Minimizing the Homogeneous L2\mathcal{L}_2-Gain of Homogeneous Differentiators

The differentiation of noisy signals using the family of homogeneous differentiators is considered. It includes the high-gain (linear) as well as robust exact (discontinuous) differentiator. To characterize the effect of noise and disturbance on the differentiation estimation error, the generalized, homogeneous $\mathcal{L}_2$-gain is utilized. Analog to the classical $\mathcal{L}_p$-gain, it is not defined for the discontinuous case w.r.t. disturbances acting on the last channel. Thus, only continuous differentiators are addressed. The gain is estimated using a differential dissipation inequality, where a scaled Lyapunov function acts as storage function for the homogeneous $\mathcal{L}_2$ supply rate. The fixed differentiator gains are scaled with a gain-scaling parameter similar to the high-gain differentiator. This paper shows the existence of an optimal scaling which (locally) minimizes the homogeneous $\mathcal{L}_2$-gain estimate and provides a procedure to obtain it. Differentiators of dimension two are considered and the results are illustrated via numerical evaluation and a simulation example

Fractional-Order Partial Cancellation of Integer-Order Poles and Zeros

The key idea of this contribution is the partial compensation of non-minimum phase zeros or unstable poles. Therefore the integer-order zero/pole is split into a product of fractional-order pseudo zeros/poles. The amplitude and phase response of these fractional-order terms is derived to include these compensators into the loop-shaping design. Such compensators can be generalized to conjugate complex zeros/poles, and also implicit fractional-order terms can be applied. In the case of the non-minimum phase zero, its compensation leads to a higher phase margin and a steeper open-loop amplitude response around the crossover frequency resulting in a reduced undershooting in the step-response, as illustrated in the numerical example.publishedVersio

Applying Linked Data Technologies in the Social Sciences

In recent years Linked Open Data (LOD) has matured and gained acceptance across various communities and domains. Large potential of Linked Data technologies is seen for an application in scientific disciplines. In this article, we present use cases and applications for an application of Linked Data in the social sciences. They focus on (a) interlinking domain-specific information, and (b) linking social science data to external LOD sources (e.g. authority data) from other domains. However, several technical and research challenges arise, when applying Linked Data technologies to a scientific domain with its specific data, information needs and use cases. We discuss these challenges and show how they can be addressed. (author's abstract

Assessment of environmental and economic efficiency of iron ore breaking technology using emulsion explosives

Methodology for calculating parameters of drilling and blasting operations for stoping works in mines of Kryvorozhskiy basin and PJSC «Zaporizhskiy iron-ore plant» has been improved with the help of established coefficient of relative capacity for the Ukrainit-PM-2B explosive. A new technology of stoping operations for ore breaking by square-chamber methods at deposit thickness more than 5 m is proposed which assumes usage of emulsion explosives and downward drilling of production hole rings in the direction of underlying drilling horizons. Ecological and economical effectiveness of the proposed ore breaking technology implemented in the extraction chambers was estimated. Regularities of harmful substances hazard index changing were established depending on distance to the emission point when trotyl-contained and emulsion explosives are used. Implementation of the proposed technology allows decreasing prime-cost of 1 ton of ore by 15 % per one extraction unit

EmoCycling – Analysen von Radwegen mittels Humansensorik und Wearable Computing

Radfahren erfreut sich einer zunehmenden Wertschätzung. Einerseits als neuer Lifestyle, andererseits als wichtiges Thema der städtischen Mobilitätsplanung: Bike-Sharing-Angebote, Radwegekonzepte und Förderung eines umweltfreundlichen Mobilitätsmix sind hierbei wichtige Stichworte. Daher fördern zunehmend mehr Städte den Ausbau der Radwege-Infrastruktur, um das Radfahren attraktiver zu gestalten. Wie stark Radfahren aber tatsächlich angenommen und praktiziert wird, hängt von ganz verschiedenen Faktoren ab: Verkehrslage, Quantität und Qualität der Infrastruktur, Topografie sowie das subjektive Sicherheitsempfinden z.B. an unübersichtlichen Kreuzungen beeinflussen die Verkehrsmittelwahl. Insbesondere die Erfassung und Analyse des subjektiven Sicherheitsempfindens stellt hierbei eine große Herausforderung dar – wird aber durch neue Methoden der Humansensorik (Exner et al. 2012) möglich. Entwicklungen in den Bereichen des Wearable Computing sowie der Geoinformatik ermöglichen es, das subjektive Sicherheitsempfinden während der Fahrt genauer zu analysieren. Anknüpfend an Projekte zur emotionalen Stadtkartierung (Höffken et al. 2008, Zeile et al. 2010) erfolgt ein Live-Monitoring der Probanden während der Fahrt. Mittels eines Sensorarmbands (Smartband) zur Erfassung psychophysiologischer Reaktionen des Körpers in Kombination mit Video-Kamera-Daten und GPS-Koordinaten wird der emotionale Zustand der Probanden sekundengenau gemessen. Dadurch lassen sich Emotionen, insbesondere Stress, interpretieren und auf einer Karte verorten sowie die Auslöser (Trigger) identifizieren. Zudem kann auf diese Weise der Verkehr kontinuierlich erfasst und in die Analyse mit aufgenommen werden, um Gefahrenstellen zu lokalisieren. Nach einer Einführung in das Thema Radfahren in der Untersuchungsgemeinde Kaiserslautern, gibt das Paper einen Überblick über den aktuellen Stand der Methodik, die Konzeptionierung der Teststrecken sowie die Methodik im konkreten Projekt EmoCycling. Darauf basierend werden die Ergebnisse des Projektes vorgestellt und daraus resultierende weiterführende Fragenstellungen aufgezeigt