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Besides the optimisation of the car, energy-efficiency and safety can also be increased by optimising the driving behaviour. Based on this fact, a driving system is in development whose goal is to educate the driver in energy efficient and safe driving. It monitors the driver, the car and the environment and gives energy-efficiency and safety relevant recommendations. However, the driving system tries not to distract or bother the driver by giving recommendations for example during stressful driving situations or when the driver is not interested in that recommendation. Therefore, the driving system monitors the stress level of the driver as well as the reaction of the driver to a given recommendation and decideswhether to give a recommendation or not. This allows to suppress recommendations when needed and, thus, to increase the road safety and the user acceptance of
the driving system.
A lot of people need help in their daily life to wash, select and manage their clothing. The goal of this work is to design an assistant system (eKlarA) to support the user by giving recommendations to choose the clothing combinations, to find the clothing and to wash the clothing. The idea behind eKlarA is to generate a system that uses sensors to identify the clothing and their state in the clothing cycle. The clothing cycle consists of the stations: closets, laundry basket and washing machine in one or several places. The system uses the information about the clothing, weather and calendar to support the user in the different steps of the clothing cycle. The first prototype of this system has been developed and tested. The test results are presented in this work.
Stress is recognized as a predominant disease with raising costs for rehabilitation and treatment. Currently there are several different approaches that can be used for determining and calculating the stress levels. Usually the methods for determining stress are divided in two categories. The first category do not require any special equipment for measuring the stress. This category useless the variation in the behaviour patterns that occur while stress. The core disadvantage for the category is their limitation to specific use case. The second category uses laboratories instruments and biological sensors. This category allow to measure stress precisely and proficiently but on the same time they are not mobile and transportable and do not support real-time feedback. This work presents a mobile system that provides the calculation of stress. For achieving this, the of a mobile ECG sensor is analysed, processed and visualised over a mobile system like a smartphone. This work also explains the used stress measurement algorithm. The result of this work is a portable system that can be used with a mobile system like a smartphone as visual interface for reporting the current stress level.
Stress is becoming an important topic in modern life. The influence of stress results in a higher rate of health disorders such as burnout, heart problems, obesity, asthma, diabetes, depressions and many others. Furthermore individual’s behavior and capabilities could be directly affected leading to altered cognition, inappropriate decision making and problem solving skills. In a dynamic and unpredictable environment, such as automotive, this can result in a higher risk for accidents. Different papers faced the estimation as well as prediction of drivers’ stress level during driving. Another important question is not only the stress level of the driver himself, but also the influence on and of a group of other drivers in the near area. This paper proposes a system, which determines a group of drivers in a near area as clusters and it derives the individual stress level. This information will be analyzed to generate a stress map, which represents a graphical view about road section with a higher stress influence. Aggregated data can be used to generate navigation routes with a lower stress influence to decrease stress influenced driving as well as improve road safety.
The digital transformation of our society changes the way we live, work, learn, communicate, and collaborate. The digitization of software-intensive products and services is enabled basically by four megatrends: Cloud computing, big data mobile systems, and social technologies. This disruptive change interacts with all information processes and systems that are important business enablers for the current digital transformation. The internet of things, social collaboration systems for adaptive case management, mobility systems and services for big data in cloud services environments are emerging to support intelligent user-centered and social community systems. Modern enterprises see themselves confronted with an ever growing design space to engineer business models of the future as well as their IT support, respectively. The decision analytics in this field becomes increasingly complex and decision support, particularly for the development and evolution of sustainable enterprise architectures (EA), is duly needed. With the advent of intelligent user-centered and social community systems, the challenging decision processes can be supported in more flexible and intuitive ways. Tapping into these systems and techniques, the engineers and managers of the enterprise architecture become part of a viable enterprise, i.e. a resilient and continuously evolving system that develops innovative business models.
The evolution of Services Oriented Architectures (SOA) presents many challenges due to their complex, dynamic and heterogeneous nature. We describe how SOA design principles can facilitate SOA evolvability and examine several approaches to support SOA evolution. SOA evolution approaches can be classified based on the level of granularity they address, namely, service code level, service interaction level and model level. We also discuss emerging trends, such as microservices and knowledge-based support, which can enhance the evolution of future SOA systems.
Industrie 4.0 - Ausblick
(2016)
Für Unternehmen ist es wichtig, frühzeitig die strategischen Weichen für ihre Industrie 4.0-Stoßrichtung zu stellen und Erfahrung im Umgang mit Industrie 4.0-Technologien aufzubauen. Allerdings werden einige der Industrie 4.0-relevanten Technologien voraussichtlich erst in 5 bis 10 Jahren ihr Effizienzpotential voll ausschöpfen können. Die Einführung von Industrie 4.0 betrifft nahezu alle Bereiche eines Unternehmens und ist deshalb nicht nur als digitale Transformation, sondern auch als Kulturwandel in der Organisation zu verstehen, zu planen und aktiv zu managen. Themen wie Datenschutz und IT-Sicherheit sind nicht nur wichtige Voraussetzungen für eine erfolgreiche Industrie 4.0-Einführung, sondern müssen als wesentliche Akzeptanz- und Erfolgsfaktoren konsequent und durchgängig in den digitalen Systemen verankert werden.
KMUs sehen sich häufig aus finanziellen Gründen nicht in der Lage, in grundlegende Technologien der Industrie 4.0 zu investieren. So wird als Hauptvorbehalt eine vermeintlich schlechte Kosten-Nutzen-Relation bzw. langfristige Pay-Back-Zyklen angegeben. Die aktuellen Herausforderungen liegen derzeit eher bei der immer weiter voranschreitenden Internationalisierung sowie dem ansteigenden Innovationsdruck durch den Wettbewerb. Natürlich ist bekannt, dass die zunehmende Vernetzung der Produktionsanlagen in der Industrie 4.0 zudem Risiken in der IT- und Datensicherheit mit sich bringt. Auch Datenqualitäts-, Stabilitäts-, Schnittstellenprobleme oder rechtliche Probleme sind ausschlaggebend für die Verunsicherung der Unternehmen. Durch die zukünftig immer weiter ansteigende Vernetzung zwischen Unternehmen und Stakeholdern, müssen sich insbesondere Zulieferunternehmen in der Pflicht sehen, das Thema Industrie 4.0 aufzugreifen und sich damit auseinander zu setzen. Gerade diese Unternehmen müssen sich vor Augen führen, dass sie nur durch den zukünftigen Einsatz geeigneter Informations- und Kommunikationstechnologien noch in der Lage sein werden, Teil der Wertschöpfungskette zwischen ihren Kunden und Lieferanten zu sein.
Rapidly growing data volumes push today's analytical systems close to the feasible processing limit. Massive parallelism is one possible solution to reduce the computational time of analytical algorithms. However, data transfer becomes a significant bottleneck since it blocks system resources moving data-to-code. Technological advances allow to economically place compute units close to storage and perform data processing operations close to data, minimizing data transfers and increasing scalability. Hence the principle of Near Data Processing (NDP) and the shift towards code-to-data. In the present paper we claim that the development of NDP-system architectures becomes an inevitable task in the future. Analytical DBMS like HPE Vertica have multiple points of impact with major advantages which are presented within this paper.