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Öğe 3D residual spatial-spectral convolution network for hyperspectral remote sensing image classification(Springer London Ltd, 2023) Firat, Huseyin; Asker, Mehmet Emin; Bayindir, Mehmet Ilyas; Hanbay, DavutHyperspectral remote sensing images (HRSI) are 3D image cubes that contain hundreds of spectral bands and have two spatial dimensions and one spectral dimension. HRSI analysis are commonly used in a wide variety of applications such as object detection, precision agriculture and mining. HRSI classification purposes to assign each pixel in HRSI to a unique class. Deep learning is seen as an effective method to improve HRSI classification. In particular, convolutional neural networks (CNNs) are increasingly used in remote sensing field. In this study, a hybrid 3D residual spatial-spectral convolution network (3D-RSSCN) is proposed to extract deep spatiospectral features using 3D CNN and ResNet18 architecture. Simultaneously spatiospectral features extraction is provided using 3D CNN. In deeper CNNs, ResNet architecture is used to achieve higher classification performance as the number of layers increases. In addition, thanks to the ResNet architecture, problems such as degradation and vanishing gradient that may occur in deep networks are overcome. The high dimensionality of the HRSIs increases the computational complexity. Thus, most of studies apply dimension reduction as preprocessing. In the proposed study, principal component analysis (PCA) is used as the preprocessing step for optimum spectral band extraction. The proposed 3D-RSSCN method is tested with Indian pines, Pavia University and Salinas datasets and compared against various deep learning-based methods (SAE, RPNet, 2D CNN, 3D CNN, M3D CNN, HybridSN, FC3D CNN, SSRN, FuSENet, S3EResBoF). As a result of the applications, the best classification accuracy among these methods compared in all datasets is obtained with the proposed 3D-RSSCN. The proposed 3D-RSSCN method has the best accuracy and time performance in classifying.Öğe Automated Fault Classification in Solar Panels Using Transfer Learning with EfficientNet and ResNet Models(Hibetullah KILIÇ, 2024) Akınca, Rojbin; Fırat, Hüseyin; Asker, Mehmet EminClassifying and detecting faults in solar panels using deep learning methods is crucial to ensuring their efficiency and longevity. In this study, we propose a model that concatenates ResNet and EfficientNet to classify faults in solar panel images. ResNet's advantage lies in its residual connections, which help mitigate the vanishing gradient problem and improve training of deep networks. EfficientNet is known for its scalability and efficiency, providing a balanced trade-off between accuracy and computational cost by optimizing network depth, width, and resolution. Together, these models enhance fault classification accuracy while maintaining efficiency. To evaluate the performance of the proposed model, experimental studies were conducted using a solar panel dataset with six classes: bird-drops, covered snow, dusty, clean, electrical and physical damage on the surfaces of solar panels. The results demonstrated that the ResNet101 + EfficientNetB1 concatenation achieved superior performance, with an accuracy of 87.55%, F1-score of 88.13%, recall of 88.75%, and precision of 87.92%. This concatenation provided significant improvements in fault classification metrics compared to individual models.Öğe Changes in the Electrical Output Power and Efficiency of a Photovoltaic Panel Cooled by a Hybrid Method(Hibetullah KILIÇ, 2023) Karaozan, Ömer; Asker, Mehmet EminDuring the process of generating electrical energy from photovoltaic panels, high ambient temperatures and radiation tend to cause excessive heating of the photovoltaic panel, resulting in a decrease in its efficiency. In this experimental study, two cooling methods were employed. The first method involved active cooling using water, while the second method combined active cooling with passive cooling using an aluminum heat sink, all while using water as the cooling medium. The experiment involved the analysis of changes in electrical output power and efficiency from three identical 100 W monocrystalline photovoltaic panels, one of which served as the reference. The first panel was considered the reference