Elektrik ve Enerji Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12514/174

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Browsing Elektrik ve Enerji Bölümü Koleksiyonu by browse.metadata.publisher "IEEE"

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    Abnormal Heart Sound Detection Using Ensemble Classifiers
    (IEEE, 2018) Zan, Hasan; Yildiz, Abdulnasir
    Phonocardiogram is used for ambulatory diagnostic to assess health status of heart and detect cardiovascular disease. The goal of this study is to develop automatic classification method of PCG recordings collected from different databases and recorded in a different way. For this purpose, after various time and frequency domain features are extracted from PCG recordings obtained from two databases, recordings are subjected to pre-classification in order determine which database they are obtained from. Before final classification, various time, frequency and time-frequency domain features of classified recordings are extracted. These features are fed into four different classification ensembles trained with training dataset. With final decision rule, proposed algorithm achieved an accuracy of 98.9%, a sensitivity of 93.75% and a specify of 99.5%.
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    Sleep arousal detection using one dimensional local binary pattern-based convolutional neural network
    (IEEE, 2021) Zan, Hasan; Yıldız, Abdulnasır
    Sleep arousal is defined as a shift from deep sleep to light sleep or complete awakening. Arousals cause sleep deprivation by fragmenting sleep, and ultimately, many health problems. Arousals can be induced by well-studied apneas and hypopneas or other sleep orders such as hypoventilation, bruxism, respiratory effort-related arousals. Thus, detection of less-studied non-apnea/hypopnea arousals is important for diagnosis and treatment of sleep disorders. Traditionally, polysomnography (PSG) test that is recording and inspecting overnight physiological signals is used for sleep studies. In this work, a novel method based on one dimensional local binary pattern (1D-LBP) and convolutional neural network (CNN) for automatic arousal detection from polysomnography recordings is proposed. 25 recordings from PhysioNet Challenge 2018 PSG dataset are used for experiments. Each signal in PSG recordings is transformed to a new signal using 1D-LBP, and then segmented using 10-s-long sliding window. The segments are fed to a CNN model formed by stacking 25 layers for classification of non-apnea/hypopnea arousal regions from non-arousal regions. Area under precision-recall curve (AUPRC) and area under receiver operating characteristic curve (AUROC) metrics are used for performance measurement. Experimental results reflect that the proposed method shows a great promise and obtains an AUPRC of 0.934 and an AUROC of 0.866.
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    Citation - Scopus: 6
    Thevenin Equivalent of Solar PV Cell Model and Maximum Power Transfer
    (IEEE, 2021) Adak, Süleyman; Cangi, Hasan; Yılmaz, A. Serdar
    Photovoltaic (PV) is the conversion of solar energy into DC electrical energy using PV cells. In addition, solar energy is an important renewable energy source. In this study, it is proposed that Thevenin's equivalent PV cell model produces a voltage-current characteristic that is quite representative of the operation of the PV source. Thevenin's elements depend on ambient temperature conditions, so charging is derived and simplified to construct a model that closely predicts and demonstrates adequate PV cell characteristic for different ambient temperature conditions. This method is very useful for estimating the desired performance and also for examining different Maximum Power Point Tracking (MPPT) algorithms. Theoretically, the simulation was supplemented with test data, then used to develop an equivalent Thevenin model in which the resistance is non-linear and voltage dependent. Thevenin's method and variable pitch is to improve the maximum power transfer to the load by increasing the performance of the PV cell. These methods were modeled and studied in a simulation program.