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Full-Text Articles in Computer Engineering
Grey Wolf Optimization Algorithm-Based Robust Neural Learning Control Of Passive Torque Simulators With Predetermined Performance, Seyyed Amirhossein Saadat, Mohammad Mehdi Fateh, Javad Keighobadi
Grey Wolf Optimization Algorithm-Based Robust Neural Learning Control Of Passive Torque Simulators With Predetermined Performance, Seyyed Amirhossein Saadat, Mohammad Mehdi Fateh, Javad Keighobadi
Turkish Journal of Electrical Engineering and Computer Sciences
In flight control systems, the actuators need to tolerate aerodynamic torques and continue their operations without interruption. To this end, using the simulators to test the actuators in conditions close to the real flight is efficient. On the other hand, achieving the guaranteed performance encounters some challenges and practical limitations such as unknown dynamics, external disturbances, and state constraints in reality. Thus, this article attempts to present a robust adaptive neural network learning controller equipped with a disturbance observer for passive torque simulators (PTS) with load torque constraints. The radial basis function networks (RBFNs) are employed to identify the unknown …
Cognitive Digital Modelling For Hyperspectral Image Classification Using Transfer Learning Model, Mohammad Shabaz, Mukesh Soni
Cognitive Digital Modelling For Hyperspectral Image Classification Using Transfer Learning Model, Mohammad Shabaz, Mukesh Soni
Turkish Journal of Electrical Engineering and Computer Sciences
Deep convolutional neural networks can fully use the intrinsic relationship between features and improve the separability of hyperspectral images, which has received extensive in recent years. However, the need for a large number of labelled samples to train deep network models limits the application of such methods. The idea of transfer learning is introduced into remote sensing image classification to reduce the need for the number of labelled samples. In particular, the situation in which each class in the target picture only has one labelled sample is investigated. In the target domain, the number of training samples is enlarged by …
A Novel Algorithm For Frequency Extraction Of Abs Signals By Using Dtdnns, Mohammad Ali Shafieian, Hamed Banizaman, Shahrzad Sedaghat
A Novel Algorithm For Frequency Extraction Of Abs Signals By Using Dtdnns, Mohammad Ali Shafieian, Hamed Banizaman, Shahrzad Sedaghat
Turkish Journal of Electrical Engineering and Computer Sciences
Intelligent transportations system (ITSs) have emerged to increase safety and convenience of people in vehicles. In an ITS, communication devices in the vehicle or along the streets send the information gathered from the vehicle to information management centers as well as sending processed information to the vehicle. Furthermore, it is necessary to locate the exact location of the vehicle on a digital map in order to navigate the vehicle precisely in control and navigation systems. One of the technologies for this purpose is the antilock brake system (ABS), which can avoid accidents effectively and can also be utilized to determine …
Particle Swarm Optimization-Based Collision Avoidance, Ti̇mur İnan, Ahmet Fevzi̇ Baba
Particle Swarm Optimization-Based Collision Avoidance, Ti̇mur İnan, Ahmet Fevzi̇ Baba
Turkish Journal of Electrical Engineering and Computer Sciences
Collision risk assessment and collision avoidance of vessels have always been an important topic in ocean engineering. Decision support systems are increasingly becoming the focus of many studies in the maritime industry today as vessel accidents are often caused by human error. This study proposes an anticollision decision support system that can determine surrounding obstacles by using the information received from radar systems, obtain the position and speed of obstacles within a certain time period, and suggest possible routes to prevent collisions. In this study we use a neural network to predict the subsequent positions of surrounding vessels, a fuzzy …
Neural Network Approach On Loss Minimization Control Of A Pmsm With Core Resistance Estimation, Hüseyi̇n Erdoğan, Mehmet Özdemi̇r
Neural Network Approach On Loss Minimization Control Of A Pmsm With Core Resistance Estimation, Hüseyi̇n Erdoğan, Mehmet Özdemi̇r
Turkish Journal of Electrical Engineering and Computer Sciences