panel. The second panel featured active cooling, with a liquid reservoir created on its rear surface to be filled with transformer oil. Copper pipes were placed at specific intervals within this liquid reservoir, and the rear surface was covered with a thin flat metal plate. The third panel was prepared for the hybrid method, featuring a liquid reservoir covered with a rectangular finned aluminum heat sink, distinct from the second panel. In both methods, transformer oil was used for electrical insulation and thermal conduction between the panel and the copper pipes at the rear. The copper pipes were connected to an automotive radiator and a pump to form a closed circuit. The water inside the radiator was cooled using a radiator fan and circulated by a pump. In the first method, active cooling was achieved by cooling through the radiator, while in the hybrid method, active cooling through the radiator was combined with passive cooling using the rectangular finned aluminum heat sink. In the experiment setup, temperature and liquid flow were measured using radiation, electrical sensors, and other measuring instruments. The data obtained from the measurements were used to compare the increases in electrical power and efficiency of the panels. The electrical power increase and efficiency were calculated as follows: in the hybrid method, it was found to be 4.7% and 0.84%, respectively, while in the active method, it was 2.94% and 0.52%, respectively. The energy consumed in the study was provided by wind energyÖğe Chaotic analysis of the gloabal solar irradiance(Institute of Electrical and Electronics Engineers Inc., 2017) Yılmaz, Musa; Gümüş, Bilal; Kılıç, Heybet; Asker, Mehmet Emin; 0000-0002-2306-6008; 0000-0002-6119-0886The use of solar energy is increasing for power generation and other uses. In order to meet these demands and make better predictions, it is necessary to understand and explain the dynamic^ of the solar parameters. Nonlinear dynamics and associated tools can provide better results in the analysis. The meteorological events are based time series and has a dynamic chacteristic therefore this paper proposes a different approach to analysis of solar parameter That is called chaotic analysis of the solar parameters such as global solar irradiance(GSI) based on times series approach. The chaotic behavior of global solar irradiation and sunshine duration are tested by phase spaces and Lyapunov Exponents. It is crucial to measure and analysis with a high accuracy solar parameters to benefit maximally form a specific region solar energy potential. In the application, four solar irradltion sites are considered from different solar energy potential locations in Turkey, namely, at Diyarbak,r, Gaziante^ Batman and Mardin cities. © 2017 IEEE.Öğe Classification of hyperspectral remote sensing images using different dimension reduction methods with 3D/2D CNN(Elsevier, 2022) Fırat, Hüseyin; Asker, Mehmet Emin; Hanbay, DavutThe high dimensionality of hyperspectral remote sensing images (HRSI) affects the classification performance. Therefore, most HRSI classification methods use dimension reduction methods as a solution for high dimensionality. It is aimed to extract useful features with dimension reduction methods. At the end of this process, the data dimension is reduced and the transaction cost is decreased. In this study, LDA, PCA, IPCA, ICA, SPCA, RPCA and SVD dimension reduction methods were applied as a preprocessing step to improve HRSI classification performance. Since HRSI is volumetric data and has a spectral dimension, 2D CNN cannot extract good distinguishing features from spectral dimensions. Because 2D CNN only considers spatial information. With 3D CNN, spectral-spatial features are extracted simultaneously. However, 3D CNN increases the computational cost. Therefore, in this study, Hybrid 3D/2D CNN method is used together with dimension reduction methods. Hybrid CNN method consists of a combination of 3D CNN, 2D CNN and depthwise separable convolution. While 3D CNN extracts common spectral-spatial features, more spatial features are learned with 2D CNN used after 3D CNN. With depthwise separable convolution, it reduces the number of parameters and overfitting is prevented. The applications performed on the frequently used HRSI benchmark datasets show that the classification performance of the proposed method is better than the compared methods. In addition, Indian pines, HyRANK-Loukia, Botswana and Pavia of University datasets are used to examine the effect of dimension reduction methods used together with the hybrid 3D/2D CNN method on classification performance. As a result of the applications, the best classification accuracies were obtained in PCA, LDA and IPCA with Indian pines, PCA with Pavia of university, PCA and IPCA with Salinas, PCA, RPCA and LDA dimension reduction methods with HyRANK-Loukia.