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Full-Text Articles in Computer Engineering

Verifying Data-Oriented Gadgets In Binary Programs To Build Data-Only Exploits, Zachary David Sisco Jan 2018

Verifying Data-Oriented Gadgets In Binary Programs To Build Data-Only Exploits, Zachary David Sisco

Browse all Theses and Dissertations

Data-Oriented Programming (DOP) is a data-only code-reuse exploit technique that "stitches" together sequences of instructions to alter a program's data flow to cause harm. DOP attacks are difficult to mitigate because they respect the legitimate control flow of a program and by-pass memory protection schemes such as Address Space Layout Randomization, Data Execution Prevention, and Control Flow Integrity. Techniques that describe how to build DOP payloads rely on a program's source code. This research explores the feasibility of constructing DOP exploits without source code-that is, using only binary representations of programs. The lack of semantic and type information introduces difficulties …

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A Semantically Enhanced Approach To Identify Depression-Indicative Symptoms Using Twitter Data, Ankita Saxena Jan 2018

A Semantically Enhanced Approach To Identify Depression-Indicative Symptoms Using Twitter Data, Ankita Saxena

Browse all Theses and Dissertations

According to the World Health Organization, more than 300 million people suffer from Major Depressive Disorder (MDD) worldwide. PHQ-9 is used to screen and diagnose MDD clinically and identify its severity. With the unprecedented growth and enthusiastic acceptance of social media such as Twitter, a large number of people have come to share their feelings and emotions on it openly. Each tweet can indicate a user's opinion, thought or feeling. A tweet can also indicate multiple symptoms related to PHQ-9. Identifying PHQ-9 symptoms indicated by a tweet can provide crucial information about a user regarding his/her depression diagnosis. The current …

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Modeling Of Cloud-Based Digital Twins For Smart Manufacturing With Mt Connect, Liwen Hu, Ngoc-Tu Nguyen, Wenjin Tao, Ming C. Leu, Xiaoqing Frank Liu, Md Rakib Shahriar, S M Nahian Al Sunny Jan 2018

Modeling Of Cloud-Based Digital Twins For Smart Manufacturing With Mt Connect, Liwen Hu, Ngoc-Tu Nguyen, Wenjin Tao, Ming C. Leu, Xiaoqing Frank Liu, Md Rakib Shahriar, S M Nahian Al Sunny

Computer Science and Computer Engineering Faculty Publications and Presentations

The common modeling of digital twins uses an information model to describe the physical machines. The integration of digital twins into productive cyber-physical cloud manufacturing (CPCM) systems imposes strong demands such as reducing overhead and saving resources. In this paper, we develop and investigate a new method for building cloud-based digital twins (CBDT), which can be adapted to the CPCM platform. Our method helps reduce computing resources in the information processing center for efficient interactions between human users and physical machines. We introduce a knowledge resource center (KRC) built on a cloud server for information intensive applications. An information model …

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Energy Slices: Benchmarking With Time Slicing, Katarina Grolinger, Hany F. Elyamany, Wilson Higashino, Miriam Am Capretz, Luke Seewald Jan 2018

Energy Slices: Benchmarking With Time Slicing, Katarina Grolinger, Hany F. Elyamany, Wilson Higashino, Miriam Am Capretz, Luke Seewald

Electrical and Computer Engineering Publications

Benchmarking makes it possible to identify low-performing buildings, establishes a baseline for measuring performance improvements, enables setting of energy conservation targets, and encourages energy savings by creating a competitive environment. Statistical approaches evaluate building energy efficiency by comparing measured energy consumption to other similar buildings typically using annual measurements. However, it is important to consider different time periods in benchmarking because of differences in their consumption patterns. For example, an office can be efficient during the night, but inefficient during operating hours due to occupants’ wasteful behavior. Moreover, benchmarking studies often use a single regression model for different building categories. …

