Yucel Kocyigit

An adaptive neuro-fuzzy inference system approach for prediction of tip speed ratio in wind turbines

This paper introduces an adaptive neuro-fuzzy inference system (ANFIS) model to predict the tip speed ratio (TSR) and the power factor of a wind turbine. This model is based on the parameters for LS-1 and NACA4415 profile types with 3 and 4 blades. In model development, profile type, blade number, Schmitz coefficient, end loss, profile type loss, and blade number loss were taken as input variables, while the TSR and power factor were taken as output variables. After a successful learning and training process, the proposed model produced reasonable mean errors. The results indicate that the errors of ANFIS models in predicting TSR and power factor are less than those of the ANN method.

Classification of EEG Recordings by Using Fast Independent Component Analysis and Artificial Neural Network

Since there is no definite decisive factor evaluated by the experts, visual analysis of EEG signals in time domain may be inadequate. Routine clinical diagnosis requests to analysis of EEG signals. Therefore, a number of automation and computer techniques have been used for this aim. In this study we aim at designing a MLPNN classifier based on the Fast ICA that accurately identifies whether the associated subject is normal or epileptic. By analyzing a data set consisting of 100 normal and 100 epileptic EEG time series, we have found that the MLPNN classifier based on the Fast ICA achieved and sensitivity rate of 98%, and specificity rate of 90.5%. The results demonstrate that the testing performance of the neural network diagnostic system is found to be satisfactory and we think that this system can be used in clinical studies. Since the time series analysis of EEG signals is unsatisfactory and requires specialist clinicians to evaluate, this application brings objectivity to the evaluation of EEG signals.

Differentiating types of muscle movements using a wavelet based fuzzy clustering neural network

: The electromyographic signals observed at the surface of the skin are the sum of many small action potentials generated in the muscle fibres. After the signals are processed, they can be used as a control source of multifunction prostheses. The myoelectric signals are represented by wavelet transform model parameters. For this purpose, four different arm movements (elbow extension, elbow flexion, wrist supination and wrist pronation) are considered in studying muscle contraction. Wavelet parameters of myoelectric signals received from the muscles for these different movements were used as features to classify the electromyographic signals in a fuzzy clustering neural network classifier model. After 1000 iterations, the average recognition percentage of the test was found to be 97.67% with clustering into 10 features. The fuzzy clustering neural network programming language was developed using Pascal under Delphi.

Imbalanced data classifier by using ensemble fuzzy c-means clustering

Pattern classifiers developed with the imbalanced data set tend to classify an object to the class with the highest number of samples, resulting in higher overall classifier accuracy but lower sensitivity. A new approach based on a dynamic under-sampling procedure is therefore proposed to improve the classification of imbalanced datasets that are quite common in bio-medicine. To overcome a class imbalance, the dataset is resampled by using the ensemble fuzzy c-means clustering method. The under-sampling procedure is then applied to the majority class to balance the size of the classes. Compared to the existing classifiers, the proposed method yields not only higher classification accuracy and sensitivity but also more stable classification performance under different data sets, classifiers and their parameters, indicating that it is independent of particular clustering or classification methods.

Landing sequencing modelling with fuzzy logic: Opportunistic approach for unmanned aerial systems

From the beginning of 21th century, the quantities and types of Unmanned Aerial Systems (UAS) have grown enormously and they become to boost substitution for the manned systems. UAS are creating advancement with a massive potential to change military operations and also enabling the new civilian applications. The vital issue for the airspace designers and managers is how to integrate manned and unmanned systems to the interoperability airspace. It is global arrangement that UAS operation in the integrated airspace must meet in any operational standards, procedures and safety issues as manned aircraft. For today, Air Traffic Management (ATM) is a dynamic and integrated environment including both manned and unmanned systems. Air Traffic Control (ATC) systems have the obligation to sustain an efficient and safe airspace utilization of manned and unmanned systems together. On the other hand, tendency for civilian and military applications about future is substituting unmanned aerial systems for manned aerial systems. In this paper, we present an analytic approach for UAS landing sequencing modelling in the dynamic airspace including different aerodynamic specifications or mission types for both military and civilian UAS via fuzzy logic modelling. During the designing model, the MATLAB Fuzzy FIS (Fuzzy Inference System) is used with realistic data and the user friendly interface is created via MATLAB/GUI.

Hybrid imbalanced data classifier models for computational discovery of antibiotic drug targets.

Identification of drug candidates is an important but also difficult process. Given drug resistance bacteria that we face, this process has become more important to identify protein candidates that demonstrate antibacterial activity. The aim of this study is therefore to develop a bioinformatics approach that is more capable of identifying a small but effective set of proteins that are expected to show antibacterial activity, subsequently to be used as antibiotic drug targets. As this is regarded as an imbalanced data classification problem due to smaller number of antibiotic drugs available, a hybrid classification model was developed and applied to the identification of antibiotic drugs. The model was developed by taking into account of various statistical models leading to the development of six different hybrid models. The best model has reached the accuracy of as high as 50% compared to earlier study with the accuracy of less than 1% as far as the proportion of the candidates identified and actual antibiotics in the candidate list is concerned.

Fast global Fuzzy C-Means clustering for ECG signal classification

Fuzzy clustering plays an important role in solving problems in the areas of pattern recognition and fuzzy model identification. The Fuzzy C-Means algorithm is one of widely used algorithms. It is based on optimizing an objective function, being responsive to initial conditions; the algorithm usually leads to local minimum results. Aiming at above problem, the fast global Fuzzy C-Means clustering algorithm (FGFCM) has been proposed, which is an incremental approach to clustering, and does not depend on any initial conditions. The algorithm was applied on ECG signals to classification.

Using LBG algorithm for extracting the features of EMG signals

The Electromyographic (EMG) signals observed at the surface of the skin is the sum of many small action potentials generated in the muscle fibers. There is only a pattern for each EMG signals, which are generated by biceps and triceps muscles. There are different types of signal processing in order to find out the feature values for true classification in this pattern. In this study, the Feature values belong to 4 different arm movements are obtained by using clustering methods, i.e K-means, Fuzzy C-means, and LBG after applying Wavelet Transform to EMG signals . Then these feature values are compared each other by KEYK and Quadratic Discriminant Analysis classifier.