Öğe Effect on model performance of regularization methods(Dicle Üniversitesi Mühendislik Fakültesi, 2021) Budak, Cafer; Mençik, Vasfiye; Asker, Mehmet EminArtificial Neural Networks with numerous parameters are tremendously powerful machine learning systems. Nonetheless, overfitting is a crucial problem in such networks. Maximizing the model accuracy and minimizing the amount of loss is significant in reducing in-class differences and maintaining sensitivity to these differences. In this study, the effects of overfitting for different model architectures with the Wine dataset were investigated by Dropout, AlfaDropout, GausianDropout, Batch normalization, Layer normalization, Activity normalization, L1 and L2 regularization methods and the change in loss function the combination with these methods. Combinations that performed well were examined on different datasets using the same model. The binary cross-entropy loss function was used as a performance measurement metric. According to the results, the Layer and Activity regularization combination showed better training and testing performance compared to other combinations.Öğe Hiperspektral görüntü sınıflandırması için derinlemesine ayrılabilir evrişim tabanlı artık ağ mimarisi(Gazi Üniversitesi, 2022) Fırat, Hüseyin; Asker, Mehmet Emin; Hanbay, DavutHiperspektral uzaktan algılama görüntüleri (HUAG), yüzlerce spektral bant içeren ve iki uzamsal-bir spektral boyuta sahip 3B görüntü küpleridir. Sınıflandırma, HUAG’de en popüler konulardan biridir. Son yıllarda HUAG sınıflandırması için çok sayıda derin öğrenme yöntemi önerilmiştir. Özellikle Evrişimli Sinir Ağları (ESA), HUAG'lerin sınıflandırılmasında yaygın olarak kullanılmaktadır. ESA, daha yüksek kaliteli HUAG sınıflandırması için daha ayırt edici özellikler sağlayabilen güçlü bir özellik öğrenme yeteneğine sahiptir.Bu çalışma kapsamında 3B/2B ESA, Artık ağ mimarisi ve Derinlemesine ayrılabilir evrişimin birlikte kullanıldığı bir yöntem önerilmiştir. Daha derin ESA'larda, katman sayısı arttıkça daha yüksek sınıflandırma performansı elde etmek için artık ağ kullanılmaktadır. Ayrıca artık ağ sayesinde derin ağlarda oluşabilecek bozulma ve gradyanların yok olması gibi sorunların üstesinden gelinmektedir. Öte yandan, hesaplama maliyetini azaltan, aşırı öğrenmeyi önleyen ve daha fazla uzamsal özellik çıkarımı sağlayan Derinlemesine ayrılabilir evrişimler kullanılmıştır. Son olarak, 3B ESA ile HUAG’lerden uzamsal-spektral özellikler eş zamanlı olarak çıkarılmaktadır. Ancak sadece 3B ESA kullanımı hesaplama karmaşıklığını arttırmaktadır. Yalnızca 2B ESA kullanımı ile de HUAG’lerden sadece uzamsal özellikler çıkarılmaktadır. Spektral özellikler çıkarılamamaktadır. 3B ESA ile 2B ESA’nın birlikte kullanılmasıyla bu iki problem çözülmüştür. Ayrıca önerilen yöntemde optimum spektral bant çıkarımı için temel bileşen analizi bir ön işleme adımı olarak kullanılmıştır. Popüler iki HUAG kıyaslama veriseti olan Indian pines ve Salinas verisetleri kullanılarak uygulamalar gerçekleştirilmiştir. Uygulamalar sonucunda Indian pines ile %99.45 ve Salinas ile %99.95 genel doğruluk sonucu elde edilmiştir. Elde edilen sınıflandırma sonuçları, önerilen yöntemin sınıflandırma performansının mevcut yöntemlerden daha iyi olduğunu göstermektedir.