Person: YİĞİT, ENES
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YİĞİT
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ENES
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Publication Hybrid nanoparticles embedded polyvinyl butyral nanocomposites for improved mechanical, thermal and microwave absorption performance(Sage Publications, 2021-08-13) Akman, Erdi; Sönmezoğlu, Savaş; Yiğit, Enes; Eskizeybek, Volkan; Avcı, Ahmet; YİĞİT, ENES; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Elektrik Elektronik Mühendisliği Bölümü.; 0000-0002-0960-5335; JFJ-3503-2023Polymer-based nanocomposites have been broadly investigated to improve its specific properties such as thermal and mechanical properties to use in different application areas. In this study, we aimed to ameliorate the desired physical properties of polyvinyl butyral (PVB) by introducing various amounts of silver (Ag) and cobalt (Co) nanoparticles (NPs) in the polymer matrix. The arc-discharge method submerged in liquid nitrogen was performed to synthesize the metal NPs. To produce hybrid nanocomposites, we demonstrated embedding Ag:Co nanoparticles in the PVB matrix via easy/low-cost solution casting process without any additional materials. In the results of analysis for nanocomposites, it was observed that there were improvements in thermal, mechanical and microwave absorption characteristics of the PVB polymer with interaction of NPs with the polymer. As a result of these interactions, the hybridization of PVB with the metal NPs resulted in the improved thermal stability since the glass transition temperature was increased from 45.6 to 55.1 degrees C. Besides, while the tensile strength (sigma) of the bare PVB film was calculated as 20.52 MPa, the strength of the corresponding tensile strength (sigma) of 1.0 wt.% Ag:Co nanocomposite film was improved to 43.41 MPa. Moreover, in order to determine the effect of these changes on the radar absorption feature of nanocomposites, one-dimensional A-Scan measurements were performed on 2-18 GHz frequency band. In the results, it was observed that 1.0%.wt Ag:Co nanocomposite film absorbed approximately 90% of the incoming energy. The characterization results revealed that a positive synergetic effect raised in the case of the modification of the PVB matrix with both Ag and Co NPs. In the light of these data, it was understood that the characteristics of PVB were improved with the NPs combining, and the usage area of that will also increase thanks to this improvement. These regenerated properties made the hybrid nanocomposite a promising substrate material with considerable potential applications for various transparent, flexible, and portable surface coatings.Publication Determination of the amount of grain in silos with deep learning methods based on radar spectrogram data(IEEE-inst Electrical Electronics Engineers Inc, 2021-01-01) Duysak, Hüseyin; Özkaya, Umut; Yiğit, Enes; YİĞİT, ENES; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Elektrik Elektronik Mühendisliği Bölümü; 0000-0002-0960-5335; JFJ-3503-2023Since the grain is a crucial food source, the determination of the quantity of stored grain in silos is inevitable in terms of commercial and correct inventory planning. In this study, a convolutional neural network (CNN) is developed to determine grain quantity using the spectrograms of the radar backscattering data. The radar backscattering signals of different amounts of grain for different grain surface condition types are collected using a stepped frequency continuous-wave radar system. In the scaled model silo, a total of 5681 measurements are carried out for grain stacks with different surface patterns and different weights (0-20 kg). Then, the dataset is constituted by using the spectrograms of these radar measurements. Randomly selected 4261 data corresponding to 75% of the dataset are used for training and the remaining 1420 data are used for testing. The proposed method is compared with pretrained CNN. Accuracy of the methods is given with metric parameters for both classification and regression. The classification task results of the proposed method are obtained as 98.45% accuracy, 98.15% sensitivity, 99.07% specitivity, 98.77% precision, 98.45% F1-Score, and 97.62% Matthews correlation coefficient. The regression task results are calculated as 0.3228 mean absolute error, 0.5150 mean absolute percentage error (MAPE), 0.9649 mean squared error, and 0.9823 root-mean-squared error. The proposed method is also compared with previous studies in the literature (with 3.29 MAPE) and its superiority is demonstrated