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Combined Ash and Soot Monitoring in Gasoline Particulate Filters with a Radio-Frequency-Based Sensor
(2022)
In recent years, it turned out that primarily the number of ultrafine particles and not their mass is relevant in terms of harmful health effects. Therefore, exhaust emission standards are constantly being tightened with regard to the particulate number. To meet the emission limits, particulate filters have become necessary for gasoline engines. Similar to diesel applications, gasoline particulate filters (GPFs) can be monitored via differential pressure measurement or via a radio-frequency approach (RF-sensor). The latter is based on the influence of the soot conductivity on the electromagnetic field. To control the GPF, ash detection is of crucial importance besides the soot load monitoring. With the differential pressure system, this is only possible with high uncertainties due to the low pressure increase caused by ash. The RF-sensor, however, has already demonstrated its potential for ash detection in diesel applications. In order to verify the applicability of the system for GPFs, filters were loaded via a method for fast ash loading. Thereby, it was shown that by evaluating resonant modes – especially the resonant frequency – the ash loading can easily be measured as long as the filter is soot-unloaded. In contrast, soot loading can be determined independently from this by considering a frequency-averaged transmission signal between the antennas of the RF-sensor. Using a setup for radio-frequency material characterization, the dielectric parameters of the generated ash were also measured. In comparison with the properties of the synthetic soot PrintexU, ash showed significantly lower dielectric losses. Thus, the evaluation of the loss-dependent quality factor, which is only possible at low soot loadings, allows a precise conclusion whether the filter is completely soot-free. Furthermore, using synthetic soot loading, it could be shown that soot detection via the averaged transmission signal is not affected by ash. In summary, it could be shown that with the RF-sensor, ash and soot can be simultaneously detected.
Der Weg in eine Professur an einer Hochschule für angewandte Wissenschaften (HAW) setzt eine „Dreifachqualifikation“ in Lehre, Forschung und Berufserfahrung voraus. Dieser hohe Anforderungsstandard limitiert aber nicht nur den Pool geeigneter Bewerber:innen (In der Smitten et al., 2017). Diese Personen sind auch bei Wirtschaftsbetrieben, Universitäten und anderen Organisationen begehrt, so dass sich der „Kampf“ um Talente mit Potenzial für eine HAW-Professur in Zukunft eher verschärft. Wissen um die Karrierewege, förderliche sowie hinderliche Aspekte in HAW-Professuren, können Hochschulen dabei helfen, geeignete Personen auf das Berufsbild aufmerksam zu machen und das Arbeitgebermarketing gegenüber potenziellen Bewerber:innen zu verbessern.
Der Vortrag präsentiert erste Ergebnisse zu den Karrierewegen in eine HAW-Professur und fasst hierfür Erkenntnisse aus einer qualitativen Studie sowie Überlegungen zu deren quantitativen Überprüfung zusammen. Semi-strukturierte Interviews mit Professor:innen, welche einen Ruf an einer HAW in Bayern angenommen hatten, konnten u. a. verschiedene Karrierewege in eine Professur aufzeigen, ebenso wie die Motive, sich für dieses Berufsbild zu entscheiden. Des Weiteren wurden mögliche unterstützenden Faktoren und Hindernisse, welche den Weg in eine HAW-Professur erleichtern oder erschweren können, identifiziert. Zum Abschluss wird ein Ausblick auf eine geplante quantitativen Online-Befragung von HAW-Professor:innen gegeben, um die qualitativen Erkenntnisse zu validieren.
Die Ergebnisse werden unter Bezugnahme auf die relevante Hochschulforschung (z. B. Armutat, 2022; Braun & Wilson, 2023; In der Smitten et al., 2017) ebenso wie relevante Karrieretheorien (z. B. Lent et al., 1994) diskutiert. Die abschließenden Handlungsempfehlungen für die Hochschulentwicklung sowie das Hochschulmarketing sind von praktischer Relevanz.
Diese Forschung wird mit Mitteln des Bundesministeriums für Bildung und Forschung und des Landes Bayern als Teil des Projekts Zukunft akademisches Personal der OTH Regensburg (ZAP.OTHR) unter dem Programm „FH-Personal“ gefördert.
Conventional dialogue management centers around an interaction style that is best described as a ping-pong game, with full turns being the units at which speech is delivered (and expected) by the system, which greatly simplifies the interaction management, delivery and understanding components of the system. While the resulting mode of interaction works well for task-based systems, it is insufficient for more conversational interaction styles, where content is delivered and grounded in units finer than full turns (Poesio and Traum 1997) and where turns are delivered concurrently by both interlocutors and hence overlap more frequently. One domain with particularly frequent overlapping contributions is interactive storytelling: a storyteller that is responsive to listeners will integrate their feedback immediately while still speaking a current contribution, and listener’s remarks or propositions regarding the story will typically be uttered immediately when the related content is delivered. We present our work on a dialogue manager that leverages recent advances in incremental speech delivery and reception (Baumann 2013) to provide for an interactive and concurrent storytelling experience. Our system uses an interaction graph, which uses a word-by-word granularity and allows to specify for individual stretches of speech where (and with what content) users may interrupt/comment, how this is interpreted, and how it is integrated into the storytelling process, thus providing for in-utterance alternatives to be spoken even without audibly interrupting the system’s ongoing utterance. The system’s interaction graph is specified in an XML language and may be hand-crafted by a story designer but can also be automatically generated. In our current system, speech recognition results are interpreted only when the user utterance is finished; however, we plan to integrate incremental speech recognition and understanding capabilities, and to
immediately react to the start of user contributions.