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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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.
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A Holistic Approach For The Optimization of Offshore Wind Farm Layouts Considering Cable Layouts
(2019-08) Alabaş Uslu, Çiğdem; ÜLKÜ, İLAYDA; 51700
A wind farm, mainly, is composed of a set of turbines, one or more transmitters and a set of electrical cable connections between turbines and transmitters. Determination of turbine locations within the farm to maximize total power generation is called turbine location (TL) problem. Relative turbine positions affect the amount of overall energy because of wake effects. Determination of cable connections among turbines and transmitters to collect produced energy by turbines at transmitters is called cable layout (CL) problem. While TL problem is directly effective on the total energy production in the farm, CL problem indirectly affects the total energy due to the power losses. In the literature, TL and CL problems are solved sequentially where the layout found by solving of TL is used as an input of CL problem. To minimize wake effects in TL problem, distances between turbine pairs should be increased, however, as the distances are increased the cable cost increases in CL problem. A new mathematical model is developed to deal with simultaneously solving of TL and CL problems. A set of test instances are used to show the performance of the proposed model. The experiments show the practical use of the proposed holistic model.
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A novel fuzzy TOPSIS method using emerging interval-valued spherical fuzzy sets
(2019-06-03) Kahraman, Cengiz; GÜNDOĞDU, FATMA KUTLU
All the extensions of ordinary fuzzy sets with three-dimensional membership functions such as intuitionistic fuzzy sets, second type intuitionistic fuzzy sets (or Pythagorean fuzzy sets) and neutrosophic sets aim at defining the judgments of decision makers/ experts with a more detailed description. As a new extension of intuitionistic fuzzy sets of second type, the emerging spherical fuzzy sets (SFS) have been proposed by Kutlu Gundogdu and Kahraman (2019b). In spherical fuzzy sets, the sum of membership, non-membership and hesitancy degrees must satisfy the condition0 <= mu(2) + v(2) + pi(2) <= 1 in which these parameters are assigned independently. SFS is an integration of Pythagorean fuzzy sets and neutrosophic sets. In this paper, novel interval-valued spherical fuzzy sets are introduced with their score and accuracy functions; arithmetic and aggregation operations such as interval-valued spherical fuzzy weighted arithmetic mean operator and interval-valued spherical fuzzy geometric mean operator. Later, interval-valued spherical fuzzy sets are employed in developing the extension of TOPSIS under fuzziness. Then, we use the proposed interval-valued spherical fuzzy TOPSIS method in solving a multiple criteria selection problem among 3D printers to verify the developed approach and to demonstrate its practicality and effectiveness. A comparative analysis with single-valued spherical TOPSIS is also performed.
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A Novel Input Set for LSTM based Transport Mode Detection
(2019-03) Güvensan, M.Amaç; AŞCI, GÜVEN; 285689
The capability of mobile phones are increasing with the development of hardware and software technology. Especially sensors on smartphones enable to collect environmental and personal information. Thus, smartphones become the key components of ambient intelligence. Human activity recognition and transport mode detection (TMD) are the main research areas for tracking the daily activities of a person. This study aims to introduce a novel input set for daily activities mainly for transportation modes in order to increase the detection rate. In this study, the frame-based novel input set consisting of time-domain and frequency-domain features are fed to LSTM network. Thus, the classification ratio on HTC public dataset is climbed up to 97% which is 2% more than the state-of-the-art method in the literature.
Open Access
A novel spherical fuzzy analytic hierarchy process and its renewable energy application
(2020-03-01) Kutlu Gündoğdu Fatma; Kahraman, Cengiz
The extensions of ordinary fuzzy sets such as intuitionistic fuzzy sets, Pythagorean fuzzy sets, and neutrosophic sets, whose membership functions are based on three dimensions, aim at collecting experts’ judgments more informatively and explicitly. In the literature, generalized three-dimensional spherical fuzzy sets have been introduced by Kutlu Gündoğdu and Kahraman (J Intell Fuzzy Syst 36(1):337–352, 2019a), including their arithmetic operations, aggregation operators, and defuzzification operations. In this paper, our aim is to extend classical analytic hierarchy process (AHP) to spherical fuzzy AHP (SF-AHP) method and to show its applicability and validity through a renewable energy location selection example and a comparative analysis between neutrosophic AHP and SF-AHP.
