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https://hdl.handle.net/20.500.12587/8645

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Design of Integer Order Approximation Fractional Order Controller with for Automatic Voltage Regulation System
(Ieee, 2019) Yildirim, Burak; Ozdemir, Mahmut Temel; Eke, Ibrahim
Maintaining the voltage value, which is one of the most important parameters of electrical power systems, is vital for modern power systems. In this study, the application of an Integer Order Approximation Fractional Order (IOAFO) controller to an automatic voltage regulation (AVR) system is shown. Matlab/Simulink model was developed for AVR system controlled by IOAFOPI, which is formed by using continued fractional expansion method (CFE) and the performance of IOAFOPI was compared with classical PID. Ant Colony Optimization (ACO) method was used to obtain controller gains in the analysis studies. A multi-purpose cost function consisting of important properties of both time domain and frequency domain was used to perform the optimization of the models. The results obtained in the analysis for the purposed IOAFOPI controller were compared with the classical PID for both time domain and frequency domain responses. The stability of the controllers against system uncertainties are also shown in the results.
No-Reference Evaluation of 3 Dimensional Video Quality Using Spatial and Frequency Domain Components
(Ieee, 2018) Bayrak, Huseyin; Nur Yilmaz, Gokce
Video Quality Assessment (VQA) plays an important role both for evaluating the performance of the transmitter-receiver system and for delivering the video in an efficient manner via the feedback it provides to the transmitter side. Full Reference (FR) VQA metrics currently utilized in the literature are not too efficient during the applications due to the requirement of the original video sequence at the receiver side. Therefore, the tendency of the researchers is recently on to develop Reduced Reference (RR) or No-Reference (NR) VKD metrics. In this paper, a NR VKD metric considering spatial and frequency domain components of the color and depth map based 3 Dimensional (3D) video important for Human Visual System (HVS) is developed. Canny operator which is an efficient algorithm to extract edge information is used to obtain the components in the spatial domain. Discrete Cosine Transform (DCT) is exploited to obtain the components in the frequency domain. The efficient results obtained show that the proposed algorithm is capable of superseding the FR metrics existing in the literature.
Wide Area Scanning Trap Camera System with Multi-Cameras and Distinctive Motion Detection Sensor
(Ieee, 2018) Simsek, Murat; Yalcinkaya, Fikret; Ugurlutan, Rifat
The Trap Camera Systems in lots of different fields such as defense, medical, ecological research, wildlife surveillance, reconnaissance and surveillance, scientific research, nest ecology, rare species detection, ecological population estimation and rich species identification, border security, illegal migration detection, habitat occupation, orienteering, sports activities, military and civilian target detection and sporting events, provide limited situational awareness with single camera and motion detection sensor having ability Access restricted distances. Unlike the conventional systems, in this paper, we present a wide-area scanning system, which is created with four cameras and image processing software, designed to penetrate far distances with a unique designed motion detection sensors. The designed system is adaptive and compact and extra features can be optionally added with completely original software.
Signal and Noise Subspace Decomposition for a Linear Antenna Array Using SVD
(Ieee, 2018) Aytaş, Nilay; Afacan, Erkan; İnanç, Nihat
In the vast majority of signal processing applications, the signal is intended to be analyzed to obtain a variety of data from the signal. It is decomposed into signal subspaces to perform this analysis. The noise in the signal during analysis is undesirable. If the signal incoming to an antenna array contains noise, it should be filtered before processing signal. In this study, we have tried to divide the uniform linear antenna array into subspaces of incoming signals from multiple signal sources with various frequencies, noise and arrival angles. The Singular Value Decomposition (SVD) method is used to separate the signal subspace and noise subspace. According to the singular values obtained, the information about the frequencies of the signals and whether the signal contains noise. Also, the number of sources can be estimated by means of singular values.
Depth Perception Prediction of 3D Video for Ensuring Advanced Multimedia Services
(Ieee, 2018) Nur Yilmaz, Gokce; Battisti, Federica
A key role in the advancement of 3 Dimensional TV services is played by the development of 3D video quality metrics used for the assessment of the perceived quality. Moreover, this key role can only be supported when the features associated with the 3D video nature is reliably and efficiently characterized in these metrics. In this study, z-direction motion incorporated with significant depth levels in depth map sequences are considered as the main characterizations of the 3D nature. The 3D video quality metrics can be classified into three categories based on the need for the reference video during the assessment process at the user end: Full Reference (FR), Reduced Reference (RR) and No Reference (NR). In this study we propose a NR quality metric, PNRM, suitable for on-the-fly 3D video services. In order to evaluate the reliability and effectiveness of the proposed metric, subjective experiments are conducted in this paper. Observing the high correlation with the subjective experimental results, it can be clearly stated that the proposed metric is able to mimic the Human Visual System (HVS).
