Mühendislik Fakültesi / Faculty of Engineering

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2D UAV path planning with radar threatening areas using simulated annealing algorithm for event detection
(2018) Basbous, Bilal;
Path Planning for Unmanned Aerial Vehicles (UAVs) can be used for many purposes. However, the problem becomes more and more complex when dealing with a large number of points to visit for detecting and catching different type of events and simple threat avoidance such as Radar Areas. In the literature different type of algorithms (especially evolutionary algorithms) are preferred. In this project, Simulated Annealing (SA) Algorithm is used for solving the path planning problem. Firstly, problem is converted to a part of Travelling Salesman Problem (TSP), and then the solutions are optimized with the 2-Opt approach and other simple algorithms. The code is implemented in MATLAB by using its visualization. Circular avoidance approach is developed and applied with the Simulated Annealing in order to escape from circular radar threats. Tests have been made to observe the results of SA algorithm and radar threats avoidance approaches, where the results show that after a period of time, SA algorithm gives acceptable solutions with the capacities of escaping from radar area threats. Where SA algorithm gives better solutions in less period of time when there are no radar threats. Experimental results depicted that the proposed model can result in an acceptable solution for UAVs in sufficient execution time. This model can be used as an alternative solution to the similar evolutionary algorithms.
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3-Phase Induction Motor Drive with PWM Modulator Using a 8-Bit Low Cost Micrcontroller
(2004) Küçükgüzel, E.; Bilgiç, Mehmet Oruç; 1616
This paper describes a study on a well studied subject, driving 3-phase induction motor. But the hardware used for the study is different than the conventional 16-bit microcontrollers or DSPs. The aim of the project is to lower the price of control hardware and achieve a satisfying performance with less expensive 8-bit microcontrollers.
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4. Endüstri Devrimi’nin getireceği yeni liderlik anlayışı
(2018-07) Tarhan, İbrahim Ethem; 114926
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4. Endüstri Devriminin getireceğiyeni Liderlik Anlayışı
(2018-06) Tarhan, İbrahim Ethem; 114926
Bu çalışmada, 4. Endüstri Devrimi olarak ifade edilen Endüstri 4.0 kavramının liderlik stillerini ne şekilde etkileyeceği konusu üzerinde durulacaktır. Bilindiği üzere Almanya öncülüğünde gelişmiş olan ülkeler özellikle uzak doğu ülkelerinin düşük işçilik maliyetlerinden dolayı birçok sektörlerdeki olası hakimeyetini azaltacağı ve az nitelikli işgücüne bağımlılığı azaltmak amacı ile Dördüncü Sanayi Devrimi’ni 2011 yılında başlatmışlardır. Endüstri 4.0’ın içerisinde yer alan önemli araçların bazıları akıllı fabrikalar, siber-fiziksel sistem, veri analitiği ve nesnelerin incelenmesidir. Özellikle, siber-fıziksel sistem ve akıllı fabrikaların üretimde insan faktörünün rolünü değiştireceği düşünülmektedir. Robotların da içinde bulunduğu birçok makinalar üretim süreçlerine dahil olacaklardır. Otomasyonun devreye girmesiyle üretimdeki insan faktörünün rolü değişmiştir. Sözü geçen bu teknolojik değişimler, beraberinde yönetim ve liderlik kavramlarını da değiştirecektir. Dünya genelinde yaygın değişik liderlik stilleri bulunmaktadır. Uygulanmakta olan liderlik modellerinin önde gelenleri etkileşimci, bir başka değişle geleneksel, mükafat ve cezaya dayalı ( transaksiyonel) liderlik ve dönüşümcü (transformasyonel) liderlik modelleridir. Günümüzde örgütsel değişimlerde en çok önerilen liderlik modeli dönüşümcü liderlik modelidir çünkü dönüşümcü liderler çalışanları motive ederek, organizasyonun vizyonu doğrultusunda yönlendirebilir ve güvenlerini artırarak işletmedeki görevlerinin kendi bireysel beklentilerinin üzerine çıkartarak verimi artırmayı başarabilirler. Bu değişime önderlik edenlere dönüştürücü lider denilmektedir. Değişimlerde etkiliği olan dönüşümcü liderlik modelinin 4. Endüstri Devriminin getireceği teknolojik değişikliklerde tek başına uygulandığında yeterli olamayacağı tahmin edilmektedir. Endüstri 4.0 kavramının beraberinde getirdiği marjinal teknolojik değişimleri sadece dönüşümcü liderlik stili ile yönlendirmemiz yeterli olamayacaktır. Bilgiye ve buluşa dayalı (knowledge-innovation based) liderlere ihtiyaç duyulacaktır. Karizmatik, dönüşümcü liderlerin aynı zamanda Elon Musk gibi okuyan, araştıran, kritik düşünebilen, konusunda bilgiye önem veren girişimci liderlerin Endüstri 4.0 kavramınma daha uygun olacağı düşünülmektedir. Bu çalışmada, 4.Endüstri Devrimine en uygun olabilecek liderlik modelleri konusu tartışılacaktır.
