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Articles 1 - 30 of 335
Full-Text Articles in Computer Engineering
Limited-Data Automatic Speaker Verification Algorithm Using Band-Limitedphase-Only Correlation Function, Ángel Pedroza, José De La Rosa, José De Jesus Villa, Aldonso Becerra
Limited-Data Automatic Speaker Verification Algorithm Using Band-Limitedphase-Only Correlation Function, Ángel Pedroza, José De La Rosa, José De Jesus Villa, Aldonso Becerra
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, a new method to deal with automatic speaker verification based on band-limited phaseonly correlation (BLPOC) is proposed. The aim of this study is to validate the use of the BLPOC function as a new limited-data automatic speaker verification technique. Although some speaker verification techniques have high accuracy, efficiency usually depends on the extraction of complex theoretical information from speech signals and the amount of the data for training the algorithms. The BLPOC function is a high-accuracy biometric technique traditionally implemented in human identification by fingerprints (through image-matching). When applying the BLPOC function in automatic speaker verification through …
Energy Saving Scheduling In A Fog-Based Iot Application By Bayesian Task Classification Approach, Gholamreza Heydari, Dadmehr Rahbari, Mohsen Nickray
Energy Saving Scheduling In A Fog-Based Iot Application By Bayesian Task Classification Approach, Gholamreza Heydari, Dadmehr Rahbari, Mohsen Nickray
Turkish Journal of Electrical Engineering and Computer Sciences
The Internet of things increases information volume in computer networks and the concept of fog will help us to control this volume more efficiently. Scheduling resources in such an environment would be an NP-Hard problem. This article has studied the concept of scheduling in fog with Bayesian classification which could be applied to gain the task requirements like the processing ones. After classification, virtual machines will be created in accordance with the predicted requirements. The ifogsim simulator has been applied to study our fog-based Bayesian classification scheduling (FBCS) method performance in an EEG tractor application. Algorithms have been evaluated on …
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, …
A Novel Randomized Recurrent Artificial Neural Network Approach: Recurrent Random Vector Functional Link Network, Ömer Faruk Ertuğrul
A Novel Randomized Recurrent Artificial Neural Network Approach: Recurrent Random Vector Functional Link Network, Ömer Faruk Ertuğrul
Turkish Journal of Electrical Engineering and Computer Sciences
The random vector functional link (RVFL) has successfully been employed in many applications since 1989. RVFL has a single hidden layer feedforward structure that also has direct links between the input layer and the output layer. Although nonlinearity, high generalization capacity, and fast training ability can be provided in RVFL, it can be found from the literature that higher nonlinearity can be obtained by adding recurrent feedback to an artificial neural network. In this paper, the recurrent type of RVFL (R-RVFL), which has both outer feedbacks and also inner feedbacks, is proposed. In order to evaluate and validate the proposed …
Electron Energy Loss Spectroscopy Simulation By A Frequency Domain Surface Integral Equation Solver, İsmai̇l Enes Uysal
Electron Energy Loss Spectroscopy Simulation By A Frequency Domain Surface Integral Equation Solver, İsmai̇l Enes Uysal
Turkish Journal of Electrical Engineering and Computer Sciences
Plasmonic nanoparticles have been mostly studied using conventional light sources. Recently, electron energy loss spectroscopy (EELS) has started to be used to analyze plasmonic nanoparticles where incident electromagnetic fields are created by swift electrons. To accurately simulate EELS experiments, several numerical methods have been adapted. In this paper, a frequency domain surface integral equation (FDSIE) solver is modified to simulate EELS for plasmonic nanoparticles of gold and silver. Accuracy and versatility of the proposed FDSIE solver are shown by several numerical examples and compared to existing numerical, analytical, and experimental results.