Recognition of Daily and Sports Activities
(Ieee, 2018) İnanç, Nihat; Kayri, Murat; Ertuğrul, Ömer Faruk
Since being physically inactive was reported as one of the major risk factor of mortality, classifying daily and sports activities becomes a critical task that may improve human life quality. In this paper, the daily and sports activities dataset was used in order to evaluate and validate the employed approach. In this approach, the statistical features were extracted from the histograms of the local changes in the wearable sensors logs were obtained by one-dimensional local binary patterns. Later, extracted features were classified by extreme learning machines. Results were showed that the proposed approach is enough to recognize the action type, but in order to recognize the actions, or gender, different feature extraction methods must be employed.
Depth Perception Prediction of 3D Video QoE for Future Internet Services
(Ieee, 2018) Nur Yilmaz, Gokce
3 Dimensional (3D) video Quality of Experience (QoE) metrics are at utmost importance to enable enhancement of Future Internet Services. This enhancement can only be supported when the 3D video is characterized in the most reliable way as possible in these metrics. In light of this fact, a QoE metric including significant depth level and aerial perspective cue to support this reliable way is developed to predict the depth perception of the 3D video. Considering that No Reference (NR) QoE metric type is the most efficient one compared to the other types (i.e., Full Reference (FR) and Reduced Reference (RR)) in terms of transmission requirement, it is used as the metric type to develop the proposed metric. Conducted subjective experiments which are currently the "gold standard" in terms of reliable depth perception assessment are exploited to assess the performance of the proposed metric. Observing the effectiveness of the performance results, it can be clearly concluded that the advancement of the 3D video communication technologies can be ensured to assist Future Internet Services in a timely fashion.
An Abstraction and Structural Information Based Depth Perception Evaluation Metric
(Ieee, 2017) Nur Yilmaz, Gokce; Bayrak, Huseyin
Developing reliable and efficient 3 Dimensional (3D) video depth perception evaluation metrics is currently a trending research topic for supporting the advancement of the 3D video services. This support can be proliferated by utilizing effective 3D video features while modeling these metrics. In this study, a Reduced Reference (RR) depth perception evaluation metric using significant depth level and structural information as effective 3D video features is developed. The significant depth level and structural information in the Depth Maps (DM) are determined using abstraction filter and Canny edge detection algorithm, respectively. The performance assessment results of the proposed RR metric present that it is quite effective for ensuring advanced 3D video services.
Scene Detection via Depth Maps Of 3 Dimensional Videos
(Ieee, 2017) Bayrak, Huseyin; Nur Yilmaz, Gokce
Scene detection via processing of multimedia data is a significant research area for the advancement of the video technologies and applications. Currently, the scene detection is mostly performed manually. Thus, it is time consuming and costly. Therefore, it is important to develop algorithms that can automatically segment scenes to support the advancement of these technologies and applications. With the wide-spread utilization of the 3 Dimensional (3D) videos, researchers working in the field of the video scene detection start using them in this field as well. However, there is still a gap in the application of the scene detection algorithms to Depth Maps (DMs) that are a part of the 3D video and important for temporal video scene detection. In this study, dominant clusters and K-means method is proposed to detect the temporal 3D video segments using the DMs. The experimental studies performed using the scene detection method present that the video scenes can be edited efficiently without human assistance. Moreover, unlike similar studies in the literature, the proposed method can provide successful results on video sequences thanks to the dominant clusters and the K-means clustering approach utilized.
Optimal Scheduling of Distributed Energy Resources by Modern Heuristic Optimization Technique
(Ieee, 2017) Bai, Wenlei; Eke, Ibrahim; Lee, Kwang Y.
The increasing number and types of energy resources and prosumers has complicated the operation in microgrid greatly. Such problem becomes a hard-to-solve or even impossible-to-solve for traditional mathematical algorithms without necessary approximation. However, modern heuristic optimization techniques have proven their efficiency and robustness in complex non-linear, non-convex and large-size problems. In this paper, we propose a comprehensive microgrid which consists of renewables, distributed generators, demand response, marketplace, energy storage system and prosumers, and investigate the behaviors of such system. A novel heuristic method, artificial bee colony, is proposed to solve the day-ahead optimal scheduling of the microgrid. Case studies have shown that such algorithm is able to solve the problem fast, reliable with satisfactory solutions. For the first case, the computational time is 9 minutes compared with 19 hours by a traditional methodical tool which has not taken necessary approximation of the original problem.