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Using Regular Languages To Explore The Representational Capacity Of Recurrent Neural Architectures, Abhijit Mahalunkar, John D. Kelleher Jan 2018

Using Regular Languages To Explore The Representational Capacity Of Recurrent Neural Architectures, Abhijit Mahalunkar, John D. Kelleher

Conference papers

The presence of Long Distance Dependencies (LDDs) in sequential data poses significant challenges for computational models. Various recurrent neural architectures have been designed to mitigate this issue. In order to test these state-of-the-art architectures, there is growing need for rich benchmarking datasets. However, one of the drawbacks of existing datasets is the lack of experimental control with regards to the presence and/or degree of LDDs. This lack of control limits the analysis of model performance in relation to the specific challenge posed by LDDs. One way to address this is to use synthetic data having the properties of subregular languages. …

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Cyber Security And Risk Society: Estonian Discourse On Cyber Risk And Security Strategy, Lauren Kook Jan 2018

Cyber Security And Risk Society: Estonian Discourse On Cyber Risk And Security Strategy, Lauren Kook

Copyright, Fair Use, Scholarly Communication, etc.

The main aim of this thesis is to call for a new analysis of cyber security which departs from the traditional security theory. I argue that the cyber domain is inherently different in nature, in that it is lacking in traditional boundaries and is reflexive in nature. Policy-makers are aware of these characteristics, and in turn this awareness changes the way that national cyber security strategy is handled and understood. These changes cannot be adequately understood through traditional understanding of security, as they often are, without missing significant details. Rather, examining these changes through the lens of Ulrich Beck’s risk …

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Interactive Process Miner: A New Approach For Process Mining, İsmai̇l Yürek, Derya Bi̇rant, Kökten Ulaş Bi̇rant Jan 2018

Interactive Process Miner: A New Approach For Process Mining, İsmai̇l Yürek, Derya Bi̇rant, Kökten Ulaş Bi̇rant

Turkish Journal of Electrical Engineering and Computer Sciences

Process mining is a technique for extracting knowledge from event logs recorded by an information system. In the process discovery phase of process mining, a process model is constructed to represent the business processes systematically and to give a general opinion about the progressive of processes in the event log. The constructed process model can be very complex as a result of structured and unstructured processes recorded in real life. Previous studies proposed different approaches to filter or eliminate some processes from the model to simplify it by implementing some statistical or mathematical formulas rather than user interactions. The main …

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An Integrated Approach For The Development Of An Electric Vehicle Powertrain: Design, Analysis, And Implementation, Özgür Üstün, Ramazan Nejat Tuncay, Mert Safa Mökükcü, Ömer Ci̇han Kivanç, Gürkan Tosun, Can Gökce, Murat Çakan Jan 2018

An Integrated Approach For The Development Of An Electric Vehicle Powertrain: Design, Analysis, And Implementation, Özgür Üstün, Ramazan Nejat Tuncay, Mert Safa Mökükcü, Ömer Ci̇han Kivanç, Gürkan Tosun, Can Gökce, Murat Çakan

Turkish Journal of Electrical Engineering and Computer Sciences

Electric motor and power electronic systems are essential elements for the performance and efficiency of electric vehicles (EVs) and hybrid electric vehicles. The inadequacy of the range due to battery limitations is compensated by powertrain solutions and innovative control algorithms. Future targets of electric powertrains are mostly based on weight, space, and efficiency issues. Highly efficient low-volume and light-weight propulsion systems increase the performance of EVs and also enhance their importance as an alternative to internal combustion engine vehicles. In this paper, a detailed propulsion system design study is presented by considering all of the important constraints of the electric …

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Real-Time Implementation Of Three-Level Inverter-Based D-Statcom Using Neuro-Fuzzy Controller, Resul Çöteli̇, Hakan Açikgöz, Beşi̇r Dandil, Servet Tuncer Jan 2018

Real-Time Implementation Of Three-Level Inverter-Based D-Statcom Using Neuro-Fuzzy Controller, Resul Çöteli̇, Hakan Açikgöz, Beşi̇r Dandil, Servet Tuncer

