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Öğe APPLICATION OF ARTIFICIAL NEURAL NETWORK FORECASTING OF DAILY MAXIMUM TEMPERATURE IN KONYA(BRNO UNIV TECHNOLOGY VUT PRESS, 2011) Tasdemir, Sakir; Cinar, Ahmet CevahirWeather forecast is one of the most effective factors on human beings and other living creatures. Maximum air temperature is one of the most important parameters to be estimated for meteorology, because the maximum and minimum temperature data is the outlook of the institution and the most interesting aspect of weather forecast presentations. Many meteorological variables play an important role in estimating the lowest and highest temperature of the day. Today, numerical models are mainly used in weather forecasting. The incredible success of Artificial Neural Networks (ANN) in classification and estimation makes it necessary to use this approach in the area of meteorology. Apart from known methods, ANN, which is an artificial intelligence technique, was used to forecast maximum temperature, which is the modeling of a non-linear process. In this study, the data for the years 2008 and 2009 was used that were obtained from the Turkish Meteorological Data Archive System and The Directorate of Konya Airfield Meteorology Station, which are the institutions of the General Directorate of Turkish State Meteorological Services. The developed ANN has 6 inputs and 1 output. The six input variables were respectively the temperature at 850 hpa level (t(850)-degrees C), daily average actual pressure (P-mb), daily minimum temperature (t(min)-degrees C), daily mean temperature (t(mean)-degrees C), daily average relative humidity (H-%) and daily sunshine duration (SD-hour). The output parameter value was the daily maximum temperature (t(max)-degrees C). Feed-forward back-propagation ANN model was used in this study. Levenberg-Marquardt (trainlm) training algorithm and Hyperbolic Tangent Sigmoid (tansig) and Logarithmic Sigmoid (logsig) transfer function were tried in the software developed in MATLAB and the results were obtained. The study put forth that accuracy rates and mean absolute error (MSE) obtained from training and test operations can be used in determining the maximum air temperature in the generated model.Öğe Artificial neural network and fuzzy expert system comparison for prediction of performance and emission parameters on a gasoline engine(PERGAMON-ELSEVIER SCIENCE LTD, 2011) Tasdemir, Sakir; Saritas, Ismail; Ciniviz, Murat; Allahverdi, NovruzThis 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. (C) 2011 Elsevier Ltd. All rights reserved.Öğe Artificial neural network based on predictive model and analysis for main cutting force in turning(SILA SCIENCE, 2012) Tasdemir, SakirIn manufacturing technology, the foremost issue influencing the usability and cost of products is metal cutting operations. In this operation, it is very difficult to develop a model including all the cutting parameters and tool geometry. Tool geometry that will enable the most suitable cutting conditions will increase the quality of workpiece surface and so the efficiency of the process. The incredible success of Artificial Neural Networks (ANN) in classification and estimation makes it necessary to use this approach in the area. Apart from known methods, ANN, which is an artificial intelligence technique, was used to estimate main cutting force, which is the modeling of a non-linear process. In this study, a novel artificial neural network model was developed in turning operation to determine the main cutting force. The developed ANN has 3 inputs and 1 output. The three input variables were feedrate (f-mm/rev), approaching angle (chi-degrees), rake angle (gamma-degrees), respectively. The output parameter value was the main cutting force (Fc-N). The results of ANN and experimental data were compared by statistical. The study put forth that accuracy rates obtained from training and test operations can be used in determining the main cutting force in the generated model.Öğe Artificial neural network model for prediction of tool tip temperature and analysis(2018) Tasdemir, SakirTechnological improvements put computer systems in the center of our life and various scientific disciplines. These can range from controlling a device in our home to public institutions and the industry. One of these disciplines is a sub-area in mechanical engineering called machining is concerned with not only mechanical systems but also computer aided systems. Artificial Neural Networks -an area of artificial intelligence- which is concerned with learning and decision making of computers is a field that scientists are very interested in. In this study, an Artificial Neural Network system was designed for predicting the temperature at the tool tip in the machining process. In the metal cutting process, tool tip temperature is one of the conditions that must be identified, analyzed and monitored. For this purpose, an ANN model was developed to determine the tool tip temperature in the turning process. In the designed ANN model, parameters consisting of three inputs and one output were used. The three input variables were rake angle (?