Classification of Uroflowmetry and EMG Signals of Pediatric Patients using Artificial Neural Networks
(Ieee, 2017) Yalcinkaya, Fikret; Caliskan, Ozan; Erogul, Osman; Irkilata, Cem; Kopru, Burak; Coguplugil, Emrah
UF-EMG test, in which non-invasive uroflowmetry (UF) and electromyography (EMG) signals are simultaneously recorded, is frequently used in children diagnosed with lower urinary tract dysfunction disease (AUSD) and its treatment. In the literature, independent (single) UF signals and integrated (dual) UF-EMG signals are graded many times but there is no classification study of UF-EMG integrated signals with Artificial Neural Networks (ANN), although studies have been done to classify UF signals with ANN. In this paper, it was aimed to classify the UF-EMG signals recorded from pediatric patients during the UF-EMG tests in Urodinami Center of Gulhane Education and Research Hospital using ANN. 773 (80%) of the 967 patients with an average age of 8 were used for training and 194 (20%) were used for the test. In YSA, the contribution of the features obtained from the EMG signals played a crucial role and was the main reason to improve the signal classification from 58% to 84.02%. The new classification method created by the obtained data does facilitate the interpretation of UF-EMG results for the clinical personnel in diagnosis, follow-up and treatment of patients. It is also aimed that the pediatric patients living in regions with less access to health care can be treated by providing an early and easy preliminary diagnostic tool.
Solving the Optimal Power Flow Quadratic Cost Functions using Vortex Search Algorithm
(Elsevier Science Bv, 2017) Aydin, O.; Tezcan, S. S.; Eke, I; Taplamacioglu, M. C.
This study proposes solving the constraint optimal power flow problem (OPF) by using vortex search algorithm (VSA). VSA is inspired by natural vortexes. Piecewise quadratic fuel cost and quadratic cost curve with valve point loadings test cases are solved on IEEE-30 bus test system by taking into consideration the system constraints such as generation limits, voltages at nodes, tap settings. The obtained test results show that VSA gives better results than any other algorithms which are used to solve the OPF problem. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
A Depth Perception Evaluation Metric For Immersive 3d Video Services
(Ieee, 2017) Nur Yilmaz, Gokce
Burgeoning advances in 3 Dimensional (3D) video services provide a big leap on the proliferation of the investigations for developing reliable and competent perceptual 3D video Quality of Experience (QoE) metrics. This proliferation can only be supported by exploiting key features characterizing 3D video nature in these investigations. In this paper, a Reduced Reference (RR) metric is developed considering that the spatial resolution and perceptually significant depth level are two effective features for efficiently evaluating depth perception of the 3D video. In order to determine the perceptually significant depth levels in the depth map sequences, abstraction filter is exploited in the development of the proposed metric. Owing to the fact that the depth perception significantly differs for the depth map sequences having dissimilar relative depth levels, this feature is also incorporated with the proposed metric through normalized standard deviation. Structural SIMilarity metric (SSIM) is utilized to predict the depth perception degraded with the change in the perceptually important levels of the compressed depth maps having dissimilar spatial resolutions and relative depth levels. The performance assessment of the proposed RR metric proves the effectiveness of the proposed metric for ensuring immersive 3D video services.
Matlab/Simulink Based Comparative Analysis of the Effect of Ion Concentration on Action Potantial by Using Hodgkin-Huxley and Morris-Lecar Neuron Models
(Ieee, 2017) Yalcinkaya, Fikret; Unsal, Hamza
In pharmacological experiments, the effect of ion concentrations on Action potential (AP) and its association with important neurological disorders such as stroke, epilepsy and depression has been investigated. In this paper, Hodgkin-Huxley (HH) and Morris-Lecar (ML) membrane models, which have contributed to the literature of theoretical neural science, have been studied. In the first step, these two models have been simulated individually in Simulink environment using Matlab software. In the second step, the ratio of the amount of extracellular ion to the amount of intracellular ion was expressed as Hd/Hi. Based on Na. and K. as ions, the effect of Hd/Hi ratio change on AP signal was investigated based on HH and ML as a model. In the HH model, it was searched that the Hd/Hi change due to sodium ions influenced the amplitude of AP and the depolarization phase. The change in Hd/Hi ratio due to potassium ion concentrations was found to affect the threshold and hyper-polarization phase of AP. In the ML membrane model, it was observed that the change in Hd/Hi ratio due to intracellular and extracellular Ca+2 and K- ion concentrations affected the formation time of AP, firing time, amplitude and formation phases. HH and ML models showed similarity in the same parameters.