Permanent magnet synchronous motors (PMSMs) are often used in industry for high-performance applications. Their key features are high power density, linear torque control capability, high efficiency, and fast dynamic response. Today, PMSMs are prevalent especially for their use in hybrid electric vehicles. Since operating the motor at high efficiency values is critically important for electric vehicles, as for all other applications, minimum loss control appears to be an inevitable requirement in PMSMs. In this study, a neural network-based intelligent minimum loss control technique is applied to a PMSM. It is shown by means of the results obtained that the total …
Study On The Recognition Method Of Airport Perimeter Intrusion Incidents Based On Laser Detection Technology, Huazhu Wu, Zengcai Wang, Changyou Wang
Study On The Recognition Method Of Airport Perimeter Intrusion Incidents Based On Laser Detection Technology, Huazhu Wu, Zengcai Wang, Changyou Wang
Turkish Journal of Electrical Engineering and Computer Sciences
Currently, detection technology is very important for airport perimeter security. When the perimeter is invaded or destroyed, the perimeter security alarm system can promptly alert personnel. In this paper, based on analysis and comparison of several detection technologies commonly used in airport perimeter security and according to the characteristics of airport perimeters and laser detection, an airport perimeter security alarm system based on laser detection is proposed. It analyzes factors that affect the performance of a laser alarm system, divides intrusions into six categories, estimates the different alarm thresholds by testing, and judges the intrusion category according to the number …
Speech Recognition Using Ann And Predator-Influenced Civilized Swarm Optimization Algorithm, Teena Mittal, Rajendra Kumar Sharma
Speech Recognition Using Ann And Predator-Influenced Civilized Swarm Optimization Algorithm, Teena Mittal, Rajendra Kumar Sharma
Turkish Journal of Electrical Engineering and Computer Sciences
This paper proposes a hybrid optimization technique, predator-influenced civilized swarm optimization, by integrating civilized swarm optimization (CSO) and predator-prey optimization (PPO) techniques. CSO is the integration of the attributes of particle swarm optimization and a society civilization algorithm (SCA). In the SCA, the swarm is divided into a few societies, and each society has its own society leader (SL); other individuals of the society are termed society members. The combination of all such societies forms a civilization, and the best-performing SL becomes the civilization leader (CL). In CSO, SLs and members update their positions through the guidance of their own …
Bandwidth Extension Of Narrowband Speech In Log Spectra Domain Using Neural Network, Sara Pourmohammadi, Mansour Vali, Mohsen Ghadyani
Bandwidth Extension Of Narrowband Speech In Log Spectra Domain Using Neural Network, Sara Pourmohammadi, Mansour Vali, Mohsen Ghadyani
Turkish Journal of Electrical Engineering and Computer Sciences
In recent years, there have been significant advances in communication technology, but speech signals still suffer from low perceived quality caused by bandwidth limitations of telephone networks. The bandwidth extension (BWE) approach adds high-frequency components of the speech signal to band-limited telephone speech and increases speech perception significantly. In this work, we develop a new method for representation of vocal tract filter coefficients using log of filter bank energy (LFBE) parameters as an alternative for mel-frequency cepstral coefficients (MFCCs). This approach is based on a strong correlation between the spectral components of low- and high-band spectrums. Furthermore, the performances of …
An Intelligence-Based Islanding Detection Method Using Dwt And Ann, Mehrdad Heidari, Ghodratollah Seifossadat, Morteza Razaz
An Intelligence-Based Islanding Detection Method Using Dwt And Ann, Mehrdad Heidari, Ghodratollah Seifossadat, Morteza Razaz
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, a new method based on artificial neural network (ANN) and discrete wavelet transform (DWT) is proposed for electrical islanding detection. Transient signals produced during an event are used in the proposed method. ANN is trained to classify the transient events as islanding and nonislanding. The required features for classifying are extracted through DWT of voltage and current transient signals. The proposed method is then simulated on a medium voltage distribution system of CIGRE with 2 kinds of DGs. Results show that this method can detect electrical islands more rapidly and accurately.
