Adapazarı Vocational High School https://hdl.handle.net/20.500.12619/476 2026-04-14T22:02:28Z 2026-04-14T22:02:28Z Ünal, Doğan Çelik, Muhsin Güngör, Mehmet Ali https://hdl.handle.net/20.500.12619/33260 2020-02-13T08:55:07Z 2015-01-01T00:00:00Z

ON THE TWO PARAMETER MOTIONS IN THE COMPLEX PLANE Ünal, Doğan; Çelik, Muhsin; Güngör, Mehmet Ali In this article, we investigate two-parameter motions in the complex plane. Also, we refer to some definitions, theorems and corollaries related to velocities, accelerations, poles and hodograph of a point in the complex planar motion.

2015-01-01T00:00:00Z Varan, Metin Yurtsever, Ulaş https://hdl.handle.net/20.500.12619/3983 2020-01-15T07:48:12Z 2018-01-01T00:00:00Z

Multi-DGAS: A pattern based educational framework design for power transformers faults interpretation and comparative performance analysis Varan, Metin; Yurtsever, Ulaş Taking necessary measures in advance for early prediction of the transformer failures are very crucial. Dissolved gas in oil analysis (DGA) techniques that used in electrical power systems has become an internationally accepted standard in condition based maintenance (CBM) studies have high complexity and increasing demands of the quick-service-ready paradigm in power transformers. This standard includes the implementation steps of various complex DGA methods named basic gas, Rogers, Doernenburg, Duval triangle, and pentagon. Mentioned methods are quite complicated and their application steps are strict rules defined in detailed procedures. The adaptation of these methods to transformer maintenance procedures that prepared according to standards and tested for correctness creates a valuable tool. In this study, a pattern based educational framework has been designed to integrate state of arts DGA techniques help for trainers, graduate researchers working on DGA techniques, and also for maintenance teams while taking critical decisions on power transformers. Developed Multi-DGAS educational framework is designed for modular construction that can be integrated with online condition monitoring and diagnostic systems for electric power systems. Success of the proposed framework revealed with newly proposed pentagon method in regard with comparison of classical methods by using oil samples which have been taken under the energized condition of different 135 transformers within multi-phase which failure histories already known. Analysis of dissolved gases in transformer oil is performed according to ASTM-D 3612 procedure using 3800 GC Varian gas chromatograph apparatus.

2018-01-01T00:00:00Z Yurtsever, Ulaş Yurtsever, Meral Şengil, İsmail Ayhan Kıratlı Yılmazçoban, Nursel https://hdl.handle.net/20.500.12619/3981 2020-01-15T07:48:12Z 2015-01-01T00:00:00Z

Fast artificial neural network (FANN) modeling of Cd(II) ions removal by valonia resin Yurtsever, Ulaş; Yurtsever, Meral; Şengil, İsmail Ayhan; Kıratlı Yılmazçoban, Nursel In the existing research, firstly, Cd adsorption properties and kinetics were studied on valonia tannin resin (VTR) from aqueous solutions at optimized process parameters such as temperature, pH of solution, initial ion concentration, and contact time. Then, a four-layer fast artificial neural network was constructed and tested to model the equilibrium data of Cd metal ions onto VTR. The properties of the VTR and the experimental conditions were used as inputs to predict the corresponding cadmium uptake at equilibrium conditions. The constructed ANN was also found to be precise in modeling the cadmium adsorption isotherms and kinetics for all inputs during the training process. ANN models were setup with varying numbers of hidden layers and different neuron numbers at each hidden layer as input parameters, mean squared error values were calculated for the train, test, and overtraining caution system status and the proper model according to these values was determined. The obtained simulation results showed that the applied technique of ANN has better adjusted the equilibrium data of the Cd adsorption when compared with the conventional isotherm models.

2015-01-01T00:00:00Z Yurtsever, Meral Yurtsever, Ulaş https://hdl.handle.net/20.500.12619/3985 2020-01-15T07:48:12Z 2019-01-01T00:00:00Z

Use of a convolutional neural network for the classification of microbeads in urban wastewater Yurtsever, Meral; Yurtsever, Ulaş Scientists are on the lookout for a practical model that can serve as a standard for sorting out, identifying, and characterizing microplastics which are common occurrences in water sources and wastewaters. The microbeads (MBs) used in cosmetics and discharged into the sewer systems after use cause substantial microplastics pollution in the receiving waters. Today, the use of plastic microbeads in cosmetics is banned. The existing use cases are to be discontinued within a few years. Yet, there are no restrictions regarding the use of microbeads in a number of industries, cleaning products, pharmaceuticals and medical practices. In this context, the determination and classification of MBs which had so far been discharged to water sources and which continue to be discharged, represent crucial problems. In this work, we examined a new approach for the classification of MBs based on microscopic images. For classification purposes, Convolutional Neural Network (CNN) -a Deep Learning algorithm- was employed, whereas GoogLeNet architecture served as the model. The network is built from scratch, and trained then after tested on a total of 42928 images containing MBs in 5 distinct cleansers. The study performed with the CNN which achieved a classification performance of 89% for MBs in wastewater. (C) 2018 Elsevier Ltd. All rights reserved.

2019-01-01T00:00:00Z