WoS İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394

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Measuring Temperature Change on Photothermal Au Nanorod and Nanocage Upon Laser Irradiation
(Amer Chemical Soc, 2015) Cavusoglu, Halit; Sakalak, Huseyin; Buyukbekar, Burak Zafer; Demirel, Gokhan; Citir, Murat; Yavuz, Mustafa Selman
Quasi-Static Operation of 2-Axis Microscanners With AlN Piezoelectric Quad-Actuators
(Institute of Electrical and Electronics Engineers Inc., 2021) Hah, D.
Aluminum nitride (AlN) started to draw attentions as a material for piezoelectric actuation owing to its CMOS process compatibility and safeness for biomedical applications. Due to its relatively low piezoelectric coefficients, AlN-based piezoelectric actuators have been mostly operated in resonance modes, especially in optical scanning. This paper presents a novel design of a 2-axis-tilt microscanner with AlN piezoelectric quad-actuators and meander-shaped hinges for reasonable quasi-static operation. Through finite-element-method simulation, it is shown that the proposed device can have about 9 degree of optical scan angle in two dimensions with the voltage amplitude of 50 V. Lissajous scanning operation of the device is demonstrated as well via simulation. © 2021 Elsevier B.V., All rights reserved.
Citation - Scopus: 5
Emotion Detection Using Multivariate Synchrosqueezing Transform via 2D Circumplex Model
(Institute of Electrical and Electronics Engineers Inc., 2018) Ozel, Pinar; Akan, Aydin; Yilmaz, Bulent; Özel, Pınar; Akan, Aydin I.; Yilmaz, Bulent
Emotion detection by utilizing signal processing methods is a challenging area. An open issue in emotional modeling is to obtain an optimum feature set to use for the classification process. This study proposes an approach for emotional state classification by the investigation of EEG signals via multivariate synchrosqueezing transform (MSST). MSST is a post-processing technique to compose a localized time-frequency representation yielding multivariate syncyrosqueezing coefficients. After obtaining these coefficients from EEG signals for 18 subjects from DEAP dataset, coefficients and self-assessment-mannequins (SAM) labels of those subjects are used for emotional state classification by using support vector machines (SVM) nearest neighbor, decision tree, and ensemble methods. The accuracy rate is 70.6% for high valence high arousal (HVHA), 75.4% for low valence high arousal (LVHA), 77.8% for high valence low arousal (HVLA), and 77.2% for low valence low arousal (LVLA) cases using SVM. © 2019 Elsevier B.V., All rights reserved.
Citation - Scopus: 1
Detection of Epileptic Seizures With Tangent Space Mapping Features of EEG Signals
(IEEE, 2021) Altindis, Fatih; Yilmaz, Bulent
Detection of epileptic seizures from EEG signals is well-studied topic for the last couple of decades. Lately, automated signal processing and machine learning methods were developed to detect epileptic seizures. However, most of the methods are tailored to subjects and require fine tuning of many parameters. In this study, we proposed to use Riemannian geometry-based signal processing method that already showed superior performance on brain-computer interface problems, to extract features. We showed that tangent space mapping features of EEG signals can be used to detect seizures with high accuracy and precision.
Text Classification Experiments on Contextual Graphs Built by N-Gram Series
(Springer International Publishing AG, 2025) Sen, Tarik Uveys; Yakit, Mehmet Can; Gumus, Mehmet Semih; Abar, Orhan; Bakal, Gokhan
Traditional n-gram textual features, commonly employed in conventional machine learning models, offer lower performance rates on high-volume datasets compared to modern deep learning algorithms, which have been intensively studied for the past decade. The main reason for this performance disparity is that deep learning approaches handle textual data through the word vector space representation by catching the contextually hidden information in a better way. Nonetheless, the potential of the n-gram feature set to reflect the context is open to further investigation. In this sense, creating graphs using discriminative ngram series with high classification power has never been fully exploited by researchers. Hence, the main goal of this study is to contribute to the classification power by including the long-range neighborhood relationships for each word in the word embedding representations. To achieve this goal, we transformed the textual data by employing n-gram series into a graph structure and then trained a graph convolution network model. Consequently, we obtained contextually enriched word embeddings and observed F1-score performance improvements from 0.78 to 0.80 when we integrated those convolution-based word embeddings into an LSTM model. This research contributes to improving classification capabilities by leveraging graph structures derived from discriminative n-gram series.
