Elektrik-Elektronik Mühendisliği Bölümü Koleksiyonu

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https://hdl.handle.net/20.500.14034/36

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Analysis of C-shaped compact microstrip antennas using deep neural networks optimized by Manta Ray foraging optimization with Lévy-Flight Mechanism
(2021) Biçer, Mustafa Berkan
In recent years, microstrip antennas have become a popular research subject with the increasing use of mobile technologies. With the development of neural networks, the design and analysis of microstrip antennas are carried out quickly with high accuracy. However, optimizing the weight matrices and bias vectors of deep neural learning models is an important challenge for engineering problems. This study presents a deep neural network-based (DNN-based) neural model to estimate the gain and scattering parameter (S11) of C-shaped compact microstrip antennas (CCMAs). For this purpose, the S11 and gain values of 324 CCMAs with different physical and electrical properties were obtained using full-wave electromagnetic simulation software based on the finite integration technique (FIT). The data related to 324 CCMAs were used for the training and testing process. The improved manta ray foraging optimization (MRFO) algorithm based on the Lévy-flight (LF) mechanism was used to optimize the connection weights matrices and bias vectors. The MRFO-optimized model has estimation success for training and testing data as 0.925 and 0.922, in terms of R2 score, respectively. The estimated resonant frequencies using the trained model are compared with the studies in the literature, and an average percentage error (APE) of 0.933% is obtained.
İzmir Menemen Bölgesinin güneş ışınım tahmini için angström-prescott modeli temelli yaklaşımların değerlendirilmesi
(2022) Yıldırım, Hatice Başak
Öz Güneş enerjisi, sınırlı bir ömre sahip olan ve çevreye zarar veren fosil yakıtların yerini alacak önemli bir role ve potansiyele sahiptir. Güneş enerjili sistemleri uygun şekilde tasarlamak, projelendirmek, inşa etmek ve işletmek için güneş ışınımı bilgisi gereklidir. Güneş ışınımı tüm dünyada nadiren ölçülmekle birlikte, diğer meteorolojik parametreler hemen hemen tüm istasyonlarda ölçülmektedir. Güneş ışınımının ölçüldüğü istasyonların veri tabanlarında da boşluklar bulunmaktadır. Bu noktada güneş ışınımının tahmini önem kazanmaktadır. Literatürde güneş ışınımının güneşlenme süresine bağlı olarak hesaplandığı Angström-Prescott modeli ve bu modelin geliştirilmesi yaklaşımıyla oluşturulmuş farklı modeller bulunmaktadır. Bu çalışmada, Menemen ilçesinde küresel güneş radyasyonunu tahmin etmek için yıllık ve mevsimlik modeller oluşturulmuştur. Ayrıca Angström-Prescott yöntemi temelli modellerin güneş ışınım şiddeti tahmini amacıyla kullanımında hangi modelin daha etkin olduğu Menemen ilçesi için araştırılarak karşılaştırılmalar gösterilmiştir. Bu modelleri oluşturmak için MATLAB programı kullanılmıştır. Son olarak bu modeller ile hesaplanan global güneş ışınımı değerleri ve yer ölçümleri R2, SSE, RMSE gibi istatistiksel testler ve hata analizleri ile karşılaştırılmış, hesaplanan ve ölçülen değerler grafiklerle gösterilmiştir.
Frequency analysis of rounded shaped inductive metallic objects in waveguides via some PEC approximations and GSM method
(2022) Aydoğan, Ahmet
A hybrid method is proposed for the frequency analysis of rounded metallic objects inductively loaded in rectangular waveguides. The proposed method combines the efficiency of the generalized scattering matrix method (GSM) and the flexibility of the method of moments (MoM) and the fact that fields cannot exist inside perfect electric conductors. Metallic discontinuities are modelled as a dielectric medium with extreme conductivity and the volume is emptied except the surrounding area. The proposed method is tested against several structures including a band-pass filter composed of metallic rods and an arbitrarily shaped discontinuity. The accuracy of the method is compared to commercial software based on the finite element method. The proposed method is exclusively competent for the frequency analysis of rounded or arbitrarily shaped metallic discontinuities.