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A novel VIKOR method using spherical fuzzy sets and its application to warehouse site selection
(2019) Kahraman, Cengiz; GÜNDOĞDU, FATMA KUTLU
The extensions of ordinary fuzzy sets such as intuitionistic fuzzy sets (IFS), Pythagorean fuzzy sets (PFS), and neutrosophic sets (NS), whose membership functions are based on three dimensions, aim at collecting experts' judgments more informatively and explicitly. In the literature, generalized three-dimensional spherical fuzzy sets have been developed by Kutlu Gundogdu and Kahraman (2019), including their arithmetic operations, aggregation operators, and defuzzification operations. Spherical Fuzzy Sets (SFS) are a new extension of Intuitionistic, Pythagorean and Neutrosophic Fuzzy sets, a SFS is characterized by a membership degree, a nonmembership degree, and a hesitancy degree satisfying the condition that their squared sum is equal to or less than one. These sets provide a larger preference domain in 3D space for decision makers (DMs). In this paper, our aim is to extend classical VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method to spherical fuzzy VIKOR (SF-VIKOR) method and to show its applicability and validity through an illustrative example and to present a comparative analysis between spherical fuzzy TOPSIS (SF-TOPSIS) and SF-VIKOR. We handle a warehouse location selection problem with four alternatives and four criteria in order to demonstrate the performance of the proposed SF-VIKOR method.
Open Access
A pipeline for adaptive filtering and transformation of noisy left-arm ECG to its surrogate chest signal
(MDPI AG, 2020-05) Tanneeru, Akhilesh; Lee, Bongmook; Misra, Veena; Mohaddes, F.; Zhou, Y.; Lobaton, E.; AKBULUT, FATMA PATLAR
The performance of a low-power single-lead armband in generating electrocardiogram (ECG) signals from the chest and left arm was validated against a BIOPAC MP160 benchtop system in real-time. The filtering performance of three adaptive filtering algorithms, namely least mean squares (LMS), recursive least squares (RLS), and extended kernel RLS (EKRLS) in removing white (W), power line interference (PLI), electrode movement (EM), muscle artifact (MA), and baseline wandering (BLW) noises from the chest and left-arm ECG was evaluated with respect to the mean squared error (MSE). Filter parameters of the used algorithms were adjusted to ensure optimal filtering performance. LMS was found to be the most effective adaptive filtering algorithm in removing all noises with minimum MSE. However, for removing PLI with a maximal signal-to-noise ratio (SNR), RLS showed lower MSE values than LMS when the step size was set to 1 × 10−5. We proposed a transformation framework to convert the denoised left-arm and chest ECG signals to their low-MSE and high-SNR surrogate chest signals. With wide applications in wearable technologies, the proposed pipeline was found to be capable of establishing a baseline for comparing left-arm signals with original chest signals, getting one step closer to making use of the left-arm ECG in clinical cardiac evaluations.
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A tree learning approach to web document sectional hierarchy extraction
(2010) Pembe, F.Canan; Göngör, Tunga
There is an increasing availability of documents in electronic form due to the widespread use of the Internet. Hypertext Markup Language (HTML) which is mostly concerned with the presentation of documents is still the most commonly used format on the Web, despite the appearance of semantically richer markup languages such as XML. Effective processing of Web documents has several uses such as the display of content on small-screen devices and summarization. In this paper, we investigate the problem of identifying the sectional hierarchy of a given HTML document together with the headings in the document. We propose and evaluate a learning approach suitable to tree representation based on Support Vector Machines.