Turkish Journal of Electrical Engineering and Computer Sciences

A distribution static compensator (D-STATCOM) is a custom power device connected in parallel to a power system to address electric power quality problems caused by reactive power and harmonics. To obtain high performance from a D-STATCOM, the D-STATCOM's \textit{dq}-axis currents must be controlled in an internal control loop. However, control of the D-STATCOM's currents is difficult because of its nonlinear structure, cross-coupling effect between the \textit{d}- and \textit{q}-axis, undefined dynamics, and fast changing load. Therefore, the controller to be preferred for a D-STATCOM should have a nonlinear and robust structure. In this study, a neuro-fuzzy controller (NFC), which is a …

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Datanet: Deep Learning Based Encrypted Network Traffic Classification In Sdn Home Gateway, Pan Wang, Feng Ye, Xuejiao Chen, And Yi Qian Jan 2018

Datanet: Deep Learning Based Encrypted Network Traffic Classification In Sdn Home Gateway, Pan Wang, Feng Ye, Xuejiao Chen, And Yi Qian

Electrical and Computer Engineering Faculty Publications

A smart home network will support various smart devices and applications, e.g., home automation devices, E-health devices, regular computing devices, and so on. Most devices in a smart home access the Internet through a home gateway (HGW). In this paper, we propose a software-defined- network (SDN)-HGW framework to better manage distributed smart home networks and support the SDN controller of the core network. The SDN controller enables efficient network quality-of-service management based on real-time traffic monitoring and resource allocation of the core network. However, it cannot provide network management in distributed smart homes. Our proposed SDN-HGW extends the control to …

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Multilabel Learning For The Online Transient Stability Assessment Of Electric Power Systems, Peyman Beyranvand, Veysel Murat İstemi̇han Genç, Zehra Çataltepe Jan 2018

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 …

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Novel Modified Impedance-Based Methods For Fault Location In The Presence Of A Fault Current Limiter, Javad Barati, Aref Doroudi Jan 2018

Novel Modified Impedance-Based Methods For Fault Location In The Presence Of A Fault Current Limiter, Javad Barati, Aref Doroudi

Turkish Journal of Electrical Engineering and Computer Sciences

A fault current limiter (FCL) is promising novel electric equipment to effectively reduce excessive short circuit current in power networks. The presence of a FCL at the time of a fault occurrence makes it necessary to consider new settings for protective relays and fault locators. This paper examines the presence of a FCL in power networks and its effects on single-ended impedance-based fault location methods. It will be shown that FCL deployment in a transmission line makes the traditional fault location method inefficient. Two modified methods are presented to solve the problem. The modified methods locate the fault point using …

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Wi-Fi Finger-Printing Based Indoor Localization Using Nano-Scale Unmanned Aerial Vehicles, Appala Narasimha Raju Chekuri Jan 2018

Wi-Fi Finger-Printing Based Indoor Localization Using Nano-Scale Unmanned Aerial Vehicles, Appala Narasimha Raju Chekuri

Electronic Theses and Dissertations

Explosive growth in the number of mobile devices like smartphones, tablets, and smartwatches has escalated the demand for localization-based services, spurring development of numerous indoor localization techniques. Especially, widespread deployment of wireless LANs prompted ever increasing interests in WiFi-based indoor localization mechanisms. However, a critical shortcoming of such localization schemes is the intensive time and labor requirements for collecting and building the WiFi fingerprinting database, especially when the system needs to cover a large space. In this thesis, we propose to automate the WiFi fingerprint survey process using a group of nano-scale unmanned aerial vehicles (NAVs). The proposed system significantly …

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Transformer Incipient Fault Diagnosis On The Basis Of Energy-Weighted Dga Usingan Artificial Neural Network, Md Danish Equbal, Shakeb Ahmad Khan, Tarikul Islam Jan 2018