with metric parameters. The results point out that, if CNN is properly modeled and trained, the combination of CNN and proper signal processing can provide effective results in the quantity measurement applications of the grain stacks.Publication Efficient multitask learning analyses on grain silo measurement(Spie-soc Photo-optical Instrumentation Engineers, 2021-08-03) Özkaya, Umut; Duysak, Hüseyin; Yiğit, Enes; YİĞİT, ENES; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Elektrik-Elektronik Mühendisliği; 0000-0002-2748-0660; ACC-3432-2022; JFJ-3503-2023Determining the amount of grain stored in silos is very important for accurate commercial inventory planning. A convolutional neural network (CNN) is developed for the first time to determine the amount of the grain using step-frequency continuous wave radar (SFCWR) signals. The radar reflection signal of different grain quantity for different grain surface patterns is gathered by means of a constructed experimental setup. 5681 measurements are performed in the scaled model silo containing different weights (0 to 20 kg) grain stacked as different surface patterns. The dataset is then created using the spectrograms of SFCWR signals. While 1420 data randomly selected from the dataset are used for testing, the remaining 4261 data are used for training. The results are then compared with the pretrained CNNs, demonstrating the superiority of the proposed method. The accuracy of the methods is given with metric parameters for both classification and regression. The proposed multitask CNN model obtained higher performance with 0.2865 MAE, 0.5053 MAPE, 0.8047 MSE, and 0.8971 RMSE for regression task and 99.23% accuracy, 99.09% sensitivity, 99.52% specitivity, 99.42% precision, 99.25% F1-score, and 98.83% MCC for classification. These metric performances are better than the previous study with 3.29 MAPE in the literature. The results obtained reveal that, with proper modeling and successful training, CNNs can be effectively used for the quantity measurement applications of the grain stacks. (C) 2021 Society of Photo-Optical Instrumentation Engineers (SPIE)Publication A machine learning approach for the estimation of photocatalytic activity of ald zno thin films on fabric substrates(Elsevier Science Sa, 2023-11-03) Akyıldız, Halil I.; Yiğit, Enes; YİĞİT, ENES; Islam, Shafiqul; Arat, Asife B.; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Elektrik ve Elektronik Mühendisliği Bölümü.; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Tekstil Mühendisliği Bölümü.; 0000-0002-0960-5335; 0000-0002-3290-1386; JFJ-3503-2023; AAQ-2513-2021Research in the field of photocatalytic wastewater treatment is striving to enhance catalyst materials to achieve high-performance systems. A promising approach to this goal has been immobilizing photocatalytic materials on fibrous substrates via atomic layer deposition (ALD). Nevertheless, both the ALD process and the assessment of photocatalytic performance involve a multitude of parameters necessitating thorough investigation. In this study, we employ popular machine-learning algorithms, including Support Vector Regression (SVR) and Artificial Neural Networks (ANN), to predict the photocatalytic activity of ALD-coated textiles. The photocatalytic activity is evaluated through methylene blue and methyl orange degradation tests. Machine learning algorithms are tested and trained using the k-fold cross-validation technique. The findings demonstrate that the ANN and SVR methods utilized in this research can predict catalytic activity with mean absolute percentage errors (MAPE) of 2.35 and 3.25, respectively. This study illuminates that, within the defined range of process parameters, the photocatalytic activity of ALD-coated textiles can be precisely estimated with suitable machine-learning algorithms.Publication Conical differential range based back-projection algorithm for concealed object detection with three-dimensional mmw imaging(Int Information & Engineering Technology Assoc, 2022-12-01) Duysak, Hüseyin; Seyfi, Levent; Yiğit, Enes; YİĞİT, ENES; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi.; 0000-0002-2748-0660; JFJ-3503-2023Millimeter wave(mmW) imaging has spread to a wide range of applications in the last quarter. One of the most important research areas of mmW is three-dimensional (3D) imaging systems. In this study, conical differential range-based back-projection (BP) algorithm is proposed for three-dimensional mmW imaging. In the algorithm, the differential range is created using points inside a conical volume, thus the number of interpolation