Suchergebnisse

Fabian Walter stellt den übergreifend benötigten Informationsaustausch im Rahmen der maritimen Transportkette in den Mittelpunkt seiner Untersuchung. Er zeigt, dass die Bereitschaft, Informationen innerhalb der maritimen Transportkette zu tauschen, entscheidend für die Verbesserung der Leistungsfähigkeit im Datenfluss ist. Seine Ergebnisse deuten hinsichtlich der Leistungsfähigkeit insgesamt auf einen höheren Effekt durch verstärkten bi- und multilateralen Informationsaustausch hin, als das durch die Bildung z.B. neuer IT-Strukturen oder das Beheben von Medienbrüchen erreicht werden könnte. Wichtig für eine verbesserte Auslastung der Kapazitäten ist ein frühzeitiger Informationsaustausch, z.B. in Form von containerisierter ETA. Der Inhalt Maritime Transportkette - Grundlegende Zusammenhänge und Sichtweisen Theoriegeleitetes Aussagemodell zum Informationsaustausch in der maritimen Transportkette Qualitative, empirische und systemdynamische Untersuchung der maritimen Transportkette Die Zielgruppen Dozierende und Studierende der Fachgebiete Logistik und Transport sowie Verkehrswesen, Wirtschaftsingenieurwesen und Betriebswirtschaftslehre Praktiker der maritimen Transportwirtschaft, z.B. Reeder, Häfen- und Terminalbetreiber, Seefrachtspediteure, Intermodal-Operateure, Eisenbahnverkehrsunternehmen, verladende Industrie Der Autor Fabian Walter ist wissenschaftlicher Mitarbeiter als Postdoc am Fachgebiet Unternehmensführung und Logistik am Fachbereich Rechts- und Wirtschaftswissenschaften der Technischen Universität Darmstadt

Abstract. In mountainous areas, rockfalls, rock avalanches, and debris flows constitute a risk to human life and property. Seismology has proven a useful tool to monitor such mass movements, while increasing data volumes and availability of real-time data streams demand new solutions for automatic signal classification. Ideally, seismic monitoring arrays have large apertures and record a significant number of mass movements to train detection algorithms. However, this is rarely the case, as a result of cost and time constraints and the rare occurrence of catastrophic mass movements. Here, we use the supervised random forest algorithm to classify windowed seismic data on a continuous data stream. We investigate algorithm performance for signal classification into noise (NO), slope failure (SF), and earthquake (EQ) classes and explore the influence of non-ideal though commonly encountered conditions: poor network coverage, imbalanced data sets, and low signal-to-noise ratios (SNRs). To this end we use data from two separate locations in the Swiss Alps: data set (i), recorded at Illgraben, contains signals of several dozen slope failures with low SNR; data set (ii), recorded at Pizzo Cengalo, contains only five slope failure events albeit with higher SNR. The low SNR of slope failure events in data set (i) leads to a classification accuracy of 70 % for SF, with the largest confusion between NO and SF. Although data set (ii) is highly imbalanced, lowering the prediction threshold for slope failures leads to a prediction accuracy of 80 % for SF, with the largest confusion between SF and EQ. Standard techniques to mitigate training data imbalance do not increase prediction accuracy. The classifier of data set (ii) is then used to train a model for the classification of 176 d of continuous seismic recordings containing four slope failure events. The model classifies eight events as slope failures, of which two are snow avalanches, and one is a rock-slope failure. The other events are local or regional earthquakes. By including earthquake detection of a permanent seismic station at 131 km distance to the test site into the decision-making process, all earthquakes falsely classified as slope failures can be excluded. Our study shows that, even for limited training data and non-optimal network geometry, machine learning algorithms applied to high-quality seismic records can be used to monitor mass movements automatically.

Der Beitrag stützt sich auf Protokolle einer im November 1982 im Forschungsinstitut für Arbeiterbildung Recklinghausen durchgeführten Tagung "auf der mit einem kleinen Kreis von 'Veteranen' die Frage der Kontinuität und Diskontinuität zwischen der Arbeiterbildung der Weimarer Republik und der Nachkriegszeit erörtert wurde". In Diskussionsbeiträgen und Gesprächen werden biographische Bezüge entwickelt und Ziele, Inhalte und Methoden der Arbeiterbildung dargestellt. Abschließend wird eine Bewertung der Nachkriegsentwicklung vorgenommen. (IAB2)

Abstract. Heavy precipitation can mobilize tens to hundreds of thousands of cubic meters of sediment in steep Alpine torrents in a short time. The resulting debris flows (mixtures of water, sediment and boulders) move downstream with velocities of several meters per second and have a high destruction potential. Warning protocols for affected communities rely on raising awareness about the debris-flow threat, precipitation monitoring and rapid detection methods. The latter, in particular, is a challenge because debris-flow-prone torrents have their catchments in steep and inaccessible terrain, where instrumentation is difficult to install and maintain. Here we test amplitude source location (ASL) as a processing scheme for seismic network data for early warning purposes. We use debris-flow and noise seismograms from the Illgraben catchment, Switzerland, a torrent system which produces several debris-flow events per year. Automatic in situ detection is currently based on geophones mounted on concrete check dams and radar stage sensors suspended above the channel. The ASL approach has the advantage that it uses seismometers, which can be installed at more accessible locations where a stable connection to mobile phone networks is available for data communication. Our ASL processing uses time-averaged ground vibration amplitudes to estimate the location of the debris-flow front. Applied to continuous data streams, inversion of the seismic amplitude decay throughout the network is robust and efficient, requires no manual identification of seismic phase arrivals and eliminates the need for a local seismic velocity model. We apply the ASL technique to a small debris-flow event on 19 July 2011, which was captured with a temporary seismic monitoring network. The processing rapidly detects the debris-flow event half an hour before arrival at the outlet of the torrent and several minutes before detection by the in situ alarm system. An analysis of continuous seismic records furthermore indicates that detectability of Illgraben debris flows of this size is unaffected by changing environmental and anthropogenic seismic noise and that false detections can be greatly reduced with simple processing steps.