Open Access
A Bayesian Deep Neural Network Approach to Seven-Point Thermal Sensation Perception
(IEEE-Inst Electrical Electronics Engineers Inc., 2022) ÇAKIR, MUSTAFA; AKBULUT, AKHAN
To create and maintain comfortable indoor environments, predicting occupant thermal sensation is an important goal for architects, engineers, and facility managers. The link between thermal comfort, productivity, and health is common knowledge, and researchers have developed many state-of-the-art thermal-sensation models from dozens of research projects over the last 50 years. In addition to these, the use of intelligent data-analysis techniques, such as black-box artificial neural networks (ANNs), is receiving research attention with the aim of designing building thermal-behavior models from collected data. With the convergence of the internet of things (IoT), cloud computing, and artificial intelligence (AI), smart buildings now protect us and keep us comfortable while saving energy and cutting emissions. These types of smart buildings play a vital role in building smart cities of the future. The aim of this study is to help facility managers predict the thermal sensation of the occupants under the given circumstances. To achieve this, we applied a data-driven approach to predict the thermal sensation of occupants of an indoor environment using previously collected data. Our main contribution is to design and evaluate a deep neural network (DNN) for predicting thermal sensations with a high degree of accuracy regardless of building type, climate zone, or a building's heating and/or ventilation methods. We used the second version of the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) Global Thermal Comfort Database to train our model. The hyperparameter-tuning process of the proposed model is optimized using the Bayesian strategy and predicts the thermal sensation of occupants with 78% accuracy, which is much higher than the traditional predicted mean vote (PMV) model and the other shallow and deep networks compared.
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A Biomimetic Application Manufacturing of Superhydrophobic Cotton Fabrics and Their Surface Properties
(2013) Özay, Serap; Ukuser, Gökçen; SARIER, NİHAL; ARAT, REFİK; 114920
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A capacity curve model for confined clay brick infills
(Springer, Van Godewijckstraat 30, 3311 Gz Dordrecht, Netherlands, 2016-03) Özkaynak, Hasan; Yüksel, Ercan; SÜRMELİ, MELİH; 40475; 175417; 154472
Experimental studies have proven that clay brick infills, confined with carbon-fiber-reinforced polymers (CFRP) in reinforced concrete (RC) frames, have some advantages in terms of stiffness, strength, energy dissipation capability and damage intensity. Owing to these advantages, existing infill walls in RC frames may be retrofitted with CFRP strips, especially in low-rise buildings in earthquake-prone areas. There is a gap in the literature concerning their behavior model, for use in structural analysis. A piecewise linear capacity curve model called "DUVAR'' is proposed here, which estimates the envelope of force-vs.-displacement hysteresis, depending on the data compiled from the literature and the completed experimental studies. A nonlinear shear spring element is utilized in the model to represent the bare and retrofitted infills. The ultimate shear strength and the corresponding displacement, the ratio of cracking stiffness to initial stiffness, the ratio of ultimate strength to cracking strength, and the ductility ratio are the five key parameters of the model. The model is validated against the experimental results of two sovereign studies. Finally, the model is employed in the performance evaluation of an existing three-story RC building to exemplify its straightforward application.