Novel saturated flow boiling heat transfer correlation for R32 refrigerant
(AIAA International, 2022) Turgut Oğuz Emrah; Asker Mustafa; Genceli Hadi; Çoban Mustafa Turhan
[No abstract available]
Parameter estimation of the wind speed distribution model by dragonfly algorithm
(Gazi Universitesi, 2023) Köse, Bayram; Aygün, Hilmi; Pak, Semih
In order to meet the increasing energy demand and to solve environmental problems, the interest in renewable energy sources continues with technology development studies and economic investments. Various methods are used to determine and estimate sustainable and renewable energy sources. Probability distribution functions are used in wind characterization and potential calculation of wind energy. In this study, the Dragonfly Algorithm (DA) is proposed to estimate the Weibull probability distribution function (Wpdf) parameters used in wind speed modeling and the two-component mixed Weibull distribution parameters used in modeling non-single peak wind speed data. The performance of the proposed method has been evaluated by comparing not only the classical methods which are the moment method (MM) and the least squares method (LSM) but also metaheuristic optimization algorithms which are Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Determination coefficient (R2) and root mean square error (RMSE) were used to evaluate the performance of these parameter estimation methods. Data obtained from 6 measurement stations were used in the study. According to the performance criteria, the two-component Weibull distribution was found to be more effective at all stations compared to the Weibull distribution model. It has been concluded that the proposed DA algorithm can be used effectively for parameter estimation in wind speed modeling. © 2023 Gazi Universitesi Muhendislik-Mimarlik. All rights reserved.
Multi-objective particle swarm optimization of the k-type shell and tube heat exchanger (case study)
(Yildiz Technical University, 2021) Nadi M.; Ehyaei M.A.; Ahmadi A.; Turgut, Oğuz Emrah
This paper investigated optimization of two objectives function include the total amount of heat transfer between two mediums and the total cost of shell and tube heat exchanger. The study was carried out for k-type heat exchanger of the cryogenic unit of gas condensates by multiple objective particle swarm optimization. Six decision variables including pipe pitch ratio, pipe diameter, pipe number, pipe length, baffle cut ratio, and baffle distance ratio were taking into account to conduct this simulation-based research. The results of mathematical modeling confirmed the actual results (data collected from the evaporator unit of the Tehran refinery’s absorption chiller). The optimization results revealed that the two objective functions of heat transfer rate and the total cost were in contradiction with each other. The results of the sensitivity analysis showed that with change in the pitch ratio from 1.25 to 2, the amount of heat transfer was reduced from 420 to 390 kW about 7.8%. Moreover, these variations caused reduction in cost function from 24,500 to 23,500 $, less than 1%. On the other hand, an increase in pipe length from 3 to 12 meters, the heat transfer rate raised from 365 to 415 kW by 13.7%, while the cost increased from 20,000$ to 24500$ about 22%. © 2021. All rights reserved.
Local search enhanced Aquila optimization algorithm ameliorated with an ensemble of Wavelet mutation strategies for complex optimization problems
(Elsevier, 2023) Turgut, Oğuz Emrah; Turgut, Mert Sinan
Aquila Optimization Algorithm (AQUILA) is a newly emerged metaheuristic optimizer for solving global optimization problems, which is based on intrinsic hunting behaviors of the foraging aquila individuals. However, this stochastic optimization method suffers from some algorithm-specific drawbacks, such as premature convergence to the local optimum points over the search hyperspace due to the lack of solution diversity in the population. To conquer this algorithmic deficiency, an ensemble of Wavelet mutation operators has been implemented into the standard AQUILA to enhance the explorative capabilities of the algorithm by diversifying the search domain as much as possible. Furthermore, a brand-new local search scheme empowered by the synergetic interactions of elite opposition-based learning and a simple-yet-effective exploitative manipulation equation is introduced into the base AQUILA to intensify on the previously visited promising regions. The proposed learning schemes are stochastically applied to the obtained solutions from the base Aquila algorithm to refine the overall solution quality and amend the premature convergence problem. It is also aimed to investigate whether the collective application of Wavelet mutation operators with different types entails a significant improvement in the general search effectivity of the algorithm rather than their individual efforts. Numerical experiments made on a suite of unconstrained unimodal and multimodal benchmark functions reveal that this hybridization with AQUILA has improved the general solution accuracy and stability to very high standards, outperforming its contemporary counterparts in the comparative statistical analysis. Furthermore, an exhaustive benchmark analysis has been performed on fourteen constrained real-world complex engineering problems.(c) 2022 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.