Economic Load Dispatch Using Vortex Search Algorithm
(Ieee, 2017) Saka, Mustafa; Eke, Ibrahim; Tezcan, Suleyman Sungur; Taplamacioglu, M. Cengiz
Economic load dispatch (ELD) is one of most fundamental issue for energy generation and distribution in power systems. For this purpose, different optimization techniques are developed and applied to ELD problem. In this paper, vortex search algorithm (VSA) is proposed and used for solving ELD problem. VSA method was developed from nature by observing the state of stirring liquids. Transmission losses, valve point loading effect, ramp rate limits and prohibited operating zone constraints are considered to solve ELD problem with VSA method. The feasibility and effectivity of this method is demonstrated for different cases. Obtained results are compared with different developed algorithms and these results clearly point out that proposed VSA method gives successfully outputs.
Sparse Representation-based Dictionary Learning Methods for Hyperspectral Super-Resolution
(Ieee, 2016) Simsek, Murat; Polat, Ediz
Due to hardware limitations, hyperspectral imagery has low spatial resolution. It can be obtained super-resolution hyperspectral imagery by means of sparse representation-based methods that are designed for improving spatial resolution. In this paper, the effect of sparse representation-based dictionary learning algorithms including K-SVD, ODL and Bayes on obtaining superresolution images with low error and high quality has been investigated. The method with best results has been identified.
Video Content Analysis Method for Audiovisual Quality Assessment
(Ieee, 2016) Konuk, Baris; Zerman, Emin; Nur, Gokce; Akar, Gozde Bozdagi
In this study a novel, spatio-temporal characteristics based video content analysis method is presented. The proposed method has been evaluated on different video quality assessment databases, which include videos with different characteristics and distortion types. Test results obtained on different databases demonstrate the robustness and accuracy of the proposed content analysis method. Moreover, this analysis method is employed in order to examine the performance improvement in audiovisual quality assessment when the video content is taken into consideration.
Structural Depth Estimation Via Depth Maps Of 3 Dimensional Videos
(Ieee, 2016) Bayrak, Huseyin; Yilmaz, Gokce Nur
3-Dimensional (3D) Video Quality Assessment (VQA) has been an important area for researchers working in this area. The reason is there isn't any well accepted and standardized VQA method for 3 Dimensional (3D) as for 2-Dimensional (2D) video. Depth perception assessment (DPE) is the most critical part of 3D VQA because of visual realism. Subjective tests are currently in use for the 3D VQA because there aren't any 3D VQA algorithms for measuring this perception accepted by researchers in literature. Subjective tests are not ergonomic methods from the stand point of time and cost. Therefore, it is quite important to develop objective 3D VQA metrics for predicting the depth perception of users. The VQA algorithms developed without using a reference video is called No-Reference (NR) metrics in literature and they are considered efficient compared to the other metrics. In this study, Depth Maps (DM) in 2D+depth based 3D videos are utilized to measure Structural Average Depth (SAD) in a NR manner. The results of this study presents that the YOD algorithm can be considered as a part of a 3D VQA metric assessing the depth perception and approved by researchers.
The Effect of Dictionary Learning Algorithms on Super-resolution Hyperspectral Reconstruction
(Ieee, 2015) Simsek, Murat; Polat, Ediz
The spatial resolutions of hyperspectral images are generally lower due to imaging hardware limitations. Super-resolution algorithms can be applied to obtain higher resolutions. Many algorithms exist to achieve super-resolution hyperspectral images from low resolution images acquired in different wavelengths. One of the popular algorithms is sparse representation-based algorithms that employ dictionary learning methods. In this study, a comparative framework is developed to investigate which dictionary learning algorithm leads to better super-resolution images. In order to achieve that, K-SVD and ODL dictionary learning algorithms are employed for comparison. A sparse representation-based algorithm G-SOMP+ is used for hyperspectral super-resolution reconstruction. The experimental results show that ODL algorithm outperforms K-SVD in terms of both reconstruction quality and processing times.
Improved Artificial Bee Colony Based on Orthognal Learning for Optimal Power Flow
(Ieee, 2015) Bai, Wenlei; Eke, İbrahim; Lee, Kwang Y.
Optimal power flow (OPF) problem is to optimize an objective function (usually total cost of generation), while satisfying system constraints. The OPF is a non-linear and non-convex problem, and an artificial bee colony (ABC) algorithm is utilized to handle the problem. Heuristic methods are credited for their simplicity to solve complex non-linear optimization problem without simplifying approximation of the system. However, the original ABC has poor efficiency on exploitation search, thus in order to find better global optimum, this paper proposes an improved ABC (IABC) based on orthogonal learning. The IABC implements the idea of orthogonal experiment design (OED) based on the orthogonal learning. The validity and effectiveness of the method are tested in the IEEE-30 bus system.

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