Öğe Hybrid 3D Convolution and 2D Depthwise Separable Convolution Neural Network for Hyperspectral Image Classification(2022) Asker, Mehmet Emin; Fırat, Hüseyin; Hanbay, DavutConvolutional neural networks (CNNs) are one of the popular deep learning methods used to solve the hyperspectral image classification (HSIC) problem. CNN has a strong feature learning ability that can ensure more distinctive features for higher quality HSIC. The traditional CNN-based methods mainly use the 2D CNN for HSIC. However, with 2D CNN, only spatial features are extracted in HSI. Good feature maps cannot be extracted from spectral dimensions with the use of 2D CNN alone. By using 3D CNN, spatial-spectral features are extracted simultaneously. However, 3D CNN is computationally complex. In this study, a hybrid CNN method, which is a combination of 3D CNN and 2D CNN, is improved to solve the two problems described above. Using hybrid CNN decreases the complexity of the method compared to using only 3D CNN and can perform well against a limited number of training samples. On the other hand, in Hybrid CNN, depthwise separable convolution (DSC) is used, which decreases computational cost, prevents overfitting and enables more spatial feature extraction. By adding DSC to the developed hybrid CNN, a hybrid depthwise separable convolutional neural network is obtained. Extensive applications on frequently used HSI benchmark datasets show that the classification performance of the proposed network is better than compared methods.Öğe Hybrid 3D/2D Complete Inception Module and Convolutional Neural Network for Hyperspectral Remote Sensing Image Classification(Springer, 2023) Firat, Huseyin; Asker, Mehmet Emin; Bayindir, Mehmet Ilyas; Hanbay, DavutClassification in hyperspectral remote sensing images (HRSIs) is a challenging process in image analysis and one of the most popular topics. In recent years, many methods have been proposed to solve the HRSIs classification problem. Compared to traditional machine learning methods, deep learning, especially convolutional neural networks (CNNs), is commonly used in the classification of HRSIs. Deep learning-based methods based on CNNs show remarkable performance in HRSIs classification and greatly support the development of classification technology. In this study, a method in which the Hybrid 3D/2D Complete Inception module and the Hybrid 3D/2D CNN method are used together has been proposed to solve the HRSIs classification problem. In the proposed method, multi-level feature extraction is performed by using multiple convolution layers with the Inception module. This improves the performance of the network. Conventional CNN-based methods use 2D CNN for feature extraction. However, only spatial features are extracted with 2D CNN. 3D CNN is used to extract spatial-spectral features. However, 3D CNN is computationally complex. Therefore, in the proposed method, a hybrid approach is used by first using 3D CNN and then 2D CNN. This reduces computational complexity and extracts more spatial features. In addition, PCA is used as a preprocessing step for optimum spectral band extraction in the proposed method. The proposed method has been tested using Indian pines, Salinas, University of Pavia, HyRANK-Loukia and Houston datasets, which are frequently used in studies for HRSIs classification. The overall accuracy of the proposed method in these five datasets are 99.83%, 100%, 100%, 90.47% and 98.93%, respectively. These results reveal that the proposed method provides higher classification performance compared to state-of-the-art methods.Öğe A hybrid approach consisting of 3D depthwise separable convolution and depthwise squeeze-and-excitation network for hyperspectral image classification(Springer Heidelberg, 2024) Asker, Mehmet Emin; Gungor, MustafaHyperspectral image classification is crucial for a wide range of applications, including environmental monitoring, precision agriculture, and mining, due to its ability to capture detailed spectral information across numerous wavelengths. However, the high dimensionality and complex spatial-spectral relationships in hyperspectral data pose significant challenges. Deep learning, particularly Convolutional Neural Networks (CNNs), has shown remarkable success in automatically extracting relevant features from high-dimensional data, making them well-suited for handling the intricate spatial-spectral relationships in hyperspectral images.This study presents a hybrid approach for hyperspectral image classification, combining 3D Depthwise Separable Convolution (3D DSC) and Depthwise Squeeze-and-Excitation Network (DSENet). The 3D DSC efficiently captures spatial-spectral features, reducing computational complexity while preserving essential information. The DSENet further refines these features by applying channel-wise attention, enhancing