İşletme Fakültesi

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Citation Count: 1
Active and Reactive Power Load Profiling Using Dimensionality Reduction Techniques and Clustering
(Institute of Electrical and Electronics Engineers Inc., 2019) Yetkin, E. Fatih; Ceylan, Oğuzhan; Papadopoulos, Theofilos A.; Kazaki, Anastasia G.; Barzegkar-Ntovom, Georgios A.
This paper proposes a methodology to characterize active and reactive power load profiles. Specifically, the approach makes use of fast Fourier Transform for conversion into frequency domain, principle component analysis to reduce the dimension and K-means++ to determine the representative load profiles. The data set consists of five-year measurements taken from the Democritus University of Thrace Campus. Test days were also classified as working and non-working. From the results it is observed that the proposed methodology determines representative load profiles effectively both regarding active and reactive power.
Citation Count: 1
A Comparative Study of Surrogate Based Learning Methods in Solving Power Flow Problem
(IEEE, 2020) Ceylan, Oğuzhan; Taşkın, Gülsen; Paudyal, Sumit
Due to increasing volume of measurements in smart grids, surrogate based learning approaches for modeling the power grids are becoming popular. This paper uses regression based models to find the unknown state variables on power systems. Generally, to determine these states, nonlinear systems of power flow equations are solved iteratively. This study considers that the power flow problem can be modeled as an data driven type of a model. Then, the state variables, i.e., voltage magnitudes and phase angles are obtained using machine learning based approaches, namely, Extreme Learning Machine (ELM), Gaussian Process Regression (GPR), and Support Vector Regression (SVR). Several simulations are performed on the IEEE 14 and 30-Bus test systems to validate surrogate based learning based models. Moreover, input data was modified with noise to simulate measurement errors. Numerical results showed that all three models can find state variables reasonably well even with measurement noise.
Citation Count: 5
A Competitive Intelligence Practices Typology in an Airline Company in Turkey
(Springer, 2020) Şahin, Murat; Bisson, Christophe
Oil prices, political instabilities, travel legislations, and many other competitive factors make it essential for any international airline with the instinct to survive to be on constant watch in such a fiercely competitive environment. To meet this need, it is vital for international airline companies to integrate competitive intelligence (CI) into their strategy building process. In this study, we create for the first time a typology of competitive intelligence practices of an international airline company (in Turkey), based on the model developed by (Wright et al.Journal of Strategic Marketing, 20(1), 19-33,2012), and it is one of the very first to investigate Competitive intelligence in this sector. Furthermore, we made a two-step cluster analysis to uncover hidden clusters that change the way of thinking within the company. Our findings show where the company would need to make improvements on the 6 strands of the model which are attitude, gathering, use, location, technological support, and IT support. Yet, that could lead towards stronger business performance. It might also inspire other companies of the airline sector and beyond.
Citation Count: 7
Daily and Intraday Herding Within Different Types of Investors in Borsa Istanbul
(Routledge Journals, Taylor & Francis Ltd, 2019) Dalgıç, Nihan; Ekinci, Cumhur; Ersan, Oğuz
This paper aims to explore the daily and intraday herd behavior of various investor groups trading in an emerging equity market, Borsa Istanbul (BIST). We analyze a one-year tick-by-tick order and trade data of BIST 100 Index stocks and document differences in herding behavior of investor groups considering market capitalization, market conditions, and announcements as well as daily and intraday periodicities. We find that nonprofessional investors (brokerage houses and domestic funds) tend to herd on large (small) stocks; their herding behavior mostly exhibits a U shape (an inverse U shape) during the day. All types of investors tend to herd in down markets on a daily basis while this behavior disappears, even inverts intraday.
Citation Count: 2
Does governance affect corporate diversification behaviour in emerging markets?
(Routledge Journals, Taylor & Francıs Ltd, 2020) Sener, Pınar; Akben Selçuk, Elif
This paper investigates the role of firm-level and country-level governance on corporate diversification behaviour in emerging markets. The results show that firms with combined leadership structure are more diversified while firms with more independent directors are less diversified. There is a U-shaped relationship between ownership concentration and diversification. No significant association between country-level shareholder protection and diversification is demonstrated.
