TR-Dizin İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/396
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Research Project Yenilenebilir Enerji İçin Ödeme İstekliliği Ve Bu İstekliliği Etkileyen Faktörlerin Analiz Edilmesi(TUBİTAK, 2018) Doğan, EyüpBu projede, Türkiye?de ikamet eden hanehalkının yenilenebilir enerji için ödeme istekliliği (YÖİS) ve bu istekliliği etkileyen faktörler analiz edilecektir. İlgili literatür kapsamında, gelişmiş ve gelişmekte olan birçok ülke için YÖİS ve bu istekliliğe etki eden faktörler incelenmesine rağmen, daha önce bu alanda Türkiye üzerine bir çalışma yapılmamıştır. Bu projenin amacı, Türkiye?deki vatandaşların YÖİS ve bu istekliliği etkileyen değişkenleri inceleyerek literatürdeki bu boşluğu doldurmaktır. Ayrıca, Sundt ve Rehdanz (2015) ?ın meta-analiz çalışması, ilgili literatürdeki çoğu makalenin yaş, eğitim seviyesi, gelir düzeyi ve çevresel duyarlılık gibi faktörlerin olası etkisini analiz etmesine rağmen sadece bir kaç makalenin hanehalkı sayısını ekonometrik modele dahil ettiğini göstermiştir. Bu proje, coğunlukla kullanılan demografik faktörlerin yanısıra hanehalkı sayısınında YÖİS?i etkileyip etkilemediğini araştıracaktır. Bu projeyi gerçekleştirebilmek için koşullu değer yöntemiyle hazırlanan toplam 2 bölüm ve 26 sorudan oluşan bir anket kullanılacaktır. Yüzyüze görüşme yöntemiyle Türkiye?nin 12 farklı İBBS bölgesinden toplam 2,500 kişiyle yüzyüze görüşme yöntemiyle doldurulacak anketlerden elde edilecek bilgiler sayesinde, Türkiye?de ikamet eden hanehalkının ortalama YÖİS miktarı ve hangi faktörlerin bu istekliliği anlamlı yada anlamsız etkilediği çeşitli yöntemler kullanılarak analiz edilecektir. Türkiye, Avrupa Birliğine aday bir ülke, G-20 ekonomilerinden birisi ve NATO?ya dahil bir ülke olmasının yanısıra, Dünya ve Avrupa enerji piyasasında da önemli bir konuma sahiptir. Ayrıca, yenilenebilir enerji alanında kısa ve orta vadede yapılması hedeflenen yatırımlarda göz önüne alındığında, Türkiye bu literatür içerisinde araştırılması gereken ülkelerin arasındadır. Bununla birlikte, son zamanlarda küresel ısınma, gaz emisyonu ve çevresel kirlilik gibi faktörler global bir sorun haline gelmiştir. Yenilenebilir enerjinin kullanımı daha temiz bir çevre için önemli bir unsurdur. Türkiye enerjide dışa bağımlı bir ülkedir. Ayrıca, Türkiye'nin elektrik enerjisinin %48'inin doğal gazdan üretiliyor olmasının yarattığı kırılganlığın son dönem Rusya krizi ile görülmüş olması sonrasında enerji karmasında çeşitlendirme çok daha hassasiyet kazanmıştır. Yenilenebilir enerjinin artırılması bağımlılığı azaltacak önemli bir araçtır. Hanehalklarının katılımı, hedeflenen yenilenebilir enerji projelerinin hayata geçirilmesini kolaylaştıracaktır. Bu proje dört ana hedefe ulaşmak üzerine odaklanmıştır: i) Türkiye?de ikamet eden hanehalklarının yenilenebilir kaynalardan üretilen elektrik enerjisi almak için ödemeye razı oldukları ortalama miktarı bulmak, ii) YÖİS?i etkileyebilecek yaş, cinsiyet, gelir düzeyi, egitim seviyesi, çevreye olan duyarlılık ve hanehalkı sayısı gibi faktörleri analiz etmek, iii) yenilenebilir enerji yatırımlarının hanehalkları tarafından desteklenmesine olanak sağlayacak bir politikanın Türkiye?de uygulanabilirliğini ortaya koymak, iv) bu proje çıktılarını uluslararası indekslerce taranan bir dergide yayınlatmak.Article Citation - WoS: 7Citation - Scopus: 8Using Students' Performance to Improve Ontologies for Intelligent E-Learning System(Edam, 2015) Icoz, Kutay; Sanalan, Vehbi A.; Cakar, Mehmet Akif; Ozdemir, Esra Benli; Kaya, SukruOntologies have often been recommended for E-learning systems, but few efforts have successfully incorporated student data to represent knowledge conceptualizations. Defining key concepts and their relations between each other establishes the backbone of our E-learning system. The system guides an individual student through his/her course by evaluating their progress and suggesting instructional material to review based upon their answers. Three main tasks are performed within this framework: building ontologies for the course, measuring a student's understanding level for the concepts, and making personal suggestions to create an individualized learning environment. This paper presents: the integration of ontologies, assisted with student data, together with an intelligent Recommendation Module for the development of an E-learning system; the comparison and correction adaption of ontology from students' mind maps; and the assessment of students' actual weaknesses in comparison to what Recommendation Module suggests. The sample of 127 students, five classrooms, was conveniently selected among seventh grade students of a demographically average school in a major city in Turkey. The students' achievement was assessed and the scores for different questions were investigated for associations with concepts made in the students' minds. The results provided significant correlations among scores, and a fit model for the concepts represented by questions. The student suggested model slightly differed from the ontology map from the experts. Based on the data-supported model, the Recommendation Module more accurately determined the