Transformer Incipient Fault Diagnosis On The Basis Of Energy-Weighted Dga Usingan Artificial Neural Network, Md Danish Equbal, Shakeb Ahmad Khan, Tarikul Islam

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, a transformer incipient fault diagnosis model has been developed with the help of an artificial neural network (ANN), taking into account the difference in the energy required to produce the different fault gases. The key fault gases are indicative of the fault type prevailing in the transformer. However, in conventional studies, the energy difference in fault gas formation is not considered while adopting the key gas method for fault diagnosis. In this work, a weighting factor has been used to take into account this relative difference in energy requirement for various fault gas formations. The fault gas …

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Real-Time Chaff Generation For A Biometric Fuzzy Vault, Manvjeet Kaur, Sanjeev Sofat Jan 2018

Real-Time Chaff Generation For A Biometric Fuzzy Vault, Manvjeet Kaur, Sanjeev Sofat

Turkish Journal of Electrical Engineering and Computer Sciences

Biometric technology is rapidly being adopted in wide variety of security applications. However, the system itself is not completely foolproof and is vulnerable to many attacks. Some of the attacks on the biometric system are very severe, one of which is the attack on template security. In spite of the various template security techniques presented in the literature, none of them is able to provide security, diversity, revocability, and good performance simultaneously to the biometric system. Fuzzy vault is one of the most promising bio-cryptographic techniques to prevent the template data from being misused. To make the fuzzy vault practically …

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Construction Of A Turkish Proposition Bank, Koray Ak, Cansu Toprak, Volkan Esgel, Olcay Taner Yildiz Jan 2018

Construction Of A Turkish Proposition Bank, Koray Ak, Cansu Toprak, Volkan Esgel, Olcay Taner Yildiz

Turkish Journal of Electrical Engineering and Computer Sciences

This paper describes our approach to developing the Turkish PropBank by adopting the semantic role-labeling guidelines of the original PropBank and using the translation of the English Penn-TreeBank as a resource. We discuss the semantic annotation process of the PropBank and language-specific cases for Turkish, the tools we have developed for annotation, and quality control for multiuser annotation. In the current phase of the project, more than 9500 sentences are semantically analyzed and predicate-argument information is extracted for 1330 verbs and 1914 verb senses. Our plan is to annotate 17,000 sentences by the end of 2017.

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Analysis And Design Of A Converter Based On Noncascading Structure, Sunil Kumar, Kanwar Pal Singh Rana, Vineet Kumar Jan 2018

Analysis And Design Of A Converter Based On Noncascading Structure, Sunil Kumar, Kanwar Pal Singh Rana, Vineet Kumar

Turkish Journal of Electrical Engineering and Computer Sciences

In the present work, a converter employing two noncascading structures, combined in a single circuit, is presented. The power stored by the storage element is transferred to two subconverters by means of two storage capacitors that complement each other. The stress on the main power switch is of interest as it reduces as the load falls, which in turn reduces the power loss. By means of the storage elements, the input power factor as well as the load transient response can be improved simultaneously. The overall efficiency is high because the amount of power processed twice decreases. There is no …

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Mutatedsocioagentsim (Msas): Semisupervised Modelling Of Multiagent Simulation To Predict And Detect The Mutation In A Camouflaged Social Network, Karthika Subbaraj, Bose Sundan Jan 2018

Mutatedsocioagentsim (Msas): Semisupervised Modelling Of Multiagent Simulation To Predict And Detect The Mutation In A Camouflaged Social Network, Karthika Subbaraj, Bose Sundan

Turkish Journal of Electrical Engineering and Computer Sciences

A social network is a networked structure formed by a set of agents/actors. It describes their interrelationships that facilitate the exchange and flow of resources and information. A camouflaged social network is one such community that influences the underlying structure and the profile of the agents, to cause mutation. The proposed MSAM is a novel system that simulates a multiagent network whose community structure is analyzed to identify the critical agents by studying the mutations caused due to attachment and detachment of agents. The isolation of the tagged agents will demonstrate disruption of information flow, which leads to the dismantling …