points is considerably reduced. The performance of the algorithm is demonstrated by simulation and experimental studies. Cylindrical scanning is carried out by means of the experimental setup. Experiments are carried out at frequencies of 26.5-40 GHz. The traditional BP algorithm (BPA) and the proposed algorithm are used to reconstruct the images. With the proposed method, it is observed that ISLR for the point target increased by about 5 dB compared to the traditional method. Moreover, the computational complexity is reduced by up to 10 times, depending on the imaging area. Thanks to the proposed method, the image of the concealed weapon under the cloth in an experimental study is more clearly focused compared to the traditional method. Therefore, it can provide images that give more accurate results for applications such as automatic target detection methods.Publication Estimation of rain parameters for microwave backscattering model using PSO(Elsevier, 2023-03-23) Ermiş, Seda; Yiğit, Enes; YİĞİT, ENES; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Elektrik-Elektronik Mühendisliği Bölümü; 0000-0002-0960-5335; JFJ-3503-2023The intention of the geophysical modelling of rain is to provide a better explanation for the effect of rainfall to the microwave backscattering and thus to interpret radar measurements. However, in the model, physical characteristic of raindrops should be estimated primarily and accurately by considering observation system, measurements and suitable rain rate retrieval algorithms to calculate backscattering coefficients from rainfall. In this study, a geophysical microwave backscattering model of rain type precipitation over sea surface is con-structed by using Particle Swarm Optimization (PSO) algorithm in the multilayered Vector Radiative Transfer (VRT) model to estimate vertical profile of rain by using GPM DPR data. Rain column is partition into sublayers and for each sublayer, physical properties of raindrops such as drop radius, water volume fraction or layer thickness are estimated by using PSO to provide the best fit with measurements by searching within certain limits defined by rain rate. Backscattering coefficients from entire rain is provided by the solution of VRT equations via Matrix Doubling Method to consider multilayer effect. Results show that, vertical profile of rain parameters can be estimated accurately for moderate /high rain rates (up to 11-12 mm/h) by using presented model.Publication Determination of flowing grain moisture contents by machine learning algorithms using free space measurement data(Ieee-inst Electrical Electronics Engineers Inc, 2022-01-01) Duysak, Hüseyin; Yiğit, Enes; YİĞİT, ENES; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Elektrik ve Elektronik Mühendisliği Bölümü.; JFJ-3503-2023The measurement of the moisture content of the stored grain in the silos provides the opportunity to take the necessary precautions to store the grain without spoiling. Since it is not possible to obtain all the moisture information of the stored grain with the current methods, in this study, a new method is proposed to determine the moisture content of the grain in real time during the loading processes. For this purpose, popular machine learning (ML) algorithms, i.e., KNN, SVR, and ANN, are used to predict the moisture content of the flowing grain. In order to measure the moisture content of the grain, a free-space electromagnetic measurement setup is constructed. Reflection and transmission coefficients are measured at 103 different frequency points between 1 and 2.48 GHz using a vector network analyzer (VNA) for three different grain types (Bulgur wheat, durum wheat, and corn silage kernel) with moisture content varying between 8% and 25%. In this way, three datasets are constituted as datasets 1-3 corresponding to each grain type. The k-fold cross-validation (k-CV) technique is used to train and test the ML algorithms and the performance of the algorithms is evaluated with five different metrics. In addition, for each grain type, the error rates corresponding to each moisture content are evaluated separately and the relationship between moisture content and performance of algorithms is revealed. While the best results are obtained with KNN for durum wheat and corn silage kernel, SVR method gives the best results for bulgur wheat. This study reveals that the moisture content of flowing grain can he determined, thanks to proper modeling of ML algorithms and measurement setup.Publication Automatic soliton wave recognition using deep learning algorithms(Pergamon-Elsevier Science Ltd, 