Yazar "Kocyigit, Gokhan" seçeneğine göre listele

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    Educational software for power system analysis
    (Manchester Univ Press, 2014) Guney, Irfan; Kocyigit, Gokhan; Onat, Nevzat
    This paper presents educational software that has an unconventional structure and friendly interface and can be used in computer-aided analysis of power systems. The software provides effective and fast solutions to in-line modeling of power systems, failure and power flow analyses. In addition, students using this software can demonstrate their own models and follow the solution steps in detail. The software also presents educational details and graphical mediums, similar to solutions provided by software developed for professional use. For this reason, it has a form that can be used in a workshop environment and distance learning activities.
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    Estimating Average Power of Welding Process With Emitted Noises Based on Adaptive Neuro Fuzzy Inference System
    (IEEE-Inst Electrical Electronics Engineers Inc, 2023) Gokmen, Gokhan; Akinci, Tahir Cetin; Kocyigit, Gokhan; Kiyak, Ismail; Akbas, M. Ilhan
    In this study, the average power consumption of an electrode welding machine during the welding process was estimated using the features of the sound emitted during welding. First, the instantaneous values of electrode current and voltage and the sound emitted during the welding process were recorded simultaneously. The minimum, maximum, average, root mean square (RMS), and energy values of the sound data were found and feature extraction was performed, and the instantaneous power and average power values were calculated using the instantaneous current and voltage values. Three Adaptive Neuro-Fuzzy Inference Systems (ANFIS) using the sound features as inputs and average power values as outputs were created, and their results were compared. The average power values consumed during the welding process have been successfully estimated at a rate of 87-95%.
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    Lifetime Prediction for a Cell-on-Board (COB) Light Source Based on the Adaptive Neuro-Fuzzy Inference System (ANFIS)
    (Hindawi Ltd, 2021) Kiyak, Ismail; Gokmen, Gokhan; Kocyigit, Gokhan
    Predicting the lifetime of a LED lighting system is important for the implementation of design specifications and comparative analysis of the financial competition of various illuminating systems. Most lifetime information published by LED manufacturers and standardization organizations is limited to certain temperature and current values. However, as a result of different working and ambient conditions throughout the whole operating period, significant differences in lifetimes can be observed. In this article, an advanced method of lifetime prediction is proposed considering the initial task areas and the statistical characteristics of the study values obtained in the accelerated fragmentation test. This study proposes a new method to predict the lifetime of COB LED using an artificial intelligence approach and LM-80 data. Accordingly, a database with 6000 hours of LM-80 data was created using the Neuro-Fuzzy (ANFIS) algorithm, and a highly accurate lifetime prediction method was developed. This method reveals an approximate similarity of 99.8506% with the benchmark lifetime. The proposed methodology may provide a useful guideline to lifetime predictions of LED-related products which can also be adapted to different operating conditions in a shorter time compared to conventional methods. At the same time, this method can be used in the life prediction of nanosensors and can be produced with the 3D technique.
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    Limited Carry-Propagate Multiply-Accumulate Unit Design for Reconfigurable Systems
    (Kaunas Univ Technology, 2017) Cini, Ugur; Kocyigit, Gokhan
    Counter and compressor arrays are frequently employed in multiplier design to efficiently reduce partial products in VLSI design. On the other hand, in reconfigurable systems, fast carry chains boost the performance of carry-propagate adders. So that, in reconfigurable systems, to save logic element area, counter and compressor trees are not employed as much since they require more area than carry-propagate scheme. In this work, carry-propagate multi-operand adders are employed in smaller blocks and the outputs are merged using double carry-save encoding to increase performance in reconfigurable systems. Hence, a more compact structure is achieved, compared to full redundant partial product reduction scheme providing comparable speed performance with counter array based carry-save structure. To show the effectiveness of the implementation, fused multiply-accumulate ( MAC) units are designed for various bit-widths. The structure is implemented on Altera (TM) Stratix III and Cyclone III FPGAs and the results show that, using least depth of pipeline, the throughput is better than regular carry-propagate and fully redundant carry-save reduction schemes.