Yazar "Cekli, S." seçeneğine göre listele

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    Amplitude and frequency detection of power system signals with chaotic distortions using independent component analysis
    (ELSEVIER SCIENCE SA, 2014) Ugur, M.; Cekli, S.; Uzunoglu, C. P.
    In this paper independent component analysis (ICA) method for amplitude and frequency estimation of highly distorted power system signals is presented. In a power system the accuracy of amplitude and frequency detection is strictly related to distortion strength of the system signal. Distorted system signal may contain chaotic (ferroresonance) and non-chaotic (white, pulse, harmonic etc.) noise. In this study, chaotic distortions are used to contaminate system signal, which is the worst case scenario for a power system. The proposed method is employed to eliminate the noise and hence to improve the efficiency of the estimation. To simulate chaotic distortions Duffing Oscillator solutions are used. Computer simulations have been carried out for the performance analysis of the proposed method and the comparison of the results of the proposed method with the conventional filters are presented. (C) 2013 Elsevier B.V. All rights reserved.
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    Amplitude and frequency estimation of power system signals using independent component analysis [Bagimsiz bilesen analizi ile enerji sistemleri isaretlerinin genlik ve frekans kestirimi]
    (2013) Uzunoglu, C.P.; Ugur, M.; Turan, F.; Cekli, S.
    In this paper independent component analysis (ICA) method for amplitude and frequency estimation of distorted power system signals is proposed. In order to protect system and keep it in safe operation mode the amplitude and frequency estimation should be conducted accurately. Transient disturbances of the power system may reduce estimation performance due to the distortion strength. In this study white noise and pulse noise which are very common for power systems, are used to contaminate power system signal. Thus, the proposed method is employed to decompose noise from system signal and hence to improve the efficiency of the estimation. Computer simulations have been carried out for the performance analysis of the ICA method and the comparison of the results of the proposed method with the conventional filters are displayed by using mean square error (MSE) values. © 2013 IEEE.
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    An improved method for lossless compression of volumetric medical images
    (Institute of Electrical and Electronics Engineers Inc., 2019) Akman, A.; Cekli, S.
    Size of volumetric medical images is very large. Because of this, some problems occur while storing or transmitting this type of data. So, it is necessary to compress volumetric medical images using efficient techniques. It is more appropriate to compress medical images using lossless techniques due to it may have effects on diagnosis. In this study, a method for lossless compression of volumetric medical images was proposed. The redundancy has been reduced using DPCM technique by utilizing correlation between image slices. SAP technique has been applied to first slice. The performance is improved by applying RDPCM for each image frame after DPCM, individiually. To investigate the performance of the proposed method, some experiments have been performed using different medical images and the results are presented. © 2019 IEEE.
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    Modelling of chaotic surface tracking on the polymeric insulators with Hidden Markov Models
    (IEEE Computer Society, 2014) Uzunoğlu, C.P.; Cekli, S.; Ugur, M.
    In this study, chaotic surface tracking patterns observed on polymeric high voltage (HV) outdoor insulation materials were investigated and simulated. The polymeric samples are tested according to the IEC 587 Inclined Plane Tracking Test Standard. Since the chaotic surface tracking patterns manifests smutty and disordered images, they are preprocessed and purified by image processing tools. Internal and external effects may severely decrease insulation performance. In order to examine external effects, samples are subjected to moisture and vibration effect. Polymer samples are investigated by their fractal dimension which is a prominent tool for analyzing chaotic images. To simulate these chaotic surface tracking patterns Hidden Markov Models (HMM) are used. © 2014 IEEE.
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    NEAR FIELD LOCALIZATION OF MOVING SOURCES WITH REM ALGORITHMS
    (Istanbul Univ, Fac Engineering, 2009) Cekli, S.; Cekli, Erdinc; Kabaoğlu, Nihat
    In this paper, we present the problem of joint tracking of the direction of arrival (DOA) and range parameters of moving sources in the near field of an antenna array with two ExpectationMaximization (EM) based recursive algorithms. The main characteristic of the first Recursive EM (REM) approach is to include computation of the gradient of the log-likelihood function and some form of the complete data Fisher information matrix. Compared to first REM approach, the second one utilizes the stochastic approximation of approximate conditional expectation of the complete data sufficient statistics. The proposed recursive algorithms in this work assume that the parameters of interest are described by a linear polynomial model. This paper concludes by presenting the simulation results of the suggested algorithms in order to illustrate the computational effectiveness of the both algorithms.
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    Simultaneous remote monitoring of transformers' ambient parameters by using IoT
    (Elsevier, 2021) Hasır, M.; Cekli, S.; Uzunoğlu, C. P.
    Transformers have an important role in the uninterrupted and reliable flow of electrical power in power systems. For this reason, it is necessary to monitor and control the proper and efficient operation of transformers regularly. The increase in the number of transformers, the differentiation of transformer operations, and the prolongation of the transformers' operating time, accelerates the importance of remote monitoring. When remote monitoring structures are compared, even if conventional monitoring methods can perform many tasks, it has become suitable to establish lower cost, safer and simpler structures by using the Internet of Things (IoT). Apart from monitoring the electrical parameters of the transformer, it is possible to obtain information about the operation and fault status of the transformer by monitoring the physical and chemical changes in the environment. In this study, a measurement and remote monitoring system that detects temperature, humidity, light level and gas densities are employed for low and medium voltage transformers in the indoor environment. Tests are conducted to establish a connection between the obtained ambient data and transformer operating voltages. Different classification algorithms such as Bayesian Networks (BN), Multilayer Perceptron (MLP), and Random Forest (RF) are used to classify the operating voltages versus ambient data. (C) 2021 Elsevier B.V. All rights reserved.
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    Wavelet analysis of a memristor emulated model proposed for compact fluorescent lamp operated systems
    (ELSEVIER SCIENCE SA, 2018) Babacan, Y.; Uzunoglu, C. P.; Cekli, S.; Kacar, F.; Ugur, M.
    Compact fluorescent lamps (CFLs) are widely employed in lighting systems thanks to their energy efficient characteristics. In contrast to incandescent lamps, CFLs have highly non-linear current-voltage characteristics and exhibit harmonic distortions during their operation. Although nonlinearity and accelerated harmonic effects may cause disturbance in power networks, CFLs are frequently preferred due to their low energy consumption. In this study a test set-up is operated for investigating non-linear characteristics of commercially available CFLs. During these tests non-linear current and voltage waveforms of CFLs are analyzed via oscilloscope and compared with the proposed memristor emulator circuit output waveforms. The non-linear characteristics of CFLs are accurately modelled by proposed memristor emulator circuit, which has a tunable threshold property. To obtain proper comparison wavelet analysis is conducted for current waveforms of CFLs and memristor emulator circuit. The wavelet analysis is an efficient tool for processing non-linear signals, which are prone to display high frequency distortions. In addition to wavelet analysis, the Fourier Transform (FFT) characteristics and total harmonic distortion (THD) levels are also analyzed for correlation of CFLs and proposed memristor. (C) 2018 Elsevier B.V. All rights reserved.