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Full-Text Articles in Engineering
Integration Of Neural Network And Distance Relay To Improve The Fault Localization On Transmission Lines, Linh Tran
Turkish Journal of Electrical Engineering and Computer Sciences
Power transmission lines are integral and very important components of power systems. Because of the length of these lines and the complexity of the power grids, the lines may encounter various incidents such as lightning strike, shortage, and breakage. When an incident or a fault occurs, a fast process of identification, localization, and isolation of the fault is desired. An accurate fault localization would have a great impact in reducing the restoration time of the system. One of the most popular solutions for fault detection and localization is the distance relays using the impedance-based algorithms. However, these relays are still …
Learning To Play An Imperfect Information Card Game Using Reinforcement Learning, Buğra Kaan Demi̇rdöver, Ömer Baykal, Ferdanur Alpaslan
Learning To Play An Imperfect Information Card Game Using Reinforcement Learning, Buğra Kaan Demi̇rdöver, Ömer Baykal, Ferdanur Alpaslan
Turkish Journal of Electrical Engineering and Computer Sciences
Artificial intelligence and machine learning are widely popular in many areas. One of the most popular ones is gaming. Games are perfect testbeds for machine learning and artificial intelligence with various scenarios and types. This study aims to develop a self-learning intelligent agent to play the Hearts game. Hearts is one of the most popular trick-taking card games around the world. It is an imperfect information card game. In addition to having a huge state space, Hearts offers many extra challenges due to its nature. In order to ease the development process, the agent developed in the scope of this …
Evolutionary Neural Networks For Improving The Prediction Performance Ofrecommender Systems, Berna Şeref, Gazi̇ Erkan Bostanci, Mehmet Serdar Güzel
Evolutionary Neural Networks For Improving The Prediction Performance Ofrecommender Systems, Berna Şeref, Gazi̇ Erkan Bostanci, Mehmet Serdar Güzel
Turkish Journal of Electrical Engineering and Computer Sciences
Recommender systems provide recommendations to users using background data such as ratings of users about items and features of items. These systems are used in several areas such as e-commerce, news websites, and article websites. By using recommender systems, customers are provided with relevant data as soon as possible and are able to make good decisions. There are more studies about recommender systems and improving their performance. In this study, prediction performances of neural networks are evaluated and their performances are improved using genetic algorithms. Performances obtained in this study are compared with those of other studies. After that, superiority …
Crash Course Learning: An Automated Approach To Simulation-Driven Lidar-Basedtraining Of Neural Networks For Obstacle Avoidance In Mobile Robotics, Stanko Kruzic, Josip Music, Mirjana Bonkovic, Frantisek Duchon
Crash Course Learning: An Automated Approach To Simulation-Driven Lidar-Basedtraining Of Neural Networks For Obstacle Avoidance In Mobile Robotics, Stanko Kruzic, Josip Music, Mirjana Bonkovic, Frantisek Duchon
Turkish Journal of Electrical Engineering and Computer Sciences
This paper proposes and implements a self-supervised simulation-driven approach to data collection used for training of perception-based shallow neural networks for mobile robot obstacle avoidance. In the approach, a 2D LiDAR sensor was used as an information source for training neural networks. The paper analyzes neural network performance in terms of numbers of layers and neurons, as well as the amount of data needed for reliable robot operation. Once the best architecture is identified, it is trained using only data obtained in simulation and then implemented and tested on a real robot (Turtlebot 2) in several simulations and real-world scenarios. …
Context-Aware System For Glycemic Control In Diabetic Patients Using Neural Networks, Owais Bhat, Dawood A. Khan
Context-Aware System For Glycemic Control In Diabetic Patients Using Neural Networks, Owais Bhat, Dawood A. Khan
Turkish Journal of Electrical Engineering and Computer Sciences
