Omer Deperlioglu

Adaptive fuzzy logic controller for DC-DC converters

This paper introduces a complete design method to construct an adaptive fuzzy logic controller (AFLC) for DC-DC converter. In a conventional fuzzy logic controller (FLC), knowledge on the system supplied by an expert is required for developing membership functions (parameters) and control rules. The proposed AFLC, on the other hand, do not required expert for making parameters and control rules. Instead, parameters and rules are generated using a model data file, which contains summary of input-output pairs. The FLC use Mamdani type fuzzy logic controllers for the defuzzification strategy and inference operators. The proposed controller is designed and verified by digital computer simulation and then implemented for buck, boost and buck-boost converters by using an 8-bit microcontroller.

An educational tool for artificial neural networks

Artificial neural networks are some kind of data processing systems, which try to simulate features of the human brain and its learning process. So, they are widely used by researchers to solve different problems in optimization, classification, pattern recognition, associative memory and control. In this paper, an educational tool, which can be used to work on different kinds of neural network models and learn fundamentals of the artificial neural network, is described. At this point, the whole tool environment provides an advanced system to ensure mentioned functions. The developed system supports using MLP, LVQ and SOM models and related learning algorithms. It employs some visual, interactive tools, which enable users to compose their own neural networks and work on the developed networks easily. By using these tools, users can also understand and learn working mechanism of a typical artificial neural network, using features of different models and related learning algorithms.

INTELLIGENT LEARNING ENVIRONMENTS WITHIN BLENDED LEARNING FOR ENSURING EFFECTIVE C PROGRAMMING COURSE

This paper describes a blended learning implementation and experience supported with intelligent learning environments included in a learning management system (LMS) called @KU-UZEM. The blended learning model is realized as a combination of face to face education and e-learning. The intelligent learning environments consist of two applications named CTutor, ITest. In addition to standard e-learning tools, students can use CTutor to resolve C programming exercises. CTutor is a problem-solving environment, which diagnoses students’ knowledge level but also gives feedbacks and tips to help them to understand the course subject, overcome their misconceptions and reinforce learnt concepts. ITest provides an assessment environment in which students can take quizzes that were prepared according to their learning levels. The realized model was used for two terms in the “C Programming” course given at Afyon Kocatepe University. A survey was conducted at the end of the course to find out to what extent the students were accepting the blended learning model supported with @KU-UZEM and to discover students’ attitude towards intelligent learning environments. Additionally, an experiment formed with an experimental group who took an active part in the realized model and a control group who only took the face to face education was performed during the first term of the course. According to the results, students were satisfied with intelligent learning environments and the realized learning model. Furthermore, the use of intelligent learning environments improved the students’ knowledge about C programming.

Classification of the heart sounds via artificial neural network

Auscultation with stethoscope is a preferential method that the doctors use in order to differentiate normal cardiac systems from the abnormal ones that come out. On the other hand, the method of auscultation with stethoscope requires medical expert experience and careful listening. Because of the problems that can be faced, listening process with stethoscope, that is auscultation, falls behind in the search of the heart abnormalities. In this study, the frequency analysis of the heart sounds taken by the electronic stethoscope is implemented via a pocket computer. The parameters gained from the frequency analysis are again classified with the neural network on the pocket computer. Thus, hearing the heart sounds, medical doctors at the same time will be able to follow the information gained through the frequency and artificial neural network analysis on the pocket computer so that they will be able to make more accurate diagnoses.

Fuzzy control of dc–dc converters based on user friendly design

The aim of this paper is to show how to build a fuzzy controller and its membership functions automatically. In a fuzzy logic controller (FLC), the proposed method allows one easily to construct a set of membership functions, called shrinking-span membership functions (SSMFs). The FLC uses Mamdani-type fuzzy controllers for the defuzzification strategy and inference operators. The FLC hardware implementation is performed on an 8-bit microcontroller. Simulation results and experimental results demonstrate that the converter can be regulated with good performance even when subjected to input disturbance and load variation. The presented approach is generally valid for the design of an FLC, and can be applied to any dc–dc converter topologies.

