Browsing by Author "Üstünkök, Tolga"

Now showing 1 - 6 of 6

Citation Count: 4
Effect of PSO Tuned P, PD, and PID Controllers on the Stability of a Quadrotor
(Institute of Electrical and Electronics Engineers Inc., 2019) Ustunkok,T.; Karakaya,M.; Software Engineering; Computer Engineering
Many popular quadrotor controllers are based on PID controllers. This study compares the behavior of a quadrotor when its controller is the proportional (P) only, proportional (P) and derivative (D), and all terms of the PID controller which is tuned by a Particle Swarm Optimization (PSO) implementation. A P, PD, and PID controller integrated quadrotor model is used with realistic parameters while conducting experiments in simulation. Our goal is to find out if it is worth to use PID or some of its terms is enough to get a stable system. According to the preliminary results of the experiments, the statistical difference of results shows that PID is better than both P and PD for the given model. © 2019 IEEE.
Citation Count: 2
Image Tag Refinement with Self Organizing Maps
(Institute of Electrical and Electronics Engineers Inc., 2019) Ustunkok,T.; Acar,O.C.; Karakaya,M.; Software Engineering; Computer Engineering
Nowadays, data sharing has become faster than ever. This speed demands novel search methods. Most popular way of accessing the data is to search its tag. Therefore, creating tags, captions from an image is a research area that gains reputation rapidly. In this study, we aim to refine image captions by utilizing Self Organizing Maps. We extract image and caption pairs as feature vectors and then cluster those vectors. Vectors with similar content clustered close to each other. With the help of those clusters, we hope to get some relevant tags that do not exist in the original tags. We performed extensive experiments and presented our initial results. According to these results, the proposed model performs reasonably well with a 54% precision score. Finally, we conclude our work by providing a list of future work. © 2019 IEEE.
Citation Count: 0
Informatics Engineering Education in Turkey and Expectations of Software Industry
(Ieee, 2018) Yazici, Ali; Mishra, Alok; Karakaya, Ziya; Ustunkok, Tolga; Software Engineering; Computer Engineering
In this study, using the OSYM data, the number of intakes in Informatics Engineering programs in Turkey, accreditation data and the medium of instruction of the program are summarized for the years 2016 and 2017. In addition, the software sector's expectations from the informatics engineering graduates are reassessed based on the academic studies. The developed knowledge-skill gap set was used to evaluate the situation in Turkish informatics engineering programs. Sector expectations are discussed in the context of 2017-2019 Turkey Software Sector Strategy and Action Plan prepared by the Ministry of Science, Industry and Technology of Turkey and some proposals are made for the academia. As a result, it was observed that the expectations of the software industry were similar in all studies. Additionally, the expectations were changed in the direction of developing technologies and this change should be reflected in the informatics engineering programs.
Citation Count: 0
Informatics Engineering Education in Turkey and Expectations of Software Industry;
(Institute of Electrical and Electronics Engineers Inc., 2018) Yazici,A.; Mishra,A.; Karakaya,Z.; Ustunkok,T.; Software Engineering; Computer Engineering
In this study, using the ÖSYM data, the number of intakes in Informatics Engineering programs in Turkey, accreditation data and the medium of instruction of the program are summarized for the years 2016 and 2017. In addition, the software sector's expectations from the informatics engineering graduates are reassessed based on the academic studies. The developed knowledge-skill gap set was used to evaluate the situation in Turkish informatics engineering programs. Sector expectations are discussed in the context of 2017-2019 Turkey Software Sector Strategy and Action Plan prepared by the Ministry of Science, Industry and Technology of Turkey and some proposals are made for the academia. As a result, it was observed that the expectations of the software industry were similar in all studies. Additionally, the expectations were changed in the direction of developing technologies and this change should be reflected in the informatics engineering programs. © 2018 IEEE.
Citation Count: 0
Ss-Mla: a Semisupervised Method for Multi-Label Annotation of Remotely Sensed Images
(SPIE, 2021) Üstünkök,T.; Karakaya,M.; Software Engineering; Computer Engineering
Recent technological advancements in satellite imagery have increased the production of remotely sensed images. Therefore, developing efficient methods for annotating these images has gained popularity. Most of the current state-of-the-art methods are based on supervised machine learning techniques. We propose a method called semisupervised multi-label annotizer (SS-MLA) that adapts vector-quantized temporal associative memory to annotate remotely sensed images. One of the advantages of SS-MLA over the supervised methods is that it extracts features not only from the given sample but also from similar samples that are previously seen without using an explicit attention mechanism. Thus SS-MLA enhances the learning efficiency of the training process. We conduct extensive performance comparisons with five different methods in the literature over four datasets. The comparison results indicate the success of the proposed method over the existing ones: SS-MLA generates the best results in 7 out of 11 comparisons. © 2021 Society of Photo-Optical Instrumentation Engineers (SPIE).
Ss-mla: Uzaktan algılamalı görüntülerin çok etiketli sınıflandırması için yeni bir çözüm
(2021) Üstünkök, Tolga; Karakaya, Kasım Murat; Software Engineering; Computer Engineering
Uzaktan algılanan görüntülerin çok etiketli sınıflandırması çok önemli bir araştırma alanıdır. Kentsel büyümeyi izlemekten askeri gözetlemeye kadar birçok uygulamaya sahiptir. Uzaktan algılanan görüntülerin çok etiketli sınıflandırması için birçok algoritma ve yöntem önerilmiştir. Bu tezde iki yaklaşım sunulmaktadır. İlki, küçük veri kümelerinde karmaşık yöntemlerin daha basit olanlara göre avantajı olmadığını gösteren CNN tabanlı basit bir modeldir. İkincisi, uzaktan algılanan görüntülerin çoklu etiketli sınıflandırması için Semi-Supervised Multi-Label Annotizer (SS-MLA) adı verilen rekabetçi bir Vector-Quantized Temporal Associative Memory (VQTAM) tabanlı yöntemdir. İlk yöntem, uzaktan algılanmış dört farklı veri kümesi üzerinde F1-Skorlarına göre literatürdeki diğer son teknoloji yöntemlerle ve SS-MLA ile karşılaştırılmıştır. Deney sonuçları, yeni bir yaklaşım olarak SS-MLA'nın, karşılaştırmaların yarısından ve önerilen basit yöntemden daha iyi sonuçlar verdiğini göstermektedir. Algoritma ve yöntemlerin tüm uygulamaları için Python 3.8 ortamında Tensorflow-GPU 2.4.0 ve Numpy 1.19.5 çerçeveleri kullanılmıştır.