Diabetic patients are quite hesitant in engaging in normal physiological activities due to difficulties associated with diabetes management. Over the last few decades, there have been advancements in the computational power of embedded systems and glucose sensing technologies. These advancements have attracted the attention of researchers around the globe developing automatic insulin delivery systems. In this paper, a method of closed-loop control of diabetes based on neural networks is proposed. These neural networks are used for making predictions based on the clinical data of a patient. A neural network feedback controller is also designed to provide a glycemic response by …
An Automated Snick Detection And Classification Scheme As A Cricket Decision Review System, Aftab Khan, Syed Qadir Hussain, Muhammad Waleed, Ashfaq Khan, Umair Khan
An Automated Snick Detection And Classification Scheme As A Cricket Decision Review System, Aftab Khan, Syed Qadir Hussain, Muhammad Waleed, Ashfaq Khan, Umair Khan
Turkish Journal of Electrical Engineering and Computer Sciences
Umpire decisions can greatly affect the outcome of a cricket game. When there is doubt about the umpire?s call for a decision, a decision review system (DRS) may be brought into play by a batsman or bowler to validate the decision. Recently, the latest technologies, including Hotspot, Hawk-eye, and Snickometer, have been employed when there is doubt among the on-field umpire, batsman, or bowlers. This research is a step forward in gaging the true class of a snick generated from the contact of the cricket ball with either (i) the bat, (ii) gloves, (iii) pad, or (iv) a combination of …
Symbolic Interpretation Of Artificial Neural Networks Using Genetic Algorithms, Dounia Yedjour, Abdelkader Benyettou, Hayat Yedjour
Symbolic Interpretation Of Artificial Neural Networks Using Genetic Algorithms, Dounia Yedjour, Abdelkader Benyettou, Hayat Yedjour
Turkish Journal of Electrical Engineering and Computer Sciences
The knowledge acquired during the learning of artificial neural networks (ANNs) is coded as values in synaptic weights, which makes their interpretations difficult, hence the name of the black box. The aim of this work is to provide a comprehensible interpretation of the ANN's decisions by extracting symbolic rules. We improve the performance of our extraction algorithm by combining the ANN with a genetic algorithm. Misleading rules whose support and confidence values are less than fixed thresholds are removed and, as a result, the comprehensibility is improved. The extracted rules are evaluated and compared with other works. The results show …
Multilabel Learning For The Online Transient Stability Assessment Of Electric Power Systems, Peyman Beyranvand, Veysel Murat İstemi̇han Genç, Zehra Çataltepe
Multilabel Learning For The Online Transient Stability Assessment Of Electric Power Systems, Peyman Beyranvand, Veysel Murat İstemi̇han Genç, Zehra Çataltepe
Turkish Journal of Electrical Engineering and Computer Sciences
Dynamic security assessment of a large power system operating over a wide range of conditions requires an intensive computation for evaluating the system's transient stability against a large number of contingencies. In this study, we investigate the application of multilabel learning for improving training and prediction time, along with the prediction accuracy, of neural networks for online transient stability assessment of power systems. We introduce a new multilabel learning method, which uses a contingency clustering step to learn similar contingencies together in the same multilabel multilayer perceptron. Experimental results on two different power systems demonstrate improved accuracy, as well as …
A Cooperative Neural Network Approach For Enhancing Data Traffic Prediction, Salihu Aish Abdulkarim, Isa Abdullahi Lawal
A Cooperative Neural Network Approach For Enhancing Data Traffic Prediction, Salihu Aish Abdulkarim, Isa Abdullahi Lawal
Turkish Journal of Electrical Engineering and Computer Sciences
