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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 Banknote classification using artificial neural network approach(2016) Kaya, Esra; Yasar, Ali; Saritas, IsmailIn this study, clustering process has been performed using artificial neural network (ANN) approach on the pictures belonging to our dataset to determine if the banknotes are genuine or counterfeit. Four input parameters, one hidden layer with 10 neurons and one output has been used for the ANN. All of these parameters were real-valued continuous. Data were extracted from images that were taken from genuine and forged banknote-like specimens. For digitization, an industrial camera usually used for print inspection was used. The final images have 400x 400 pixels. Due to the object lens and distance to the investigated object gray-scale pictures with a resolution of about 660 dpi were gained. Wavelet Transform tool were used to extractfeatures from images. Four input parameters are processed in the hidden layer with 10 neurons and the output realizes the clustering process. The classification process of 1372 unit data by using ANN approach is sure to be a success as much as the actual data set. The regression results of the clustering process is considerably well. It is determined that the training regression is 0,99914, testing regression is 0,99786 and the validation regression is 0,9953, respectively. Based on the results obtained, it is seen that classification process using ANN is capable of achieving outstanding successÖğe Classification of leaf type using artificial neural networks(2015) Yasar, Ali; Saritas, Ismail; Sahman, M. Akif; Dundar, A. OktayA number of shape features for automatic plant recognition based on digital image processing have been proposed by Pauwels et al. in 2009. Then Silva et al in 2014 have presented database comprises 40 different plant species. We performed in our study a classification process using dataset and artificial neural networks which have been prepared by Silva and et al. It has been determined that classification accuracy is over 92%.Öğe The control of magnetic filters by FPGA based fuzzy controller(SILA SCIENCE, 2012) Ozkan, Ilker Ali; Saritas, Ismail; Herdem, SaadetdinOne of the most effective methods to clean magnetic particles in industrial liquids is magnetic filtration. Magnetic filters (MF) are being used increasingly since they can work under various conditions like high temperature and radioactivity. However, there are many factors like particle concentration and flow speed that affect magnetic filter performance (MFP). In this study, a portable, compact MF kit that cleans micron-sized magnetic particles and is suitable for industrial purposes was designed and realized. A FPGA based fuzzy control system that considers the factors which influence MF performance was developed and integrated with MF kit. With this control system both the MF performance is kept at maximum and MF can clean itself. Thus, besides an effective filtration process which increases quality, sustainability of production and energy save is achieved.Öğe Determination of the drug dose by fuzzy expert system in treatment of chronic intestine inflammation(SPRINGER, 2009) Saritas, Ismail; Ozkan, Ilker A.; Allahverdi, Novruz; Argindogan, MustafaIn this study, chronic intestine illness symptoms such as sedimentation and prostate specific antigen are used for the design of fuzzy expert system to determine the drug (salazopyrine) dose. Suitable drug dose for patients is obtained by using data of ten patients. The results of some patients are compared with the doses recommended to them by the physician. As a result, it has been seen that proposed system is helped to shorten the treatment duration and minimize or remove the negative effects of determination of drug dose for helping physicians.Öğe The effects of fuzzy control of magnetic flux on magnetic filter performance and energy consumption(PERGAMON-ELSEVIER SCIENCE LTD, 2010) Saritas, Ismail; Ozkan, Ilker Ali; Herdem, SaadetdinMagnetic filters are used effectively in many industrial areas to clean up technological liquids and gases from micron and submicron size magnetic particles. Performance of the magnetic filter is affected by technological parameters like flow rate of the industrial liquid and concentration and flux magnitude of the magnetic filter. These parameters exhibit differences depending on the field of work. Controlling of magnetic filters without regard to these parameters has disadvantages such as low filter performance, ineffectiveness in parameter changes and high energy consumption. To remove these disadvantages, an adaptive fuzzy control system which considers these technological parameters was designed and realized. When the realized filter is compared to the filter that ignores technological parameters, it is observed that energy can be saved at a rate of 68% annually. (C) 2010 Elsevier Ltd. All rights reserved.