Elektrik ve Enerji Bölümü Koleksiyonu

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

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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%.
Citation - WoS: 23
Citation - Scopus: 36
Classification and analysis of epileptic EEG recordings using convolutional neural network and class activation mapping
(Biomedical Signal Processing and Control, 2021) Zan, Hasan; Yıldız, Abdulnasir; Said, Sherif
Electrical bio-signals have the potential to be used in different applications due to their hidden nature and their ability to facilitate liveness detection. This paper investigates the feasibility of using the Convolutional Neural Network (CNN) to classify and analyze electroencephalogram (EEG) data with their time-frequency representations and class activation mapping (CAM) to detect epilepsy disease. Several types of pre-trained CNNs are employed for a multi-class classification task (AlexNet, GoogLeNet, ResNet-18, and ResNet-50) and their results are compared. Also, a novel convolutional neural network architecture comprised of two horizontally concatenated GoogLeNets is proposed with two inputs scalograms and spectrogram of the eplictic EEG signal. Four segment lengths (4097, 2048, 1024, and 512 sampling points) with three time-frequency representations (short-time Fourier, Wavelet, and Hilbert-Huang transform) are statistically evaluated. The dataset used in this research is collected at the University of Bonn. The dataset is reorganized as normal, interictal, and ictal. The maximum achieved accuracies for 4097, 2048, 1024, and 512 sampling points are 100 %, 100 %, 100 %, and 99.5 % respectively. The CAM method is used to analyze discriminative regions of time-frequency representations of EEG segments and networks' decisions. This method showed CNN models used different time and frequency regions of input images for each class with correct and incorrect predictions.
Citation - WoS: 26
Citation - Scopus: 28
Developed Analytical Expression for Current Harmonic Distortion of the Pv System's Inverter in Relation To the Solar Irradiance and Temperature
(Springer, 2021) Cangi, Hasan; Eid, Bilal; Yilmaz, Ahmet Serdar; Adak, Suleyman
This paper deals with modeling and simulation of the total harmonic distortion of the current (THDI) dispatched from the inverter and connected to nonlinear load. The change of THD(I)was examined in relation to the ambient temperature (T) and solar irradiance (G). The developed model is being used to extract parameters for a given THD(I)as a function of temperature and solar radiation. This study outlines the working principle of photovoltaic (PV) panel as well as PV array. Off-grid PV system is modeled by using Matlab/Simulink program, and detailed analytical study has been carried out in this work. The design, modeling and simulation of this study are performed from 50 up to 988 W/m(2)for solar irradiance. Harmonic components have negative effects on the steady-voltage stability of the PV system. Therefore, analytical expression is needed for steady-state stability analysis in order to reduce negative effects. Hence, two analytical expressions of THD(I)were obtained by two new different methods which are statistical package for the social sciences program and genetic expression programming. Eventually, two different methods have been verified by the Matlab/Simulink program in order to find out THD(I)and demonstrated the effectiveness of the proposed strategy. As a result of this study, it is observed that input current THDI of nonlinear load is too high at low irradiance. It is suggested that active harmonic filters should be used at low irradiance in order to produce better quality energy and avoid damages in the PV system.
Citation - WoS: 32
Citation - Scopus: 32
Development software program for extraction of photovoltaic cell equivalent circuit model parameters based on the Newton–Raphson method
(SpringerLink, 2022) Adak, Süleyman; Cangi, Hasan; Yılmaz, Ahmet Serdar; Arifoğlu, Uğur
Finding the equivalent circuit parameters for photovoltaic (PV) cells is crucial as they are used in the modeling and analysis of PV arrays. PV cells are made of silicon. These materials have a nonlinear characteristic. This distorts the sinusoidal waveform of the current and voltage. As a result, harmonic components are formed in the system. The PV cell is the smallest building block of the PV system and produces voltages between 0.5 V and 0.7 V. It serves as a source of current. The amount of radiation hitting the cell determines how much current it produces. In an ideal case, a diode and a parallel current source make up the equivalent circuit of the PV cell. In practice, the addition of a series and parallel resistor is made to the ideal equivalent circuit. There are many equivalent circuits in the literature on modeling the equivalent circuit of a PV cell. The PV cell single–diode model is the most used model due to its ease of analysis. In this study, the iterative method by Newton–Raphson was used to find the equivalent circuit parameters of a PV cell. This method is one of the most widely used methods for determining the roots of nonlinear equations in numerical analysis. In this study, five unknown parameters (Iph, Io, Rs, Rsh and m) of the PV cell equivalent circuit were quickly discovered with the software program prepared based on the Newton–Raphson method in MATLAB
Citation - WoS: 11
Citation - Scopus: 15
Development software program for finding photovoltaic cell open-circuit voltage and fill factor based on the photovoltaic cell one-diode equivalent circuit model
(Springer, 2024) Adak, Süleyman; Cangi, Hasan
The photovoltaic (PV) cell is the smallest building block of the PV solar system and produces voltages between 0.5 and 0.7 V. It acts as a current source in the equivalent circuit. The amount of radiation hitting the cell determines how much current it produces. The equivalent circuit of an ideal PV cell consists of a diode and a parallel current source. In order to express losses in applications, series and parallel resistance are added to the ideal equivalent circuit of the PV cell. There are many equivalent circuits in the literature for modeling the equivalent circuit of a PV cell. The single-diode equivalent circuit is the most widely used model because of its simplicity and ease of analysis. There are several methods available to estimate and analyze the parameters of PV cell models, such as Newton Raphson method, Lambert-W function, etc. In this study, the Newton Raphson method was used to find the equivalent circuit parameters of a PV cell. Fill factor is used to determine the quality of electricity generated by the photovoltaic cell. Open-circuit voltage is the maximum voltage value that the PV cell can transmit. The analysis of PV cell fill factor and open-circuit voltage was carried out using the developed software program. Then, the open-circuit voltage and fill factor were found using the software program prepared in MATLAB and given in Appendix.