students' learning deficiencies and suggested concepts to be reviewed.Article Solutions to Nonlinear Second-Order Three-Point Boundary Value Problems of Dynamic Equations on Time Scales(Tubitak Scientific & Technological Research Council Turkey, 2019-05-29) Dogan, AbdulkadirIn this paper, we consider existence criteria of three positive solutions of three-point boundary value problems for p-Laplacian dynamic equations on time scales. To show our main results, we apply the well-known Leggett-Williams fixed point theorem. Moreover, we present some results for the existence of single and multiple positive solutions for boundary value problems on time scales, by applying fixed point theorems in cones. The conditions we used in the paper are different from those in [Dogan A. On the existence of positive solutions for the one-dimensional p-Laplacian boundary value problems on time scales. Dynam Syst Appl 2015; 24: 295-304].Article Citation - WoS: 2Citation - Scopus: 2Prediction of Preference and Effect of Music on Preference: A Preliminary Study on Electroencephalography from Young Women(Tubitak Scientific & Technological Research Council Turkey, 2019-03-01) Yilmaz, Bulent; Gazeloglu, Cengiz; Altindis, FatihNeuromarketing is the application of the neuroscientific approaches to analyze and understand economically relevant behavior. In this study, the effect of loud and rhythmic music in a sample neuromarketing setup is investigated. The second aim was to develop an approach in the prediction of preference using only brain signals. In this work, 19-channel EEG signals were recorded and two experimental paradigms were implemented: no music/silence and rhythmic, loud music using a headphone, while viewing women shoes. For each 10-sec epoch, normalized power spectral density (PSD) of EEG data for six frequency bands was estimated using the Burg method. The effect of music was investigated by comparing the mean differences between music and no music groups using independent two-sample t-test. In the preference prediction part sequential forward selection, k-nearest neighbors (k-NN) and the support vector machines (SVM), and 5-fold cross-validation approaches were used. It is found that music did not affect like decision in any of the power bands, on the contrary, music affected dislike decisions for all bands with no exceptions. Furthermore, the accuracies obtained in preference prediction study were between 77.5 and 82.5% for k-NN and SVM techniques. The results of the study showed the feasibility of using EEG signals in the investigation of the music effect on purchasing behavior and the prediction of preference of an individual.Article Citation - WoS: 5Citation - Scopus: 6Performance Analysis of Hamming Code for WSN-Based Smart Grid Applications(Tubitak Scientific & Technological Research Council Turkey, 2018) Yigit, Melike; Gungor, Vehbi Cagri; Boluk, PinarMany methods have been employed to detect, compare, and correct errors to increase communication reliability and efficiency in wireless sensor networks (WSNs). However, to the best of our knowledge, no existing study has compared the performance of error control codes by using different modulation techniques in a smart grid communication environment when multichannel scheduling is used. This paper presents a detailed performance evaluation and makes a comparison of different modulation techniques, such as frequency shift keying (FSK), differential phase shift keying (DPSK), binary phase shift keying (BPSK), and offset quadrature phase-shift keying (OQPSK), using Hamming codes in a 500-kV line-of-sight substation smart grid environment with multichannel scheduling. A link-quality-aware routing algorithm is used as a routing protocol and a log-normal shadowing channel is employed as a channel model. Simulations are performed in MATLAB and the performance of the Hamming code with various modulation techniques is compared with the results obtained without using any error correction codes for throughput, delay, and bit error rate. The results show that the performance of the Hamming code with OQPSK modulation is better than its performance with other modulation techniques. Moreover, the results show that the performance of Hamming code improves with multichannel scheduling for all modulation techniques.Article Citation - WoS: 4Citation - Scopus: 5Noise-Assisted Multivariate Empirical Mode Decomposition Based Emotion Recognition(Istanbul Univ-Cerrahapasa, 2018-08-03) Ozel, Pinar; Akan, Aydin; Yilmaz, BulentEmotion state detection or emotion recognition cuts across different disciplines because of the many parameters that embrace the brain's complex neural structure, signal processing methods, and pattern recognition algorithms. Currently, in addition to classical time-frequency methods, emotional state data have been processed via data-driven methods such as empirical mode decomposition (EMD). Despite its various benefits, EMD has several drawbacks: it is intended for univariate data; it is prone to mode mixing; and the number of local extrema must be enough before the EMD process can begin. To overcome these problems, this study employs a multivariate EMD and its