Meslek Yüksek Okulları / Vocational Schools

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    The Transnational Spread of Turkish Television Soap Operas
    (2014) Karlidag, Serpil; Bulut, Selda; J-2443-2015
    Turkish soap operas that are primarily popular in Turkic Republics, Middle East, and the Balkans have in recent years spread to different countries. With the entry into Ukrainian, Pakistani, Russian, and Chinese markets last year, tens of Turkish soap operas now reach their viewers in over 50 countries, and generate export revenues. Short-term return on investment, new communication technologies, Istanbul's ` magnetic nature attracting new talents' and different sociocultural forces and policies play key role in such extensive spread of Turkish TV series. As cultural commodities, TV series, while the images and identities they contain spread, reach at the same time new customers through geographical expansion, and increase the earnings of producers. Yet, the distribution of soap operas is important as much as their production. This requires focusing on the commodities with geo-linguistic and geo-cultural markets rather than companies producing these commodities. Therefore, demand for these non-Western commodities of different geographies points to a contra-flow. In a sense, while new media centers are emerging elsewhere other than the United States of America (USA)-Europe axis, it can be spoken of a regional opposition to the Western hegemony, though there is not an important historical leitmotive or transformation. Thus, in this study, relations between power, cultural commodity, and geography and the spread of Turkish soap operas in different geographies will be discussed with a political economic approach by also drawing attention to the historical commonality.
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    A Novel Approach for Estimating Heat Transfer Coefficients of Ethylene Glycol-Water Mixtures
    (2014) Bulut, Murat; Ankishan, Haydar; Demircioglu, Erdem; Ari, Seckin; Sengul, Orhan; https://orcid.org/0000-0002-6240-2545; AAH-4421-2019
    Ethylene glycol-water mixtures (EGWM) are vital for cooling engines in automotive industry. Scarce information is available in the literature for estimating the heat transfer coefficients (HTC) of EGWM using knowledge-based estimation techniques such as adaptive neuro-fuzzy inference systems (ANFIS) and artificial neural networks (ANN) which offer nonlinear input-output mapping. In this paper, the supervised learning methods of ANFIS and ANN are exploited for estimating the experimentally determined HTC. This original research fulfills the preceding modeling efforts on thermal properties of EGWM and HTC applications in the literature. An experimental test setup is designed to compute HTC of mixture over a small circular aluminum heater surface, 9.5 mm in diameter, placed at the bottom 40-mm-wide wall of a rectangular channel 3 mm x 40 mm in cross section. Measurement data are utilized as the train and test data sets of the estimation process. Prediction results have shown that ANFIS provide more accurate and reliable approximations compared to ANN. ANFIS present correlation factor of 98.81 %, whereas ANN estimate 87.83 % accuracy for test samples.
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    Optimization of Waiting and Journey Time in Group Elevator System Using Genetic Algorithm
    (2014) Tartan, Emre Oner; Erdem, Hamit; Berkol, Ali
    Efficient elevator group control is an important issue for vertical transportation in high-rise buildings. From the engineering design perspective, regulation of average waiting time and journey time while considering energy consumption is an optimization problem. Alternatively to the conventional algorithms for scheduling and dispatching cars to hall calls, intelligent systems based methods have drawn much attention in the last years. This study aims to improve the elevator group control system's performance by applying genetic algorithm based optimization algorithms considering two systems. Firstly, average passenger waiting time is optimized in the conventional elevator systems in which a hall call is submitted by indicating the travel direction. Secondly, a recent development in elevator industry is considered and it is assumed that instead of direction indicators there are destination button panels at floors that allow passengers to specify their destinations. In this case optimization of average waiting time, journey time and car trip time is investigated. Two proposed algorithms have been applied considering preload conditions in a building with 20 floors and 4 cars. The simulation results have been compared with a previous study and conventional duplex algorithm.