A Multiseed-Based Svm Classification Technique For Training Sample Reduction, Imran Sharif, Debasis Chaudhuri
A Multiseed-Based Svm Classification Technique For Training Sample Reduction, Imran Sharif, Debasis Chaudhuri
Turkish Journal of Electrical Engineering and Computer Sciences
A support vector machine (SVM) is not a popular method for a very large dataset classification because the training and testing time for such data are computationally expensive. Many researchers try to reduce the training time of SVMs by applying sample reduction methods. Many methods reduced the training samples by using a clustering technique. To reduce its high computational complexity, several data reduction methods were proposed in previous studies. However, such methods are not effective to extract informative patterns. This paper demonstrates a new supervised classification method, multiseed-based SVM (MSB-SVM), which is particularly intended to deal with very large datasets …
Assessment Of Techno-Economic Benefits For Smart Charging Scheme Of Electric Vehicles In Residential Distribution System, Kumari Kasturi, Manas Ranjan Nayak
Assessment Of Techno-Economic Benefits For Smart Charging Scheme Of Electric Vehicles In Residential Distribution System, Kumari Kasturi, Manas Ranjan Nayak
Turkish Journal of Electrical Engineering and Computer Sciences
Connecting multiple electric vehicles (EVs) to a power system network for the purpose of charging has major setbacks like decrease in power quality, instability in voltage profile, and increase in power losses and thus electricity price. This paper focuses on devising an optimal charging scheme to reduce the negative impacts of EVs' presence in the distribution network by limiting the charging process to only off-peak demand periods when the electricity price is comparatively lower. The salp swarm algorithm, an efficient, fast, and reliable optimization technique, is used to obtain the optimal locations for the EVs and their charging schedule in …
Design And Fabrication Of A Dual-Polarized, Dual-Band Reflectarray Using Optimal Phase Distribution, Iman Aryanian, Arash Ahmadi, Mehdi Rabbani, Sina Hassibi, Majid Karimipour
Design And Fabrication Of A Dual-Polarized, Dual-Band Reflectarray Using Optimal Phase Distribution, Iman Aryanian, Arash Ahmadi, Mehdi Rabbani, Sina Hassibi, Majid Karimipour
Turkish Journal of Electrical Engineering and Computer Sciences
Two main factors limiting the reflectarray bandwidth are different phase slopes versus the frequency at every point on the aperture and the phase limitation of comprising elements at different frequencies. Considering these two factors, a novel design method is proposed to implement a dual-band, dual-polarized reflectarray antenna in X and Ku bands. An optimization algorithm is adopted to find the optimum phase for each unit cell on the reflectarray aperture. The best geometrical parameters of the phasing elements are suggested based on the phase variation of the element versus frequency and the element position with respect to the antenna feed. …
Multi-Objective Design Optimization Of A Permanent Magnet Axial Flux Eddy Current Brake, Rasul Tarvirdilu Asl, Hüseyi̇n Murat Yüksel, Ozan Keysan
Multi-Objective Design Optimization Of A Permanent Magnet Axial Flux Eddy Current Brake, Rasul Tarvirdilu Asl, Hüseyi̇n Murat Yüksel, Ozan Keysan
Turkish Journal of Electrical Engineering and Computer Sciences
The main aim of this study is to optimize an axial flux eddy current damper to be used in a specific aviation application. Eddy current dampers are more advantageous compared to conventional mechanical dampers as they are maintenance-free due to contactless structure and have higher reliability, which is very desirable in aerospace applications. An initial eddy current brake prototype is manufactured and the test results are used to verify the 3-D finite element simulations. The effect of temperature on the brake performance is investigated. Finally, a multiobjective genetic algorithm optimization is applied to find the optimum pole number and geometric …
A Comparative Study Of Author Gender Identification, Tuğba Yildiz
A Comparative Study Of Author Gender Identification, Tuğba Yildiz
Turkish Journal of Electrical Engineering and Computer Sciences
In recent years, author gender identification has gained considerable attention in the fields of information retrieval and computational linguistics. In this paper, we employ and evaluate different learning approaches based on machine learning (ML) and neural network language models to address the problem of author gender identification. First, several ML classifiers are applied to the features obtained by bag-of-words. Secondly, datasets are represented by a low-dimensional real-valued vector using Word2vec, GloVe, and Doc2vec, which are on par with ML classifiers in terms of accuracy. Lastly, neural networks architectures, the convolution neural network and recurrent neural network, are trained and their …