Chaotic quasi-oppositional arithmetic optimization algorithm for thermo-economic design of a shell and tube condenser running with different refrigerant mixture pairs
(Springer London Ltd, 2022) Turgut, Mert Sinan; Turgut, Oğuz Emrah; Abualigah, Laith
This theoretical research study proposes a novel Chaotic Quasi-Oppositional Arithmetic Optimization Algorithm (COAOA) for thermo-economic optimization of a shell and tube condenser working with refrigerant mixtures. Arithmetic Optimization Algorithm (AOA) is a recently emerged metaheuristic algorithm considering different mathematical operators to optimize the candidate solutions over a wide range of search domains. The effectiveness the COAOA is assessed by applying it to a set of benchmark optimization problems and comparing the obtained solutions with that of the original AOA and its quasi-oppositional variant. The COAOA has been employed to acquire the minimum value of the total annual cost of the shell and tube condenser by iteratively varying nine decision variables of mass flow rate, shell diameter, the tube inside diameter, tube length, number of tube passes, tube layout, tube pitch ratio, the total number of baffles, and diameter ratio. Three different case studies are solved using different refrigerant pairs used for in-tube flow to show the proposed metaheuristic optimizer's efficiency and effectivity on real-world mixed-integer optimization problem. Optimal results retrieved for different mixture pairs with varying mass fractions are compared with each other, and parametric configuration yielding the minimum total cost is decided. Finally, a comprehensive sensitivity analysis is performed to investigate the influences of the design variables over the considered problem objective. Overall analysis results indicate that COAOA can be an excellent optimizer to obtain a shell and tube condenser's optimal configuration within a reasonable computation time.
Dielectric measurement via partially filled and short-circuited circular waveguides
(Ieee, 2020) Aydoğan, Ahmet; Sağlam, Serkan; Akleman, Funda
In this study, the dielectric measurement of homogeneous materials via short-circuited circular waveguides is examined. The proposed method is numerically tested against homogeneous materials with varying permittivities, as well as in different shapes and positions. Synthetic data is generated via the moment method solution of the related integral equation. In the inversion algorithm, Newton-Raphson method with multi-frequency data is used. The complex part of the material is modeled in a linear fashion.
Brain tumor classification using the fused features extracted from expanded tumor region
(Elsevier Sci Ltd, 2022) Öksüz, Coşku; Urhan, Oğuzhan; Güllü, Mehmet Kemal
In this study, a brain tumor classification method using the fusion of deep and shallow features is proposed to distinguish between meningioma, glioma, pituitary tumor types and to predict the 1p/19q co-deletion status of LGG tumors. Brain tumors can be located in a different region of the brain, and the texture of the surrounding tissues may also vary. Therefore, the inclusion of surrounding tissues into the tumor region (ROI expansion) can make the features more distinctive. In this work, pre-trained AlexNet, ResNet-18, GoogLeNet, and ShuffleNet networks are used to extract deep features from the tumor regions including its surrounding tissues. Even though the deep features are extremely important in classification, some low-level information regarding tumors may be lost as the network deepens. Accordingly, a shallow network is designed to learn low-level information. Next, in order to compensate the information loss, deep features and shallow features are fused. SVM and k-NN classifiers are trained using the fused feature sets. Experimental results achieved on two publicly available data sets demonstrate that using the feature fusion and the ROI expansion at the same time improves the average sensitivity by about 11.72% (ROI expansion: 8.97%, feature fusion: 2.75%). These results confirm the assumption that the tissues surrounding the tumor region carry distinctive information. Not only that, the missing low-level information can be compensated thanks to the feature fusion. Moreover, competitive results are achieved against state-of-the-art studies when the ResNet-18 is used as the deep feature extractor of our classification framework.