Citation Count: 10
Dynamic Network Analysis of Online Interactive Platform
(Springer, 2019) Aydın, Mehmet Nafiz; Perdahci, N. Ziya
The widespread use of online interactive platforms including social networking applications community support applications draw the attention of academics and businesses. The basic trust of this research is that the very nature of these platforms can be best described as a network of entangled interactions. We agree with scholars that these platforms and features necessitate the call for theory of network as a novel approach to better understand their underpinnings. We examine one of the leading online interactive health networks in Europe. We demonstrate that the interactive platform examined exhibits essential structural properties that characterize most real networks. In particular we focus on the largest connected component so-called a giant component (GC) to better understand network formation. Dynamic network analysis helps us to observe how the GC has evolved over time and to identify a particular pattern towards emerging a GC. We suggest that the network measures examined for the platform should be considered as novel and complementary metrics to those used in conventional web and social analytics. We argue that various stages of GC development can be a promising indicator of the strength and endurance of the interactions on the platform. Platform managers may take into account basic stages of the emergence of the GC to determine varying degrees of product attractiveness.
Citation Count: 44
Economic Policy Uncertainty and Bank Credit Growth: Evidence From European Banks
(Elsevier B.V., 2020) Danışman, Gamze Öztürk; Ersan, Oğuz; Demir, Ender
Using a sample of 2977 private and listed banks in the EU-5 countries (the United Kingdom, Germany, Spain, Italy, France) for the years 2009–2018, this paper explores the impact of Economic Policy Uncertainty (EPU) on credit growth. Using panel data fixed effects methodology and controlling for endogeneity using two-step difference GMM estimators, our findings indicate that uncertainty in economic policies hampers the credit growth of European banks. Our bank type-based analyses indicate that the effect is mainly valid for cooperative banks. Additional analyses imply that the negative impact of EPU on credit growth is more pronounced in civil law countries, increases with debt maturity, and weakens for banks with a larger number of employees and branches. Furthermore, the unfavorable effects are stronger in well-capitalized banks, banks with foreign subsidiaries, and banks with a higher share of wholesale funding. We also provide several policy implications for different economic actors.
Citation Count: 0
Graph Optimized Locality Preserving Projection Via Heuristic Optimization Algorithms
(IEEE, 2019) Ceylan, Oğuzhan; Taşkın, Gülşen
Dimensionality reduction has been an active research topic in hyperspectral image analysis due to complexity and non-linearity of the hundreds of the spectral bands. Locality preserving projection (LPP) is a linear extension of the manifold learning and has been very effective in dimensionality reduction compared to linear methods. However, its performance heavily depends on construction of the graph affinity matrix, which has two parameters need to be optimized: k-nearest neighbor parameter and heat kernel parameter. These two parameters might be optimally chosen simply based on a grid search when using only one representative kernel parameter for all the features, but this solution is not feasible when considering a generalized heat kernel in construction the affinity matrix. In this paper, we propose to use heuristic methods, including harmony search (HS) and particle swarm optimization (PSO), in exploring the effects of the heat kernel parameters on embedding quality in terms of classification accuracy. The preliminary results obtained with the experiments on the hyperspectral images showed that HS performs better than PSO, and the heat kernel with multiple parameters achieves better performance than the isotropic kernel with single parameter.
