Lisansüstü Eğitim Enstitüsü (Yayınlar)
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Item “Kazısız teknoloji” yöntemiyle atıksu altyapısı : İstanbul-Bayrampaşa örneği(Bahçeşehir Üniversitesi Fen Bilimleri Enstitüsü, 2013-04) Yiğit, Ayten; Yakar, HülyaAtıksu altyapısı, kentsel sistemin vazgeçilmez öğelerindendir. Sağlıklı bir çevre ve su kaynaklarının temizliğinin korunması için sağlam bir atıksu altyapısı gerekmektedir. Artan nüfus yoğunluğu, kentleşme hızı ile sayıları giderek artan kentsel alanlar ile birlikte kentsel altyapı ve özellikle yeni atıksu hatlarının yapımı, var olan hatların da bakım ve onarımı konunun önemini artırmaktadır. Kazarak yapılan atıksu altyapı uygulamaları sırasında ortaya çıkan trafik problemi ile soucunda ekonomik ve sosyal maliyetin artması nedeniyle geliştirilen, kazısız altyapı uygulamaları kazısız teknolojiler (KT) olarak adlandırılmaktadır. 20.Yüzyılın başlarından itibaren gelişen kazısız teknolojiler ile yolları bozmadan, çevreye, tarihi dokuya zarar vermeden ve kentlilerin yaşam kalitesini zedelemeden atıksu altyapı uygulamaları yapmak mümkün olmaktadır. Bu çalışma kazısız teknoloji yönteminin kent ve kentli için önemini, uygulama yöntemlerini, İstanbul- Bayrampaşa İlçesi’ndeki örnek uygulamalar ile açıklanmaya çalışmaktadır.Item Lojistikte 3PL performans değerlendirmesi ve sağlık sektöründe bir uygulama(Bahçeşehir Üniversitesi Fen Bilimleri Enstitüsü, 2013-05) Oktay, Eda; Sümen, HalefşanLojistik faaliyetleri, iş dünyasında rekabet avantajını artırmak isteyen bir firmanın daha büyük yatırımlar ve daha yüksek uzmanlık düzeyi isteyen bir fonksiyonu haline gelmiştir. Özellikle depo yönetimi tüm lojistik faaliyetler içerisinde en büyük paya sahip dilimlerden biridir. Ürün maliyetinin düşürülmesi, deponun iyi yönetilmesine, doğru noktalara doğru sevkiyatın yapılmasına ve stok yönetimine bağlıdır. Günümüzde 3PL firmaları, bilişim altyapısı, teknik bilgisi ve deneyimi ile birlikte son tüketici memnuniyetine dayanan üst düzey bir yaklaşım sergilenmektedir. Bu çalışma kapsamında lojistikte dış kaynak kullanımı için 3PL yaklaşımı incelenmiştir. 3PL seçim kriterleri ve performans ölçümü sağlık sektöründe faaliyet gösteren bir firma üzerinde uygulanmış ve sonuçlar değerlendirilmiştir.Item Fine-tuning convolutional neural networks for maritime vessel classification, verification and recognition(Bahçeşehir Üniversitesi Fen Bilimleri Enstitüsü, 2018) Gürkaynak, Cahit Deniz; Arıca, NafizAutonomous maritime vessel surveillance systems have enormous implications to national defense and global supply chain. Therefore, ship detection and classification problems have been widely studied for a long time. Most of the studies have used satellite imagery, the real-time satellite imaging access is not public and image resolutions is insufficient for high-quality classification and recognition systems. As an alternative approach, consumer-level surveillance cameras have attracted great attention recently due to its cost-effectiveness and easy installation process. Recently, deep learning has become the state-of-the-art method in computer vision field. Deep network architectures have emerged by surpassing human-level accuracy on image classification problems. Many old but powerful ideas have been revised and applied to these networks in various computer vision problems. However, the applications of deep learning methods in the analysis of maritime vessel images are limited in the literature. In this thesis, we employ the state-of-the-art deep network architectures for maritime vessel classification, verification and recognition problems. In the experiments, the most popular three convolutional neural network architectures; AlexNet, VGGNet and ResNet are used. MARVEL dataset is utilized for benchmark purposes, which contains 2M ship images. Since these networks are very difficult to train and they require lots of training samples, we follow transfer learning approach. The main contribution of this thesis is the implementation, tuning and evaluation of specific applications for maritime vessels domain. For classification task, we conduct experiments on different transfer learning techniques and we investigate their performance