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    A Greedy Algorithm for Minimum Cut into Bounded Sets Problem
    (IEEE, 2021) Ugurlu, Onur; Akram, Vahid Khalilpour; Eliiyi, Deniz Tursel
    Finding critical links and weak points is an important task in almost all types of networks. Minimum cuts provide useful information about the critical links. However, finding a minimum cut of a network may provide insufficient or misleading information on critical links since the number of disconnected nodes in the residual network is not taken into account in this problem. In this work, we study the minimum cut into bounded sets problem, which limits the number of nodes in portioned sets. Finding the minimum cut into bounded sets can provide useful information on important critical links in a different network, whose failure has a hard and unacceptable effect. The minimum cut into bounded sets problem is an open NP-Complete problem. We propose a greedy algorithm for this problem with O(c x n(2)) time complexity and present computational results on random networks. To the best of our knowledge, the proposed algorithm is the first heuristic for the minimum cut into bounded sets problem.
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    Minimization of the threshold voltage parameter of the co-doped ZnO doped liquid crystals by machine learning algorithms
    (Nature Portfolio, 2023) Onsal, Guelnur; Ugurlu, Onur; Kaynar, Umit H.; Eliiyi, Deniz Tursel
    This study aims to examine the influence of the co-doped semiconductor nanostructure (Al-Cu):ZnO on the electro-optical properties of the E7 coded pure nematic liquid crystal structures and minimize the threshold voltage of pure E7 liquid crystal. To determine the ideal concentration ratios of the materials for the minimum threshold voltage, we employed different machine learning algorithms. In this context, we first produced twelve composite structures through lab experimentation with different concentrations and created an experimental dataset for the machine learning algorithms. Next, the ideal concentration ratios were estimated using the AdaBoost algorithm, which has an R-2 of 96% on the experimental dataset. Finally, additional composite structures having the estimated concentration ratios were produced. The results show that, with the help of the employed machine learning algorithms, the threshold voltage of pure E7 liquid crystal was reduced by 19% via the (Al-Cu):ZnO doping.
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    Reliability-based optimization of imperfect preventive maintenance with Bayesian estimation
    (Taylor & Francis Inc, 2022) Gürler, Selma; Göksülük, Dinçer; Eliiyi, Deniz Tursel
    In this study, we present a sequential imperfect preventive maintenance model for a component subject to degradation. We use an age reduction reliability model for scheduling preventive maintenance, which is performed whenever the component's reliability falls below the reliability threshold level, R. The problem is considered under lack of data, in which the Bayesian methodology is more appropriate than the frequentist view for estimating the unknown parameters of the model. We assume a fixed duration for preventive maintenance, whereas the duration of corrective maintenance is an exponential random variable. We model the total expected cost over all cycles and find the optimal preventive maintenance plan until a replacement based on the reliability threshold value. We also conducted a numerical study for sensitivity analysis of the developed model.