Cilt:62 Sayı:02 (2020)

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https://dspace.ankara.edu.tr/handle/20.500.12575/76533

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Browsing Cilt:62 Sayı:02 (2020) by Author "Mühendislik Fakültesi"

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    A Concatenated Up And Down Tapered Fıber For Sımultaneous Measurement Of Straın And Temperature
    (Ankara Üniversitesi Fen Fakültesi, 2020-07-01) Bilsel, Mustafa; Navruz, İsa; Elektrik-Elektronik Mühendisliği; Mühendislik Fakültesi
    A novel fiber optical sensor based on in-line fiber Mach-Zehnder interferometer for simultaneous measurement of strain and temperature is proposed and demonstrated experimentally. The interferometer is simple, extremely robust and highly sensitive and consists of two concatenated parts; one is a down-tapered fiber (DTF) and the other is an up-tapered fiber (UTF). UTF and DTF sections of the sensor are fabricated by using a commercial fiber splicer and a non-commercial setup based on heating and stretching a portion of a standard single-mode fiber, respectively. While UTF section behaves as a beam splitter to decompose the injected light into core and cladding modes, DTF section provides evanescent field to access the surrounding environment. Experimental results indicate that the resolutions of 0.83 °C and 45.80 micro-epsilon were achieved in temperature and strain, respectively, for simultaneous measurement with a 10 pm of wavelength resolution.
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    Modern Learnıng Technıques And Plant Image Classıfıcatıon
    (Ankara Üniversitesi Fen Fakültesi, 2020-07-01) Ünal, Metehan; Bostancı, Erhan; Güzel, Mehmet Serdar; Aydın, Ayhan; Bilgisayar Mühendisliği; Mühendislik Fakültesi
    The intelligent machines concept is born in sci-fi scenarios. Today it seems to be we are much closer to realizing this idea than ever before. By imitating the human nervous system, machines can learn many things. This paper explains modern learning techniques like artificial neural networks, transfer learning. Later purposes an experiment to classify plant seedling images to test the transfer learning with two different CNN architectures. Although the architects were not actually created for this task, result were quite accurate for a different classification task.