Citation Count: 19
Grey Wolf Optimizer for Allocation and Sizing of Distributed Renewable Generation
(Institute of Electrical and Electronics Engineers Inc., 2019) Ahmadi, Bahman; Ceylan, Oğuzhan; Özdemir, Aydoğan
Increasing penetration of distributed energy resources (DERs) have brought operational and control philosophy changes in Smart Grids (SGs). Renewable energy based technologies are becoming more important due to their economic and environmental impacts. Distributed generations (DGs) in the form of small renewable energy resources such as solar photovoltaics (PVs) and Wind Turbines (WTs) are connected in radial distribution networks near to the loads. This paper presents optimal siting and sizing of distributed renewable energy resource to maintain voltage magnitude profiles. Bus voltage magnitude differences for each hour in a day of a distribution system are formulated as an objective function. Three consecutive days are taken into account representing the three seasons of a year. A new nature inspired algorithm Grey Wolf Optimizer (GWO) is used as a solution tool. The proposed formulation is applied to 33 bus and 69 bus radial distribution networks. MATLAB simulations are performed to validate the performance of the approach. Simulation results are discussed and compared with of the several available ones'.
Citation Count: 1
Judgments of Capability and Conformity as Distinct Forms of Social Judgments, and the Way They Interact To Shape Evaluator Decisions
(Wiley, 2020) Topaler, Başak; Küp, Eyuep Tolunay
Objective Social judgments are evaluators' opinions about the social properties of a set of actors. Different types of judgments rendered by the evaluators and potential interactions between them may have major consequences for the actors who are evaluated. In this article, we distinguish between judgments of capability and conformity, and examine their concurrent and interdependent effects on evaluator impressions. Methods We investigate these dynamics in the context of authors competing for the best paper award at the Academy of Management (AoM) conference. Results Findings of our empirical analyses demonstrate interdependent effects of capability and conformity judgments on the committee members' decisions. We demonstrate that evaluators expect greater conformity to their ideal template from more capable actors who have greater potential to contribute to these ideals. Conclusion Our study advances the literature on social judgments by showing that congruence (or incongruence) among distinct types of judgment shape evaluators' decisions, beyond their independent effects.
Citation Count: 3
Machine Learning Model To Predict an Adult Learner's Decision To Continue Esol Course or Not
(Springer, 2019) Dahman, Mohammed R.; Dağ, Hasan
This study investigated the ability of the demographic and the affective variables to predict the adult learners' decision to continue ESOL courser. 278 adult learners, enrolled on ESOL course at FLS institution in Istanbul, Turkey, participated in the study. The result showed that the continued or dropped out groups, demonstrated statistical differences in the demographic variable (the placement test score) with a magnitude of large effect size (.378). Additionally, the result showed the effect size in the perception of the affective variables (motivation, attitude, and anxiety), accounts for about 50% of the variation between the continuation and dropout groups. Following that, three machine learning models were proposed; all possible subset regression analysis was used to compare the three models. The adequate model, which fitted the demographic variable (the placement test score) and the affective variables (motivation, attitude, and anxiety), correctly predicted 83.3% of the adult learners' decision to continue ESOL course. The model showed about 68% goodness-of-fit. The cultural implications of these findings are discussed, along with suggestions for future research.
Citation Count: 1
A Max–min Model of Random Variables in Bivariate Random Sequences
(Elsevier, 2021) Bayramoğlu, Ismihan; Gebizlioğlu, Ömer Lütfi
We introduce a max–min model to bivariate random sequences and applying bivariate binomial distribution in fourfold scheme derive the distributions of associated order statistics in a new model. Some examples for special cases are presented and applications of the results in reliability analysis and actuarial sciences are discussed.
Citation Count: 0
Multi-Agent Model of Electricity Networks - a Perspective on Distribution Network Charges
(Institute of Electrical and Electronics Engineers Inc., 2019) Pisica, Ioana; Ceylan, Oğuzhan
In the UK, DNOs are regulated by Ofgem to use two common distribution use of system charging methodologies: Common Distribution Charging Methodology and Extra-High Voltage Distribution Charging Methodology. To account for the changing landscape of the energy sector, Ofgem has recently published a consultation paper on changes to DUoS charging structure. This paper looks into the implications of distribution network charging in consumer-level adoption of low carbon technologies and vice-versa, using an agent-based model approach.