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A CNN based rotation invariant fingerprint recognition system
(Istanbul Unıv, Fac Engineering, Elektrik-Elektronik Mühendisliği Bölümü, Avcılar Kampüsü, İstanbul, 34320, Turkey, 2017) Çelik Mayadağlı, Tuba; Saatçı, Ertuğrul; Rifat, Edizkan; 10488; 16117; 16117
This paper presents a Cellular Neural Networks (CNN) based rotation invariant fingerprint recognition system by keeping the hardware implementability in mind. Core point was used as a reference point and detection of the core point was implemented in the CNN framework. Proposed system consists of four stages: preprocessing, feature extraction, false feature elimination and matching. Preprocessing enhances the input fingerprint image. Feature extraction creates rotation invariant features by using core point as a reference point. False feature elimination increases the system performance by removing the false minutiae points. Matching stage compares extracted features and creates a matching score. Recognition performance of the proposed system has been tested by using high resolution PolyU HRF DBII database. The results are very encouraging for implementing a CNN based fully automatic rotation invariant fingerprint recognition system.
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A Combined Fuzzy AHP-goal Programming Approach to Assembly-Line Selection
(IOS PRESS, NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS, 2007) Ayağ, Zeki; Özdemir, Rifat Gürcan; TR141173; TR8785
In mass production, assembly-line balancing (ALB) problem has been a critical and repetitive issue for companies for long time. On the other hand, equipment selection for stations has also been another important problem at the design stage of an assembly-line system. In this paper, both problems are handled simultaneously. Therefore first, goal programming (GP) method, a well-suited technique is used to develop a preemptive formulation to joint both of the problems, when the nature of the problem consists of several conflicting objectives, and some mathematical constraints on solutions. Second, an AHP method based on fuzzy scales which is incorporated with the GP is also used due to the fact that it takes both qualitative and quantitative judgments of decision-maker(s) into consideration to rank the equipment alternatives for stations by weight. The fuzzy AHP as one of the most commonly used multiple-criteria decision making (MCDM) methods has been effectively used for more than decade in both academic research and practice, and takes the vagueness and uncertainty on judgments of decision-maker(s) into consideration due to the fact that the crisp pairwise comparison in the conventional AHP seems to insufficient and imprecise to capture the right judgments of decision-maker(s). In short, in this study, a combined fuzzy AHP-GP approach is proposed to evaluating assembly-line design alternatives with equipment selection. An integer GP formulation is constructed, which also uses the fuzzy AHP scores of equipment alternatives, and employs them as one of the goals. Then, the mathematical model is solved to find out the ultimate alternative in terms of the minimized equipment cost and the maximized preference measures of decision-maker(s). The proposed approach is also illustrated on a sample case study.
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A Comparative Study of Smoothing a Vehicle s Trajectory which is Calculated by an Evolutionary Algorithm
(2016-06) Buran, Bayram Ali; Çağlar, Süleyman Hikmet; ŞAHİNGÖZ, ÖZGÜR KORAY; 214903; 243931; 114368
Determining a vehicle’s trajectory is a complex and hard to solve type problem in the literature and it is identified as a NP-Hard optimization problem which is studied in different engineering disciplines such as computer, electrical and industrial engineering. It has been observed that such complex problems can be solved by using various approaches and lots of them are focused on the usage of Evolutionary Algorithms especially in case of a large number of controls points which are needed to be visited. Although these algorithms provide near optimal solutions, in the real world, vehicles are not able to follow this determined path (trajectory) without any deviation. Because vehicles are moving objects and each one moves with a certain speed. Therefore it is impossible for a vehicle to make a sharp turn after visiting control points. These vehicles need to make smoothed turns over these points. Therefore there will be a certain difference between the calculated path and the real path. It is needed to determine the real path by using necessary mathematical solutions for smoothing these paths. To ensure the motion continuity of vehicles, they need to follow paths determined according to a certain criterion. In this study, the most common smoothing methods which are used to ensure these continuities (Bezier, B-Spline and Dubins) have been compared and it is aimed to show the different approaches in an application area of path planning problems as a comparative study.