A New Approach For Wind Turbine Placement Problem Using Modified Differential Evolution Algorithm, Hüseyi̇n Hakli
A New Approach For Wind Turbine Placement Problem Using Modified Differential Evolution Algorithm, Hüseyi̇n Hakli
Turkish Journal of Electrical Engineering and Computer Sciences
Energy use is increasing worldwide with industrialization and advancing technology. Following this increase, renewable energy resources are increasingly preferred to reduce the costs of energy production. Wind energy is preferred as a renewable energy resource because it is clean and safe. Wind turbines are used to meet the demand for wind energy. They are placed close to each other to generate higher amounts of energy. However, the wake effect problem arises in these types of layouts, and this hinders the turbines from producing the desired yield. A modified differential evolution (MDE) algorithm was proposed in this study to solve the …
A Transmission Optimization Algorithm For Smart Load Controllers, Ohyoung Song
A Transmission Optimization Algorithm For Smart Load Controllers, Ohyoung Song
Turkish Journal of Electrical Engineering and Computer Sciences
This paper introduces a transmission optimization algorithm for wireless transmissions between smart load controllers and a corresponding gateway in wireless personal area networks, where smart load controllers connect several electrical appliances through their corresponding load interfaces, measure the power consumption from each electrical appliance connected to the load controller, and control on/off switching through its load interface. The aim of this paper is to reduce the traffic load of power consumption data in electrical appliances used in the building area network and the smart grid network. The proposed algorithm allows the smart load controller to efficiently reduce traffic load even …
A Comparative Study On Handwritten Bangla Character Recognition, Md. Atiqul Islam Rizvi, Kaushik Deb, Md. Ibrahim Khan, Mir Md. Saki Kowsar, Tahmina Khanam
A Comparative Study On Handwritten Bangla Character Recognition, Md. Atiqul Islam Rizvi, Kaushik Deb, Md. Ibrahim Khan, Mir Md. Saki Kowsar, Tahmina Khanam
Turkish Journal of Electrical Engineering and Computer Sciences
Recognition of handwritten Bangla characters has drawn considerable attention recently. The Bangla language is rich with characters of various styles such as numerals, basic characters, and compound and modifier characters. The inherent variation in individual writing styles, along with the complex, cursive nature of characters, makes the recognition task more challenging. To compare the outcomes of handwritten Bangla character recognition, this study considers two different approaches. The first one is classifier-based, where a hybrid model of the feature extraction technique extracts the features and a multiclass support vector machine (SVM) performs the recognition. The second one is based on a …
A Robust Ensemble Feature Selector Based On Rank Aggregation For Developing New Vo\Textsubscript{2}Max Prediction Models Using Support Vector Machines, Fatih Abut, Mehmet Fati̇h Akay, James George
A Robust Ensemble Feature Selector Based On Rank Aggregation For Developing New Vo\Textsubscript{2}Max Prediction Models Using Support Vector Machines, Fatih Abut, Mehmet Fati̇h Akay, James George
Turkish Journal of Electrical Engineering and Computer Sciences
This paper proposes a new ensemble feature selector, called the majority voting feature selector (MVFS), for developing new maximal oxygen uptake (VO2max) prediction models using a support vector machine (SVM). The approach is based on rank aggregation, which meaningfully utilizes the correlation among the relevance ranks of predictor variables given by three state-of-the-art feature selectors: Relief-F, minimum redundancy maximum relevance (mRMR), and maximum likelihood feature selection (MLFS). By applying the SVM combined with MVFS on a self-created dataset containing maximal and submaximal exercise data from 185 college students, several new hybrid (VO2max) prediction models have been created. To compare the …
A Novel Hardware-Efficient Spatial Orientation Tree-Based Image Compression Algorithm And Its Field Programmable Gate Array Implementation, Mohd Rafi Lone, Najeeb-Ud-Din Hakim
A Novel Hardware-Efficient Spatial Orientation Tree-Based Image Compression Algorithm And Its Field Programmable Gate Array Implementation, Mohd Rafi Lone, Najeeb-Ud-Din Hakim
Turkish Journal of Electrical Engineering and Computer Sciences