by transferring the weights layer by layer. We reach the state-of-the-art results by fine-tuning VGG-16 architecture. For both verification and recognition tasks, we use triplet loss heavily inspired by recent advances in the field of face verification and recognition. We achieve closely comparable state-of-the-art results on MARVEL’s both verification and recognition benchmarks.Item Unmanned aerial vehicle digital forensic investigation(Bahçeşehir Üniversitesi Ekonomik ve Toplumsal Araştırmalar Merkezi, 2018) Gülataş, İbrahimThe Unmanned Aerial Vehicle (UAV) technology is a rapidly emerging technology and it has found widespread usage. While UAVs are still in their development phase without any existing commonly accepted standards for their underlying technologies and their forensic investigation, they have an increasing record of criminal usage. This urges the research community to develop techniques to detect and prevent illegal usage of UAVs. With this work, a seven-phase UAV digital forensics investigation framework is proposed to standardize the investigation process for UAVs. The framework was tested on the DJI Phantom III Professional UAV which is one of the most popular commercial UAVs in the market. Three kinds of forensic artifacts are found on the sample UAV and these artifacts are examined deeply. Two of these artifacts are log files stored as binary files and the other artifact is the EXIF header of the images that are captured by UAV's onboard camera. The log files of the UAV has a proprietary data structure. By reverse engineering this data structure, the flight paths for all the flights taken by the investigated UAV, could be derived. At the end of the whole investigation process, it is observed that the proposed seven-phased investigation framework works successfully and significantly helps with the forensic investigation of UASs in a systematic manner.Item IR image edge detection using neural network and clustering(Bahçeşehir Üniversitesi Ekonomik ve Toplumsal Araştırmalar Merkezi, 2018) Mohammadzadeh Meymandi, Tala; Aydın, TarkanNowadays image processing and feature extraction methods provide significantly important knowledge about images. The first step for identifying objects in an image is extracting the image properties. Edge detection is one the common features of image processing, because edges include useful information about an image. Although general public may not deal with Infrared images directly, this field is widely benefited in many sciences. Therefore, a proper infrared image edge detection method could result in thorough comprehension. In this study, infrared images are selected for edge detection due to their application in various technologies such as medical, military fields and surveillance purposes. According to the structure of these images, it is not possible to extract their edges using common methods. Therefore, a new method is proposed for edge detection of infrared images. In the proposed method, first the image is segmented by a clustering algorithm. Then, Neural Network algorithm is selected to extract the region of interest among the segmented clusters. In the last step, morphological operators are used to extract the edges from the Region of Interest. For segmentation, two K-means and Mean Shift clustering methods are applied separately, and their cluster features are used as the Neural Network inputs. Pursuant to the advantage of Mean Shift clustering algorithm in cluster number determination this method may be favorable in many cases. The evaluation results of the proposed method and comparison with other available methods indicate the method’s good performance for infrared image edge detection.Item Türk hukukunda gazetecinin parasal ve dinlenme hakları(Bahçeşehir Üniversitesi Sosyal Bilimleri Enstitüsü, 2009) Karaçöl, Dürdane; Tuncay, Aziz CanItem Kurumsal kaynak planlama yazılım paketleri ve kuruma özel yazılımların seçim aşamasında karşılaştırılması(Fen Bilimleri Enstitüsü, 2009) Köstence, N. Tuğrul; Karahoca, AdemItem Logistics outsourcing and selection of third party logistics service provider (3PL) via fuzzy ahp(Fen Bilimleri Enstitüsü, 2009) Çakır, Erdal; Vayvay, Özalp