Öğe Effects of nanographene added to the matrix material at different rates on the Charpy impact energy(Selçuk Üniversitesi, 2021) Demirci, Ibrahim; Saritas, IsmailIn this study, the Charpy impact behaviors and hardness values of nanographene-added epoxies, which are used in the aviation and space, automotive, and maritime industries that require advanced technology, were investigated. The additive ratio of nanographene used as the filling material in the matrix should be determined. Otherwise, poorly determined additive ratios reduce the impact resistance of the epoxies. In this study, nano graphene additive rates were determined as 0.5%, 0.8% and 1.2% by weight. Charpy impact tests were performed on the epoxy composites at these ratios and their hardness values were determined. A comment was made regarding the link between the increased nanographene values and hardness.Öğe Evaluation of The Classification Performance of Methods Developed in ANN Training(Selçuk Üniversitesi, 2021) Yasar, Ali; Saritas, IsmailIn this study, the performance and success of some models used in the training of artificial neural networks are compared. The UCI machine learning database was used for the performance evaluation and comparison process and for the IRIS dataset), which we often encounter in performance evaluations. In the study, the classification performances of our data sets were calculated using Feedforward Neural Network Cross-Validation (FNN-CV), Probabilistic Neural Network Cross-Validation (PNN-CV) and Recurrent Neural Network Cross-Validation (RNNCV) learning techniques. Our data has been classified using a 10-fold cross validation technique. The RNN-CV method for the IRIS data set proved to be a good classification method by achieving 100% accuracy success which shows that it should be used in classification problems.Öğe FPGA-based self-organizing fuzzy controller for electromagnetic filter(SPRINGER, 2017) Ozkan, Ilker Ali; Herdem, Saadetdin; Saritas, IsmailElectromagnetic filters (EMF) are used to clean magnetic particles in industrial liquids which play important roles to sustain the high-quality material production in industrial fields. In this study, an FPGA-based adaptive fuzzy controller is realized to sustain high performance of an EMF. An experiment is performed by using realized adaptive controller on an EMF set. The results obtained from the experiment are compared with the results of the conventional fuzzy controller. It is observed that adaptive fuzzy controller is performed better than the conventional fuzzy controller.Öğe Fuzzy expert system design for operating room air-condition control systems(PERGAMON-ELSEVIER SCIENCE LTD, 2009) Etik, Nazmi; Allahverdi, Novruz; Sert, Ibrahim Unal; Saritas, IsmailIn this study, a controlled fuzzy expert system (FES) was designed to provide the conditions necessary for operating rooms. For this purpose, existing operating rooms have been Studied to see if there are more useful, reliable and comfortable ones. How an operating room can be controlled with FES and its advantages and disadvantages have also been researched. For a theoretically visible FES to show system's advantage a prototype operating room was built and a suitable configuration was designed. In this system, heat, humidity, oxygen and particles were used as input parameters, and a fresh air entrance and fail circulation were chosen as output parameters. With the help of all expert, appropriate linguistic expressions and the membership function of these expressions were defined. The sensors were classified and sensor information was transferred to computer by means of an interface designed. In order to transfer the data to the system simultaneously, an interface was written in C#. Whether it provides the most suitable control for the system prototype was determined by simulating the operation with varying numbers of patients and operation personnel. In these trials, input, output and other necessary parameters were collected in the computer. In the study, we obtained excellent results in prototype operating room control with FES. The analyses of the results carried out indicated that the controls performed with FES provide more economical, comfortable, reliable and consistent controls and that they are feasible in a real operating room. (C) 2009 Elsevier Ltd. All rights reserved.Öğe Prediction of Breast Cancer Using Artificial Neural Networks(SPRINGER, 2012) Saritas, IsmailIn this study, an artificial neural network (ANN) was developed to determine whether patients have breast cancer or not. Whether patients have cancer or not and if they have its type can be determined by using ANN and BI-RADS evaluation and based on the age of the patient, mass shape, mass border and mass density. Though this system cannot diagnose cancer conclusively, it helps physicians in deciding whether a biopsy is required by providing information about whether the patient has breast cancer or not. Data obtained from 800 patients who were diagnosed with cancer definitively through biopsy. The definitive diagnosis corresponding to each patient and the data from ANN model results were investigated using Confusion matrix and ROC analyses. In the test data of the ANN model that was implemented as a result of these analyses, disease prediction rate was 90.5% and the health ratio was 80.9%. It is seen from these high predictive values that the ANN model is fast, reliable and without any risks and therefore can be of great help to physicians.