COVID-19 detection with severity level analysis using the deep features, and wrapper-based selection of ranked features
(Wiley, 2021) Öksüz, Coşku; Urhan, Oğuzhan; Güllü, Mehmet Kemal
The SARS-COV-2 virus, which causes COVID-19 disease, continues to threaten the whole world with its mutations. Many methods developed for COVID-19 detection are validated on the data sets generally including severe forms of the disease. Since the severe forms of the disease have prominent signatures on X-ray images, the performance to be achieved is high. To slow the spread of the disease, effective computer-assisted screening tools with the ability to detect the mild and the moderate forms of the disease that do not have prominent signatures are needed. In this work, various pretrained networks, namely GoogLeNet, ResNet18, SqueezeNet, ShuffleNet, EfficientNetB0, and Xception, are used as feature extractors for the COVID-19 detection with severity level analysis. The best feature extraction layer for each pre-trained network is determined to optimize the performance. After that, features obtained by the best layer are selected by following a wrapper-based feature selection strategy using the features ranked based on Laplacian scores. The experimental results achieved on two publicly available data sets including all the forms of COVID-19 disease reveal that the method generalized well on unseen data. Moreover, 66.67%, 90.32%, and 100% sensitivity are obtained in the detection of mild, moderate, and severe cases, respectively.
Ensemble-LungMaskNet: Automated lung segmentation using ensembled deep encoders
(Institute of Electrical and Electronics Engineers Inc., 2021) Öksüz, Coşku; Urhan, Oğuzhan; Güllü, Mehmet Kemal
Automated lung segmentation has importance because it gives clues about several diseases to the experts. It is the step that comes before further detailed analyses of the lungs. However, segmentation of the lungs is a challenging task since the opacities and consolidations are caused by various lung diseases. As a result, the clarity of the borders of the lungs may be lost which makes the segmentation task difficult. The presence of various medical equipment such as cables in the image is another factor that makes segmentation difficult. Therefore, it is a necessity to develop methods that can handle such situations. Learning the most useful patterns related to various diseases is possible with deep learning methods. Unlike conventional methods, learning the patterns improves the generalization ability of the models on unseen data. For this purpose, a deep segmentation framework including ensembles of pre-trained lightweight networks is proposed for lung region segmentation in this work. The experimental results achieved on two publicly available data sets demonstrate the effectiveness of the proposed framework. © 2021 IEEE.
Field programmable gate arrays implementation of two-point non-uniformity correction and bad pixel replacement algorithms
(Institute of Electrical and Electronics Engineers Inc., 2021) Njuguna, J.C.; Alabay, E.; Çelebi, A.; Çelebi, A.; Güllü, Mehmet Kemal
In this paper, the hardware architecture for two-point non-uniformity correction (TPNUC) and bad pixel replacement (BPR) algorithms are presented based on field-programmable gate arrays (FPGA) for infrared focal plane arrays (IRFPA). An efficient hardware architecture modeled using C++ in the High-Level Synthesis (HLS) tool is presented. The design is tested on an FPGA fabricated at a 16 nm technology node. The design achieves a maximum frequency of 300 MHz and one pixel per clock. A thermal camera development platform (FullScale USB3A) with a resolution of 640×480 is used as the source for the raw video. The simulation results from MATLAB and FPGA posed close similarities. © 2021 IEEE.
Almost global stability of nonlinear switched systems with mode-dependent and edge-dependent average dwell time
(Elsevier Sci Ltd, 2021) Kıvılcım, Ayşegül; Karabacak, Özkan; Wisniewski, Rafael
It has recently been shown that almost global stability of nonlinear switched systems can be characterized using multiple Lyapunov densities. This has been accomplished for switched systems subject to a minimum dwell time or an average dwell time constraint. In this paper, as an extension of the aforementioned results, we provide a sufficient condition on mode-dependent and edge-dependent average dwell time to ensure almost global stability of a nonlinear switched system. The relations between average dwell time, mode-dependent, and edge-dependent average dwell time have been discussed. The obtained results for nonlinear switched systems imply the existing results for linear switched systems. (C) 2021 Elsevier Ltd. All rights reserved.