-o), approaching angle (-o), feedrate (fmm/rev) respectively. The output parameter was the tool tip temperature (T-0C). The most appropriate model was determined according to Mean Squared Error ratio. In the test phase of the Artificial Neural Network, the smallest Mean Squared Error was obtained with the Artificial Neural Network topology formed as 3-4-1. In this Artificial Neural Network model, calculations were Mean Squared Error0.00144, R20.9956 (absolute fraction of variance) in the training phase and Mean Squared Error0.00231, R20.9954 in the test phase. The results show that the designed Artificial Neural Network model can be used for predicting and analyzing tool tip temperatureÖğe Determination of body measurements on the Holstein cows using digital image analysis and estimation of live weight with regression analysis(ELSEVIER SCI LTD, 2011) Tasdemir, Sakir; Urkmez, Abdullah; Inal, SerefIn this study, the body measurements (BMs) of Holstein cows were determined using digital image analysis (IA) and these were used to estimate the live weight (LW) of each cow. For this purpose, an image capture arrangement was established in a dairy cattle farm. BMs including wither height (WH), hip height (HH), body length (BL), hip width (HW), plus the LWs of cows were first determined manually, by direct measurement. Then the digital photos of cows were taken from different directions synchronously and analyzed by IA software to calculate WH, HH, BL and HW of each cow. After comparing the BMs obtained by IA with the manual measurements, the accuracy was determined as 97.72% for WH, 98.00% for HH, 97.89% for BL and 95.25% for HW. The LW estimation using BMs was then performed by the aid of the regression equations, and the correlation coefficient between the estimated and real (manual) LW values obtained by weighing was calculated as 0.9787, which indicates the IA method is appropriate for LW estimation of Holstein cows. (C) 2011 Elsevier B.V. All rights reserved.Öğe FUZZY LOGIC MODELING OF PERFORMANCE OF MICROBIAL FUEL CELL WITH 10x10 AND 11x11 CM2 MEMBRANE(BAKU STATE UNIV, INST APPLIED MATHEMATICS, 2018) Tasdemir, Sakir; Hayder, Mustafa Akram; Dincer, Kevser[Abstract not Available]Öğe A fuzzy rule-based system for predicting the live weight of Holstein cows whose body dimensions were determined by image analysis(TUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEY, 2011) Tasdemir, Sakir; Urkmez, Abdullah; Inal, SerefThe aim of this study was to determine the body measurement of Holstein cows through image analysis (IA) and to estimate their live weight (LW) by means of a fuzzy rule-based model using the body measurements. For this purpose, a photography environment was established at a dairy cattle farm where a large number of cows were kept. First, digital photographs of each animal were synchronously taken from different directions with Canon EOS 400D cameras. At the same time, body dimensions, namely wither height (WH), hip height (HH), body length (BL), and hip width (NW), of the cows were manually measured using a laser meter and measuring stick. The LWs of the cows were found with a weighing scale and the data were automatically saved on a computer. In the second stage, the photos were analyzed by IA software developed in the Delphi programming language and body measurements were computed. Manually measured values were very close to IA results. Finally, a fuzzy system was developed by using these body measurements. This fuzzy system was developed by using MATLAB software. Weights that were estimated with the developed knowledge-based system were compared with those found by the platform scale. The correlation coefficient was calculated (r = 0.99). There was a statistically meaningful relationship between the compared data. The developed system can be used confidently, and the system on which the experiments were performed can be modeled successfully.Öğe The use of artificial neural network for prediction of grain size of 17-4 pH stainless steel powders(ACADEMIC JOURNALS, 2010) Findik, Tayfun; Tasdemir, Sakir; Sahin, IsmailThis study is aimed to deals with artificial neural network (ANN) approach for prediction grain size (GS) of 17 - 4 pH stainless steel powders. Experimental data which were obtained from experimental studies in a laboratory environment have been used for this modeling. Using some of the experimental data for training and testing an ANN for GS was developed. In these systems, output parameters GS has been determined using input parameters including environment, time, speed, ball diameter, ball ratio, and material. When experimental data and results obtained from ANN were compared by regression analysis in Matlab, it was determined that both groups of data are consistent. The correlation coefficient between estimated GS values and experimental data obtained are 0.99 for traing and 0.98 for testing respectively. The correlation coefficient is closely to 1. This coefficient shows that there is a strong relationship between these data. Also, the accuracy rate was 98.97% for GS. As a result, it has been shown that designed ANN can be used reliably in powder metallurgic industry and engineering.
