Search results for: Ozan Mut

Authors: Kevser Dincer, Basma Waisi, M. Ozan Ozdemir, Ugur Pasaogullari, Jeffrey McCutcheon

Abstract:

Nanofibers are defined as fibers with diameters less than 100 nanometers. In this study, behaviours of activated carbon nanofiber (ACNF), carbon nanofiber (CNF), polyacrylonitrile/ carbon nanotube (PAN/CNT), polyvinyl alcohol/nanosilver (PVA/Ag) in proton exchange membrane (PEM) fuel cells are investigated experimentally. This material was used as gas diffusion layer (GDL) in PEM fuel cells. In this study, the electrical conductivities of nanofiber and nanofiber/nanoparticles have been studied to understand their effects on PEM fuel cell performance. According to the experimental results, the maximum electrical conductivity performance of the fuel cell with nanofiber was found to be at PVA/Ag (at UConn condition). The electrical conductivities of CNF, ACNF, PAN/CNT are lower for PEM. The resistance of cell with PVA/Ag is lower than the resistance of cell with PAN/CNT, ACNF, CNF.

Keywords: Proton exchange membrane fuel cells, electrospinning, carbon nanofiber, activate carbon nanofiber, PVA fiber, pan fiber, carbon nanotube, nanoparticle, nanocomposites.

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Authors: Ozan Mut, Mehmet Göktürk

Abstract:

Matching algorithms have significant importance in speaker recognition. Feature vectors of the unknown utterance are compared to feature vectors of the modeled speakers as a last step in speaker recognition. A similarity score is found for every model in the speaker database. Depending on the type of speaker recognition, these scores are used to determine the author of unknown speech samples. For speaker verification, similarity score is tested against a predefined threshold and either acceptance or rejection result is obtained. In the case of speaker identification, the result depends on whether the identification is open set or closed set. In closed set identification, the model that yields the best similarity score is accepted. In open set identification, the best score is tested against a threshold, so there is one more possible output satisfying the condition that the speaker is not one of the registered speakers in existing database. This paper focuses on closed set speaker identification using a modified version of a well known matching algorithm. The results of new matching algorithm indicated better performance on YOHO international speaker recognition database.

Keywords: Automatic Speaker Recognition, Voice Recognition, Pattern Recognition, Digital Audio Signal Processing.

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