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Improvement Of Air Pollution Prediction In A Smart City And Its Correlation With Weather Conditions Using Metrological Big Data, Talat Zaree, Ali Reza Honarvar Jan 2018

Improvement Of Air Pollution Prediction In A Smart City And Its Correlation With Weather Conditions Using Metrological Big Data, Talat Zaree, Ali Reza Honarvar

Turkish Journal of Electrical Engineering and Computer Sciences

Smart cities are an important concept for urban development. This concept addresses many current critical urban problems including traffic and environmental pollution. As utilization of the Internet of things and technology in smart cities increases, large volumes of big data are generated and collected by sensors embedded at different places in the city, which present a real-time display of what is happening throughout the city at all times. Such data should be processed and analyzed as a response to ensure effectiveness and improvement in quality of provided services; correct use and analysis of such data is valuable. Big data mining …

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Topological Feature Extraction Of Nonlinear Signals And Trajectories And Its Application In Eeg Signals Classification, Saleh Lashkari, Ali Sheikhani, Mohammad Reza Hashemi Golpayegani, Ali Moghimi, Hamid Reza Kobravi Jan 2018

Topological Feature Extraction Of Nonlinear Signals And Trajectories And Its Application In Eeg Signals Classification, Saleh Lashkari, Ali Sheikhani, Mohammad Reza Hashemi Golpayegani, Ali Moghimi, Hamid Reza Kobravi

Turkish Journal of Electrical Engineering and Computer Sciences

This study introduces seven topological features that characterize attractor dynamic of nonlinear and chaotic trajectories in a phase space. These features quantify volume, occupied space, nonuniformity, and curvature of trajectory. The features are evaluated as initial point invariant measures by a practical approach, which means that a feature is only sensitive to dynamic changes. The Lorenz and Rossler system trajectories are employed in this evaluation. Moreover, the proposed features are used in a real world application, i.e. epileptic seizure electroencephalogram signal classification. As the result shows, these features are efficient in this task in comparison with others studies that used …

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Optimum, Projected, And Regularized Extreme Learning Machine Methods With Singular Value Decomposition And L$_{2}$-Tikhonov Regularization, Mohanad Abd Shehab, Ni̇han Kahraman Jan 2018

Optimum, Projected, And Regularized Extreme Learning Machine Methods With Singular Value Decomposition And L$_{2}$-Tikhonov Regularization, Mohanad Abd Shehab, Ni̇han Kahraman

Turkish Journal of Electrical Engineering and Computer Sciences

The theory and implementation of an extreme learning machine (ELM) have proved that it is a simple, efficient, and accurate machine learning methodology. In an ELM, the hidden nodes are randomly initiated and fixed without iterative tuning. However, the optimal hidden layer neuron number ($L_{opt})$ is the key to ELM generalization performance where initializing this number by trial and error is not reasonably satisfied. Optimizing the hidden layer size using the leave-one-out cross validation method is a costly approach. In this paper, a fast and reliable statistical approach called optimum ELM (OELM) was developed to determine the minimum hidden layer …

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Novel Low-Loss Microstrip Triplexer Using Coupled Lines And Step Impedance Cells For 4g And Wimax Applications, Abbas Rezaei, Leila Noori Jan 2018

Novel Low-Loss Microstrip Triplexer Using Coupled Lines And Step Impedance Cells For 4g And Wimax Applications, Abbas Rezaei, Leila Noori

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, a new microstrip triplexer with flexible resonance frequencies is designed based on the properties of coupled lines, steps, and spiral cells. It operates at 2.67 GHz for 4G LTE and at 3.1 GHz and 3.43 GHz for IEEE 802.16 WiMAX. The close resonance frequencies make it suitable for frequency division duplex applications. In order to improve insertion loss, the LC equivalent circuit of the proposed resonator is analyzed. Moreover, careful alignment of the coupled lines and step impedance structures is performed to improve the insertion and return losses so that they are 0.72/0.63/0.81 dB and 24.5/24/24.7 dB, …