Citation - WoS: 50
Citation - Scopus: 149
An Investigation on the Determinants of Carbon Emissions for OECD Countries: Empirical Evidence From Panel Models Robust to Heterogeneity and Cross-Sectional Dependence
(Springer Heidelberg, 2016) Dogan, Eyup; Seker, Fahri
This empirical study analyzes the impacts of real income, energy consumption, financial development and trade openness on CO2 emissions for the OECD countries in the Environmental Kuznets Curve (EKC) model by using panel econometric approaches that consider issues of heterogeneity and cross-sectional dependence. Results from the Pesaran CD test, the Pesaran-Yamagata's homogeneity test, the CADF and the CIPS unit root tests, the LM bootstrap cointegration test, the DSUR estimator, and the Emirmahmutoglu-Kose Granger causality test indicate that (i) the panel time-series data are heterogeneous and cross-sectionally dependent; (ii) CO2 emissions, real income, the quadratic income, energy consumption, financial development and openness are integrated of order one; (iii) the analyzed data are cointegrated; (iv) the EKC hypothesis is validated for the OECD countries; (v) increases in openness and financial development mitigate the level of emissions whereas energy consumption contributes to carbon emissions; (vi) a variety of Granger causal relationship is detected among the analyzed variables; and (vii) empirical results and policy recommendations are accurate and efficient since panel econometric models used in this study account for heterogeneity and cross-sectional dependence in their estimation procedures.
Citation - WoS: 1
Citation - Scopus: 2
Vision-Based Autonomous Aerial Refueling
(Amer Inst Aeronautics & Astronautics, 2022) Erkin, Tevfik; Abdo, Omer; Sanli, Yilmaz; Celik, Harun; Isci, Hasan
Aerial refueling tasks are very challenging due to the high risk of aircraft close proximity. Currently, within the drogue-probe method, the receiver aircraft pilot manages the refueling task in accordance with the tanker aircraft pilot. Therefore, autonomous aerial refueling is still an unaccomplished task for aircrafts. In this paper, a fully automated aerial refueling procedure based on digital visual inspection is proposed. A nonlinear dynamic model of receiver aircraft is derived to track the motion of drogue. In order to control the receiver aircraft affected by tanker aircraft vortex during approach, and ensure the receiver aircraft to automatically track and dock the tanker aircraft, an autopilot system that considers visual sensing of drogue motion is designed. The receiver aircraft is controlled by the autopilot system via translational motion of tanker aircraft projected by a cameramounted on the receiver aircraft. Thanks to this vision-based controllers, the need of tanker aircraft positioning is denied since camera projection has the capability of perception of three-dimensional direction of tanker aircraft. In order to test the autopilots include vision-based controllers and algorithms, the vision-based autonomous aerial refueling is operated under presence of turbulence and vortex. Finally, the simulation results demonstrate that the proposed guidance-navigation-control system achieve aerial refueling autonomously, and make it feasible and realizable for aircrafts.
Citation - WoS: 3
Citation - Scopus: 3
Template Scoring Methods for Protein Torsion Angle Prediction
(Springer-Verlag Berlin, 2015) Aydin, Zafer; Baker, David; Noble, William Stafford
Prediction of backbone torsion angles provides important constraints about the 3D structure of a protein and is receiving a growing interest in the structure prediction community. In this paper, we introduce a three-stage machine learning classifier to predict the 7-state torsion angles of a protein. The first two stages employ dynamic Bayesian and neural networks to produce an ab-initio prediction of torsion angle states starting from sequence profiles. The third stage is a committee classifier, which combines the ab-initio prediction with a structural frequency profile derived from templates obtained by HHsearch. We develop several structural profile models and obtain significant improvements over the Laplacian scoring technique through: (1) scaling templates by integer powers of sequence identity score, (2) incorporating other alignment scores as multiplicative factors (3) adjusting or optimizing parameters of the profile models with respect to the similarity interval of the target. We also demonstrate that the torsion angle prediction accuracy improves at all levels of target-template similarity even when templates are distant from the target. The improvement is at significantly higher rates as template structures gradually get closer to target.