Power electronics converter control based on rule based algorithm

The exact modeling of power converter circuit that includes several semiconductor switching devices is not easy due to the non-linear and time varying characteristics of the switching devices. Thus, controlling the system effectively without exact mathematical model is very important. The rule based controller RBC can easily be used in the control of any systems when an exact mathematical model of the system cannot be obtained. In this paper, a RBC for DC-DC converter is proposed for output voltage control of DC-DC converter. As compared to conventional fuzzy logic control FLC, it provides improved performances in terms of overshoot limitation and sensitivity to load and line voltage variations. Simulation and experimental results of buck converter confirm the validity of proposed control technique.

Underwater image enhancement based on contrast adjustment via differential evolution algorithm

Due to the absorption and scattering of light in underwater environment, underwater images have poor contrast and resolution. This situation generally causes to a color, which became more dominant than the other ones. Because of that, analyzing underwater images effectively and identifying any object under the water has become a difficult task. In this paper, an underwater enhancement approach by using differential evolution algorithm was proposed. In the approach, a contrast enhancement in the RGB space is done. By using the approach, both scattering and absorption effects are reduced.

Diabetes determination via vortex optimization algorithm based support vector machines

Approaches performed based on computer supported systems within the medical field gain more popularity day by day. In such systems, Artificial Intelligence techniques are often used for several disease diagnostics. Diabetes is one of these diseases. In this study, a diabetes diagnosis system based on Support Vector Machines has been proposed. Along training of SVM, Vortex Optimization Algorithm was used for determining the sigma parameter of the Gauss (RBF) kernel function, and a classification process has been done over the diabetes data set related to Pima Indians.

Performing discrete fourier transform of the heart sounds on the pocket computer

The heart is one of the two significant organs for human life. Today the most important method used by the medical doctors in diagnosing the disorders in connection with the heart is listening with the stethoscope. The sounds created by the flow of the blood entering the heart and movements of the valvular connected to the flow are listened and then the anormalities related to the heart are determined. On the other hand, the method of listening with the stethoscope has many constrains. In this study, from the heart sounds taken from the patient via electronic stethoscope digital data have been acquired, and with the discrete Fourier transform, which is one of the signal processing methods, frequency analysis has been performed, and both sound graphic and frequency spectrum have been displayed on the pocket computer. Thus, examining the frequency spectrum of the normal and anormal heart sounds, it is thought that the differences on the frequency axis of the normal and anormal sounds may be beneficial in diagnosing. Because unlike normal heart sounds, the diseased heart sounds taken from a patient who has any kind of heart disorder include higher frequency components. As the developed system can be operated in a mobile way, it is appropriate for the medical doctors to use it in clinical practically.

A Novel Underwater Image Enhancement Approach with Wavelet Transform Supported by Differential Evolution Algorithm

In this paper, a novel underwater image enhancement approach was proposed. This approach includes use of a method formed by the wavelet transform and the differential evolution algorithm. In the method, the contrast adjustment function was applied to the original underwater image first. Then, the homomorphic filtering technique was used to normalize the brightness in the image. After these steps, the underwater image was separated into its R, G, and B components. Then wavelet transform function was performed on each of the R, G, and B channels with Haar wavelet decomposition. Thus, detailed images were obtained for each of the color channels by wavelet transform low-pass approximation (cA), horizontal (cH), vertical (cV) and diagonal (cD) coefficients. Four parameters of weights (w) of each component cA, cH, cV, and cD situated in the R, G, and B color channels were optimized using differential evolution algorithm. In the proposed method, differential evolution algorithm was employed to find the optimum w parameters for Entropy and PSNR in separate approaches. Finally, unsharp mask filter was used to enhance the edges in the image. As an evaluation approach, performance of the proposed method was tested by using the criteria of entropy, PSNR, and MSE. The obtained results showed that the effectiveness of the proposed method was better than the existing techniques. Likewise, the visual quality of the image was also improved more thanks to the proposed method.