Rfid Card Security For Public Transportation Applications Based On A Novel Neural Network Analysis Of Cardholder Behavior Characteristics, Gürsel Düzenli̇
Rfid Card Security For Public Transportation Applications Based On A Novel Neural Network Analysis Of Cardholder Behavior Characteristics, Gürsel Düzenli̇
Turkish Journal of Electrical Engineering and Computer Sciences
This paper proposes a novel approach that applies neural network forecasting to security for closed-loop prepaid cards based on low-cost technologies such as RFID and 1-Wire. The security vulnerability of low-cost RFID closed-loop prepaid card systems originates mostly from the card itself. Criminal organizations counterfeit or clone card data. Although high-security prepaid cards exist, they are often too expensive for transport ticketing, and even their security is not guaranteed for a well-defined period of time. Therefore, data encryption systems are used widely against counterfeiting with success. However, it has not been possible to develop countermeasures with comparable success against cloning. …
Improving The Drain-Current Expression Of Bsim4 For Hot-Carrier Degradation Modeling That Is Suitable For Analog Applications, Gürsel Düzenli̇
Improving The Drain-Current Expression Of Bsim4 For Hot-Carrier Degradation Modeling That Is Suitable For Analog Applications, Gürsel Düzenli̇
Turkish Journal of Electrical Engineering and Computer Sciences
The reliability evaluation of MOS transistors is one of the most important subjects in device engineering and VLSI design. The down-scaling of device dimensions adversely affects device reliability and lifetime. Although different factors contribute to device reliability and lifetime, the most influential factor is hot-carrier degradation. Furthermore, hot-carrier degradation affects each application uniquely. In analog applications, hot-carrier degradation is more complex and diverse relative to digital applications. In this study, we improve the BSIM4 drain-current model to develop a hot-carrier degradation model that is suitable for both analog and digital applications. Our approach is readily applicable to all process technologies …
Applications Of Wavelets And Neural Networks For Classification Of Power System Dynamics Events, Samir Avdakovic, Amir Nuhanovic, Mirza Kusljugic, Elvisa Becirovic
Applications Of Wavelets And Neural Networks For Classification Of Power System Dynamics Events, Samir Avdakovic, Amir Nuhanovic, Mirza Kusljugic, Elvisa Becirovic
Turkish Journal of Electrical Engineering and Computer Sciences
This paper investigates the possibility of classifying power system dynamics events using discrete wavelet transform (DWT) and a neural network (NN) by analyzing one variable at a single network bus. Following a disturbance in the power system, it will propagate through the system in the form of low-frequency electromechanical oscillations (LFEOs) in a frequency range of up to 5 Hz. DWT allows the identification of components of the LFEO, their frequencies, and magnitudes. After determining the energy components' share of the analyzed signal using DWT and Parseval's theorem, the input data for the classification process using a NN are obtained. …
Controlling The Chaotic Discrete-Hénon System Using A Feedforward Neural Network With An Adaptive Learning Rate, Kürşad Gökce, Yilmaz Uyaroğlu
Controlling The Chaotic Discrete-Hénon System Using A Feedforward Neural Network With An Adaptive Learning Rate, Kürşad Gökce, Yilmaz Uyaroğlu
Turkish Journal of Electrical Engineering and Computer Sciences
This paper proposes a feedforward neural network-based control scheme to control the chaotic trajectories of a discrete-Hénon map in order to stay within an acceptable distance from the stable fixed point. An adaptive learning back propagation algorithm with online training is employed to improve the effectiveness of the proposed method. The simulation study carried in the discrete-Hénon system verifies the validity of the proposed control system.
Review Of Distinctive Phonetic Features And The Arabic Share In Related Modern Research, Yousef Alotaibi, Ali Meftah
Review Of Distinctive Phonetic Features And The Arabic Share In Related Modern Research, Yousef Alotaibi, Ali Meftah
Turkish Journal of Electrical Engineering and Computer Sciences
Most research in the field of digital speech technology has traditionally been conducted in only a few languages, such as English, French, Spanish, or Chinese. Numerous studies using distinctive phonetic features (DPFs) with different techniques and algorithms have been carried out during the last 3 decades, mainly in English, Japanese, and other languages of industrialized countries. DPF elements are based on a technique used by linguists and digital speech and language experts to distinguish between different phones by considering the lowest level of actual features during phonation. These studies have investigated the best performances, outcomes, and theories, especially those regarding …
Feature-Based Fault Detection Of Industrial Gas Turbines Using Neural Networks, Abbas Rasaienia, Behzad Moshiri, Mohammadamin Moezzi
Feature-Based Fault Detection Of Industrial Gas Turbines Using Neural Networks, Abbas Rasaienia, Behzad Moshiri, Mohammadamin Moezzi
Turkish Journal of Electrical Engineering and Computer Sciences
Gas turbine (GT) fault detection plays a vital role in the minimization of power plant operation costs associated with power plant overhaul time intervals. In other words, it is helpful in generating pre-alarms and paves the way for corrective actions in due time before incurring major equipment failures. Hence, finding an efficient fault detection technique that is applicable in the online operation of power plants involved with minor computations is an urgent need in the power generation industry. Such a method is studied in this paper for the V94.2 class of GTs. As the most leading stage for developing a …
A Complete Motor Protection Algorithm Based On Pca And Ann: A Real Time Study, Okan Özgönenel, Turgay Yalçin
A Complete Motor Protection Algorithm Based On Pca And Ann: A Real Time Study, Okan Özgönenel, Turgay Yalçin
Turkish Journal of Electrical Engineering and Computer Sciences
Protection of an induction motor (IM) against possible faults, such as a stator winding fault, due to thermal deterioration, rotor bar and bearing failures, is very important in environments in which it is used intensively, as in industry as an actuator. In this work, a real time digital protection algorithm based on principal component analysis (PCA) and neural network method is presented for induction motors. The proposed protection algorithm covers internal winding faults (also known as stator faults), broken rotor bar faults, and bearing faults. Many laboratory experiments have been performed on a specially designed induction motor to evaluate the …