Hava Trafik Kontrolörlerinin Stres Seviyelerinin Makine Öğrenme Teknikleriyle Algılanması

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2020

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Department of Electrical & Electronics Engineering
Department of Electrical and Electronics Engineering (EE) offers solid graduate education and research program. Our Department is known for its student-centered and practice-oriented education. We are devoted to provide an exceptional educational experience to our students and prepare them for the highest personal and professional accomplishments. The advanced teaching and research laboratories are designed to educate the future workforce and meet the challenges of current technologies. The faculty's research activities are high voltage, electrical machinery, power systems, signal and image processing and photonics. Our students have exciting opportunities to participate in our department's research projects as well as in various activities sponsored by TUBİTAK, and other professional societies. European Remote Radio Laboratory project, which provides internet-access to our laboratories, has been accomplished under the leadership of our department with contributions from several European institutions.

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Hava Trafik Kontrolünde olduğu gibi, iletişimin çoğunlukla ses üzerinden sağlandığı ve yoğun stresin iş kalitesini ve dolayısıyla da insan hayatını doğrudan etkileyebildiği koşullarda, stresin tespitinin makinalarla doğru bir şekilde anlaşılması, istenmeyen durumlar için önleyici tedbirler alınabilmesine olanak tanıyacaktır. Bu tez kapsamında, ses üzerinden özellikle mesleki stres altında olduğu düşünülen Hava Trafik Kontrolörlerinin, stres seviyelerinin ölçülmesi amaçlandı. Bu amaçla tez için benzersiz bir veri seti oluşturulup ses özellikleri çıkarıldı ve yapay sinir ağları ile farklı stres düzeylerinin algılanması için sınıflandırma çalışmaları gerçekleştirildi. Yapılan testler sonucunda 26 özellik seçilerek yapılan testlerde ortalama başarım, yapay sinir ağları kullanıldığında %31.2, destek vektör makinaları kullanıldığında ise %25.9'dır.
An accurate understanding of stress detection with machines will allow preventive measures to be taken for undesirable situations, such as in air traffic control, where communication is mostly through sound and intense stress can directly affect the quality of work and hence human life. Within the scope of this thesis, it was aimed to measure the stress levels of Air Traffic Controllers, which are considered to be under occupational stress, from their speeches on duty. For this purpose, a unique data set was created for the thesis, sound features were extracted and classification studies were carried out with artificial neural networks. As a result of the tests, the average performance for 26 features was 31.2% for NN and 25.9% for SVM.

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Elektrik ve Elektronik Mühendisliği, Electrical and Electronics Engineering, Sivil Havacılık, Civil Aviation

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90