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    Öğe
    Fiber quantity analysis of cementitious composite using multifocus imagery
    (Wiley, 2018) Akcay, Ozgun; Pekmezci, Bekir Yilmaz; Seker, Dursun Zafer; Tuncel, Erman Yigit; Atahan, Hakan Nuri
    After static and impact loading tests applied to PVA fiber reinforced cementitious composites, depending on the loading type and mixture properties of the composite, different fiber failure types are observed at the fractured surface of test samples. It is very hard to determine the pulled out fiber amount with conventional methods. The purpose of this study is to display these differences in a quantitative way using digital imaging processing techniques and exhibit the relation between absorbed energy and pulled out fiber area. Digital image processing might provide useful techniques to determine structure of the cracked surface of samples. Since close-up images of the failure surfaces of test samples are needed to examine thin and short fibers, from the point view of science of measurement and modeling from stereo images, digital photogrammetry does not produce prospective results appropriately. In this work, in order to reveal fibers, two complementary image-processing techniques were used successively. First, images, which obtained from macro photographic lenses at different focal lengths, were combined with multifocus imaging. Second, processed multifocused image is automatically clustered in order to extract fibers with maximum likelihood classification algorithm. Consequently, state of side-views of the fractured surfaces of composites is drawn precisely. The amount of pulled out fibers were estimated by using the area calculated. The relation between the absorbed energy and pulled fiber area was obtained. With increase of absorbed fracture energy, pulled out fiber amount for both static and dynamic tests increases. POLYM. COMPOS., 39:2126-2134, 2018. (c) 2016 Society of Plastics Engineers
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    Öğe
    THE ROLE OF NATIONAL AND INTERNATIONAL GEOSPATIAL DATA SOURCES IN COASTAL ZONE MANAGEMENT
    (Parlar Scientific Publications (P S P), 2017) Bayram, Bulent; Avsar, Ozgur; Seker, Dursun Zafer; Kayi, Abdullah; Erdogan, Mustafa; Eker, Oktay; Janpaule, Inese
    Coastline changes are increasing rapidly due to both natural and human effects. The expansion of touristic, industrial and culture fishing establishments through coastal areas has brought about uncontrolled and unplanned urbanization. In this study, the coastline in Karasu district of Turkey has been extracted by using Unmanned Aerial Vehicle (UAV) images. For this purpose; 7 cm ground sample distance (GSD) UAV images taken in 2013 by Gatewing-X100 by 40% side and 70% forward overlap was used as post images. Produced ortho images were used as the base map for extraction of the coastline. An object-oriented approach has been applied to capture the shoreline from these ortho images. In the presented study, the eCognition object-oriented fuzzy image processing software has been used. This commercial software has the ability to develop custom tools for image classification in addition to its standard object feature tools. Customized arithmetic features have been used to achieve more accurate shoreline segmentation results. Three main object classes have been created as sea, shoreline buffer and land to extract shoreline. The segmentation results were converted into dxf vector data format. The results were compared with manual digitizing of 55 blind readers. An algorithm has been developed by using Matlab to analyze the differences between object-oriented classification and manual digitizing results. Results were evaluated according to students' gender, age, spent time, used software and courses taken. The root mean square error (RMSE) was calculated as 7.15 m.