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Secure Access Control In Multidomain Environments And Formal Analysis Of Model Specifications, Fatemeh Nazerian, Homayun Motameni, Hossein Nematzadeh Jan 2018

Secure Access Control In Multidomain Environments And Formal Analysis Of Model Specifications, Fatemeh Nazerian, Homayun Motameni, Hossein Nematzadeh

Turkish Journal of Electrical Engineering and Computer Sciences

Distributed multiple organizations interact with each other. If the domains employ role-based access control, one method for interaction between domains is role-mapping. However, it may violate constraints in the domains such as role hierarchy, separation of duty, and cardinality. Therefore, autonomy of the domains is lost. This paper proposes secure interoperation in multidomain environments. For this purpose, a cross-domain is created by foreign permission assignment. In an effort to maintain the autonomy of every domain, several rules are defined formally. Then, a decentralized scheme is used to provide permission mapping between domains. At the next stage, the proposed cross-domain is …

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Prediction Of Gross Calorific Value Of Coal Based On Proximate Analysis Using Multiple Linear Regression And Artificial Neural Networks, Mustafa Açikkar, Osman Si̇vri̇kaya Jan 2018

Prediction Of Gross Calorific Value Of Coal Based On Proximate Analysis Using Multiple Linear Regression And Artificial Neural Networks, Mustafa Açikkar, Osman Si̇vri̇kaya

Turkish Journal of Electrical Engineering and Computer Sciences

Gross calorific value (GCV) of coal was predicted by using as-received basis proximate analysis data. Two main objectives of the study were to develop prediction models for GCV using proximate analysis variables and to reveal the distinct predictors of GCV. Multiple linear regression (MLR) and artifcial neural network (ANN) (multilayer perceptron MLP, general regression neural network GRNN, and radial basis function neural network RBFNN) methods were applied to the developed 11 models created by different combinations of the predictor variables. By conducting 10-fold cross-validation, the prediction accuracy of the models has been tested by using $ R^2 $, $ RMSE …

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Q-Axis Current Perturbation Based Active Islanding Detection For Converter Interfaced Distributed Generators, Suman Murugesan, Venkatakirthiga Murali Jan 2018

Q-Axis Current Perturbation Based Active Islanding Detection For Converter Interfaced Distributed Generators, Suman Murugesan, Venkatakirthiga Murali

Turkish Journal of Electrical Engineering and Computer Sciences

Thanks to the incessant developments in technology towards extracting electric power from renewable energy resources, incorporation of distributed generators has been gaining great importance in recent years. The key expedients for such power generation include reduction in power loss and improvement in the power quality and reliability. In spite of the numerous advantages, it is mandatory to ascertain the island formation and shut down the distributed generators (DGs) during an unplanned islanding. An analyzing technique subsequent to an active islanding detection technique is proposed in this work for faster and accurate detection of island formation. The proposed technique is investigated …

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Estimation Of The Depth Of Anesthesia By Using A Multioutput Least-Square Support Vector Regression, Mercedeh Jahanseir, Kamal Setarehdan, Sirous Momenzadeh Jan 2018

Estimation Of The Depth Of Anesthesia By Using A Multioutput Least-Square Support Vector Regression, Mercedeh Jahanseir, Kamal Setarehdan, Sirous Momenzadeh

Turkish Journal of Electrical Engineering and Computer Sciences

Today, most surgeries are performed under general anesthesia where one of the most growing methods for anesthesia depth monitoring is using electroencephalogram (EEG). The bispectral index (BIS) is the most commonly used parameter for anesthesia depth monitoring using EEG, the validity of which is still to be studied before being accepted as a routine method by clinicians. This paper proposes a new technique for detecting the depth of anesthesia by means of EEG, which is based on multioutput least-squares support vector regression (MLS-SVR), which provides the probability that the patient is in the four different possible anesthesia states. In this …