Citation - Scopus: 5
Development of Knowledge Based Response Correction for a Reconfigurable N-Shaped Microstrip Antenna Design
(Institute of Electrical and Electronics Engineers Inc., 2015) Aoad, Ashrf; Simsek, Murat; Aydin, Zafer
This study presents the use of prior knowledge of inverse artificial neural network (ANN) to model and optimize a reconfigurable N-shaped microstrip antenna. Three accurate prior knowledge inverse ANNs with large amount training data are proposed where the frequency information is incorporated into the structure of ANN. The complexity of the input/output relationship is reduced by using prior knowledge. Three separate methods of incorporating knowledge in the second step of the training process with a multilayer perceptron (MLP) in the first step are demonstrated and their results are compared to EM simulation. © 2023 Elsevier B.V., All rights reserved.
Range-Based Wireless Sensor Network Localization by a Circumnavigating Mobile Anchor Without Position Information
(IEEE, 2024) Guler, Samet
Typical range-based wireless sensor network (WSN) localization approaches aim at estimating the sensor node positions by using a set of anchors with known positions. In some applications, assuming the knowledge of the anchors' positions may be impractical, and estimation of the sensors' positions in an arbitrary fixed frame may be sufficient. Considering such scenarios, we propose a WSN localization algorithm by single mobile anchor without self location information. The mobile anchor obtains distance measurements from the sensors while tracking a custom trajectory which is shown to improve the localization performance over time for high signal-to-noise ratio cases. By utilizing two stationary reference nodes within the WSN, the proposed framework generates sensor node position estimation up to translation and rotation with sufficient precision in the absence of global positioning aids. We foresee that the proposed framework can demonstrate benefits in several WSN applications ranging from internet-of-things to service robotics.
Citation - WoS: 3
Mol-Eye: A New Metric for the Performance Evaluation of a Molecular Signal
(IEEE, 2018) Turan, Meric; Kuran, Mehmet Sukru; Yilmaz, H. Birkan; Chae, Chan-Byoung; Tugcu, Tuna
Inspired by the eye diagram in classical radio frequency (RF) based communications, the MOL-Eye diagram is proposed for the performance evaluation of a molecular signal within the context of molecular communication. Utilizing various features of this diagram, three new metrics for the performance evaluation of a molecular signal, namely the maximum eye height, standard deviation of received molecules, and counting SNR (CSNR) are introduced. The applicability of these performance metrics in this domain is verified by comparing the performance of binary concentration shift keying (BCSK) and BCSK with consecutive power adjustment (BCSK-CPA) modulation techniques in a vessel-like environment with laminar flow. The results show that, in addition to classical performance metrics such as bit-error rate and channel capacity, these performance metrics can also be used to show the advantage of an efficient modulation technique over a simpler one.
Citation - WoS: 1
Citation - Scopus: 3
Endüstriyel Kablosuz Algılayıcı Ağlarda Hata Kontrol Sistemlerinin Ağ Yaşam Süresine Etkileri
(IEEE, 2019) Tekin, Nazli; Gungor, V. Cagri
Due to the harsh channel conditions of the industrial environment, the data transmission over wireless channel suffers from erroneous packets. The energy consumption of error control schemes is of vital importance for battery-powered Wireless Sensor Networks (WSNs). In this paper, the performance evaluation of error control schemes namely, Automatic Repeat Request (ARQ), Forward Error Correction (FEC) and Hybrid ARQ (HARQ) in industrial environment in terms of energy efficiency is presented. The impact of the existing error control schemes on the industrial wireless sensor network lifetime is analyzed. A novel Mixed Integer Programming (MIP) framework is developed to maximize network lifetime. Performance results show that utilizing BCH (31,21,5) for Telos at the link layer maximizes the network lifetime while attaining the desired application reliability rate.
Citation - WoS: 3
Citation - Scopus: 3
Performance Improvements of Photonic Lantern Based Coherent Receivers
(Institute of Electrical and Electronics Engineers Inc., 2014) Ozdur, Ibrahim T.; Toliver, Paul; Woodward, Ted K.
In this work, the signal-to-noise ratio improvement of photonic lantern-based coherent receivers over single-mode coherent receivers is demonstrated. The signal-to-noise ratio is improved by a factor of 2.8 when other parameters kept constant. © 2021 Elsevier B.V., All rights reserved.
Sustainable Water Management and Rehabilitation in the Mining Lakes, Ilgin-Konya, Turkey
(Agro Arge Danismanlik San ve Tic As, 2016) Delibalta, M. S.; Uzal, N.; Lermi, A.
