Sakir Tasdemir

Artificial neural network and fuzzy expert system comparison for prediction of performance and emission parameters on a gasoline engine

This study is deals with artificial neural network (ANN) and fuzzy expert system (FES) modelling of a gasoline engine to predict engine power, torque, specific fuel consumption and hydrocarbon emission. In this study, experimental data, which were obtained from experimental studies in a laboratory environment, have been used. Using some of the experimental data for training and testing an ANN for the engine was developed. Also the FES has been developed and realized. In this systems output parameters power, torque, specific fuel consumption and hydrocarbon emission have been determined using input parameters intake valve opening advance and engine speed. When experimental data and results obtained from ANN and FES were compared by t-test in SPSS and regression analysis in Matlab, it was determined that both groups of data are consistent with each other for p > 0.05 confidence interval and differences were statistically not significant. As a result, it has been shown that developed ANN and FES can be used reliably in automotive industry and engineering instead of experimental work.

Temperature increase during orthodontic bonding with different curing units using an infrared camera

Abstract Aim. To evaluate the effects of different curing units and light-tip tooth surface distances on the temperature increase generated during orthodontic bonding, using an infrared camera (IR) and artificial neural networks (ANN). Materials and methods. Fifty-two freshly extracted human premolar teeth were used. Metallic orthodontic brackets were bonded to the buccal surfaces of the teeth and thermal records were taken using an IR camera and ANN. Brackets were cured with a light-emitting diode (LED) and high intensity halogen (HQTH). Teeth were divided into four groups according to the curing units (LED and HQTH) and curing distances (from tooth surface and 10 mm away from tooth surface). The results were analyzed with analysis of variance (ANOVA) and the Tukey HSD test. Results. The ANOVA and Tukey HSD tests revealed that temperature changes were influenced by the type of light source and exposure times. All groups revealed significant differences between each other (p < 0.001). The highest surface temperature increase was gained from curing with a LED unit from the tooth surface (11.35°C ± 0.91°C). The lowest surface temperature increase was gained from curing with a HQTH unit 10 mm away from the tooth surface (2.57°C ± 0.6°C). Conclusion. The LED unit induced significantly higher temperature changes than did the HQTH. The temperature increase during orthodontic bonding was increased with long exposure time. A shorter light-tip tooth surface distance leads to greater increases in temperature.

Determination of the resistance characteristics of self-compacting concrete samples by artificial neural network

In this study, in order to determine the resistance characteristics of self-compacting concrete (SCC) samples, experiments were done in the Konya Cement Factory, Ready-mix Concrete Establishment. Four different mixture proportions were chosen in the experimental study. 24 samples of the 4 mixtures were selected in order to set the cube compression strength. For each mixture, these 24 samples were broken down within 28 days and the characteristics of cube compression strength were obtained. After 28 days, compression strength average was found to be 50.0300 MPa. A model of Artificial Neural Network (ANN) was designed for this study and the results were obtained in this model of ANN. Both experimental and ANN data was analyzed with SPSS statistical packet software. The result of statistical analysis (p=0.9972) has been done in 95% of confidence interval. It has been seen that the ANN can be used as reliable modelling method for similar studies.