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Optimized Bilevel Classifier For Brain Tumor Type And Grade Discrimination Using Evolutionary Fuzzy Computing, Kavitha Srinivasan, Mohanavalli Subramaniam, Bharathi Bhagavathsingh
Optimized Bilevel Classifier For Brain Tumor Type And Grade Discrimination Using Evolutionary Fuzzy Computing, Kavitha Srinivasan, Mohanavalli Subramaniam, Bharathi Bhagavathsingh
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, an optimized bilevel brain tumor diagnostic system for identifying the tumor type at the first level and grade of the identified tumor at the second level is proposed using genetic algorithm, decision tree, and fuzzy rule-based approach. The dataset is composed of axial MRI of brain tumor types and grades. From the images, various features such as first and second order statistical and textural features are extracted (26 features). In the first level, tumor type classification was done using decision tree constructed with all features. Further evolutionary computing using genetic algorithms (GA) was applied to select the …
Performance Comparison Of Optimization Algorithms In Lqr Controller Design For A Nonlinear System, Ümi̇t Önen, Abdullah Çakan, İlhan İlhan
Performance Comparison Of Optimization Algorithms In Lqr Controller Design For A Nonlinear System, Ümi̇t Önen, Abdullah Çakan, İlhan İlhan
Turkish Journal of Electrical Engineering and Computer Sciences
The development and improvement of control techniques has attracted many researchers for many years. Especially in the controller design of complex and nonlinear systems, various methods have been proposed to determine the ideal control parameters. One of the most common and effective of these methods is determining the controller parameters with optimization algorithms.In this study, LQR controller design was implemented for position control of the double inverted pendulum system on a cart. First of all, the equations of motion of the inverted pendulum system were obtained by using Lagrange formulation. These equations were linearized by Taylor series expansion around the …
Transmission Expansion Planning Based On A Hybrid Genetic Algorithm Approachunder Uncertainty, Ercan Şenyi̇ği̇t, Selçuk Mutlu, Bi̇lal Babayi̇ği̇t
Transmission Expansion Planning Based On A Hybrid Genetic Algorithm Approachunder Uncertainty, Ercan Şenyi̇ği̇t, Selçuk Mutlu, Bi̇lal Babayi̇ği̇t
Turkish Journal of Electrical Engineering and Computer Sciences
Transmission expansion planning (TEP) is one of the key decisions in power systems. Its impact on the system?s operation is excessive and long-lived. The aim of TEP is to determine new transmission lines effectively for a current transmission grid to fulfill the model objectives. However, to obtain a solution, especially under uncertainty, is extremely difficult due to the nonlinear mixed-integer structure of the TEP problem. In this paper, first genetic algorithm (GA) approaches for TEP are reviewed in the literature and then a new hybrid GA with linear modeling is proposed. The proposed GA method has a flexible structure and …
Automatic Prostate Segmentation Using Multiobjective Active Appearance Model In Mr Images, Ahad Salimi, Mohammad Ali Pourmina, Mohamma-Shahram Moien
Automatic Prostate Segmentation Using Multiobjective Active Appearance Model In Mr Images, Ahad Salimi, Mohammad Ali Pourmina, Mohamma-Shahram Moien
Turkish Journal of Electrical Engineering and Computer Sciences
Prostate cancer is the second largest cause of mortality among men. Prostate segmentation, i.e. the precise determination of the prostate region in magnetic resonance imaging (MRI), is generally used for prostate volume measurement, which can be used as a potential prostate cancer indicator. This paper presents a new fully automatic statistical model called the multiobjective active appearance model (MOAAM) for prostate segmentation in MR images. First, in the training stage, the appearance model, including the shape and texture model, is developed by applying principal component analysis to the training images, already outlined by a physician. Then noise and roughness are …
Power System Loading Margin Enhancement By Optimal Statcom Integration:A Case Study, Sasidharan Shreedharan, Tibin Joseph, Sebin Joseph, Chittesh Veni Chandran, Vishnu J., Vipin Das P
Power System Loading Margin Enhancement By Optimal Statcom Integration:A Case Study, Sasidharan Shreedharan, Tibin Joseph, Sebin Joseph, Chittesh Veni Chandran, Vishnu J., Vipin Das P
Articles
Safe and secure network operation with acceptable voltage level has become a challenging task for utilities requiring corrective measures to be implemented. Network upgrades using Flexible Alternating Current Transmission System devices are being considered to serve this purpose. To this end, static loading margin enhancement by optimal static synchronous compensator (STATCOM) allocation to enhance the power transfer capability with minimal voltage variation is presented. Maximum loadability is formulated as an optimization problem, subjected to voltage and small-signal stability constraints. Stability indices are presented and incorporated with the optimization problem to ensure secure operation under maximum loading. The scheme is executed …
Gacnn Sleeptunenet: A Genetic Algorithm Designing The Convolutional Neural Network Architecture For Optimal Classification Of Sleep Stages From A Single Eeg Channel, Shahnawaz Qureshi, Seppo Karilla, Sirirut Vanichayobon
Gacnn Sleeptunenet: A Genetic Algorithm Designing The Convolutional Neural Network Architecture For Optimal Classification Of Sleep Stages From A Single Eeg Channel, Shahnawaz Qureshi, Seppo Karilla, Sirirut Vanichayobon
Turkish Journal of Electrical Engineering and Computer Sciences
This study presents a method for designing--by a genetic algorithm, without manual intervention--the feature learning architecture for classification of sleep stages from a single EEG channel, when using a convolutional neural network called GACNN SleepTuneNet. Two EEG electrode positions were selected, namely FP2-F4 and FPz-Cz, from two available datasets. Twenty-five generations were involved in diagnosis without hand-crafted features, to learn the architecture for classification of sleep stages based on AASM standard. Based on the results, our model not only achieved the highest classification accuracy, but it also distinguished the sleep stages based on either of the two EEG electrode signals, …
Performance Enhancement Of Photovoltaic System Using Genetic Algorithm- Based Maximum Power Point Tracking, Brammanayagam Nagarani, Jothiswaroopan Nesamony
Performance Enhancement Of Photovoltaic System Using Genetic Algorithm- Based Maximum Power Point Tracking, Brammanayagam Nagarani, Jothiswaroopan Nesamony
Turkish Journal of Electrical Engineering and Computer Sciences
In recent years, enormous progress has been made on power generation using photovoltaic (PV) system. Solar power is one of the most promising renewable energy sources that is providing its benefit specifically in rural areas. With the increasing need for solar energy, it becomes necessary to extract maximum power from the PV array. The output power of the solar cells varies directly with the ambient temperature and Irradiation. Therefore, the challenge is to track maximum power from the PV array when environmental factors change. This paper focuses on increasing the efficiency of a PV array by incorporating artificial intelligence techniques. …
Generation Rescheduling Using Multiobjective Bilevel Optimization, Kiran Babu Vakkapatla, Srinivasa Varma Pinni
Generation Rescheduling Using Multiobjective Bilevel Optimization, Kiran Babu Vakkapatla, Srinivasa Varma Pinni
Turkish Journal of Electrical Engineering and Computer Sciences
This paper presents a new multiobjective optimization method that can be used for generation rescheduling in power systems. Generation rescheduling in restructured power systems is performed by the system operator for different operations like congestion management, day-ahead scheduling, and preventive maintenance. The nonlinear nature of the equations involved and the constraints on decision variables pose a challenge to find the global optimum. In order to find the global optimum using a genetic algorithm, a bilevel optimization method is proposed. In the proposed multiobjective optimization method, the objectives are classified as primary and secondary based on their relative importance. The best …