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A Wearable Device for Virtual Cyber Therapy of Phantom Limb Pain
(2018-09) Tarakçı, Ela; Aydın, Muhammed; Zaim, Abdul Halim; AKBULUT, AKHAN; AŞCI, GÜVEN; 285689; 116056; 101760; 176402; 8693
Phantom limb pain (PLP) is the condition most often occurs in people who have had a limb amputated and it is may affect their life severely. When the brain sends movement signals to the phantom limb, it returns and causes a pain. Many medical approaches aim to treat the PLP, however the mirror therapy still considered as the base therapy method. The aim of this research is to develop a wearable device that measures the EMG signals from PLP patients to classify movements on the amputated limb. These signals can be used in virtual reality and augmented reality environments to realize the movements in order to reduce pain. A data set was generated with measurements taken from 8 different subjects and the classification accuracy achieved as 90% with Neural Networks method that can be used in cyber therapies.This type of therapy provides strong visuals which make the patient feel he/she really have the limb. The patient will have great therapy session time with comparison to the other classical therapy methods that can be used in home environments.
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Adapazarı Zeminlerinde Sıvılaşabilirliğin Lojistik Regresyon ile Değerlendirilmesi
(2018-09) MERT, AHMET CAN; AREL, ERSİN; ÖNALP, AKIN; 258088; 136055; 2172
Adapazarı bölgesinde, geniş veri tabanına sahip koni penetrasyon deney (PCPT) sonuçları kullanılarak zemin sıvılaşıma olasılığının belirlenmesi amacıyla bir lojistik regresyon modeli geliştirilmiştir. Model girdileri olarak, temiz kum eşdeğeri normalize koni uç direnci (q ciN,cs) ve 7.5 moment büyüklüğü için düzeltilmiş çevrimsel gerilme oranı (CSRm=7.s) kullanılmıştır. Lojistik regresyon, iki değişkenli sonuç çıktısına sahip olayların gerçekleşme olasılıklarının belirlenmesinde kullanılan bir doğrusal regresyon modelidir. Sıvılaşmanın olası iki sonucunun aranması ('sıvılaşma var' veya 'sıvılaşma yok') lojistik regresyon modeline uygundur. CPT lokasyonlarının koordinatları ve koni uç direnci verileri, 0-6m derinlik aralığı için elde edilen ortalama sıvılaşma olasılıkları ile birlikte noktalanarak şehrin üç boyutlu sıvılaşma olasılık yüzeyi oluşturulmuştur. Sıvılaşma potansiyelinde derinliğin etkisinin ifade edilmesi amacıyla üç boyutlu yüzey 0-2m, 2-4m ve 4-6m derinlik aralıklarına bölünerek tekrar oluşturulmuştur. Her bir derinlik aralığı için sıvılaşma olasılıkları, 1999 Adapazarı depreminde belirlenmiş olan sıvılaşan ve sıvılaşmayan bölgeler bilgileri ile karşılaştırıImıştır. Elde edilen sıvılaşma olasılıklarının yüzeye yakın derinliklerde en yüksek olduğu ve artan derinlikle azaldığı belirlenmiştir. Sıvılaşma olasılıkları; 0-2m için en yüksek, 4-6m arasında ise en düşük bulunmuştur. Bu durum sıvılaşmanın yüzeye yakın birkaç metre içerisinde etkili olduğunu göstermektedir. Önerilen model ile uyum, modifiye olabilirlik oranı indisinin p2=0.95 bulunması ile oldukça başarılı olmuştur.