Citation - Scopus: 22
Effects of distributed generations' integration to the distribution networks case study of solar power plant
(İlhami Çolak, 2017) Shobole, Abdulfetah; Baysal, Mustafa; Wadi, Mohammed; Tur, Mehmet Rida
All over the world, the Distributed Generations (DGs) integration to power system has increased in the recent years due to economic, environmental and technical advantages. Turkey which has the huge solar potential has focused on integrating both licensed and unlicensed solar power plants by providing 10 years of purchasing guarantee as an incentive for the electricity producers from solar energy. However, the integration of DGs has several negative effects on the distribution networks (DNs). This work is concerned with investigating the possible challenges that may arise due to integration of PV based DGs on the existing distribution networks. Short circuit current level with respect to variation in MW integration is studied for the case the utility network is weak and strong. When the utility network is strong, the integration effect of inverter based DGs like solar power plants were observed insignificant. However, for the weak utility networks, the integration of inverter based DGs has been observed to have significant influence. Finally, directly integrated DGs (without inverter) are considered to reveal its difference with the non-inverter based DGs. As the case study, the distribution network integration of a solar power project, which is found in the Antalya region of Turkey, is investigated. This is 12 MW solar power plant designed to be connected to the local distribution network in Antalya. It is concluded that the effects of directly integrated DGs are observed more prominent compared to the inverter based DGs. DigSILENT Power Factory simulation tool is used for the study.
Citation - WoS: 46
Citation - Scopus: 57
Harmonics Mitigation of Stand-Alone Photovoltaic System Using LC Passive Filter
(Journal of Electrical Engineering and Technology, 2021) Adak, Süleyman
This article investigates modeling and simulation of the off-grid photovoltaic (PV) system, and elimination of harmonic components using an LC passive filter. Pulse width modulation (PWM) inverter is used to convert the direct current to alternating current. It is very important in terms of energy quality that the inverter output current total harmonic distortion (THDI) is below the value given by standards. Harmonic components have negatively effect on off-grid PV power system. THDI should be kept below a certain level in order to prevent damage to the equipment in the off-grid system and to ensure a higher quality energy flow to reduce the total harmonic distortion (THD) of the solar inverter output current; LC passive filter must be connected to the output of the PWM inverter. There are many types of passive filters for solar inverters. One of the most widely used filter types is the LC filter. LC filters are used in off-grid systems. LC filter is smaller in size and lower cost than other filters. But it is more complicated to determine the parameters of the LC filter. Therefore, in order for the system to remain in a steady state, the parameters must be accurately calculated and analyzed. In this study, the output power of the solar inverter, switching frequency, bus voltage etc. values were determined and LC filter parameters were calculated. Since high inductance values are used in LC filters, the voltage drop increases in these filters. To reduce the voltage drop, the DC bus voltage must be increased, which increases the switching losses. LC filter is connected between the inverter and the nonlinear load to filter the harmonic components produced by the DC/DC boost converter, DC/AC inverter and non-linear load. Matlab/Simulink program was used in Simulation and analysis of off-grid solar system. Solar inverter output current THD was measured as 91.55%. After the LC filter is connected to the system, this value has dropped to 2.62%.