The processes during the search, production and enrichment of mining operations naturally affects the air, soil, water resources in turn the natural environment and living organisms. In general, the environmental impact of coal opencast mining operations is much more significant than that of underground mining and mineral processing. After stripping of the material filling the holes in coal opencast production, with the rise of surface water and ground water level is composed of large or small ponds. Low pH (acidic characteristic) and high metal concentrations (Al, Ca, Mn, Fe, Cu, Zn, Pb) of these ponds, containing sulfide minerals and the waste materials, for the sustainability of natural resources is one of the biggest environmental problems. This paper is to investigate geochemical characteristics of the pond waters in the Ilgm Coal deposit area. Geochemical analyses were made by ICP-MS in waters taken from ponds in each three-month periods. Highest heavy metal contents 1839 ppb Mn and 9777 ppb Fe, the average pH values 6.49-7.81, turbidity (NTU) 0.1263.6, sulphate content 0.05-2.67 mg SO4/L, chemical oxygen demand 4-136 mg O-2/L, and electrical conductivity 285 mu S/cm4.68 mS/cm have been measured during the monitoring study of five different lignite opencast mine post-production lakes of the TKI GLI Ilgm. Analyses were performed in three-month periods. The results were evaluated within the framework of relevant laws and regulations.
Citation - WoS: 5
Citation - Scopus: 4
Single-Country Versus Multiple-Country Studies
(Academic Press Ltd-Elsevier Science Ltd, 2019) Aslan, Alper; Dogan, Eyup; Altinoz, Buket
Citation - WoS: 1
Citation - Scopus: 2
Experimental Study on Increase of Bonding Strength of FRP Reinforcement in Concrete
(Springer-Verlag Singapore Pte Ltd, 2022) Taskin, Furkan; Ciftci, Cihan
In the last two decades, the use of fiber-reinforced polymer (FRP) bars is of great interest to reinforce concrete beam structures due to its high specific strength, effective corrosion resistance, and low cost fabrication. Therefore, the flexural performance of these reinforced concrete beams containing FRP bars has been investigated by researchers for years with great interest. According to these investigations, one of the major problems is weak bonding strength between these bars and concrete material. Since, this major problem causes low flexural capacity, high deflection, and high crack widths for the reinforced concrete beams. Hence, the use of FRP bars by engineers does not sufficiently become widespread and also the engineering applications of these useful materials are still limited today. In this study, it is aimed to present an applicable solution regarding the bonding failures of the FRP bars in structurally reinforced concrete beams. For this solution, reinforced concrete beam samples were produced by using FRP materials on which knotted structures were formed. Then these samples were tested under 3-point bending tests. Furthermore, smooth-surfaced FRP bars and traditional deformed steel rebars were also used as reinforcing materials in the concrete beam samples for the comparison of the flexural capacities of each sample in order to investigate the effects of the reinforcing materials on the bonding strength. To conclude, the knotted FRP bars provide a significant contribution on the flexural capacity due to the increase of the bonding strength between the reinforcing material and the concrete in the beams.
Email Clustering & Generating Email Templates Based on Their Topics
(Assoc Computing Machinery, 2021) Coskun, Fatih; Gezer, Cengiz; Gungor, V. Cagri
Email templates have a significant impact on users in terms of productivity. Using an email template that is produced successfully is going to transfer the main information with a considerable impression. While the previous studies were focused on the email generation by text-differences in the content of the emails, generated templates based on email topics can provide better productivity for the companies. This article proposes a system, in which user emails are clustered according to the topics of the emails, and introduces an email template generation system that utilizes the sample emails belonging to the formed email clusters. For this purpose, the Enron email dataset has been used and the performance of different text preprocessing and topic modeling algorithms, such as DMM, GPU-DMM, GPU-PDMM, LF-DMM, LDA, LF-LDA, BTM, WNTM, PTM, SATM, have been investigated and compared to determine the most efficient one. After obtaining the email topics, the system shows the examples of the emails representing the selected topics and enables the authorized users to create templates that generalize these topics.