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An analytical approach for analysing the impact of risks on production planning: Case of Öztiryakiler
(2020) Telatar, E.; Bekeç, T.; Başaran, A.; Balıkçı, N.; Bilgin, B.; İlbay, E.; AKTİN, AYŞE TÜLİN
Accurate and applicable production plans are a must for manufacturing companies. Although companies tend to prepare ideal production plans, some exogenous factors can affect their validity. These risks, which occur unexpectedly, will have a negative influence on the plan. This study aims to determine the exogenous factors affecting the success of production planning of square and rectangular food containers manufactured by Öztiryakiler, and analyse their impacts on the plans. The risk factors are evaluated using Failure Mode and Effects Analysis, and their risk priority numbers are calculated. A mixed-integer linear programming model with the objective of total cost minimisation is developed to obtain the production plan of containers. Initially, an ideal data set is used as input; hence, this model’s output displays a risk-free plan. Similarly, for each of the risk factor scenarios, mathematical models are solved with risk-related data. GAMS software and CPLEX solver is utilised in the solution of all models. Finally, for each of the selected high risk alternative, the expected total costs are calculated. This is achieved by multiplying the normalized risk priority number obtained from the Failure Mode and Effects Analysis with the corresponding optimal total cost of the risky plan. This analysis highlights the most critical risks, and comparison with the risk-free plan helps in proposing system improvements. © 2020, Springer Nature Switzerland AG.
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An Integrated Pythagorean Fuzzy AHP & TOPSIS Method for the Selection of the Most Appropriate Clean Energy Technology
(2019-07) Karaşan, Ali; Kahraman, Cengiz; GÜNDOĞDU, FATMA KUTLU; 273471; 227871; 9178
Clean energy technologies which include renewable energy, electric vehicles, nuclear power and biofuels focus on ways to boost demand and de ployment by societies. Governments in many countries aim to increase the clean energy technologies and oner incentives to increase tendency on that sec tor. One of the reasons of these incentives is to prevent the global warming. For example. 30% of global electricity can be produced from wind and solar power in the long tenn, without adding to the total cost of reaching a low-carbon fu ture. In this study, we use an integrated Pythagorean fuzzy MCDM method consist of AHP and TOPSIS for the selection of the most appropriate clean en ergy7 technology for the Marmara Region.
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An Unsupervised Data Mining Approach for Clustering Customers of Abrasive Manufacturer
(2019-07) AKBURAK, DİLEK; 275470
Customer segmentation is the process of dividing customers into groups based on common similar characteristics such as value, location, demography etc. Companies can communicate with each group effectively and appropriately by considering these common properties. Data mining algorithms are the most utilized techniques which lead direct marketers to develop their marketing strategies tailored to particular segments and/or individuals. Clustering is one of the unsupervised data mining methods used for grouping set of objects such a way that objects in the same group have maximum similarity while between group similarities are low. K-means clustering is a commonly used non-hierarchical clustering method for performing non-parametrical learning tasks. This study aims to identify customer types according to their profitability, value and risk in order to take appropriate action for each group via clustering. In this study, data items are grouped according to coded customer profile with respect to the consumers’ total expenditures. Customers are segmented as VIP, Platinum, Gold, and Bronze into 4 groups according to their values within 2 years.
Open Access
Analysis of Facial Emotion Expression in Eating Occasions Using Deep Learning
(Springer, 2023) ELİF, YILDIRIM; AKBULUT, FATMA PATLAR; Çatal, Çağatay
Eating is experienced as an emotional social activity in any culture. There are factors that influence the emotions felt during food consumption. The emotion felt while eating has a significant impact on our lives and affects different health conditions such as obesity. In addition, investigating the emotion during food consumption is considered a multidisciplinary problem ranging from neuroscience to anatomy. In this study, we focus on evaluating the emotional experience of different participants during eating activities and aim to analyze them automatically using deep learning models. We propose a facial expression-based prediction model to eliminate user bias in questionnaire-based assessment systems and to minimize false entries to the system. We measured the neural, behavioral, and physical manifestations of emotions with a mobile app and recognize emotional experiences from facial expressions. In this research, we used three different situations to test whether there could be any factor other than the food that could affect a person’s mood. We asked users to watch videos, listen to music or do nothing while eating. This way we found out that not only food but also external factors play a role in emotional change. We employed three Convolutional Neural Network (CNN) architectures, fine-tuned VGG16, and Deepface to recognize emotional responses during eating. The experimental results demonstrated that the fine-tuned VGG16 provides remarkable results with an overall accuracy of 77.68% for recognizing the four emotions. This system is an alternative to today’s survey-based restaurant and food evaluation systems.
