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NOĞAY, HIDIR SELÇUK

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NOĞAY

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HIDIR SELÇUK

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    Diagnosis of chaotic ferroresonance phenomena using deep learning
    (IEEE-Institute of Electrical and Electronics Engineers, 2023-01-01) Nogay, Hıdır Selçuk; Akıncı, Tahir Çetin; Akbaş, Mustafa İlhan; Tokiç, Amir; NOĞAY, HIDIR SELÇUK; Teknik Bilimler Meslek Yüksekokulu; Elektrik ve Enerji Mühendisliği Bölümü; JPK-1615-2023
    Ferroresonance is a non-linear and dangerous resonance phenomenon that can affect power networks and damage electrical equipment. The ferroresonance phenomenon is examined by dividing it into classes, with chaotic ferroresonance being the most dangerous type that causes overvoltage's. Detecting chaotic ferroresonance in a short period of time is of great importance in terms of taking measures and reducing equipment damage. In this study, we explored the application of deep convolutional neural networks (DCNNs) for the identification and classification of chaotic ferroresonance phenomena. Two pre-trained AlexNet models were adapted using transfer learning to perform these tasks. The first model was utilized to identify chaotic ferroresonance, while the second was employed to distinguish between different subtypes of chaotic ferroresonance by dividing voltage curve graphs into different periods and shapes. The training and testing of both DCNN models were conducted using snapshot images extracted from the voltage curves of all phase voltages. The results of the experiments showed high accuracy in both the identification and classification of chaotic ferroresonance phenomena.
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    Diagnostic of autism spectrum disorder based on structural brain mri images using, grid search optimization, and convolutional neural networks
    (Elsevier, 2022-09-27) Noğay, Hıdır Selçuk; Adeli, Hojjat; NOĞAY, HIDIR SELÇUK; Teknik Bilimler Meslek Yüksekokulu; Elektrik ve Enerji Bölümü; JPK-1615-2023
    In this study, an automatic autism diagnostic model based on sMRI is proposed. This proposed model consists of two basic stages. The first stage is the preprocessing stage, which consists of removing unclear images, identi-fying the edges of the images by applying the canny edge detection (CED) algorithm, cropping them to the size required by the system, and finally enlarging the images five times with data augmentation. The data augmentation method should not affect the discrimination in the images such as coloring, and also since it is applied to both groups of autism spectrum disorders (ASD) and typical development (TD), it is performed with care not to cause any manipulation in the data. In the second stage, the grid search optimization (GSO) algorithm is applied to the deep convolutional neural networks (DCNN) used in the system to have optimal hyper -parameters. As a result, the proposed diagnostic method of ASD based on sMRI achieves an outstanding success rate of 100%. The reliability of the proposed model is validated by testing with five-fold cross-validation, and its superiority is demonstrated by comparing it with recent studies and widely-used pre-trained models.