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Öğe Comparison of experimental measurements and machine learning predictions of dielectric constant of liquid crystals(Indian Acad Sciences, 2022) Taşer, Pelin Yıldırım; Önsal, Gülnur; Uğurlu, OnurIn this study, we investigated the dielectric properties of the phthalocyanine (Pc)-doped nematic liquid crystal (NLC) composite structures. 4-Pentyl-4 & PRIME;-cyanobiphenyl (5CB) NLC was dispersed with 1 and 3% wt/wt Pc to investigate the doping concentration effect. Dielectric measurements of the samples were carried out using the dielectric spectroscopy method. Moreover, the real and imaginary components of the dielectric constant values were estimated based on the input parameters (frequency, voltage value and dispersion rate) using two different traditional regression algorithms (k-Nearest Neighbor and Decision Tree Regression) and five different ensemble-based regression algorithms (Extreme Gradient Boosting, Random Forest, Extra Tree Regression, Voting and Bagging using k-Nearest Neighbor as a base learner). According to the obtained results, the Extra Tree Regression algorithm had the best prediction performance on real and imaginary components of the dielectric constant values. Moreover, it is seen from the obtained results that the ensemble-based regression algorithms are more successful than the traditional ones.Öğe A distributed depth first search based algorithm for edge connectivity estimation(IEEE, 2020) Uğurlu, Onur; Akram, Vahid Khalilpour; Eliiyi, Deniz TürselThe edge connectivity of a network is the minimum number of edges whose removal disconnect the network. The edge connectivity determines the minimum number of edge-disjoint paths between all nodes. Hence finding the edge connectivity can reveal useful information about reliability, alternative paths and bottlenecks. In this paper, we propose a cost-effective distributed algorithm that finds a lower bound for the edge connectivity of a network via finding at most c depth-first-search trees, where c is the edge connectivity. The proposed algorithm is asynchronous and does not need any synchronization between the nodes. In the proposed algorithm, the root node starts a distributed depth-first-search algorithm, and the nodes select next node in the tree based on their available edges to maximize the total number of established trees. The simulation results show that the proposed algorithm finds the edge connectivity with an average of 48% accuracy ratio.Öğe Independent strong weak domination: A mathematical programming approach(World Scientific Publ Co Pte Ltd, 2020) Berberler, Murat Ersen; Uğurlu, Onur; Berberler, Zeynep NihanLet G = (V, E) be a graph. A subset S subset of V of vertices is a dominating set if every vertex in V - S is adjacent to at least one vertex of S. The domination number is the minimum cardinality of a dominating set. Let u, v is an element of V. Then, u strongly dominates v and v weakly dominates u if (i) uv is an element of E and (ii) deg u >= deg v. A subset D of V is a strong (weak) dominating set of G if every vertex in V - D is strongly (weakly) dominated by at least one vertex in D. The strong (weak) domination number of G is the minimum cardinality of a strong (weak) dominating set. A set D subset of V is an independent (or stable) set if no two vertices of D are adjacent. The independent domination number of C (independent strong domination number, independent weak domination number, respectively) is the minimum size of an independent dominating set (independent strong dominating set, independent weak dominating set, respectively) of G. In this paper, mathematical models are developed for the problems of independent domination and independent strong (weak) domination of a graph. Then test problems are solved by the GAMS software, the optima and execution times are implemented. To the best of our knowledge, this is the first mathematical programming formulations for the problems, and computational results show that the proposed models are capable of finding optimal solutions within a reasonable amount of time.Öğe Parallel identification of central nodes in wireless multi-hop networks(IEEE, 2020) Eliiyi, Deniz Türsel; Arslan, Hilal; Akram, Vahid Khalilpour; Uğurlu, OnurA wireless multi-hop network is a collection of nodes that communicate by message passing over multiple links. Sending a message to a remote node can consume some energy from all intermediary nodes. In a network, the nodes with minimum distance to all other nodes are called Jordan central nodes. Selecting the central nodes as sink or base station can considerably reduce the overall energy consumption and increase the network life time. This paper proposes a new parallel algorithm to find all central nodes of a network by finding BFS trees of a subset of nodes. The roots of the trees with smallest height are selected as Jordan central nodes. After finding each tree the algorithm eliminates some nodes from the search space. Available processors construct the BFS tree for different nodes in parallel and eliminate a group of unvisited nodes after creating each tree. The implementation results of the algorithm using different number of processors on topologies with up to 250 nodes showed that the proposed algorithm can find all central nodes by examining less than 20% of nodes in less than 0.034 seconds.Öğe A progressive search algorithm for the minimum hitting set problem(2021) Arslan, Hilal; Uğurlu, Onur; Akram, Vahid Khalilpour; Eliiyi, Deniz TürselGiven a finite universe and a collection of the subsets of the universe, the minimum hitting set of thecollection is the smallest subset of the universe that has non-empty intersection with each set in thecollection. Finding the minimum hitting set is an NP-Hard problem that has many real worldapplications. In this study, we propose a progressive search-based approach to find the minimumhitting set of a given collection. The algorithm starts searching for the hitting sets of size 1 andincrease the expected size of the minimum hitting set by a factor of d. After each unsuccessful search,it increases the expected size by d and generate the candidate sets with the expected size. After eachsuccessful search, the algorithm takes the average of last unsuccessful and successful searches andcontinue the searching with the new expected size. The algorithm terminates when the detectedupper bound coincides with the detected lower bound. The effect of different values for d on theperformance of the algorithm has been experimented on various data sets. Experimental resultsreveal that the proposed method effectively computes the minimum hitting set on real-world dataand random dataset.Öğe A sequential workforce scheduling and routing problem for the retail industry: a case study(2022) Özcan, Sel; Uğurlu, Onur; Eliiyi, UğurThis study investigated the operational workforce scheduling and routing problem of a leading international retail company. Currently, the company plans to launch a new product into the Turkish market, which will be used in all its retail stores across the country. For the best marketing outcome, branding of all retail stores needs to be renewed by an outsourced workforce with a minimum of cost and time. We framed this as a workforce scheduling and routing optimization problem. Therefore, a two-stage solution was proposed. The retail stores were partitioned into disjoint regions in the first stage, and the schedules were optimized in the second stage. We employed the k-means clustering algorithm for constructing these regions. Two different heuristic approaches were applied to solve regional scheduling in the second stage of the algorithm since the resulting scheduling problem is NP-hard. Finally, a computational analysis was performed with real data and the results are discussed.