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Analysis of linear lung models based on state-space models
(Elsevier Ireland Ltd., 2020-01) Saatçi, Ertuğrul; Akan, Aydın; SAATÇI, ESRA
Background and Objectives: Linear parametric respiratory system models have been used in the model-based analysis of the respiratory system. Although there are studies exploring the physiological correctness and fitting accuracy of the models, they are not analysed in terms of interaction between parameters and dynamics of the model. In this study we propose to use state-space modelling to yield the time-varying nature of the system incorporated by the parameters. Methods: We tested controllability, observability and stability characteristics of the equation of motion, 2-comp. parallel, 2-comp. series, viscoelastic, 6-element and mead models while using the parameters given in the literature. In the sensitivity analysis we proposed to use dual Desensitized Linear Kalman Filter (DKF) and Extended Kalman Filter (EKF) method. In this method, state error covariance revealed the parameter sensitivities for each model. Results: Results showed that all models, except 2-comp. parallel and mead models, are both controllable and observable models. On the other hand all models, except mead model, are stable models. Regarding to the sensitivity analysis, dual DKF - EKF method estimated states of the models successfully with a low estimation error. Sensitivity analysis results showed that airway parameters have higher effects on the state estimation than the other parameters have. Conclusion: We proved that state-space evaluation of the previously proposed parametric models of the respiratory system led us to quantitative and qualitative assessments of the respiratory models. Moreover parameter values found in the literature have different effects on the models. (C) 2019 Elsevier B.V. All rights reserved.
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Analysis of the use of computational intelligence techniques for air-conditioning systems: A systematic mapping study
(SAGE PUBLICATIONS LTD, 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND, 2019) Çakır, Mustafa; AKBULUT, AKHAN; ÖNEN, YUSUF HATAY
In our systematic mapping study, we examined 289 published works to determine which intelligent computing methods (e.g. Artificial Neural Networks, Machine Learning, and Fuzzy Logic) used by air-conditioning systems can provide energy savings and improve thermal comfort. Our goal was to identify which methods have been used most in research on the topic, which methods of data collection have been employed, and which areas of research have been empirical in nature. We observed the rules for literature reviews in identifying published works on databases (e.g. the Institute of Electrical and Electronics Engineers database, the Association for Computing Machinery Digital Library, SpringerLink, ScienceDirect, and Wiley Online Library) and classified identified works by topic. After excluding works according to the predefined criteria, we reviewed selected works according to the research parameters motivating our study. Results reveal that energy savings is the most frequently examined topic and that intelligent computing methods can be used to provide better indoor environments for occupants, with energy savings of up to 50%. The most common intelligent method used has been artificial neural networks, while sensors have been the tools most used to collect data, followed by searches of databases of experiments, simulations, and surveys accessed to validate the accuracy of findings.
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Anticipation for Customer Analytics
(2019-11) Şenvar, Özlem; Yel, Necla; AKBURAK, DİLEK; 275470; 193864
In the digitalization era, enterprises are required to proactive customer relationships via analytical techniques to execute customer treatment strategies based on predictive analytics drawing on a wealth of data about customer behaviors. Nowadays, predictive analytics with automatic decision making concepts are integrated for better understanding preferences of customers, segmenting customers, promoting right products to right customers, improving customer experience, and driving revenue. Customer value anticipation becomes inevitable for understanding customer requirements and expectations. Anticipation can provide capacities in shaping customer perceptions and expectations to make sense of novelty by enlarging and enhancing the analytical and operational approaches to incorporate decision making. In this regard, customer analytics is handled within the perspective of the process by which data from customer behavior utilized to make key business decisions through market segmentation and predictive analytics in order to maximize organizational throughput and maintain long lasting relationships with customers. From this standpoint, this study aims to provide an overview for customer analytics considering anticipation, customer experience and big data analytics involving strategic decision making processes for customer loyalty and relationships. Moreover, customer experience quality is discussed for anticipating customer expectation.