Almost global stability of nonlinear switched system with stable and unstable subsystems
(IEEE, 2020) Kıvılcım, Ayşegül; Karabacak, Özkan; Wisniewski, Rafael
This paper presents sufficient conditions for almost global stability of nonlinear switched systems consisting of both stable and unstable subsystems. Techniques from the stability analysis of switched systems have been combined with the multiple Lyapunov density approach - recently proposed by the authors for the almost global stability of nonlinear switched systems composed of stable subsystems. By using slow switching for stable subsystems and fast switching for unstable subsystems lower and upper bounds for mode-dependent average dwell times are obtained. In addition to that, by allowing each subsystem to perform slow switching and using some restrictions on total operation time of unstable subsystems and stable subsystems, we have obtained a lower bound for an average dwell time.
MultiTempLSTM: prediction and compression of multitemporal hyperspectral images using LSTM networks
(Spie-Soc Photo-Optical Instrumentation Engineers, 2021) Karaca, Ali Can; Güllü, Mehmet Kemal
Since multitemporal hyperspectral imaging has an excellent ability to observe the Earth's surface over time, it has been used for various remote sensing applications. On the other hand, multitemporal hyperspectral images (HSIs) contain HSI sequences acquired multiple times over the same scene, resulting in large amounts of data. Conventional HSI compression methods cannot benefit from temporal correlation, which can be very high, depending on the acquisition cycle. We propose a prediction and compression framework that directly considers temporal correlation for the compression of HSIs. The main objective of the proposed method is to predict each spectral signature in the target HSI from the corresponding spectral signature of the reference HSI using a long short-term memory network model that supports clustering. Then, the residual image between the predicted HSI and the target HSI is quantized and entropy encoded for the compression purpose. The experiments are conducted on a ground-based multitemporal dataset named Noguiero, which contains nine HSIs, in terms of prediction and compression performances. Experiments show that the proposed method not only provides the best quality metrics from the perspective of prediction but also has convincing compression performances compared to the other methods. (C) 2021 Society of Photo-Optical Instrumentation Engineers (SPIE)
Almost global stability of nonlinear switched systems with time-dependent switching
(Institute of Electrical and Electronics Engineers Inc., 2020) Karabacak, Özkan; Kıvılcım, Ayşegül; Wisniewski, Rafael
For a dynamical system, it is known that the existence of a Lyapunov density implies almost global stability of an equilibrium. It is then natural to ask whether the existence of multiple Lyapunov densities for a nonlinear switched system implies almost global stability, in the same way as the existence of multiple Lyapunov functions implies global stability for nonlinear switched systems. In this paper, the answer to this question is shown to be affirmative as long as switchings satisfy a dwell time constraint with an arbitrarily small dwell time. Specifically, as the main result, we show that a nonlinear switched system with a minimum dwell time is almost globally stable if there exist multiple Lyapunov densities that satisfy some compatibility conditions depending on the value of the minimum dwell time. This result can also be used to obtain a minimum dwell time estimate to ensure almost global stability of a nonlinear switched systems. In particular, the existence of a common Lyapunov density implies almost global stability for any arbitrary small minimum dwell time. The results obtained for continuous-time switched systems are based on some sufficient conditions for the almost global stability of discrete-time nonautonomous systems. These conditions are obtained using the duality between Frobenius-Perron operator and Koopman operator. © 1963-2012 IEEE.
MultiTempGAN: Multitemporal multispectral image compression framework using generative adversarial networks
(Academic Press Inc Elsevier Science, 2021) Karaca, Ali Can; Kara, Ozan; Güllü, Mehmet Kemal
Multispectral satellites that measure the reflected energy from the different regions on the Earth generate the multispectral (MS) images continuously. The following MS image for the same region can be acquired with respect to the satellite revisit period. The images captured at different times over the same region are called multitemporal images. Traditional compression methods generally benefit from spectral and spatial correlation within the MS image. However, there is also a temporal correlation between multitemporal images. To this end, we propose a novel generative adversarial network (GAN) based prediction method called MultiTempGAN for compression of multitemporal MS images. The proposed method defines a lightweight GAN-based model that learns to transform the reference image to the target image. Here, the generator parameters of MultiTempGAN are saved for the reconstruction purpose in the receiver system. Due to MultiTempGAN has a low number of parameters, it provides efficiency in multitemporal MS image compression. Experiments were carried out on three Sentinel-2 MS image pairs belonging to different geographical regions. We compared the proposed method with JPEG2000-based conventional compression methods and three deep learning methods in terms of signal-tonoise ratio, mean spectral angle, mean spectral correlation, and laplacian mean square error metrics. Additionally, we have also evaluated the change detection performances and visual maps of the methods. Experimental results demonstrate that MultiTempGAN not only achieves the best metric values among the other methods at high compression ratios but also presents convincing performances in change detection applications.