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A Comparative Study to Determine the Effective Window Size of Turkish Word Sense Disambiguation Systems
(Springer, 233 Spring Street, New York, Ny 10013, United States, 2013) Adalı, Eşref; Tantuğ, Ahmet Cüneyd; İLGEN, BAHAR; 141812; 8786; 21833
In this paper, the effect of different windowing schemes on word sense disambiguation accuracy is presented. Turkish Lexical SampleDataset has been used in the experiments. We took the samples of ambiguous verbs and nouns of the dataset and used bag-of-word properties as context information. The experi-ments have been repeated for different window sizes based on several machine learning algorithms. We follow 2/3 splitting strategy (2/3 for training, 1/3 for test-ing) and determine the most frequently used words in the training part. After re-moving stop words, we repeated the experiments by using most frequent 100, 75, 50 and 25 content words of the training data. Our findings show that the usage of most frequent 75 words as features improves the accuracy in results for Turkish verbs. Similar results have been obtained for Turkish nouns when we use the most frequent 100 words of the training set. Considering this information, selected al-gorithms have been tested on varying window sizes {30, 15, 10 and 5}. Our find-ings show that Naive Bayes and Functional Tree methods yielded better accuracy results. And the window size +/-5 gives the best average results both for noun and the verb groups. It is observed that the best results of the two groups are 65.8 and 56% points above the most frequent sense baseline of the verb and noun groups respectively.
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A component-oriented process model
(IEEE Computer Soc, 10662 Los Vaqueros Circle, Po Box 3014, Los Alamitos, Ca 90720-1314 USA, 2003-07) Altunel, Yusuf; 141288
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A compressed sensing based approach on discrete algebraic reconstruction technique
(IEEE, 345 E 47th St, New York, Ny 10017 USA, 2015) Demircan Türeyen, Ezgi; Kamaşak, Mustafa Erşel; 237397; 27148
Discrete tomography (DT) techniques are capable of computing better results, even using less number of projections than the continuous tomography techniques. Discrete Algebraic Reconstruction Technique (DART) is an iterative reconstruction method proposed to achieve this goal by exploiting a prior knowledge on the gray levels and assuming that the scanned object is composed from a few different densities. In this paper, DART method is combined with an initial total variation minimization (TvMin) phase to ensure a better initial guess and extended with a segmentation procedure in which the threshold values are estimated from a finite set of candidates to minimize both the projection error and the total variation (TV) simultaneously. The accuracy and the robustness of the algorithm is compared with the original DART by the simulation experiments which are done under (1) limited number of projections, (2) limited view problem and (3) noisy projections conditions.
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A Cutting Sequencing Approach to Modular Manufacturing
(Emerald Group Publishing Limited, 2004) Özdemir, Rifat Gürcan; AKTİN, AYŞE TÜLİN; 109203
In this study, a two‐stage algorithm is developed for the cutting sequencing problem in a modular manufacturing system consisting of four basic workstations. Since the flexibility of the system is dependent upon the cutting stage of raw materials, the study focuses particularly on this workstation. In the first stage of the algorithm, an integer linear programming model is used to determine the number of hardboards that will be cut. The model is tested with two different objective functions. In the second stage, a heuristic which takes into account the due date of the products is developed to obtain the real‐time sequencing of these cutting patterns on the shop floor. The algorithm is further implemented in a furniture manufacturer that operates on a make‐to‐order basis. The results of the existing and proposed system are compared, and the proposed algorithm is found to provide a useful tool in such a real‐life planning problem.
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A Decision Support Model for Customer Value Assessment and Supply Quota Allocation
(TAYLOR & FRANCIS LTD, 11 NEW FETTER LANE, LONDON EC4P 4EE, ENGLAND, 2000-09) Barbarasoğlu, G; Yazgaç, Ayşe Tülin; AKTİN, AYŞE TÜLİN
The aim of this study is to develop a decision support tool for a supplier in a value-chain environment. The supplier under consideration is assumed to provide a strategic product to a number of customers and needs to allocate his capacity among them in a way to maximize his business value. First, an analytic hierarchy process (AHP) structure is designed to represent the criteria which are identified from the supplier's point of view to assess customer performance, and customer priorities are obtained by using the AHP composition principle. Then, these are deployed in numerical algorithms which aim to allocate the total supply capacity among customers as supply quotas. The approach is implemented in an electric motor manufacturer in Turkey, which possesses high competitive power with advanced manufacturing technology.
