@misc{Mueller2018, type = {Master Thesis}, author = {M{\"u}ller, Philipp}, title = {Mensch-Maschine-Kommunikation basierend auf Natural Language Processing: Evaluierung von M{\"o}glichkeiten f{\"u}r den Einsatz eines humanoiden Roboters im Kontext einer Hochschulbibliothek}, doi = {10.15771/ma_2018_1}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-10349}, school = {Technische Hochschule Wildau}, pages = {172}, year = {2018}, abstract = {Thematisiert wird der Weg, von einer theoretischen Betrachtung computerlinguistischer Grundlagen der Sprachverarbeitung, bis hin zu einer prototypischen Umsetzung einer Software zur nat{\"u}rlich sprachlichen Dialogkommunikation zwischen Mensch und Roboter. Die Arbeit widmet sich dabei zun{\"a}chst der Bereitstellung theoretischer Grundlagen (Definition, Geschichte, Funktionsweise, Herausforderungen, Aktuelle Lage) von Mensch-Maschine-Kommunikation unter Zuhilfenahme des Natural Language Processings. Darauf basierend werden Anforderungen aufgestellt, die eine softwarem{\"a}ßige Implementierung eines solchen Systems beschreiben. Dies geschieht unter der Beachtung von Rahmenbedingungen durch den themenstellenden Betrieb dieser Arbeit und den Grenzen des Robotersystems. Es folgt eine ausgiebige Evaluierung verschiedener NLP-Systeme anhand des Abgleichs von Anforderungen und zus{\"a}tzlicher Kennziffer-Messungen (Precision, Recall, F1 Score). In diesem Zusammenhang entsteht auch ein Testprogramm, welches ein automatisiertes Evaluierungsverfahren bereitstellt. Die beiden besten NLP-Systeme werden anschließend in ihrer Verwendung detailliert beschrieben. Eine prototypische Implementierung stellt die Verwendung eines NLP-Systems, zur Kommunikation von Mensch und Roboter, in einem konkreten Anwendungsfall unter Beweis.}, language = {de} } @incollection{BorndoerferKarbsteinLiebchenetal.2018, author = {Bornd{\"o}rfer, Ralf and Karbstein, Marika and Liebchen, Christian and Lindner, Niels}, title = {A Simple Way to Compute the Number of Vehicles That Are Required to Operate a Periodic Timetable}, series = {18th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2018)}, booktitle = {18th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2018)}, editor = {Bornd{\"o}rfer, Ralf and Storandt, Sabine}, publisher = {Schloss Dagstuhl - Leibniz-Zentrum f{\"u}r Informatik GmbH}, address = {Wadern}, isbn = {978-3-95977-096-5}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-10549}, publisher = {Technische Hochschule Wildau}, pages = {16:1 -- 16:15}, year = {2018}, abstract = {We consider the following planning problem in public transportation: Given a periodic timetable, how many vehicles are required to operate it? In [Julius Paetzold et al., 2017], for this sequential approach, it is proposed to first expand the periodic timetable over time, and then answer the above question by solving a flow-based aperiodic optimization problem. In this contribution we propose to keep the compact periodic representation of the timetable and simply solve a particular perfect matching problem. For practical networks, it is very much likely that the matching problem decomposes into several connected components. Our key observation is that there is no need to change any turnaround decision for the vehicles of a line during the day, as long as the timetable stays exactly the same.}, language = {en} } @misc{GedatFechnerFiebelkornetal.2018, author = {Gedat, Egbert and Fechner, Pascal and Fiebelkorn, Richard and Vandenhouten, Jan and Vandenhouten, Ralf}, title = {Image recognition of multi-perspective data for intelligent analysis of gestures and actions}, series = {Wissenschaftliche Beitr{\"a}ge 2018}, volume = {22}, journal = {Wissenschaftliche Beitr{\"a}ge 2018}, issn = {0949-8214}, doi = {10.15771/0949-8214_2018_3}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-10230}, pages = {25 -- 30}, year = {2018}, abstract = {The BERMUDA project started in January 2015 and was successfully completed after less than three years in August 2017. A technical set-up and an image processing and analysis software were developed to record and evaluate multi-perspective videos. Based on two cameras, positioned relatively far from one another with tilted axes, synchronized videos were recorded in the laboratory and in real life. The evaluation comprised the background elimination, the body part classification, the clustering, the assignment to persons and eventually the reconstruction of the skeletons. Based on the skeletons, machine learning techniques were developed to recognize the poses of the persons and next for the actions performed. It was, for example, possible to detect the action of a punch, which is relevant in security issues, with a precision of 51.3 \% and a recall of 60.6 \%.}, language = {en} }