Determination of freezing of gait with wearable sensors in patients with Parkinson's disease
(Wiley, 2020) Eliiyi, Uğur; Keskinoğlu, Pembe; Kahraman, Turhan; Özkurt, Ahmet; Yürdem, Betül; Duran, G.; Genç, Aslı
Objective: The aim was to determine freezing of gait (FOG) with wearable sensors in patients with Parkinson’s disease (PD). Background: PD is a neurodegenerative disorder leading to deficits in automatic motor performance. FOG is a major mobility problem for patients with PD, can be accompanied by postural instability and subsequent falls. Accurate and automatic FOG detection are essential for long-term symptom monitoring or preventing FOG via cueing. Although some studies have investigated the use of wearable sensors to detect FOG, conducted mostly with participants who were mainly in early stages of PD, there is no firm consensus regarding appropriate methodologies. Method: This study had a diagnostic accuracy design. Multi-segmental acceleration data was obtained from 12 patients with PD performing standardized tasks, and clinical assessment of FOG was performed by an experienced neurologist in real time and from video recordings. Three-axis wireless accelerometers were attached to patients’ ankles, waist and wrists. Trials were performed during the drug-free period, at least 12 hours after taking medication. The standardized tasks included standing from a chair, walking, 180o and 360o turnings, and passing a doorway. Trials were repeated at least 3 times, and up to 5 times if there was no FOG event. Sensor signals were processed as numerical data. Results: The mean age of patients was 64 years, 58% of them were males, and mean of disease duration was 10 years. Modified Hoehn and Yahr scale scores ranged from 2.5 to 4. The data matrix dimension is 103,261*18. Random forest (RF), artificial neural network (ANN), and decision tree (DT) methods, which are among the supervised learning algorithms, were used to predict FOG in the presence of misleading tremors on this big data. Algorithms’ performances were AUCRF/ANN/DT= 0.985 / 0.962 / 0.765 and sensitivityRF/ANN/DT= 97.1% / 94.3% / 93.8%. Conclusion: The predictions of ANN and RF were much better, while the sensitivity of DT was close to other methods. FOG detection is important to prevent it before occurring and decrease its effects. In this study, it has been shown that FOG can be detected by using the proposed algorithms with data collected from wearable sensors in patients with PD, even who are in late stages of PD.
A channel selection method for epilepsy seizure prediction
(Institute of Electrical and Electronics Engineers Inc., 2021) Coşgun, Erman; Çelebi, A.; Güllü, Mehmet Kemal
The development of systems that can predict epilepsy seizures in real time offers great hope for epilepsy patients. These systems aim to prevent accidents that patients may experience due to loss of consciousness during seizures. Therefore, systems that can predict epileptic seizures should both work in real time and be designed to maintain the daily activities of the patient. In this case, a system with as few electrodes as possible should be developed. In this study, it is aimed to choose the most appropriate electrode in predicting epileptic seizures. Channel selection is made according to two parameters and its effect on seizure prediction is examined. The first parameter is the difference in variance between preictal and interictal; The other parameter is the weighted average sensitivity (WAS). The Rusboosted Tree ensemble classification is used to calculate WAS. The prediction process is carried out with the method we proposed in the previous study. For performance evaluation, prediction accuracy, sensitivity (SEN) and false alarm rates per hour (FPR) are calculated. The prediction performance for the channel selected according to the variance difference results are 69%, 70.9% and 0.054 respectively and the for the channel selected according to WAS results are 69%, 71.8% and 0.031 respectively. © 2021 IEEE.

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