Citation - WoS: 12
Citation - Scopus: 14
Local Pattern Transformation-Based convolutional neural network for sleep stage scoring
(ScienceDirect, 2023) Zan, Hasan; Yildiz, Abdulnasır
Sleep stage scoring is essential for the diagnosis and treatment of sleep disorders. However, manual sleep scoring is a tedious, time-consuming, and subjective task. Therefore, this paper proposes a novel framework based on local pattern transformation (LPT) methods and convolutional neural networks for automatic sleep stage scoring. Unlike in previous works in other fields, these methods were not employed for manual feature extraction, which requires expert knowledge and the pipeline behind it might bias results. The transformed signals were directly fed into a CNN model (called EpochNet) that can accept multiple successive epochs. The model learns features from multiple input epochs and considers inter-epoch context during classification. To evaluate and validate the effectiveness of the proposed approach, we conducted several experiments on the Sleep-EDF dataset. Four LPT methods, including One-dimensional Local Binary Pattern (1D-LBP), Local Neighbor Descriptive Pattern (LNDP), Local Gradient Pattern (LGP), and Local Neighbor Gradient Pattern (LNGP), and different polysomnography (PSG) signals were analyzed as sequence length (number of input epochs) increased from one to five. 1D-LBP and LNDP achieved similar performances, outperforming other LPT methods that are less sensitive to local variations. The best performance was achieved when an input sequence containing five epochs of PSG signals transformed by 1D-LBP was employed. The best accuracy, F1 score, and Kohen’s kappa coefficient were 0.848, 0.782, and 0.790, respectively. The results showed that our approach can achieve comparable performance to other state-ofthe-art methods while occupying fewer computing resources because of the compact size of EpochNet.
Citation - WoS: 11
Citation - Scopus: 13
Multi-task learning for arousal and sleep stage detection using fully convolutional networks
(IOP Publishing, 2023) Zan, Hasan; Yıldız, Abdulnasir
Objective: Sleep is a critical physiological process that plays a vital role in maintaining physical and mental health. Accurate detection of arousals and sleep stages is essential for the diagnosis of sleep disorders, as frequent and excessive occurrences of arousals disrupt sleep stage patterns and lead to poor sleep quality, negatively impacting physical and mental health. Polysomnography is a traditional method for arousal and sleep stage detection that is time-consuming and prone to high variability among experts. Approach: In this paper, we propose a novel multi-task learning approach for arousal and sleep stage detection using fully convolutional neural networks. Our model, FullSleepNet, accepts a full-night single-channel EEG signal as input and produces segmentation masks for arousal and sleep stage labels. FullSleepNet comprises four modules: a convolutional module to extract local features, a recurrent module to capture long-range dependencies, an attention mechanism to focus on relevant parts of the input, and a segmentation module to output final predictions. Main results: By unifying the two interrelated tasks as segmentation problems and employing a multi-task learning approach, FullSleepNet achieves state-of-the-art performance for arousal detection with an area under the precision-recall curve of 0.70 on Sleep Heart Health Study and Multi-Ethnic Study of Atherosclerosis datasets. For sleep stage classification, FullSleepNet obtains comparable performance on both datasets, achieving an accuracy of 0.88 and an F1-score of 0.80 on the former and an accuracy of 0.83 and an F1-score of 0.76 on the latter. Significance: Our results demonstrate that FullSleepNet offers improved practicality, efficiency, and accuracy for the detection of arousal and classification of sleep stages using raw EEG signals as input.
Citation - WoS: 19
Citation - Scopus: 20
The Quality Problems at Low Irradiance in the Grid-Connected Photovoltaic Systems
(Springer, 2024) Adak, Suleyman; Cangi, Hasan
Solar photovoltaic (PV) energy is one of the most prominent topics that have attracted the attention of researchers in recent years. The use of solar energy is increasing rapidly in the world. Although using PV energy has various advantages, it has some disadvantages. Among these disadvantages, power factor (PF) and total harmonic distortion (THD) issues are discussed in this article. When solar PV systems are integrated into the grid, various power quality problems arise. In addition, due to low power quality and high harmonics, power system components overheat and start operating in undesirable regions; causes great damage. The magnitude of PF and THD is dependent on solar irradiation values. In order to determine how the power quality in the grid-connected solar system is affected by changes in solar irradiation (G), results for various irradiation situations are presented and analyzed. In addition, at low irradiance values, the amplitude of harmonic components and reactive power increases, whereas the power factor of the PV system decreases. Low power factor and high amplitude of harmonics cause the efficiency of the solar system to decrease. In this study, PF and THDI values were measured on a particular cloudy day for analysis. An analysis of the solar PV system was conducted using Matlab/simulation program to model the grid-connected PV system. Thus, the analytical expression of the PF and THDI, which are dependent on irradiation, was found with a new method by using the Statistical Package for the Social Sciences (SPSS) program and the curve fitting method. Obtaining the analytical expressions for both solar irradiation (G) and power factor (PF) used the SPSS program and also solar irradiation (G) and total harmonic distortion (THDI) used the MATLAB curve fitting method which contributed to the science comparing to the existing literature. It can be prevented the low power quality by using such these expressions at low solar irradiation cases.
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.
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.

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