SEARCH RESULT

Co-authorship networks provide a broad view to the connectivity properties of scholars, together with patterns of knowledge diffusion in scientific society. Network science provides a substantial framework for discovering the dynamics of these interactions those are defined by co-authoring a paper together. We constructed a complex network consisting of co-authorship links between authors, using the data retrieved from Web of Science Core Collection. Date retrieved is limited to 67248 publications addressed from Turkey in engineering field, including the timespan between 1975 and 2018. Analysis performed through this massive dataset resulted a complex network of 78883 nodes (authors) and 194232 edges (co-authorship links). Authors exhibit an average degree (neighbor) of 4.925, which increases to 6.687 in weighted analysis. Network exhibits an invincible clustering coefficient of ~0.8, while the average path length is close to 18. Together with the power-law consistent degree distribution that labels the network as scale-free, we also presentedtop “most central” authors of this network with respect to betweenness, closeness and eigenvector centrality measures, each defining the “importance” of an author in different aspects.

International Conference on Advanced Technologies, Computer Engineering and Science
ICATCES

İlker TÜRKER Rafet Durgut Oğuz Findik

Recognition and interpretation of human activities are very interesting and hot topics that are frequently studied in the field of computer vision. Especially with the advent and development of the Microsoft Kinect depth sensors, the expansion of the study fieldhas gained momentum in the positive direction. Thanks to RGBD cameras, which also provide depth information in addition to the RGB image, researchers benefit from many advantages in terms of privacy, accuracy and precision. In this study, automatic segmentation of repeated 3D human activity is proposed. A public dataset containing the repeated action sequences are recorded using the RGBD camera. The action sequence in this dataset includes similar and different action information. In order to identify and label each action in sequence, it is necessary to perform the segmentation process. To be able to perform a successful segmentation process, the data must be preprocessed to remove noise. For this purpose, a total variation based noise removal method is used. Human action recognition and detailed error analysis can be performed through the segments derived from the output of this work.

International Conference on Advanced Technologies, Computer Engineering and Science
ICATCES

Rafet Durgut C. OZCAN Oğuz Findik

Interactive 3D architectural indoor design have been more popular after it benefited from Virtual Reality (VR) technologies. VR brings computer-generated 3D content to real life scale and enable users to observe immersive indoor environments so that users can directly modify it. This opportunity enables buyers to purchase a property off-the-plan cheaper through virtual models. Instead of showing property through 2D plan or renders, this visualized interior architecture of an on-sale unbuilt property is demonstrated beforehand so that the investors have an impression as if they were in the physical building. However, current applications either use highly resource consuming software, or are non-interactive, or requires specialist to create such environments. In this study, we have created a real-estate purposed low-cost high quality fully interactive VR application that provides a realistic interior architecture of the property by using free and lightweight software: Sweet Home 3D and Unity. A preliminary study showed that participants generally liked proposed real estate-purposed VR application, and it satisfied the expectation of the property buyers.

International Workshop on GeoInformation Science
GEOADVANCES

K. Ozacar Yasin Ortakcı I. Kahraman Rafet Durgut Ismail Rakıp Karas