Citation - WoS: 1
Citation - Scopus: 2
Effects of Curved-Beam Heights to Harvested Energy in a Blanaced Comb-Drive Configuration
(IEEE, 2021) Hah, Dooyoung
Energy harvesting devices have been gaining increasing interests, especially in the areas of internet of things (IoTs) and sensor networks. Due to the broadband and random nature of typical vibration energy sources available in the environment, significant amount of research efforts have been put into the bandwidth broadening of the energy harvesters. Utilization of spring nonlinearity has been one of the most studied subject in that regard. In this work, response of an energy harvesting device with curved-beam springs to colorednoise vibration is studied numerically, based on stochastic differential equations. The harvester considered in this study is an electrostatic type with electrets and a balanced comb-drive configuration. The study mainly focuses on the effect of the beam height to the harvested power. The results show that curved-beam springs can increase the harvested electric power by 52% (2.69 mW versus 1.77 mW) in comparison to straight-beam springs of the same dimensions. Buckling-induced rapid snapping of the curved beams is attributed to such a power increase.
An Evaluation of the Rural Landscapes as Heritage From Habitus Perspective
(Geleneksel Yayincilik Ltd Stl, 2024) Elagoz Timur, Bahar; Asiliskender, Burak
Rural heritage areas consist of natural and built environments produced concerning local and traditional life practices, production -consumption habits, and intangible values of societies. This environment is created vernacularly using local materials and construction techniques due to the topographical features where it is built and is in contact with local users. For this reason, it is valuable to explain the meaning of vernacular architecture to understand its users and the habitus that emerges from it. Historical rural settlements, which have found their place in conservation theories over time, attract attention with their traditional and vernacular architecture.These areas, called "rural landscape as heritage" by definition developed by ICOMOS-IFLA, are accepted as a whole with their tangible and intangible components such as natural, archaeological, and architectural. Today, plenty of research is about integrated conservation issues of rural landscape heritages. The study, differently from theirs, plans to discuss the rural landscapes through habitus. It is possible to interpret the vernacular architecture produced in rural landscapes by understanding its user and the habitus in which it emerges. Moreover, there is a dynamic link between the traditional rural areas and the habitus of societies that produce and are produced by their daily lifestyles, traditions, collective memories, and histories. The habitus, which is always transformed, begins to adapt its environment to the change by this link. In this changing process, effects such as industrialization, technological developments, and globalization threaten rural landscapes to lose their authentic values. The first step in the conservation of rural landscapes lies in understanding these areas and their values and making change predictable. From this point of view, this study questions the role of habitus in the formation and life cycle of rural heritage. The research and the hypothesis created aim to contribute to the studies about sustainable living in rural landscapes by revealing the structuring effect of the habitus between the rural landscapes and their natural, built, and socio-cultural environments. In the study, the method developed from the literature to understand rural landscapes and their dynamics without studying the case is presented for use in rural landscape heritage conservation studies. Habitus connects the natural, tangible, and intangible components of rural landscapes by the balance it creates and contributes to the formation and maintenance of the spirit of place. In order to understand this balance and draw attention to holistic conservation approaches, the network of relations has been tried to be revealed in detail. Within the scope of the study, the definition of habitus was explained through the environment and practices, and its relationship with the rural landscape was conveyed through a single structure and settlement. The transition of living heritage is inevitable, but when it cannot be managed according to international regulations, the consequences will be the loss of rural heritage, which represents societies' traditional lifestyles. The proposed approach needs to be customized and re-established for each different rural landscape heritage site. Because each heritage site is unique and has its own conservation problems. It is critical to raise awareness about the effects of habitus change in rural landscapes and their management and to emphasize the importance of creating resilient rural heritage areas that can accompany change by preserving authentic values.
Data-Driven Local Control Design for Dead Band Control of Load Tap Changers
(IEEE, 2024) Savasci, Alper; Ceylan, Oguzhan; Paudyal, Sumit
This study presents an off-line optimization-guided machine learning approach for coordinating the local control rules of on-load tap changers (OLTCs) and step-voltage regulators (SVRs). Based on a bang-bang control rule, these legacy devices autonomously regulate the feeder voltage around the nominal level by varying the tap position in the lower or raise direction. The characterizing parameter of the local control rule is the dead band, which affects the number of tap switching in operation and is directly related to the economical use life of the equipment. The bandwidth is typically set within a standard voltage range and is generally kept constant in daily operation. However, adjusting the bandwidth dynamically can prevent excessive tap switching while maintaining satisfactory voltage regulation for varying loading and distributed generation conditions. Our approach aims to set the bandwidth parameter systematically and efficiently through a machine learning-based scheme, which is trained with a dataset formed by solving the distribution network optimal power flow (DOPF) problem. The performance of learning the bandwidth parameter is demonstrated on the modified 33-node feeder, which is promising for integrated voltage control schemes.

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