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A decision support system to determine optimal ventilator settings
(Biomed Central Ltd, 236 Grays Inn Rd, Floor 6, London Wc1X 8Hl, England, 2014) Akkur, Erkan; Akan, Aydın; Yarman, B. Sıddık; AKBULUT, FATMA PATLAR
Background: Choosing the correct ventilator settings for the treatment of patients with respiratory tract disease is quite an important issue. Since the task of specifying the parameters of ventilation equipment is entirely carried out by a physician, physician ' s knowledge and experience in the selection of these settings has a direct effect on the accuracy of his/her decisions. Nowadays, decision support systems have been used for these kinds of operations to eliminate errors. Our goal is to minimize errors in ventilation therapy and prevent deaths caused by incorrect configuration of ventilation devices. The proposed system is designed to assist less experienced physicians working in the facilities without having lung mechanics like cottage hospitals. Methods: This article describes a decision support system proposing the ventilator settings required to be applied in the treatment according to the patients ' physiological information. The proposed model has been designed to minimize the possibility of making a mistake and to encourage more efficient use of time in support of the decision making process while the physicians make critical decisions about the patient. Artificial Neural Network (ANN) is implemented in order to calculate frequency, tidal volume, FiO(2) outputs, and this classification model has been used for estimation of pressure support /volume support outputs. For the obtainment of the highest performance in both models, different configurations have been tried. Various tests have been realized for training methods, and a number of hidden layers mostly affect factors regarding the performance of ANNs. Results: The physiological information of 158 respiratory patients over the age of 60 and were treated in three different hospitals between the years 2010 and 2012 has been used in the training and testing of the system. The diagnosed disease, core body temperature, pulse, arterial systolic pressure, diastolic blood pressure, PEEP, PSO2, pH, pCO(2), bicarbonate data as well as the frequency, tidal volume, FiO(2), and pressure support / volume support values suitable for use in the ventilator device have been recommended to the physicians with an accuracy of 98,44%. Performed experiments show that sequential order weight/bias training was found to be the most ideal ANN learning algorithm for regression model and Bayesian regulation backpropagation was found to be the most ideal ANN learning algorithm for classification models. Conclusions: This article aims at making independent of the choice of parameters from physicians in the ventilator treatment of respiratory tract patients with proposed decision support system. The rate of accuracy in prediction of systems increases with the use of data of more patients in training. Therefore, non-physician operators can use systems in determination of ventilator settings in case of emergencies.
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A decision support tool for the analysis of pricing, investment and regulatory processes in a decentralized electricity market
(ELSEVIER SCI LTD, THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND, 2008-04) Paşaoğlu Kılanç, Güzay; Or, İlhan; TR207355; TR144701
After the liberalization of the electricity generation industry, capacity expansion decisions are made by multiple self-oriented power companies. Unlike the centralized environment, decision-making of market participants is now guided by price signal feedbacks and by an imperfect foresight of the future market conditions (and competitor actions) that they will face. In such an environment, decision makers need to better understand long-term dynamics of the Supply and demand sides of the power market. In this Study, a system dynamics model is developed, to better understand and analyze the decentralized and competitive electricity market dynamics in the long run. The developed simulation model oversees a 20-year planning horizon; it includes a demand module, a capacity expansion module, a power generation module, in accounting and finance module, various competitors, a regulatory body and a bidding mechanism. Many features, singularities and tools of decentralized markets, such as; capacity withholding, enforced divestment, long-term contracts, price-elastic demands, incentives/disincentives, are also incorporated into the model. Public regulators and power companies are potential users of the model, for learning and decision support in policy design and strategic planning. Results of scenario analysis are presented to illustrate potential use of the model. (C) 2008 Elsevier Ltd. All rights reserved.