This paper addresses the problem of learning a regression model for the prediction of data traffic in a cellular network. We proposed a cooperative learning strategy that involves two Jordan recurrent neural networks (JNNs) trained using the firefly algorithm (FFA) and resilient backpropagation algorithm (Rprop), respectively. While the cooperative capability of the learning process ensures the effectiveness of the regression model, the recurrent nature of the neural networks allows the model to handle temporally evolving data. Experiments were carried out to evaluate the proposed approach using high-speed downlink packet access data demand and throughput measurements collected from different cell sites …
Reduction Of Torque Ripple In Induction Motor By Artificial Neural Multinetworks, Fati̇h Korkmaz, İsmai̇l Topaloğlu, Hayati̇ Mamur, Murat Ari, İlhan Tarimer
Reduction Of Torque Ripple In Induction Motor By Artificial Neural Multinetworks, Fati̇h Korkmaz, İsmai̇l Topaloğlu, Hayati̇ Mamur, Murat Ari, İlhan Tarimer
Turkish Journal of Electrical Engineering and Computer Sciences
Direct torque control is used in the high performance control of induction motors. The most frequently faced problem of it is high torque ripples. In this study, a new approach based on artificial neural multinetworks is presented to overcome the problem. Two different artificial neural networks were suggested instead of vector selection and sector determination processes in the conventional direct torque control method. The conventional and the proposed control methods were evaluated on an induction motor through an experimental set. It was observed that the speed and torque responses of the proposed method were better than those of the conventional …
Fpga Implementations Of Scale-Invariant Models Of Neural Networks, Zeinulla Zhanabaev, Yeldos Kozhagulov, Dauren Zhexebay
Fpga Implementations Of Scale-Invariant Models Of Neural Networks, Zeinulla Zhanabaev, Yeldos Kozhagulov, Dauren Zhexebay
Turkish Journal of Electrical Engineering and Computer Sciences
Integrated circuit implementations of new models of neural networks with scale-invariant properties are presented. The specifics of such models are necessary in analysis of discrete mappings containing fractional power. We suggest an algorithm for increasing the power of a physical value by using a field-programmable gate array (FPGA). Comparisons between FPGA implementations and numerical results are demonstrated.
Application Of A Time Delay Neural Network For Predicting Positive And Negative Links In Social Networks, Saghar Babakhanbak, Kaveh Kavousi, Fardad Farokhi
Application Of A Time Delay Neural Network For Predicting Positive And Negative Links In Social Networks, Saghar Babakhanbak, Kaveh Kavousi, Fardad Farokhi
Turkish Journal of Electrical Engineering and Computer Sciences
No abstract provided.
Short-Term Load Forecasting Using Mixed Lazy Learning Method, Seyed-Masoud Barakati, Ali Akbar Gharaveisi, Seyed-Mohammad Reza Rafiei
Short-Term Load Forecasting Using Mixed Lazy Learning Method, Seyed-Masoud Barakati, Ali Akbar Gharaveisi, Seyed-Mohammad Reza Rafiei
Turkish Journal of Electrical Engineering and Computer Sciences
A novel short-term load forecasting method based on the lazy learning (LL) algorithm is proposed. The LL algorithm's input data are electrical load information, daily electricity consumption patterns, and temperatures in a specified region. In order to verify the ability of the proposed method, a load forecasting problem, using the Pennsylvania-New Jersey-Maryland Interconnection electrical load data, is carried out. Three LL models are proposed: constant, linear, and mixed models. First, the performances of the 3 developed models are compared using the root mean square error technique. The best technique is then selected to compete with the state-of-the-art neural network (NN) …
Model-Based Test Case Prioritization Using Cluster Analysis: A Soft-Computing Approach, Ni̇da Gökçe, Fevzi̇ Belli̇, Mübari̇z Emi̇nli̇, Beki̇r Taner Di̇nçer
Model-Based Test Case Prioritization Using Cluster Analysis: A Soft-Computing Approach, Ni̇da Gökçe, Fevzi̇ Belli̇, Mübari̇z Emi̇nli̇, Beki̇r Taner Di̇nçer
Turkish Journal of Electrical Engineering and Computer Sciences
Model-based testing is related to the particular relevant features of the software under test (SUT) and its environment. Real-life systems often require a large number of tests, which cannot exhaustively be run due to time and cost constraints. Thus, it is necessary to prioritize the test cases in accordance with their importance as the tester perceives it, usually given by several attributes of relevant events entailed. Based on event-oriented graph models, this paper proposes an approach to ranking test cases in accordance with their preference degrees. For forming preference groups, events are clustered using an unsupervised neural network and fuzzy …