the model's ability to focus on the most informative features. To assess the performance of the proposed hybrid model, extensive experimental studies were carried out on four commonly utilized HSI datasets, namely HyRANK-Loukia and WHU-Hi (including HongHu, HanChuan, and LongKou). As a result of the experimental studies, the HyRANK-Loukia achieved an accuracy of 90.9%, marking an 8.86% increase compared to its previous highest accuracy. Similarly, for the WHU-Hi datasets, HongHu achieved an accuracy of 97.49%, reflecting a 2.11% improvement over its previous highest accuracy; HanChuan achieved an accuracy of 97.49%, showing a 2.4% improvement; and LongKou achieved an accuracy of 99.79%, providing a 0.15% improvement compared to its previous highest accuracy. Comparative analysis highlights the superiority of the proposed model, emphasizing improved classification accuracy with lower computational costs.Öğe Hyperspectral image classification method based on squeeze-and-excitation networks, depthwise separable convolution and multibranch feature fusion(Springer Science and Business Media, 2023) Asker, Mehmet EminHyperspectral image classification (HSIC) is a hot topic discussed by most researchers. In recent years, deep learning and especially CNN have provided very good results in HSIC. However, there is still a need to develop new deep learning-based methods for HSIC. In this study, a new CNN-based method is proposed to reduce the number of trainable parameters and increase HSIC accuracy. The proposed method consists of 3 branches. Squeeze-and-excitation network (SENet) in the first branch, a hybrid method consisting of the combination of 3D CNN and 2D DSC in the second branch, and 2D DSC in the third branch are used. The main purpose of using a multi-branch network structure is to further enrich the features extracted from HSI. SENet used in the first branch are integrated into the proposed method as they increase the classification performance while minimally increasing the total number of parameters. In the second and third branches, hybrid CNN methods consisting of 3D CNN and 2D Depthwise separable convolution were used. With the hybrid CNN, the number of trainable parameters is reduced and the classification performance is increased. In order to analyze the classification performance of the proposed method, applications were carried out on the WHU-Hi-HanChuan, WHU-Hi-LongKou and Indian pines datasets. As a result of the applications, 97.45%, 99.84% and 96.31% overall accuracy values were obtained, respectively. In addition, the proposed method was compared with nine different methods developed in recent years from the literature and it was seen that it obtained the best classification result.Öğe Learning-based approaches for voltage regulation and control in DC microgrids with CPL(Mdpi, 2023) Güngör, Mustafa; Asker, Mehmet EminThis article introduces a novel approach to voltage regulation in a DC/DC boost converter. The approach leverages two advanced control techniques, including learning-based nonlinear control. By combining the backstepping (BSC) algorithm with artificial neural network (ANN)-based control techniques, the proposed approach aims to achieve accurate voltage tracking. This is accomplished by employing the nonlinear distortion observer (NDO) technique, which enables a fast dynamic response through load power estimation. The process involves training a neural network using data from the BSC controller. The trained network is subsequently utilized in the voltage regulation controller. Extensive simulations are conducted to evaluate the performance of the proposed control strategy, and the results are compared to those obtained using conventional BSC and model predictive control (MPC) controllers. The simulation results clearly demonstrate the effectiveness and superiority of the suggested control strategy over BSC and MPC.