Self-aware reliable monitoring

Cyber-Physical Systems (CPSs) can be found in almost all technical areas where they constitute a key enabler for anticipated autonomous machines and devices. They are used in a wide range of applications such as autonomous driving, traffic control, manufacturing plants, telecommunication systems, smart grids, and portable health monitoring systems. CPSs are facing steadily increasing requirements such as autonomy, adaptability, reliability, robustness, efficiency, and performance. A CPS necessitates comprehensive knowledge about itself and its environment to meet these requirements as well as make rational, well-informed decisions, manage its objectives in a sophisticated way, and adapt to a possibly changing environment. To gain such comprehensive knowledge, a CPS must monitor itself and its environment. However, the data obtained during this process comes from physical properties measured by sensors and may differ from the ground truth. Sensors are neither completely accurate nor precise. Even if they were, they could still be used incorrectly or break while operating. Besides, it is possible that not all characteristics of physical quantities in the environment are entirely known. Furthermore, some input data may be meaningless as long as they are not transferred to a domain understandable to the CPS. Regardless of the reason, whether erroneous data, incomplete knowledge or unintelligibility of data, such circumstances can result in a CPS that has an incomplete or inaccurate picture of itself and its environment, which can lead to wrong decisions with possible negative consequences. Therefore, a CPS must know the obtained data’s reliability and may need to abstract information of it to fulfill its tasks. Besides, a CPS should base its decisions on a measure that reflects its confidence about certain circumstances. Computational Self-Awareness (CSA) is a promising solution for providing a CPS with a monitoring ability that is reliable and robust — even in the presence of erroneous data. This dissertation proves that CSA, especially the properties abstraction, data reliability, and confidence, can improve a system’s monitoring capabilities regarding its robustness and reliability. The extensive experiments conducted are based on two case studies from different fields: the health- and industrial sectors

Confidence-Enhanced Early Warning Score Based on Fuzzy Logic

Cardiovascular diseases are one of the world’s major causes of loss of life. The vital signs of a patient can indicate this up to 24 hours before such an incident happens. Healthcare professionals use Early Warning Score (EWS) as a common tool in healthcare facilities to indicate the health status of a patient. However, the chance of survival of an outpatient could be increased if a mobile EWS system would monitor them during their daily activities to be able to alert in case of danger. Because of limited healthcare professional supervision of this health condition assessment, a mobile EWS system needs to have an acceptable level of reliability - even if errors occur in the monitoring setup such as noisy signals and detached sensors. In earlier works, a data reliability validation technique has been presented that gives information about the trustfulness of the calculated EWS. In this paper, we propose an EWS system enhanced with the self-aware property confidence, which is based on fuzzy logic. In our experiments, we demonstrate that - under adverse monitoring circumstances (such as noisy signals, detached sensors, and non-nominal monitoring conditions) - our proposed Self-Aware Early Warning Score (SA-EWS) system provides a more reliable EWS than an EWS system without self-aware properties.</p

Object detection and flightpath prediction : a parallelized approach using a graphics processing unit

Zsfassung in dt. SpracheDas vermehrte Verlangen nach individuellen Produkten steigert den Bedarf an flexibleren Produktionslinien. Ein zukunftsweisendes System hierfür könnte der -Wurftransport-Ansatz- sein, bei dem sich Roboterarme die zu transportierenden Güter gegenseitig zuwerfen. Obwohl auf diesem Gebiet schon viel geforscht wurde, ergab sich bis dato noch keine völlig zufriedenstellende Lösung für dieses Transportsystem. Ein neuer, biologisch inspirierter Ansatz könnte die Antwort auf dieses Problem darstellen. Wenngleich dieses System bereits hinsichtlich seiner Genauigkeit untersucht wurde, so ist die Erforschung seiner Echtzeitfähigkeit noch ausständig. Diese Arbeit zeigt, dass die Detektion des Balls und die Vorhersage seiner Flugbahn schnell genug durchführbar sind, um das Kamerasystem bei einer Bildwiederholungsrate von 130 FPS arbeiten lassen zu können. Mit Hilfe einer NVidia GTX 560 Ti GPU ist es möglich gewesen, alle nötigen Berechnungen hierfür, in durchschnittlich, unter 7,7 ms durchzuführen. Für Bildwiederholungsraten von über 85 FPS wird jedoch ein Puffer benötigt, der selten auftretende Rechenzeiten von bis zu 11,7 ms kompensiert. Darüber hinaus zeigen die Resultate ebenso ein um das 3,46- bis 7,17-fach schnellere Ausführen des implementierten Programmes, wenn anstelle einer CPU eine GPU, für die nötigen Berechnungen, verwendet wird. Basierend auf diesen Resultaten können nun weitere Forschungen angestellt werden, um die Zuverlässigkeit und mögliche Einschränkungen des Systems zu untersuchen. Etwaige zukünftige Programmänderungen, im Zuge weiterer Forschungen, könnten zu längeren Ausführungszeiten führen. Jedoch ist es möglich, diese unter Verwendung einer aktuelleren GPU oder mit Hilfe einer Rechenschrittaufteilung auf verschiedene GPUs zu kompensieren.Advanced personalized customer needs and requirements lead to the demand for more flexible types of production lines. One trendsetting system apt to replace the old and static conveyor belt could be Transport-by-Throwing, which consists of robotic arms throwing objects to each other. Much research has been carried out in the field of robotic catching, but more needs to be done to meet the challenges involved. Despite many novel approaches, no fully satisfactory solution to catching a ball has been developed so far. A new approach that deals with this problem in a biologically-inspired way could be the answer. While it has already been proven that such a solution can lead to accurate results, its real-time constraints have not been examined. This thesis shows that computing ball detection and flightpath prediction can be done fast enough to capture the scene with a frame rate of 130 FPS. With the help of a NVidia GTX 560 Ti graphics processing unit, it was possible to execute all necessary calculations for the predictions in less than 7.7 ms on average. Because of maximum times of up to 11.7 ms, a small buffer is required for frame rates over 85 FPS. The results here demonstrate that the use of a GPU greatly accelerates the entire procedure and can lead to executions 3.5 to 7.2 times faster than on a CPU. Based on these results, further research can be carried out to examine the prediction system-s reliability and limitations. Possible changes in the algorithm that lead to additional demand for computational power can be made when using a newer GPU or distributing the tasks on different GPUs.9

Wireless Mobile Communication and Healthcare : 7th International Conference, MobiHealth 2017, Vienna, Austria, November 14–15, 2017, Proceedings

Early Warning Score (EWS) systems are a common practice in hospitals. Health-care professionals use them to measure and predict amelioration or deterioration of patients’ health status. However, it is desired to monitor EWS of many patients in everyday settings and outside the hospitals as well. For portable EWS devices, which monitor patients outside a hospital, it is important to have an acceptable level of reliability. In an earlier work, we presented a self-aware modified EWS system that adaptively corrects the EWS in the case of faulty or noisy input data. In this paper, we propose an enhancement of such data reliability validation through deploying a hierarchical agent-based system that classifies data reliability but using Fuzzy logic instead of conventional Boolean values. In our experiments, we demonstrate how our reliability enhancement method can offer a more accurate and more robust EWS monitoring system.</p