von der Zeit, Verfügbarkeit und Expertise des Operators, der die Überwachung durchführt, abhängig. Ein anderer Ansatz ist die Verwendung eines dedizierten Diagnosesystems. Dieses kann das technische System permanent überwachen und selbstständig Diagnosen berechnen. Die Diagnosen können dann durch einen Experten eingesehen werden, der auf ihrer Basis Aktionen durchführen kann. Das in dieser Arbeit vorgestellte modellbasierte Diagnosesystem verwendet ein quantitatives Modell eines technischen Systems, das dessen Nominalverhalten beschreibt. Das beobachtete Verhalten des technischen Systems, gegeben durch Messwerte, wird mit seinem erwarteten Verhalten, gegeben durch simulierte Werte des Modells, verglichen und Diskrepanzen bestimmt. Jede Diskrepanz ist dabei ein Symptom. Diagnosen werden dadurch berechnet, dass zunächst zu jedem Symptom eine sogenannte Konfliktmenge berechnet wird. Dies ist eine Menge von Komponenten, sodass der Defekt einer dieser Komponenten das entsprechende Symptom erklären könnte. Mithilfe dieser Konfliktmengen werden sogenannte Treffermengen berechnet. Eine Treffermenge ist eine Menge von Komponenten, sodass der gleichzeitige Defekt aller Komponenten dieser Menge alle beobachteten Symptome erklären könnte. Jede minimale Treffermenge entspricht dabei einer Diagnose. Zur Berechnung dieser Mengen nutzt das Diagnosesystem ein Verfahren, bei dem zunächst abhängige Komponenten bestimmt werden und diese von symptombehafteten Komponenten belastet und von korrekt funktionierenden Komponenten entlastet werden. Für die einzelnen Komponenten werden Bewertungen auf Basis dieser Be- und Entlastungen berechnet und mit ihnen Diagnosen gestellt. Da das Diagnosesystem auf ausreichend genaue Modelle angewiesen ist und die manuelle Kalibrierung dieser Modelle mit erheblichem Aufwand verbunden ist, wurde ein Verfahren zur automatischen Kalibrierung entwickelt. Dieses verwendet einen Zyklischen Genetischen Algorithmus, um mithilfe von aufgezeichneten Werten der realen Komponenten Modellparameter zu bestimmen, sodass die Modelle die aufgezeichneten Daten möglichst gut reproduzieren können. Zur Evaluation der automatischen Kalibrierung wurden ein Testaufbau und verschiedene dynamische und manuell schwierig zu kalibrierende Komponenten des Qualifikationsmodells eines realen Nanosatelliten, dem SONATE-Nanosatelliten modelliert und kalibriert. Der Testaufbau bestand dabei aus einem Batteriepack, einem Laderegler, einem Tiefentladeschutz, einem Entladeregler, einem Stepper Motor HAT und einem Motor. Er wurde zusätzlich zur automatischen Kalibrierung unabhängig manuell kalibriert. Die automatisch kalibrierten Satellitenkomponenten waren ein Reaktionsrad, ein Entladeregler, Magnetspulen, bestehend aus einer Ferritkernspule und zwei Luftspulen, eine Abschlussleiterplatine und eine Batterie. Zur Evaluation des Diagnosesystems wurde die Energieversorgung des Qualifikationsmodells des SONATE-Nanosatelliten modelliert. Für die Batterien, die Entladeregler, die Magnetspulen und die Reaktionsräder wurden die vorher automatisch kalibrierten Modelle genutzt. Für das Modell der Energieversorgung wurden Fehler simuliert und diese diagnostiziert. Die Ergebnisse der Evaluation der automatischen Kalibrierung waren, dass die automatische Kalibrierung eine mit der manuellen Kalibrierung vergleichbare Genauigkeit für den Testaufbau lieferte und diese sogar leicht übertraf und dass die automatisch kalibrierten Satellitenkomponenten eine durchweg hohe Genauigkeit aufwiesen und damit für den Einsatz im Diagnosesystem geeignet waren. Die Ergebnisse der Evaluation des Diagnosesystems waren, dass die simulierten Fehler zuverlässig gefunden wurden und dass das Diagnosesystem in der Lage war die plausiblen Ursachen dieser Fehler zu diagnostizieren.