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Dynamic Liquid Level Detection Method Based On Resonant Frequency Difference For Oil Wells, Wei Zhou, Juan Liu, Liqun Gan Jan 2018

Dynamic Liquid Level Detection Method Based On Resonant Frequency Difference For Oil Wells, Wei Zhou, Juan Liu, Liqun Gan

Turkish Journal of Electrical Engineering and Computer Sciences

The dynamic liquid level of an oil well can be used to determine the oil production strategies and analyze the reservoir performance. Therefore, it is important to measure the dynamic liquid level in an oil field. This paper proposes a novel dynamic liquid level measurement method for oil wells, where the resonant frequency difference (RFD) of the resonant acoustic signal in annular is used to calculate the dynamic liquid level. To solve the noise interference problem in the resonant acoustic signal, a spectral fast Fourier transform (FFT) method based on Welch power spectrum is proposed to obtain the RFD. First, …

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Influence Of Thyristor-Controlled Series Capacitor On Wheeling Cost Incorporating The Impact Of Real And Reactive Power Losses, Kranthi Kiran Irinjila, Jaya Laxmi Askani Jan 2018

Influence Of Thyristor-Controlled Series Capacitor On Wheeling Cost Incorporating The Impact Of Real And Reactive Power Losses, Kranthi Kiran Irinjila, Jaya Laxmi Askani

Turkish Journal of Electrical Engineering and Computer Sciences

Electric power transmission and transmission pricing are the key issues in the deregulated electric power industry. Factors like fast power demand growth, competition, service outage, and scarce natural resources make transmission systems operate close to their thermal limits. However, new transmission systems cannot be built in due to economic, environmental, and political reasons. For better utilization of existing power system capacities, the power electronic technology-based power system equipment called flexible alternating current transmission system (FACTS) devices like thyristor-controlled series compensators (TCSCs) can be effectively used for operating the transmission grid economically, rapidly, dynamically, and efficiently with increased flexibility and efficiency …

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A Novel Optimization Method For Solving Constrained And Unconstrained Problems: Modified Golden Sine Algorithm, Erkan Tanyildizi Jan 2018

A Novel Optimization Method For Solving Constrained And Unconstrained Problems: Modified Golden Sine Algorithm, Erkan Tanyildizi

Turkish Journal of Electrical Engineering and Computer Sciences

Recently, the metaheuristic optimization algorithms inspired by nature and different science branches have been powerful solution methods for unconstrained, constrained, and engineering problems. Various metaheuristic optimization algorithms have been proposed and they have been applied to problems in different fields. This paper proposes a novel optimization method based on a modified version of the Golden Sine Algorithm for solving unconstrained, constrained, and engineering problems. The basic idea behind the proposed modified Golden Sine Algorithm (GoldSA-II) depends on finding the optimum solution field in search space by using the decreasing pattern of the sine function and the golden ratio. The performance …

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New Optimization Algorithm Inspired By Fluid Mechanics For Combined Economic And Emission Dispatch Problem, Ruyi Dong, Shengsheng Wang Jan 2018

New Optimization Algorithm Inspired By Fluid Mechanics For Combined Economic And Emission Dispatch Problem, Ruyi Dong, Shengsheng Wang

Turkish Journal of Electrical Engineering and Computer Sciences

With the increasing concern over environmental protection, the combined economic emission dispatch (CEED) problem has received much attention. It needs to minimize both fuel cost and emission pollution. This study aims to propose a new metaheuristic algorithm inspired by fluid mechanics to solve the CEED problem with the weighted sum method. The new algorithm simulates the inverse process of fluid flowing spontaneously from high pressure to low pressure, similar to the optimization process of the CEED problem. When applied in two real-world cases, the new algorithm achieves better performance compared with other algorithms in the literature.

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