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The evaluation of the effectiveness of different machine learning algorithms on a publicly available database of signals derived from wearable devices is presented with the goal of optimizing human activity recognition and classification. Among the wide number of body signals we choose a couple of signals, namely photoplethysmographic (optically detected subcutaneous blood volume) and tri-axis acceleration signals that are easy to be simultaneously acquired using commercial widespread devices (e.g. smartwatches) as well as custom wearable wireless devices designed for sport, healthcare, or clinical purposes. To this end, two widely used algorithms (decision tree and k-nearest neighbor) were tested, and their performance were compared to two new recent algorithms (particle Bernstein and a Monte Carlo-based regression) both in terms of accuracy and processing time. A data preprocessing phase was also considered to improve the performance of the machine learning procedures, in order to reduce the problem size and a detailed analysis of the compression strategy and results is also presented.
The transformation to an Industry 4.0, which is in general seen as a solution to increasing market challenges, is forcing companies to radically change their way of thinking and to be open to new forms of cooperation. In this context, the opening-up of the innovation process is widely seen as a necessity to meet these challenges, especially for small and medium enterprises (SMEs). The aim of the study therefore is to analyze how cooperation today can be characterized, how this character has changed since the establishment of the term Industry 4.0 at Hanover Fair in 2011 and which cooperation strategies have proven successful. The analysis consists of a quantitative, secondary data analysis that includes country-specific data from 35 European countries of 2010 and 2016 collected by the European Commission and the OECD. The research, focusing on the secondary sector, shows that multinational enterprises MNEs still tend to cooperate more than SMEs, with a slight overall trend towards protectionism. Nevertheless, there is a clear tendency towards the opening-up of SMEs. In this regard, especially universities, competitors and suppliers have become increasingly attractive as cooperation partners for SMEs.
Deep neural networks (DNNs) are known for their high prediction performance, especially in perceptual tasks such as object recognition or autonomous driving. Still, DNNs are prone to yield unreliable predictions when encountering completely new situations without indicating their uncertainty. Bayesian variants of DNNs (BDNNs), such as MC dropout BDNNs, do provide uncertainty measures. However, BDNNs are slow during test time because they rely on a sampling approach. Here we present a single shot MC dropout approximation that preserves the advantages of BDNNs without being slower than a DNN. Our approach is to analytically approximate for each layer in a fully connected network the expected value and the variance of the MC dropout signal. We evaluate our approach on different benchmark datasets and a simulated toy example. We demonstrate that our single shot MC dropout approximation resembles the point estimate and the uncertainty estimate of the predictive distribution that is achieved with an MC approach, while being fast enough for real-time deployments of BDNNs.
Die wenigen Literaturangaben zu Sorptionsisothermen von mineralischen Estrichen beziehen sich im Wesentlichen auf Calciumsulfatestriche und genormte Zementestriche, sowie i.d.R. nur auf eine festgesetzte Lufttemperatur (= 20 Grad C). Daher war es das Anliegen der im Beitrag beschriebenen Untersuchung, die Feuchtigkeitseigenschaften von Estrichen bei unterschiedlichen Klimaten mithilfe von Sorptionsisothermen zu charakterisieren. Ergänzend sollten die seit ca. 20 Jahren marktüblichen ternären Schnellzemente mit untersucht und die baupraktisch interessanten Temperaturen von 15 Grad C und 25 Grad C einbezogen werden. Ebenso wurden die Auswirkungen der Klimabedingungen auf der Baustelle (Jahreszeit, Luftfeuchtigkeit, Temperatur) auf den Hydratationsvorgang der Estriche untersucht. In Kombination mit den Ergebnissen der Gefügeuntersuchungen (u.a. Hg-Porosimetrie) wird belegt, weshalb sich die zement- und schnellzementgebundenen Estriche vollkommen anders verhalten als die calciumsulfatgebundenen Estriche. Dieses unterschiedliche Verhalten ist auch einer der Gründe, warum Estriche mit der KRL-Methode in Bezug auf ihren Feuchtegehalt nicht bewertet werden können. Deshalb folgt ein Vergleich der Materialfeuchtemessungen "KRL-Methode" mit der handwerksüblichen und seit Jahrzehnten in der Praxis bewährten "CM-Methode".
Ein Beitrag zum Beobachterentwurf und zur sensorlosen Folgeregelung translatorischer Magnetaktoren
(2020)
Botswana, a new construction project – the Maun Science Park - is to be built with a focus on sustainability and to create a new living space for the rapidly growing population in Africa. The project will be a blueprint for future projects in Africain terms of progress, technology and sustainability. This thesis will deal with its financial framework and will serve as a basis for the development of ways and means of financing such projects.
The ballistocardiography is a technique that measures the heart rate from the mechanical vibrations of the body due to the heart movement. In this work a novel noninvasive device placed under the mattress of a bed estimates the heart rate using the ballistocardiography. Different algorithms for heart rate estimation have been developed.
Globalization has increased the number of road trips and vehicles. The result has been an intensification of traffic accidents, which are becoming one of the most important causes of death worldwide. Traffic accidents are often due to human error, the probability of which increases when the cognitive ability of the driver decreases. Cognitive capacity is closely related to the driver’s mental state, as well as other external factors such as the CO2 concentration inside the vehicle. The objective of this work is to analyze how these elements affect driving. We have conducted an experiment with 50 drivers who have driven for 25 min using a driving simulator. These drivers completed a survey at the start and end of the experiment to obtain information about their mental state. In addition, during the test, their stress level was monitored using biometric sensors and the state of the environment (temperature, humidity and CO2 level) was recorded. The results of the experiment show that the initial level of stress and tiredness of the driver can have a strong impact on stress, driving behavior and fatigue produced by the driving test. Other elements such as sadness and the conditions of the interior of the vehicle also cause impaired driving and affect compliance with traffic regulations.
Kleine und mittelständische Unternehmen (KMU) sind bekannt für ihre Innovationskraft und bilden das Rückgrat der deutschen Wirtschaft. Wie Studien zeigen sind sie in Bezug auf Compliance-Maßnahmen im Vergleich zu
kapitalmarktorientierten Unternehmen jedoch im Rückstand. Eine gesonderte Betrachtung der IT-Compliance erfolgt dabei in den Studien in der Regel nicht. Auch wenn zu den Gründen und Motiven fehlender IT-Compliance-Strukturen in KMU kaum Forschungsergebnisse vorliegen, zeigen doch die vielen Publikationen, die sich mit Teilaspekten von Compliance und KMU beschäftigen, dass Handlungsbedarf besteht. Insbesondere die aktuellen Veränderungen unter dem Stichwort Digitalisierung deuten auf eine gesteigerte Bedeutung von IT-Compliance-Maßnahmen vor allem in mittelständischen Unternehmen. In dieser Arbeit sollen daher mithilfe einer Literaturrecherche die aktuell behandelten Themen in Bezug auf IT-Compliance und KMU analysiert sowie aktuelle Themenschwerpunkte herausgearbeitet werden.