Search results for: Pavle Dakić

Authors: Pavle Dakić, Dimitrije Kotur, Zoran Stojanović

Abstract:

This paper presents the results of the study in which the excitation system fault of synchronous generator is simulated. In a case of excitation system fault (loss of field), distance relay is used to prevent further damage. Loss-of-field relay calculates complex impedance using measured voltage and current at the generator terminals. In order to obtain phasors from sampled measured values, discrete Fourier transform is used. All simulations are conducted using Matlab and Simulink software package. The analysis is conducted on the two machine system which supplies equivalent load. While simulating loss of excitation on one generator in different conditions (at idle operation, weakly loaded, and fully loaded), diagrams of active power, reactive power, and measured impedance are analyzed and monitored. Moreover, in the simulations, the effect of generator load on relay tripping time is investigated. In conclusion, the performed tests confirm that the fault in the excitation system can be detected by measuring the impedance.

Keywords: loss-of-excitation, synchronous generator, distance protection, Fourier transformation

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Authors: Ketevan Epadze

Abstract:

The paper aims to examine conflicting historical narratives proposed by the Georgian and Abkhazian scholars on the territorial affiliation of Abkhazia in the 1950s, explain how these narratives were connected to the Soviet nationalities policy after WW II and demonstrate the dynamic of the narratives’ battle in the last years of the Soviet system, which was followed by military conflict in the post-Soviet era. Abkhazia –a breakaway region of Georgia- self-declared its independence in 1992. Historical dispute on the territorial rights of Abkhazia emerged long before the military conflict began and was connected to the theory of Abkhazian ethnogenesis written by the Georgian literary scholar Pavle Ingorokva. He argued that medieval Abkhazians were Georgians, while modern Abkhazians are newcomers in Abkhazia. After the de-Stalinization, Abkhazian historians developed historical narrative opposed to Ingorokva’s theory. In the 1980s, Georgian dissidents who strove for Georgia’s independence used Ingorokva’s thesis to oppose Abkhazians desire for self-determination and sovereignty. Abkhazian political actors in their turn employed opposite historical arguments to legitimate their rights over autonomy. Ingorokva’s theory is one of the principal issues, discussed during the Georgian-Abkhazian dialogue; it often confuses Georgians and gives the reasons to Abkhazians for complaining about the Georgian discrimination in the Soviet past. The study is based on the different kind of sources: archival materials of the 1950s (Communist Party Archive of Georgia, Soviet Journal ‘Mnatobi’), the book by Pavle Ingorokva ‘Giorgi Merchule’ (1947-1954) and Zurab Anchabadze’s responsive work to Ingorokva’s book – ‘From the medieval history of Abkhazia’ (1956-1959), political speeches of the Georgian and Abkhazian political actors in the 1980s, secondary sources on the Soviet nationalities policy from the 1950s to the 1990s.

Keywords: Soviet, history, ethnicity, nationalism, politics, post-Soviet, conflict

Procedia PDF Downloads 141

Authors: Annika Stechemesser, Rachel Pounds, Emma Lucas, Chris Dawson, Julia Lipecki, Pavle Vrljicak, Jan Brosens, Sean Kehoe, Jason Yap, Lawrence Young, Sascha Ott

Abstract:

Advances in technology make it now possible to retrieve the genetic information of thousands of single cancerous cells. One of the key challenges in single cell analysis of cancerous tissue is to determine the number of different cell types and their characteristic genes within the sample to better understand the tumors and their reaction to different treatments. For this analysis to be possible, it is crucial to filter out background noise as it can severely blur the downstream analysis and give misleading results. In-depth analysis of the state-of-the-art filtering methods for single cell data showed that they do, in some cases, not separate noisy and normal cells sufficiently. We introduced an algorithm that filters and clusters single cell data simultaneously without relying on certain genes or thresholds chosen by eye. It detects communities in a Shared Nearest Neighbor similarity network, which captures the similarities and dissimilarities of the cells by optimizing the modularity and then identifies and removes vertices with a weak clustering belonging. This strategy is based on the fact that noisy data instances are very likely to be similar to true cell types but do not match any of these wells. Once the clustering is complete, we apply a set of evaluation metrics on the cluster level and accept or reject clusters based on the outcome. The performance of our algorithm was tested on three datasets and led to convincing results. We were able to replicate the results on a Peripheral Blood Mononuclear Cells dataset. Furthermore, we applied the algorithm to two samples of ovarian cancer from the same patient before and after chemotherapy. Comparing the standard approach to our algorithm, we found a hidden cell type in the ovarian postchemotherapy data with interesting marker genes that are potentially relevant for medical research.

Keywords: cancer research, graph theory, machine learning, single cell analysis

Procedia PDF Downloads 79