2023-07-17) Aksoy, Abdullah; Yiğit, Enes; AKSOY, ABDULLAH; YİĞİT, ENES; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Elektrik-Elektronik Mühendisliği Bölümü.; AAH-3945-2021; JFJ-3503-2023In this study, deep learning (DL) based wave classification is performed to automatically recognize the soliton waves. Different experiments using non-linear transmission lines (NLTLs) are performed and the signal images obtained from the experiments are recorded. To demonstrate the applicability of the soliton wave in different scenarios, the waves are generated in different devices, under different noise conditions, and in various environments. Based on the images obtained from the experiments, four different classes consisting of sine, square, triangle, and soliton waves are created. 225 different images belonging to each classes are created and thus a total of 900 different image data are obtained. Five popular DL algorithms, namely DenseNet201, VGG16, VGG19, Xception, and ResNet152, are used to train and test the data. The DenseNet201 algorithm showed the best performance with 0.9904 training accuracy, 0.9630 validation accuracy, and 0.9778 test results. Thus, soliton waves are easily separated from other waveforms such as square, triangle, and sine. These results clearly demonstrate the feasibility of using DL algorithms to automatically recognize the soliton waves, which can have significant implications in various fields such as telecommunications, optics, nonlinear electronics, and nonlinear physics.Publication Investigation of the performance of different wavelet-based fusions of sar and optical images using sentinel-1 and sentinel-2 datasets(Selçuk Üniversitesi Yayınları, 2022-02-01) Duysak, Hüseyin; Yiğit, Enes; YİĞİT, ENES; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Elektrik Elektronik Mühendisliği; 0000-0002-0960-5335; JFJ-3503-2023In this study, the fusion of optical and synthetic aperture radar images with wavelet transform was investigated. Images are obtained from Sentinel-1 and sentinel-2 satellites. Images were decomposed by wavelet transform. The four main coefficients were obtained for different wavelet packages and up to ten decomposition levels. The coefficients were combined taking the maximum, minimum or mean. 1710 Fused images were obtained for all possible combinations in terms of different wavelet packets, decomposition levels and fusion rules. Fused images were evaluated according to the structural similarity index (SSI). It was seen that the missing regions in the optical images were improved in the fused images with the appropriate wavelet packets and highest SSI.Publication Ann-based estimation of mems diaphragm response: An application for three leaf clover diaphragm based fabry-perot interferometer(Elsevier Sci Ltd, 2022-06-28) Hayber, Sekip Esat; YİĞİT, ENES; Aydemir, Umut; AYDEMİR, UMUT; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Elektrik ve Elektronik Bölümü.; 0000-0001-5396-4610; AGY-4584-2022; JFJ-3503-2023In this study, an artificial neural network (ANN) based model is developed for MEMS diaphragm analysis, which does not require difficult and time-consuming FEM processes. ANN-based estimator is generated for static pressure response (d) and dynamic pressure response (f) analysis of TLC (three leaf clover) diaphragms for FabryPerot interferometers as an example. TLC is one of the unsealed MEMS design diaphragms formed by three leaves of equal angles. The diaphragms used to train ANNs are designed with SOLIDWORKS and analyzed with ANSYS. A total of 1680 TLC diaphragms are simulated with eight diaphragm parameters (3 for SiO2 material, 4 for geometry, and 1 for pressure) to create a data pool for ANN's training, validation, and testing processes. 80% of the data is used for training, 15% for validation, and the remaining for testing. Only four geometric parameters are used as input in the ANN estimator, and the material parameters are added to the model with an analytical multiplier. Thus, network models that estimate d and f values for all kinds of diaphragm materials are proposed, with a material-independently trained ANN structure. The performance of the ANN model is compared with the empirical equation suggested in the literature, and its superiority is demonstrated. In addition, the d and f parameters of TLC diaphragms designed with five different materials (Si, In2Se3, Ag, EPDM, Graphene) are estimated to be very close to the real ones. By using the proposed method, analyses of TLC diaphragms are quickly performed without the need for time-consuming and costly design and analysis programs.