Öğe Multiscale Feature Fusion for Hyperspectral Image Classification Using Hybrid 3D-2D Depthwise Separable Convolution Networks(Int Information & Engineering Technology Assoc, 2023) Firat, Hueseyin; Cig, Harun; Guellueoglu, Mehmet Tahir; Asker, Mehmet Emin; Hanbay, DavutHyperspectral remote sensing images (HRSI) comprise three-dimensional image cubes, containing a single spectral dimension alongside two spatial dimensions. HRSI are presently among the foremost essential datasets for Earth observation. The task of HRSI classification is intricate due to the influence of spectral mixing, leading to notable variability within classes and resemblances across classes. Consequently, the field of HRSI classification has garnered significant research attention in recent times. Convolutional Neural Networks (CNNs) are harnessed to address these issues, enabling both feature extraction and classification. This study introduces a novel approach for HRSI classification called the hybrid 3D-2D depthwise separable convolution network (Hybrid DSCNet), which leverages multiscale feature integration. Within the Hybrid DSCNet, diverse kernel sizes contribute to an enriched feature extraction process from HRSI. The conventional 3D-2D CNN, while effective, comes with a computational load. Instead of using the standard 3D-2D CNN, this study adopts the 3D-2D DSC architecture. This approach partitions the conventional convolution into two components: pointwise and depthwise convolution, yielding a substantial reduction in trainable parameters and computational complexity. To evaluate the proposed method, the Indian Pines dataset along with WHU-Hi subdatasets (LongKou-LK, HanChuan-HC, and HongHu-HH) were employed. Employing a 5% training sample, impressive overall accuracy scores were achieved: 94.51%, 99.78%, 97.06%, and 97.27% for Indian Pines, WHU-LK, WHU-HC, and WHU-HH, respectively. Comparative analysis of the proposed approach with cutting-edge techniques within the literature reveals its superior performance across the four HRSI datasets. Notably, the Hybrid DSCNet attains enhanced classification accuracy while maintaining lower computational overhead.Öğe A novel chaotic switched modulation for EMI suppression in electrical drive system(Balkan Yayın, 2021) Asker, Mehmet Emin; Kürüm, HasanIn electric drive systems common mod voltage (CMV) is known as voltage between power line and ground. CMV causes negative effects on both electric engine and its driver whereas switching operation occurs in power devices. In addition switching frequency and its multiples in CMV cause electromagnetic and aqustic noises. In this paper a novel FixedFrequency chaotic switched sinusoidal pulse width modulation (FFCS-SPWM) is proposed. The cruial point in proposed FFCSSPWM method is to obtained a new carrier wave by summation of carrier wave and its inverse form. In FFCS-SPWM method extra switching loses were reduced due to constant switching frequency. This method also applied to permanent magnet synchronous motor (PMSM) vector control system. The reduction of electromagnetic interference (EMI), aqustic noises and dv/dt stress effects that occur in CMV is simulated by proposed method. Moreover SPWM combined with chaoticfrequency chaotic switched (CFCS) is proposed to reduce the differential mode voltage (DMV) which occurs due to voltage between lines. Finally in this method switching frequency change chaotically and also in this paper it shown that, CFCS-SPWM provides to reduce EMI, aqustic noises which are caused by CMV and DMV.Öğe Paralel aktif güç filtresi kullanarak asenkron motorun reaktif güç kompanzasyonun PSCAD ile modellenmesi(Dicle Üniversitesi Mühendislik Fakültesi, 2022) Güngör, Mustafa; Asker, Mehmet Emin; Kurt, Muhammed BahaddinBu çalışmada, üç fazlı bir asenkron (ASM) motorun üç faz-üç telli gerilim beslemeli paralel aktif güç filtresi (PAGF) yardımı ile güç katsayısının (cosφ) düzeltilmesi amaçlı PSCAD/EMTDC yazılımı kullanılarak hazırlanan bir benzetim çalışması verilmiştir. Benzetim modeli üç fazlı bir asenkron motor, üç faz-üç telli gerilim beslemeli paralel aktif güç filtresi ve AC kaynaktan oluşmaktadır. Paralel aktif güç filtresini kontrol için anlık reaktif güç teorisi tekniğini kullanarak referans akımlar üretmektedir. Fabrikalar ve işletmelerde yoğun olarak kullanılagelen üç fazlı asenkron motorların şebekeden anlık olarak değişen reaktif güç talepleri olmaktadır. Bu güç talepleri pasif filtrelerle düzeltilebilse de birçok olumsuz yönü de bulunmaktadır. Gerçekleştirilen benzetim çalışması ile güç katsayısının hızlı bir şekilde düzeltilebildiği gösterilmiştir. Ayrıca, yapılan benzetim çalışmasında PAGF’nin dinamik yük şartlarına cevabı oldukça iyi olduğu alınan sonuçlardan anlaşılmaktadır.