Vortragender: Fabio Hellmann

Termin: Freitag, 11. Dezember 2020, 10:00

Ort: Online

Vortragender: Niklas Schröter

Termin: Freitag, 11. Dezember 2020, 10:00

Ort: Online

Vortragender: Jonas Mayr

Termin: Freitag, 11. Dezember 2020, 10:00

Ort: Online

Vortragender: Tobias Hübner

Termin: Montag, 19. Oktober 2020, 16:30

Ort: Online

Vortragende: Carmen Berndt 

Termin: Mittwoch, 17. Juni 2020, 10:00-11:00

Ort: Online

Zusammenfassung

The University Library Service now offers two new services centered around the better visibility of your research publications. During this brief, informal presentation Sonja Härkönen will provide an overview of the services available and of how they can be accessed. The focus will be on the display of publication lists on the new website (using a plugin for DjangoCMS) and on the self-archiving options available (i.e. the option to make the full text of your publications freely available online).

Dozent(in): Sonja Härkönen, Bibliothekarin, Universität Augsburg

Termin: Freitag, 21. Februar 2020, 13:45

Gebäude/ Raum: Raum 004, Eichleitnerstr. 30, F1

Zusammenfassung

The temporal information, which describes the sequential dependency across the frames naturally exists in the video-based datasets. In several machine vision tasks, including facial based analysis, these inherent features have been shown to be beneficial to model training in improving their accuracy. In affective computing, however, deep investigations of these methods are yet to be undergone given its unique challenges. One instance is that it demands more sophisticated understanding of complex interactions between humans besides of the accuracy of the model. To this end, the current task will be first to analyse of the impact of temporal modeling through generative modeling in current affect estimations problem. One such approach is to use deep auto-encoder based structure to model inherent features from each modality. Then, recurrent based deep learning models, such as RNN and LSTMs, can be used to capture relevant temporal information and final estimations through curriculum learning. Further, utilizing these findings, necessary machine learning-based techniques will be integrated to achieve the main aim of state of the art accuracy on the available benchmarks.

Biografie

Decky Aspandi is currently a Ph.D candidate in the Department of Information and Communication Technologies at the Universitat Pompeu Fabra (UPF), Barcelona. He is supervised by Xavier Binefa and funded by the Predoctoral Fellowship of the UPF. In 2014 he received his Master Degree in Computer Engineering from King Mongkuts University of Technology Thonburi, Thailand. In 2012 he received his Bachelor in Computer science from the University of Mulawarman, Indonesia. He has taught several undergraduate courses including Computer Organization and Deep Learning. He is currently working on his thesis, which focuses on the understanding of facial analysis using deep spatio-temporal neural network models.

​​​​​​​Dozent(in): Decky Aspandi

Termin: Donnerstag 16. Januar 2020, 10:00

Gebäude/Raum: Raum 004, Eichleitnerstr. 30, F1

Zusammenfassung

The exterior noise is one of the key components in the automotive NVH development process. While strict regulations have to be met, the acoustic character is of major importance for the OEMs. The simulated pass-by is a widely accepted alternative to the real test track, as it is more convenient and independent from environmental conditions. Modifications and additional measurement tasks can be added easily in order to minimize possible sources of disturbances. With the so-called contribution analysis, a ranking of the most common sources, such as intake, engine, exhaust, and tires can be easily performed in addition to the standard measurements. This indicates at which positions modifications might make sense. The process to determine these contributions is rather tiresome, as all possible sources and transfer paths have to be measured with microphones and accelerometers, in order to decompose the response at the AA line. Hence, a machine learning based approach is desired, In order to guide engineers to critical sound and vibration sources way faster. The task is now to train a system, which is able to decompose the exterior noise into tires, engine, exhaust, gear box and powertrain. Further, an easy transfer to comfort noise measurements within the vehicle is aimed at.

Biografie

Dejan Arsić received his diploma in 2004, followed by his doctoral degree in 2010 for his studies in audiovisual signal processing with focus on object tracking and behavioral analysis at the faculty of electrical engineering at Technische Universität München (TUM). He currently applies his profound knowledge in signal analysis as key account manager in the automotive industry. Here he establishes elaborate solutions for various problems in the field of NVH, with a main focus on TPA methods and workflow oriented data acquisition and analysis. Dr. Arsić has (co-)authored more than 60 publications in books, journals and conference proceedings, and is reviewer for a range of leading scientific journals.

Vortragender: Dr. Dejan Arsić, Müller-BBM VibroAkustik

Termin: Mittwoch, 8. Januar 2020, 10:00

Gebäude/Raum: Raum 004, Eichleitnerstr. 30, F1