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Arçelik Yetkili Servislerinde Optimizasyon Tabanlı Teknisyen Planlama
(2019-06) Çelebi, Ceren; Öcal, Cansu; Kurtoğlu, Gizem; YÜKSEKTEPE, FADİME ÜNEY; 108243
Arçelik, 1955 yılında Türkiye'de dayanıklı tüketim malları üreticisi olarak kuruldu. Halen dünya genelinde 32 ülkede 30.000 çalışanı, 18 farklı üretim tesisi ve 34 satış ve pazarlama şirketi bulunmaktadır. Beyaz eşya, tüketici elektroniği, ısıtma-havalandırma ve küçük ev aletleri gibi geniş ürün yelpazesi için, şirketin 145 ülkede birkaç yetkili servisi bulunmaktadır. Türkiye'de hizmet verdiği yetkili servis sayısı 574'tür. Küreselleşen dünyada satış sonrası hizmetler, müşterilerin sürdürülebilirliği sebebiyle firmalar için önemli işlevler arasında yer almaktadır. Ayrıca, teknisyenlerin görevleriyle ilgili becerilerinin de ayarlanması önemlidir. Ancak, Arçelik yetkili servislerinde çalışan teknisyenleri planlanmak için analitik bir çözüm uygulaması bulunmamaktadır. Bazı servisler bazı dönemlerde fazla sayıda teknisyen ile çalışmaktadır. Şu anda, bu servislerin karlılığı, teknisyenlerin verimsiz planlanması nedeniyle daha düşüktür. Bu nedenle, şirket teknisyen planlama sürecini geliştirmek istemektedir. Bu çalışmada, yetkili bir serviste her uzmanlık seviyesindeki teknisyenlerin optimum sayısını belirlemek için analitik bir yaklaşım önerilmiştir. İlk aşamada, Merter yetkili servisine odaklanarak arıza kategorileri, teknisyenlerin uzmanlık düzeyleri, arızaların tamir edilme süreleri, arızaların ortalama tamir süreleri ve ulaşım süreleri şirketten temin edilmiştir. Merter yetkili servisine odaklanarak, her bir arızanın ortalama işlem süresini belirlemek için bir dizi ölçüm yapılmıştır. Alman verileri kullanarak, belirli bir ay için teknisyen planlama problemini çözmek için bir matematiksel model geliştirilmiştir. Sonuç olarak, optimum teknisyen sayısı ve gerekli yetkinlikleri belirlenmiştir. Farklı senaryolar dikkate alınarak, sonuçların duyarlılığı incelenmiştir.
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Assessment of seismic behaviour and safety of a masonry heritage
(Selçuk Üniv, Meram Tıp Fakultesi, 2019-12) TÜRK, AHMET MURAT
Turkey is a highly seismic country where numerous major earthquakes have devastated or damaged the existing historic structures owing to there is a large number of historical structures, mostly religious ones like mosques, and churches. The minarets are essential parts of mosques and there are many of these historic structures across the country which were built since Seljuq Empire started to rule the Anatolia and followed by Ottomans. As Turkey located in a highly active seismic zone, the possible damage of the minarets, the tallest part of the mosques, should be examined and the safety measures, in terms of seismic retrofit, should be considered. It is engineering communities' responsibility to satisfy the longevity of these existing heritages by the help of science and knowledge. To realize this objective, a case study is planned which addresses the problem of seismic capacity of a stone masonry minaret as a part of the architectural heritage in Istanbul, and a typical historical mosque is chosen. Mathematical model of the minaret is prepared in order to obtain possible deformation profile, lateral displacements, free vibration modes and most likely failure modes under seismic excitation by using response spectrum analysis. Most recent developments in the in-situ testing of structures and computational procedures for structural analysis have made reaching to the important results about the behaviour of the old masonry structures. The numerical results have shown that the greatest damage accumulated near the shoe region and the lower part of the shaft. The evaluation of these results is promising in terms of seismic safety of these heritages with minimized intervention on the structure without compromising the authentic view and function.

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