Removing Random-Valued Impulse Noise In Images Using A Neural Network Detector, İlke Türkmen
Removing Random-Valued Impulse Noise In Images Using A Neural Network Detector, İlke Türkmen
Turkish Journal of Electrical Engineering and Computer Sciences
This paper proposes a new method using an artificial neural network to remove random-valued impulse noise (RVIN) in images. The inputs of the neural model used to detect the RVIN are formed using basic and related gradient values. The detection of the noisy pixels is realized in 3 phases using the proposed neural detector. In order to obtain a more robust detector, 2 different networks, which are trained with an artificial training image corrupted with high and low clutter densities, are used. The extensive simulation results show that the proposed method is significantly better than the compared filters in terms …
Prediction Of Emissions And Exhaust Temperature For Direct Injection Diesel Engine With Emulsified Fuel Using Ann, Görkem Kökkülünk, Erhan Akdoğan, Vezi̇r Ayhan
Prediction Of Emissions And Exhaust Temperature For Direct Injection Diesel Engine With Emulsified Fuel Using Ann, Görkem Kökkülünk, Erhan Akdoğan, Vezi̇r Ayhan
Turkish Journal of Electrical Engineering and Computer Sciences
Exhaust gases have many effects on human beings and the environment. Therefore, they must be kept under control. The International Convention for the Prevention of Pollution from Ships (MARPOL), which is concerned with the prevention of marine pollution, limits the emissions according to the regulations. In Emission Control Area (ECA) regions, which are determined by MARPOL as ECAs, the emission rates should be controlled. Direct injection (DI) diesel engines are commonly used as a propulsion system on ships. The prediction and control of diesel engine emission rates is not an easy task in real time. Therefore, in this study, an …
Role Of Energy Management In Hybrid Renewable Energy Systems: Case Study-Based Analysis Considering Varying Seasonal Conditions, Recep Yumurtaci
Role Of Energy Management In Hybrid Renewable Energy Systems: Case Study-Based Analysis Considering Varying Seasonal Conditions, Recep Yumurtaci
Turkish Journal of Electrical Engineering and Computer Sciences
The recent popularity of alternative energy technologies is mainly promoted by the increasing awareness of environmental concerns as well as the economic impacts of the depleting fossil fuel reserves. Among several alternative technologies, wind- and solar-based energy have been given specific importance with government-based support for providing a cost-effective structure to realize better penetration of such environmentally friendly sources in the energy market. Even these sources are advantageous over the conventional means of energy production from many aspects, a main drawback being the total dependence on the meteorological conditions (wind speed, solar radiation, temperature, etc.) of the wind and solar …
Forecasting Natural Gas Consumption In İstanbul Using Neural Networks And Multivariate Time Series Methods, Ömer Fahretti̇n Demi̇rel, Seli̇m Zai̇m, Ahmet Çalişkan, Pinar Özuyar
Forecasting Natural Gas Consumption In İstanbul Using Neural Networks And Multivariate Time Series Methods, Ömer Fahretti̇n Demi̇rel, Seli̇m Zai̇m, Ahmet Çalişkan, Pinar Özuyar
Turkish Journal of Electrical Engineering and Computer Sciences
The fast changes and developments in the world's economy have substantially increased energy consumption. Consequently, energy planning has become more critical and important. Forecasting is one of the main tools utilized in energy planning. Recently developed computational techniques such as genetic algorithms have led to easily produced and accurate forecasts. In this paper, a natural gas consumption forecasting methodology is developed and implemented with state-of-the-art techniques. We show that our forecasts are quite close to real consumption values. Accurate forecasting of natural gas consumption is extremely critical as the majority of purchasing agreements made are based on predictions. As a …