Citation Count: 12
Multi-Verse Optimization Algorithm- and Salp Swarm Optimization Algorithm-Based Optimization of Multilevel Inverters
(Springer, 2020) Ceylan, Oğuzhan
Renewable energy sources are installed into both distribution and transmission grids more and more with the introduction of smart grid concept. Hence, efficient usage of cascaded H-bridge multilevel inverters (MLIs) for power control applications becomes vital for sustainable electricity. Conventionally, selective harmonic elimination equations need to be solved for obtaining optimum switching angles of MLIs. The objective of this study is to obtain switching angles for MLIs to minimize total harmonic distortion. This study contributes to the solution of this problem by utilizing two recently developed intelligent optimization algorithms: multi-verse optimization algorithm and salp swarm algorithm. Moreover, well-known particle swarm optimization is utilized for MLI optimization problem. Seven-level, 11-level and 15-level MLIs are used to minimize total harmonic distortions. Simulation results with different modulation indexes for seven-, 11- and 15-level MLIs are calculated and compared in terms of the accuracy and solution quality. Numerical calculations are verified by using MATLAB/Simulink-based models.
Citation Count: 34
Municipal Solid Waste Management Via Mathematical Modeling: a Case Study in İstanbul, Turkey
(Elsevier, 2019) Çavdaroğlu Ayvaz, Nur; Çoban, Aslı; Fırtına Ertis, İrem
The prominence of managing municipal solid waste (MSW) in an efficient and effective manner is increasing from day to day. In this paper, the solid waste management (SWM) system of İstanbul is analyzed by applying the techniques from mathematical programming methodology. In this manner, the solutions of the two optimization problems which aim to minimize the total cost and the environmental effects of SWM, respectively, are presented in this study. Additionally, a sensitivity analysis is performed and a multi-objective problem that combines two problems is presented. In this regard, the application of five MSW management technologies which are currently in use in İstanbul on six waste components is analyzed; and the optimal solution regarding the best mixture of these technologies is developed on a given waste composition. Besides, this optimal solution is compared with the current practice in İstanbul; and recommendations are presented about possible future investments for the policymakers. The results of the study emphasize the importance of material recovery and incineration facilities to improve profitability and to minimize environmental side effects. In particular, material recovery facility (MRF) should be expanded to be able to treat all of metal, paper and plastic from a cost management perspective. Incineration (INC) facility should also be expanded in order to treat plastics or organic waste from a Greenhouse Gas (GHG) minimization perspective. In addition to this, landfill appears to be the most prominent treatment technique according to the current problem parameters. However, regarding the waste composition, the amount of organic waste must be decreased by more than 37% for other waste streams to be treated in different facilities other than landfill. Anaerobic digestion and composting facilities need to be more cost-effective for becoming economically feasible. The methodology represented in this study can be extended and generalized to other cities around the world once the correct problem parameters are specified.
Citation Count: 9
Ontology-Based Data Acquisition Model Development for Agricultural Open Data Platforms and Implementation of Owl2mvc Tool
(Elsevier, 2020) Aydın, Şahin; Aydın, Mehmet Nafiz
In the open data world, it is difficult to collect data in compliance with a certain data model that is of interest to different types of stakeholders within a domain like agriculture. Ontologies that provide broad vocabularies and metadata with respect to a given domain can be used to create various data models. We consider that while creating data acquisition forms to gather data related to an agricultural product, which is hazelnut in this study, from stakeholders of the relevant domain, the traits can be modeled as attributes of the data models. We propose a generic ontology-based data acquisition model to create data acquisition forms based on model-view-controller (MVC) design pattern, to publish and make use of on the agricultural open data platforms. We develop a tool called OWL2MVC that integrates the Hazelnut Ontology, which illustrates the effectiveness of the proposed model for generating data acquisition forms. Because model creation is implemented in compliance with the selection of ontology classes, stakeholders; in other words, the users of OWL2MVC Tool could generate data acquisition forms quickly and independently. OWL2MVC Tool was evaluated in terms of usability by fifty-three respondents implementing the case-study scenario. Among others the findings show that the tool has satisfactory usability score overall and is promising to provide stakeholders with required support for agricultural open data platforms.