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    The Representation of Trauma and Trauma Coping Strategies in Grace Nichols's I is a Long Memoried Woman
    (2017) Sarikaya Sen, Merve; 0000-0003-2091-2536; AEG-2545-2022
    Using the historical background of the Middle Passage and slavery and drawing on the tools provided by trauma studies, this study carries out a comprehensive analysis of Grace Nichols's I is a Long Memoried Woman (1983). The starting hypothesis explored in this study is that I is a Long Memoried Woman represents traumatic experiences of the Middle Passage and slavery as well as trauma coping strategies adopted by Afro-Caribbean slaves, especially black slave women. Accordingly, this study first delves into four main trauma representation strategies in I is a Long Memoried Woman: repression, haunting, repetition and traumatic pastoral. It then explores the representation of trauma coping strategies adopted by Afro-Caribbean peoples: return to religion and spirituality, dissociation, escape through imagination and revenge fantasies. In doing so, this study demonstrates that there is a possibility for trauma victims such as Afro-Caribbean peoples to heal their wounds and move on as represented in I is a Long Memoried Woman.
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    A New Portable Device for the Snore/Non-Snore Classification
    (2017) Ankishan, Haydar; Tuncer, A. Turgut; 0000-0002-6240-2545; AAH-4421-2019
    Snoring is widely known as a disease. The aim of this paper is to introduce and validate our newly developed snoring detection device to identify automatically snore and non-snore sounds using a nonlinear analysis technique. The developed device can analyze chaotic features of a snore related sounds such as entropy, Largest Lyapunov Exponents (LLEs) and also has the data classification ability depending on the feature values. We report that the developed snoring detection device with proposed automatic classification method could achieve an accuracy of 94.38% for experiment I and 82.02 for experiment II when analyzing snore and non-snore sounds from 22 subjects. This study revealed the efficacy of our newly developed snoring detection device and indicated that it may be used at home an alternative to diagnose snore related sounds. It is anticipated that our findings will contribute to the development of an automated snore analysis system to be used in sleep studies.
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    A New Approach for the Acoustic Analysis of the Speech Pathology
    (2017) Ankishan, Haydar; 0000-0002-6240-2545; AAH-4421-2019
    Voice disorders are a common physical problem that can be encountered today and can cause serious problems in the long term. It is necessary to analyze the voice and extract its characteristics correctly so that it can be treated. In some cases, due to their sound characteristics, they do not differ from each other characteristics exactly, and today's systems do not yet have the ability to make correct decisions. This study has taken into account those evident which from voice disturbances and tries to the analysis of these disorders by means of previously unused attributes with the help of classifier (SVMs). In this study, after the sounds are modeled with LPC and MFCC, disorder analysis is performed on the obtained signals. In the results obtained from experimental studies, it has been determined that 100% of the patients with four different diseases can be decomposed together with the used nonlinear features.
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    A Simple Population Based Hybrid Harmonic Estimation Algorithm
    (2016) Tartan, Emre Oner; Erdem, Hamit
    This paper presents a new hybrid algorithm for harmonic estimation. The algorithm combines a simple fast population based search algorithm with Least Squares Method. It is based on the structural property of the harmonic estimation problem which implies that the signal model is linear in amplitude and nonlinear in phase. The hybrid algorithm uses the search algorithm for phase estimation and LS for amplitude estimation, iteratively. Exploiting the objective function defined according to the error of single harmonic's phase estimation, the proposed search algorithm distributes the population through equal intervals and simply narrows the search space sequentially in every generation. Unlike the other heuristic optimization algorithms that uses random distribution in initialization stage, the proposed method provides more robust convergence in the limits determined by the generation number. Simulation results show that the proposed hybrid algorithm not only gives accurate results but also significantly improves the computation time when compared with other heuristic optimization algorithms. Moreover this approach can be used to reduce the search duration when involved in other evolutionary optimization algorithms in a hybrid way and then can deal with frequency deviation and subharmonic estimation which are pitfalls for DFT based algorithms.
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    Dedifferentiated Epithelial-Myoepithelial Carcinoma of the Parapharyngeal Area: Case Report
    (2016) Akcay, F. Yilmaz; Canpolat, T.; Erkan, A. N.; Borcek, P.; Gunhan, O.; 0000-0001-7138-1400; AAK-8107-2021; H-1063-2019