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A discretized tomographic image reconstruction based upon total variation regularization
(Elsevier Sci Ltd, The Boulevard, Langford Lane, Kidlington, Oxford Ox5 1Gb, Oxon, England, 2017-09) Demircan Türeyen, Ezgi; Kamasak, Mustafa E.; 237397
Tomographic image reconstruction problem has an ill-posed nature like many other linear inverse problems in the image processing domain. Discrete tomography (DT) techniques are developed to cope with this drawback by utilizing the discreteness of an image. Discrete algebraic reconstruction technique (DART) is a DT technique that alternates between an inversion stage, employed by the algebraic reconstruction methods (ARM), and a discretization (i.e. segmentation) stage. Total variation (TV) minimization is another popular technique that deals with the ill-posedness by exploiting the piece-wise constancy of the image and basically requires to solve a convex optimization problem. In this paper, we propose an algorithm which also performs the successive sequences of inversion and discretization, but it estimates the continuous reconstructions under TV-based regularization instead of using ARM. Our algorithm incorporates the DART's idea of reducing the number of unknowns through the subsequent iterations, with a 1-D TV-based setting. As a second contribution, we also suggest a procedure to be able to select the segmentation parameters automatically which can be applied when the gray levels (corresponding to the different densities in the scanned object) are not known a priori. We performed various experiments using different phantoms, to show the proposed algorithm reveals better approximations when compared to DART, as well as three other continuous reconstruction techniques. While investigating the performances, we considered limited number of projections, limited-view, noisy projections and lack of prior knowledge on gray levels scenarios. (C) 2017 Elsevier Ltd. All rights reserved.
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A fault detection strategy for software projects
(Univ Osijek, Tech Fac, Trg ivane Brlic-Mazuranic 2, Slavonski Brod, Hr-35000, Croatia, 2013-02) Çatal, Çağatay; Diri, Banu; 108363; 25308
Abstract The existing software fault prediction models require metrics and fault data belonging to previous software versions or similar software projects. However, there are cases when previous fault data are not present, such as a software company's transition to a new project domain. In this kind of situations, supervised learning methods using fault labels cannot be applied, leading to the need for new techniques. We proposed a software fault prediction strategy using method-level metrics thresholds to predict the fault-proneness of unlabelled program modules. This technique was experimentally evaluated on NASA datasets, KC2 and JM1. Some existing approaches implement several clustering techniques to cluster modules, process followed by an evaluation phase. This evaluation is performed by a software quality expert, who analyses every representative of each cluster and then labels the modules as fault-prone or not fault-prone. Our approach does not require a human expert during the prediction process. It is a fault prediction strategy, which combines a method-level metrics thresholds as filtering mechanism and an OR operator as a composition mechanism.
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A fuzzy AHP approach to evaluating machine tool alternatives
(SPRINGER, VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS, 2006-04) Ayağ, Zeki; Özdemir, Rifat Gürcan; TR8785; TR141173
Selecting process of a machine tool has been very important issue for companies for years, because the improper selection of a machine tool might cause of many problems affecting negatively on productivity, precision, flexibility and company's responsive manufacturing capabilities. On the other hand, selecting the best machine tool from its increasing number of existing alternatives in market are multiple-criteria decision making (MCDM) problem in the presence of many quantitative and qualitative attributes. Therefore, in this paper, an analytic hierarchy process (AHP) is used for machine tool selection problem due to the fact that it has been widely used in evaluating various kinds of MCDM problems in both academic researches and practices. However, due to the vagueness and uncertainty on judgments of the decision-maker(s), the crisp pair wise comparison in the conventional AHP seems to insufficient and imprecise to capture the right judgments of decision-maker(s). That is why; fuzzy number logic is introduced in the pair wise comparison of AHP to make up for this deficiency in the conventional AHP. Shortly, in this study, an intelligent approach is proposed, where both techniques; fuzzy logic and AHP are come together, referred to as fuzzy AHP. First, the fuzzy AHP technique is used to weight the alternatives under multiple attributes; second Benefit/Cost (B/C) ratio analysis is carried out by using both the fuzzy AHP score and procurement cost, of each alternative. The alternative with highest B/C ratio is found out and called as the ultimate machine tool among others. In addition, a case study is also presented to make this approach more understandable for a decision-maker(s).

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