Citation Count: 0
Optimization of Graph Affinity Matrix With Heuristic Methods in Dimensionality Reduction of Hypespectral Images
(IEEE, 2019) Ceylan, Oğuzhan; Taşkın, Gülşen
Hyperspectral images include hundreds of spectral bands, adjacent ones of which are often highly correlated and noisy, leading to a decrease in classification performance as well as a high increase in computational time. Dimensionality reduction techniques, especially the nonlinear ones, are very effective tools to solve these issues. Locality preserving projection (LPP) is one of those graph based methods providing a better representation of the high dimensional data in the low-dimensional space compared to linear methods. However, its performance heavily depends on the parameters of the affinity matrix, that are k-nearest neighbor and heat kernel parameters. Using simple methods like grid-search, optimization of these parameters becomes very computationally demanding process especially when considering a generalized heat kernel, including an exclusive parameter per feature in the high dimensional space. The aim of this paper is to show the effectiveness of the heuristic methods, including harmony search (HS) and particle swarm optimization (PSO), in graph affinity optimization constructed with a generalized heat kernel. The preliminary results obtained with the experiments on the hyperspectral images showed that HS performs better than PSO, and the heat kernel with multiple parameters achieves better performance than the heat kernel with a single parameter.
Citation Count: 4
Responses To Replica (vs. Genuine) Touristic Experiences
(Pergamon-Elsevier Science, 2020) Gülen, Sarial-Abi; Ezgi, Merdin-Uygur; Gürhan-Canlı, Zeynep
A growing trend in tourism is the use of replica experiences. Yet, consumers' responses to replica (vs. genuine) touristic experiences are mostly overlooked in the literature. In this paper, we propose that consumers' perceptions of authenticity of the replica (vs. genuine) touristic experiences mediate their responses to these experiences. We define three theoretically driven factors that influence the authenticity perceptions of consumers-experience characteristics (restricted experience vs. not restricted experience; within close vs. distant geographical proximity to the genuine) and a consumer characteristic (salient goal: collecting experiences vs. having fun/pleasure), which influence consumers' responses to replica and genuine touristic experiences. We found support for the proposed theory using two field studies from Egypt's Luxor Tutankhamun tomb and three experimental studies.
Citation Count: 33
Role Conflict Role Ambiguity and Proactive Behaviors: Does Flexible Role Orientation Moderate the Mediating Impact of Engagement?
(Routledge Journals Taylor & Francis Ltd, 2019) Maden-Eyiusta, Ceyda
This study investigates the relationships between role conflict role ambiguity and proactive behaviors at work (i.e. individual innovation and taking charge) as mediated by work engagement. It also investigates the moderating role of flexible role orientation on the relationships between role conflict and ambiguity work engagement and employee proactivity. Data were collected from 227 employees and their supervisors working in 20 small and medium-sized enterprises in Istanbul Turkey. The findings indicated that role conflict was negatively related to taking charge while role ambiguity was negatively related to individual innovation. Work engagement acted as a full mediator in the relationship between role ambiguity and individual innovation while partially mediating the relationship between role conflict and taking charge. With regard to the moderating role of flexible role orientation the findings indicated that the conditional indirect relationship between (a) role conflict and taking charge and (b) role ambiguity and individual innovation through engagement were stronger when the level of flexible role orientation was low.
Citation Count: 3
The Speed of Stock Price Adjustment To Corporate Announcements: Insights From Turkey
(Elsevier, 2020) Ersan, Oğuz; Şimşir, Serif Aziz; Şimsek, Koray D.; Afan, Hasan
The market reaction speeds to the news flow are currently measured at the millisecond level in developed markets. We investigate, using a unique setting from Turkey, whether the market reaction speeds in less sophisticated markets are on par with those of developed markets. We find that market reaction times to corporate announcements are slower than documented in recent studies, although markets react to positive news more quickly than negative news. When high-frequency traders are more active in the market prior to announcements, the speed of price adjustment is slower. Finally, we find sizable profit opportunities for investors following event-driven strategies.

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