Mapping and localization of mobile robots in an unknown environment are essential for most high-level operations like autonomous navigation or exploration. This paper presents a novel approach for combining estimated trajectories, namely curvefusion. The robot used in the experiments is equipped with a horizontally mounted 2D profiler, a constantly spinning 3D laser scanner and a GPS module. The proposed algorithm first combines trajectories from different sensors to optimize poses of the planar three degrees of freedom (DoF) trajectory, which is then fed into continuous-time simultaneous localization and mapping (SLAM) to further improve the trajectory. While state-of-the-art multi-sensor fusion methods mainly focus on probabilistic methods, our approach instead adopts a deformation-based method to optimize poses. To this end, a similarity metric for curved shapes is introduced into the robotics community to fuse the estimated trajectories. Additionally, a shape-based point correspondence estimation method is applied to the multi-sensor time calibration. Experiments show that the proposed fusion method can achieve relatively better accuracy, even if the error of the trajectory before fusion is large, which demonstrates that our method can still maintain a certain degree of accuracy in an environment where typical pose estimation methods have poor performance. In addition, the proposed time-calibration method also achieves high accuracy in estimating point correspondences.
Purpose
Pronounced differences in individual physiological adaptation may occur following various training mesocycles in runners. Here we aimed to assess the individual changes in performance and physiological adaptation of recreational runners performing mesocycles with different intensity, duration and frequency.
Methods
Employing a randomized cross-over design, the intra-individual physiological responses [i.e., peak (\(\dot{VO}_{2peak}\)) and submaximal (\(\dot{VO}_{2submax}\)) oxygen uptake, velocity at lactate thresholds (V\(_2\), V\(_4\))] and performance (time-to-exhaustion (TTE)) of 13 recreational runners who performed three 3-week sessions of high-intensity interval training (HIIT), high-volume low-intensity training (HVLIT) or more but shorter sessions of HVLIT (high-frequency training; HFT) were assessed.
Results
\(\dot{VO}_{2submax}\), V\(_2\), V\(_4\) and TTE were not altered by HIIT, HVLIT or HFT (p > 0.05). \(\dot{VO}_{2peak}\) improved to the same extent following HVLIT (p = 0.045) and HFT (p = 0.02). The number of moderately negative responders was higher following HIIT (15.4%); and HFT (15.4%) than HVLIT (7.6%). The number of very positive responders was higher following HVLIT (38.5%) than HFT (23%) or HIIT (7.7%). 46% of the runners responded positively to two mesocycles, while 23% did not respond to any.
Conclusion
On a group level, none of the interventions altered \(\dot{VO}_{2submax}\), V\(_2\), V\(_4\) or TTE, while HVLIT and HFT improved \(\dot{VO}_{2peak}\). The mean adaptation index indicated similar numbers of positive, negative and non-responders to HIIT, HVLIT and HFT, but more very positive responders to HVLIT than HFT or HIIT. 46% responded positively to two mesocycles, while 23% did not respond to any. These findings indicate that the magnitude of responses to HIIT, HVLIT and HFT is highly individual and no pattern was apparent.
Aims Acute myocardial infarction (MI) is the major cause of chronic heart failure. The activity of blood coagulation factor XIII (FXIIIa) plays an important role in rodents as a healing factor after MI, whereas its role in healing and remodelling processes in humans remains unclear. We prospectively evaluated the relevance of FXIIIa after acute MI as a potential early prognostic marker for adequate healing.
Methods and results This monocentric prospective cohort study investigated cardiac remodelling in patients with ST-elevation MI and followed them up for 1 year. Serum FXIIIa was serially assessed during the first 9 days after MI and after 2, 6, and 12 months. Cardiac magnetic resonance imaging was performed within 4 days after MI (Scan 1), after 7 to 9 days (Scan 2), and after 12 months (Scan 3). The FXIII valine-to-leucine (V34L) single-nucleotide polymorphism rs5985 was genotyped. One hundred forty-six patients were investigated (mean age 58 ± 11 years, 13% women). Median FXIIIa was 118 % (quartiles, 102–132%) and dropped to a trough on the second day after MI: 109%(98–109%; P < 0.001). FXIIIa recovered slowly over time, reaching the baseline level after 2 to 6 months and surpassed baseline levels only after 12 months: 124 % (110–142%). The development of FXIIIa after MI was independent of the genotype. FXIIIa on Day 2 was strongly and inversely associated with the relative size of MI in Scan 1 (Spearman’s ρ = –0.31; P = 0.01) and Scan 3 (ρ = –0.39; P < 0.01) and positively associated with left ventricular ejection fraction: ρ = 0.32 (P < 0.01) and ρ = 0.24 (P = 0.04), respectively.
Conclusions FXIII activity after MI is highly dynamic, exhibiting a significant decline in the early healing period, with reconstitution 6 months later. Depressed FXIIIa early after MI predicted a greater size of MI and lower left ventricular ejection fraction after 1 year. The clinical relevance of these findings awaits to be tested in a randomized trial.