Öğe Reduction of EMI with chaotic space vector modulation in direct torque control(Kauno Technologijos Universitetas, 2016) Asker, Mehmet Emin; Özer, Ahmet Bedri; Kurum, HasanIn this paper a study is presented about the reduction of electromagnetic interference (EMI) in Direct Torque Control (DTC) method by the means of chaotic space vector modulation. Space Vector Modulated Direct Torque Control (SV-DTC) is a method developed to reduce current and moment fluctuations which stem from the hysteresis controllers used in classical DTC method. The switching frequency amplitude was changed chaotically in a Space Vector Modulated Direct Torque Control (FFSV-DTC) having a Fixed switching frequency, and Chaotic Space Vector Modulated Direct Torque Control (CAFSV-DTC) method having chaotic amplitude modulated switching frequency was obtained. CAFSV-DTC method was suggested to reduce EMI, acoustic noise and current harmonics which occur in FFSV- DTC method. For this purpose, the proposed CAFSV-DTC method, using a Permanent Magnet Synchronous Motor (PMSM) drive, is compared to Random Amplitude Frequency method (RSV-DTC) and FFSV-DTC. Simulation results indicate that the proposed method yields better results.Öğe SMSM’de vektör kontrol sürücüsü için 2 DOF FOPI kontrolör tasarımı(Dicle Üniversitesi Mühendislik Fakültesi, 2023) Erdal, Hasan; Yıldırım, Burak; Asker, Mehmet EminGünümüzde sürekli mıknatıslı senkron motorların (SMSM) tahrik sistemlerinde kullanımları giderek yaygın hale gelmektedir. Bu motorların kontrollerindeki gelişmeler kullanıldıkları servo sistemlerde konum ve hız takibinde iyileştirmeler sağlamaktadır. Bu çalışmada SMSM'nin uzay vektör modülasyonlu vektör kontrol yöntemine dayalı modeli Matlab/Simulink’te modellenmiştir. Bu modelde kontrolör yapısı olarak iki serbestlik dereceye sahip kesir dereceli oransal integral (2-DOF FOPI) kontrolör yapısı kullanılmıştır. Kontrolör parametreleri karınca kolonisi optimizasyonu (KKO) ile belirlenmiştir. Önerilen kontrolörün başarısını göstermek amacıyla sonuçlar geleneksel PI kontrolör ile eşit şartlarda karşılaştırılmıştır. Elde edilen simülasyon sonuçları incelendiğinde, 2-DOF FOPI kontrolörün SMSM'nin alan yönlendirmeli kontrolünde geleneksel PI kontrolörden daha iyi performansa sahip olduğu görülmüştür.Öğe Spatial-spectral classification of hyperspectral remote sensing images using 3D CNN based LeNet-5 architecture(Elsevier, 2022) Firat, Hueseyin; Asker, Mehmet Emin; Bayindir, Mehmet Ilyas; Hanbay, DavutHyperspectral remote sensing image (HRSI) analysis are commonly used in a wide variety of remote sensing applications such as land cover analysis, military surveillance, object detection and precision agriculture. Deep learning is seen as an effective method to improve HRSI classification. In particular, convolutional neural net-works (CNNs) are increasingly used in this field. The high dimensionality of the HRSIs increases the computa-tional complexity. Thus, most of studies apply dimension reduction as preprocessing. Another problem in HRSI classification is that spatial-spectral features must be considered in order to obtain accurate results. Because, HRSI classification results are highly dependent on spatiospectral information. The aim of this paper is to build a 3D CNN-based LeNet-5 method for HRSI classification. Principal component analysis (PCA) is used as the pre-processing step for optimum spectral band extraction. 3D CNN is used to simultaneously extract spatial -spectral features in HRSIs. LeNet-5 architecture has a simple and straightforward architecture. At the same time, the number of trainable parameters is very low. With the use of the LeNet-5 architecture, the number of trainable parameters of the proposed method is considerably reduced. This is one of the most important features that distinguish the proposed method from other deep learning methods. The proposed method is tested with Indian pines, Pavia University and Salinas datasets. As a result of experimental studies, 100% overall accuracy result is obtained in all datasets. The proposed 3DLeNet method is compared against various state-of-the-art CNN based methods. From the experimental results, it is seen that our 3DLeNet method performs more accurate result. It has also been found that the proposed 3DLeNet method shows a satisfactory result with less computational complexity.
