Browsing by Publisher "IEEE Computer Society"

Now showing 1 - 6 of 6
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    On the realization of memristor based RC high pass filter
    (IEEE Computer Society, 2013) Sozen H.; Cam U.
    In 1971, Prof. Leon Chua proposed and described memristor which defines the relationship between flux and charge. Stanley Williams and his group realized a practical device that fulfills the memristor properties. In this paper, a memristor based high pass filter is presented. A comparative analysis between resistor and memristor based high pass filters is performed. The cut off frequency dependency of the high pass filter with memristor configuration is investigated. SPICE simulation results which are obtained using a memristor SPICE model with nonlinear dopant drift are included to verify theoretical analyses. The effect of change in the input frequency and initial condition of the length of doped region on the cut off frequency of the given high pass filter is investigated. The memory effect of memristor is represented by simulation results. © 2013 The Chamber of Turkish Electrical Engineers-Bursa.
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    A proposed artificial neural network model for PEM fuel cells
    (IEEE Computer Society, 2013) Sari A.; Balikci A.; Taskin S.; Aydin S.
    Fuel cells convert the chemical energy directly to the electrical energy and hence they are a very favorable alternative energy source. In the literature, there are many studies related to the modeling of fuel cells. Artificial neural networks (ANNs) is one of the promising techniques for modelling nonlinear systems such as fuel cells. The proposed model in this study doesn't require many parameters like other studies. Firstly, training and testing data was obtained the dynamic model of a PEM fuel-cell. Then, proposed ANN model outputs are compared with dynamic model ouputs Simulation results shows that the proposed ANN model can be used very efficiently for PEM fuel-cells without using many parameters. © 2013 The Chamber of Turkish Electrical Engineers-Bursa.
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    Analysis of thin dielectric cylindrical reflector having an arbitrary conic section profile illuminated by complex line source: H-polarization case
    (IEEE Computer Society, 2014) Oguzer T.; Kuyucuoglu F.; Avgin I.; Altintas A.
    Arbitrary conic section profile and thin dielectric reflector is analyzed by using the Method of Analytical Regularization (MAR) technique based on Riemann-Hilbert problem and Fourier inversion procedures. The reflector surface is assumed to be illuminated by an H-polarized complex line source type feed antenna. The convergence of the solution is verified and some changes in the radiation patterns are obtained especially for rather thicker cases. © 2014 IEEE.
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    Radiation performance of metamaterial cylindrical reflector antenna having a conic section profile illuminated by An E-polarized complex source line beam
    (IEEE Computer Society, 2014) Kuyucuoglu F.; Oguzer T.; Avgin I.
    Parabolic conic section profile, cylindrical metamaterial reflector antenna radiation characteristic is analyzed by using the Method of Analytical Regularization (MAR) techniques. E polarized complex source point is located at the focal point is used to illuminate the antenna. Far field radiation patterns are obtained for metamaterial and dielectric cases. © 2014 IEEE.
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    Naive Bayes classifier for continuous variables using novel method (NBC4D) and distributions
    (IEEE Computer Society, 2014) Yildirim P.; Birant D.
    In data mining, when using Naive Bayes classification technique, it is necessary to overcome the problem of how to deal with continuous attributes. Most previous work has solved the problem either by using discretization, normal method or kernel method. This study proposes the usage of different continuous probability distribution techniques for Naive Bayes classification. It explores various probability density functions of distributions. The experimental results show that the proposed probability distributions also classify continuous data with potentially high accuracy. In addition, this paper introduces a novel method, named NBC4D, which offers a new approach for classification by applying different distribution types on different attributes. The results (obtained classification accuracy rates) show that our proposed method (the usage of more than one distribution types) has success on real-world datasets when compared with the usage of only one well known distribution type. © 2014 IEEE.
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    Exploring PEMFCs for Powering Untethered Small-Scale Robots
    (IEEE Computer Society, 2024) Manikandan A.L.; Gurboga B.; Munzenrieder N.; Raman A.; Gardeniers H.J.G.E.; Susarrey-Arce A.; Abelmann L.; Khalil I.S.M.
    Magnetically guided untethered devices are used in a variety of medical applications. These devices are typically powered by onboard battery units. Hydrogen fuel cells (FC) are a promising alternative power source for such small-scale devices since they rely on a sustainable fuels which produce electric power from the redox reaction of hydrogen and oxygen across a proton exchange membrane (PEM). Understanding the impact of decreasing the active electrode area in FCs is crucial for deploying FCs in untethered devices and gaining insights into the challenges of downscaling the devices. This paper investigates the performance of PEM FCs (PEMFCs) when their active area is reduced, and when the FC is supplied with reactants at different flow rates from a PEM electrolyzer. PEMFCs with three active electrode areas, 3.5 × 3.5 cm2, 2.7×2.7 cm2, and 1.6×1.6 cm2were designed, fabricated, and characterised. Maximum fuel cell output powers of 0.3 W, 0.09 W, and 0.03 W (maximum power densities of 0.025 W/cm2, 0.012 W/cm2, and 0.013 W/cm2) corresponding to the three aforementioned areas were achieved. Mathematical modeling of the PEMFC simulated the FC response, providing insights into the activation kinetics of the fuel cell. The smallest PEMFC with an active area of 1.6 × 1.6 cm2was used to power an inductor coil (rated 130 mA, 150 mH, 8 Ω). This study can guide the development of FCs to power untethered devices. © 2024 IEEE.