Bölüm "MTÖ Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü" için listeleme
Toplam kayıt 33, listelenen: 21-33
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A Novel Analog Modulation Classification: Discrete Wavelet Transform-Extreme Learning Machine (DWT-ELM)
(Bitlis Eren Üniversitesi, 2021)The aim of this study is to propose a method using discrete wavelet transform and extreme learning machine (DWT-ELM) in classification of communication signals. Six types of analog modulated signals as “AM”, “DSB”, “USB”, ... -
A novel ship classification network with cascade deep features for line-of-sight sea data
(2021)In ship classifcation, selecting distinctive features and designing a proper classifer are two key points of the process. As a lack of most of the studies, these two essential points are considered separately. In this ... -
A Novel Short-Term Photovoltaic Power Forecasting Approach based on Deep Convolutional Neural Network
(2021)In this study, a novel photovoltaic power forecasting system that utilizes a deep Convolutional Neural Network (CNN) structure and an input signal decomposition algorithm is proposed. The proposed CNN architecture extracts ... -
On the modeling of the multi-segment capacitance: a fractional-order model and Ag-doped SnO2 electrode fabrication
(2022)This study proposes a methodology of electrochemical capacitor modeling via fractional-order impedance equation for porous electrodes fabricated with pure and Ag-doped SnO2 nanoparticles. It was carried out to prove the ... -
Optimal PI Kontrolör Tasarımı için Üçgenler Ağında Lineer Enterpolasyon Yöntemiyle Kararlılık Sınır Yüzeyinin Oluşturulması
(Bitlis Eren Üniversitesi, 2020)Bu çalışmada, PI parametrelerinin grafiksel olarak hesaplanması için geliştirilen kararlılık sınır eğrisi kullanılarak, yeni bir yaklaşım önerilmiştir. Geleneksel kararlılık sınır eğrisi, kapalı çevrim sistemin karakteristik ... -
Prediction of the optimal FSW process parameters for joints using machine learning techniques
(Walter de Gruyter GmbH, 2021)In this work, a couple of dissimilar AA2024/AA7075 plates were experimentally welded for the purpose of considering the effect of friction-stir welding (FSW) parameters on mechanical properties. First, the main mechanical ... -
Radiomics and deep learning approach to the differential diagnosis of parotid gland tumors
(Wolters Kluwer Health, 2022)All studies aimed at the differential diagnosis of benign vs. malignant PGTs or the identification of the commonest PGT subtypes were identified, and five studies were found that focused on deep learning-based differential ... -
SEM-Net: Deep features selections with Binary Particle Swarm Optimization Method for classification of scanning electron microscope images
(Elsevier, 2021)Materials Science is increasingly handling artificial intelligence methods to address the complexity in the field of everyday life necessities. Researchers in both academia and industry are interested in imaging techniques ... -
A Ship Detector Design Based on Deep Convolutional Neural Networks for Satellite Images
(Sakarya Üniversitesi, 2020)Ship detection and classification systems from satellite images are challenging tasks with their requirements of feature extracting, advanced pre-processing, a variety of parameters obtained from satellites and other types ... -
SolarNet: A hybrid reliable model based on convolutional neural network and variational mode decomposition for hourly photovoltaic power forecasting
(Elsevier, 2021)Photovoltaic (PV) power generation has high uncertainties due to the randomness and imbalance nature of solar energy and meteorological parameters. Hence, accurate PV power forecasts are essential in the operation of PV ... -
Two-stepped majority voting for efficient EEG-based emotion classification
(Springer, 2020)In this paper, a novel approach that is based on two-stepped majority voting is proposed for efficient EEG-based emotion classification. Emotion recognition is important for human-machine interactions. Facial features- and ... -
When machine learning meets fractional-order chaotic signals: detecting dynamical variations
(Elsevier, 2022)The challenge of classifying multivariate time series generated by discrete and continuous dynamical systems according to their chaotic or non-chaotic behavior has been studied extensively in the literature. The examination ... -
WSFNet: An efficient wind speed forecasting model using channel attention-based densely connected convolutional neural network
(Elsevier, 2021)This paper introduces a novel deep neural network (WSFNet) to efficiently forecast multi-step ahead wind speed. WSFNet forms the basis of the stacked convolutional neural network (CNN) with dense connections of different ...