noise-assisted version in the emotional state classification of electroencephalogram signals. Emotion state detection or emotion recognition cuts across different disciplines because of the many parameters that embrace the brain's complex neural structure, signal processing methods, and pattern recognition algorithms. Currently, in addition to classical time-frequency methods, emotional state data have been processed via data-driven methods such as empirical mode decomposition (EMD). Despite its various benefits, EMD has several drawbacks: it is intended for univariate data; it is prone to mode mixing; and the number of local extrema must be enough before the EMD process can begin. To overcome these problems, this study employs a multivariate EMD and its noise-assisted version in the emotional state classification of electroencephalogram signals.Article Multiple Positive Solutions of Nonlinear M-Point Dynamic Equations for P-Laplacian on Time Scales(Tubitak Scientific & Technological Research Council Turkey, 2016) Dogan, AbdulkadirIn this paper, we study the existence of positive solutions of a nonlinear m-point p-Laplacian dynamic equation (phi(p) (x(Delta)(t)))(del) w(t)f (t,x(t), x(Delta)(t)) = 0, t(1) < m-1 X(ti) - B-0 (Sigma m-1 i=2 a(i)x(Delta)(t(i))) = 0, x(Delta) (tm) = 0, or x(Delta)(t(1)) - 0, x(t(m)) + B-1(Sigma m-1 i=2 b(i)s(Delta)(t(i))) -0, where phi(p)(s) =vertical bar s vertical bar(P-2) s, p > 1. Sufficient conditions for the existence of at least three positive solutions of the problem are obtained by using a fixed point theorem. The interesting point is the nonlinear term f is involved with the first order derivative explicitly. As an application, an example is given to illustrate the result.Article Modified Self-Adaptive Local Search Algorithm for a Biobjective Permutation Flow Shop Scheduling Problem(Tubitak Scientific & Technological Research Council Turkey, 2019-07-26) Alabas Uslu, Cigdem; Dengiz, Berna; Aglan, Canan; Sabuncuoglu, Ihsan; Uslu, Çiğdem AlabaşInterest in multiobjective permutation flow shop scheduling (PFSS) has increased in the last decade to ensure effective resource utilization. This study presents a modified self-adaptive local search (MSALS) algorithm for the biobjective permutation flow shop scheduling problem where both makespan and total flow time objectives are minimized. Compared to existing sophisticated heuristic algorithms, MSALS is quite simple to apply to different biobjective PFSS instances without requiring effort or time for parameter tuning. Computational experiments showed that MSALS is either superior to current heuristics for Pareto sets or is incomparable due to other performance indicators of multiobjective problems.Article Citation - WoS: 3Citation - Scopus: 3MicroRNA Prediction Based on 3D Graphical Representation of RNA Secondary Structures(Tubitak Scientific & Technological Research Council Turkey, 2019-08-05) Sacar Demirci, Muserref Duygu; Demirci, Müşerref Duygu SaçarMicroRNAs (miRNAs) are posttranscriptional regulators of gene expression. While a miRNA can target hundreds of messenger RNA (mRNAs), an mRNA can be targeted by different miRNAs, not to mention that a single miRNA might have various binding sites in an mRNA sequence. Therefore, it is quite involved to investigate miRNAs experimentally. Thus, machine learning (ML) is frequently used to overcome such challenges. The key parts of a ML analysis largely depend on the quality of input data and the capacity of the features describing the data. Previously, more than 1000 features were suggested for miRNAs. Here, it is shown that using 36 features representing the RNA secondary structure and its dynamic 3D graphical representation provides up to 98% accuracy values. In this study, a new approach for ML-based miRNA prediction is proposed. Thousands of models are generated through classification of known human miRNAs and pseudohairpins with 3 classifiers: decision tree, naive Bayes, and random forest. Although the method is based on human data, the best model was able to correctly assign 96% of nonhuman hairpins from MirGeneDB, suggesting that this approach might be useful for the analysis of miRNAs from other species.Article Citation - WoS: 4Citation - Scopus: 4Determination of 1/V-T (P, Constant) Diagrams of Hydrogen Gases by Graph-Analytical Methods(Yildiz Technical Univ, 2017-01-01) Ibrahimoglu, Beycan; Dindar, Cigdem Kanbes; Erol, Hazal; Karasari, Salih; Kanbeş, Çiğdem DindarGraph-analytical methods provide more accurate results in the analysis of V-T (P=constant) and 1/V-T (P=constant) diagrams of gases. In this study, as a continuation of [1, 2]*, the behavior of hydrogen gas was examined by using graph-analytical method under consideration of volume and density parameters at high pressure and temperature. In this paper, graph-analytical method was applied to draw and examine V-T (P=constant) and 1/V-P (T=constant) diagrams which were based on experimental data of hydrogen and other gases (Hydrogen, carbon dioxide, oxygen, argon, helium, neon, xenon and other gases) at high pressure and temperature. The results indicate that the behavior of hydrogen gas is different from the other gases.