Kadir Has University or as mostly preferred by its students , was founded in 1997, in Istanbul. A private university, it has seven faculties, Engineering, science and Humanities, Economics and Administrative science, Communication, Law and Fine Arts, as well as its several vocational schools, and is dedicated to becoming a leader in educational and cultural fields in Turkey, as well as establishing itself as an international center for research and scientific development. Prof. Mustafa Aydın is the rector of the university. Wikipedia.
Ayag Z.,Kadir Has University |
Gurcan Ozdemir R.,Istanbul Kultur University
International Journal of Production Economics | Year: 2012
The problem of machine tool selection among available alternatives has been critical issue for most companies in fast-growing markets for a long time. In the presence of many alternatives and selection criteria, the problem becomes a multiple-criteria decision making (MCDM) machine tool selection problem. Therefore, most companies have utilized various methods to successfully carry out this difficult and time-consuming process. In this work, both of the most used MCDM methods, the modified TOPSIS and the Analytical Network Process (ANP) are introduced to present a performance analysis on machine tool selection problem. The ANP method is used to determine the relative weights of a set of the evaluation criteria, as the modified TOPSIS method is utilized to rank competing machine tool alternatives in terms of their overall performance. Furthermore, in this paper, we use a fuzzy extension of ANP, a more general form of AHP, which uses uncertain human preferences as input information in the decision-making process, because AHP cannot accommodate the variety of interactions, dependencies and feedback between higher and lower level elements. Instead of using the classical eigenvector prioritization method in AHP, only employed in the prioritization stage of ANP, a fuzzy logic method providing more accuracy on judgments is applied. The resulting fuzzy ANP enhances the potential of the conventional ANP for dealing with imprecise and uncertain human comparison judgments. The proposed approach is also applied for a real-life case in a company. © 2012 Elsevier B.V. All rights reserved.
Sengul M.,Kadir Has University
IEEE Transactions on Circuits and Systems II: Express Briefs | Year: 2013
It is always preferable to use commercially available software tools to design broadband matching networks for microwave communication systems. However, for these tools, the matching network topology and element values must be selected properly. Therefore, in this paper, a practical method is presented to generate matching networks with good initial element values. Eventually, the performance of the designed matching network is optimized by employing the commercially available computer-aided design (CAD) tools. An example is given to illustrate the utilization of the proposed method. It is shown that the proposed method provides very good initials for CAD tools. © 2013 IEEE.
Akkemik K.A.,Kadir Has University |
Goksal K.,Yildirim Beyazit University
Energy Economics | Year: 2012
Existing studies examining the Granger causality relationship between energy consumption and GDP use a panel of countries but implicitly assume that the panels are homogeneous. This paper extends the Granger causality relationship between energy consumption and GDP by taking into account panel heterogeneity. For this purpose, we use a large panel of 79 countries for the period 1980-2007. Specifically, we examine four different causal relationships: homogeneous non-causality, homogeneous causality, heterogeneous non-causality, and heterogeneous causality. The results show that roughly seven-tenths of the countries exhibit bi-directional Granger causality, two-tenths exhibit no Granger causality, and one-tenths exhibit uni-directional Granger causality. © 2012 Elsevier B.V.
Yucekaya A.,Kadir Has University
Renewable and Sustainable Energy Reviews | Year: 2013
A Compressed Air Energy Storage System is a means of storing energy which can then be used when the demand for energy increases. In this system, air is compressed in a cavern when power prices are low, and this air is used to run a natural gas-fired turbine to generate power when prices go up, with the aim of profiting from the price difference. This type of system can independently compress air, generate electricity, or do both. However, the prices of electricity and natural gas fluctuate, which directly impacts the amount of revenue that can be made, and this requires the calculating of estimates to optimize operation strategies and maximize profit. For these reasons, this is a crucial energy storage technology that requires economic analyses to justify investment decisions in power markets. In this paper, a mixed integer programming method is developed to schedule the operation of the system for forward market prices that are estimated using a markov-based probabilistic model. Then an algorithm that includes two separate modules in a simulation is employed to optimize the annual operation of the system. The paper presents a case study for Turkey as well as economic analyses based on probabilistic forward prices and the profits obtained from the optimization module. © 2013 Elsevier Ltd. All rights reserved.
Aladag A.E.,Bogazici University |
Erten C.,Kadir Has University
Bioinformatics | Year: 2013
Motivation: Given protein-protein interaction (PPI) networks of a pair of species, a pairwise global alignment corresponds to a one-to-one mapping between their proteins. Based on the presupposition that such a mapping provides pairs of functionally orthologous proteins accurately, the results of the alignment may then be used in comparative systems biology problems such as function prediction/verification or construction of evolutionary relationships.Results: We show that the problem is NP-hard even for the case where the pair of networks are simply paths. We next provide a polynomial time heuristic algorithm, SPINAL, which consists of two main phases. In the first coarse-grained alignment phase, we construct all pairwise initial similarity scores based on pairwise local neighborhood matchings. Using the produced similarity scores, the fine-grained alignment phase produces the final one-to-one mapping by iteratively growing a locally improved solution subset. Both phases make use of the construction of neighborhood bipartite graphs and the contributors as a common primitive. We assess the performance of our algorithm on the PPI networks of yeast, fly, human and worm. We show that based on the accuracy measures used in relevant work, our method outperforms the state-of-the-art algorithms. Furthermore, our algorithm does not suffer from scalability issues, as such accurate results are achieved in reasonable running times as compared with the benchmark algorithms. © 2013 The Author. Published by Oxford University Press. All rights reserved.