Set partitioning in hierarchical trees (SPIHT) has become a popular research topic for more than a decade now. This is because it is simple, besides having compression efficiency close to the state-of-the-art JPEG2000 standard. The main drawback of SPIHT is that it uses three lists to store addresses of coefficients during its operation. These lists are dynamic and in worst cases need to store more number of addresses than total coefficients. In this work, a novel hardware-efficient spatial orientation tree-based algorithm is proposed and its hardware implementation is carried out. The wavelet transformed image is partitioned into 2 $\times$ 2 …
A Novel Design Of An Electromagnetically Levitated Vibrational Viscometer For Biomedical And Clinical Applications, Ali̇ Akpek
Turkish Journal of Electrical Engineering and Computer Sciences
Accurate determination of the viscosity behavior of fluids is extremely important, especially for biomedical and clinical applications. For example, blood viscosity is used to detect cardiovascular diseases in patients. Like blood, all body fluids and biochemical solvents used in biomedical studies are very limited resources. Therefore, a viscometer that is especially focused for biomedical and clinical applications should have the ability to obtain viscosity results from a reservoir as small as possible, in a range as wide as possible and in a period of time as short as possible. The measurements must be accurate even when the fluid temperatures shift …
Turkish Lexicon Expansion By Using Finite State Automata, Mustafa Burak Öztürk, Burcu Can Buğlalilar
Turkish Lexicon Expansion By Using Finite State Automata, Mustafa Burak Öztürk, Burcu Can Buğlalilar
Turkish Journal of Electrical Engineering and Computer Sciences
Turkish is an agglutinative language with rich morphology. A Turkish verb can have thousands of different word forms. Therefore, sparsity becomes an issue in many Turkish natural language processing (NLP) applications. This article presents a model for Turkish lexicon expansion. We aimed to expand the lexicon by using a morphological segmentation system by reversing the segmentation task into a generation task. Our model uses finite-state automata (FSA) to incorporate orthographic features and morphotactic rules. We extracted orthographic features by capturing phonological operations that are applied to words whenever a suffix is added. Each FSA state corresponds to either a stem …
The Biobjective Multiarmed Bandit: Learning Approximate Lexicographic Optimal Allocations, Cem Teki̇n
The Biobjective Multiarmed Bandit: Learning Approximate Lexicographic Optimal Allocations, Cem Teki̇n
Turkish Journal of Electrical Engineering and Computer Sciences
We consider a biobjective sequential decision-making problem where an allocation (arm) is called $\epsilon$ lexicographic optimal if its expected reward in the first objective is at most $\epsilon$ smaller than the highest expected reward, and its expected reward in the second objective is at least the expected reward of a lexicographic optimal arm. The goal of the learner is to select arms that are $\epsilon$ lexicographic optimal as much as possible without knowing the arm reward distributions beforehand. For this problem, we first show that the learner's goal is equivalent to minimizing the $\epsilon$ lexicographic regret, and then, propose a …
An Improved Tree Model Based On Ensemble Feature Selection For Classification, Chandralekha M, Shenbagavadivu N
An Improved Tree Model Based On Ensemble Feature Selection For Classification, Chandralekha M, Shenbagavadivu N
Turkish Journal of Electrical Engineering and Computer Sciences
Researchers train and build specific models to classify the presence and absence of a disease and the accuracy of such classification models is continuously improved. The process of building a model and training depends on the medical data utilized. Various machine learning techniques and tools are used to handle different data with respect to disease types and their clinical conditions. Classification is the most widely used technique to classify disease and the accuracy of the classifier largely depends on the attributes. The choice of the attribute largely affects the diagnosis and performance of the classifier. Due to growing large volumes …
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 …
Task Graph Scheduling In The Presence Of Performance Fluctuations Of Computational Resources, Najmeh Malakoutifar, Hassan Motallebi
Task Graph Scheduling In The Presence Of Performance Fluctuations Of Computational Resources, Najmeh Malakoutifar, Hassan Motallebi
Turkish Journal of Electrical Engineering and Computer Sciences
Most of the existing work in the area of task graph scheduling considers resources with fixed processing capacity. The algorithms in these works rely on an estimation of the execution times of tasks on different resources. However, in practice, due to fluctuations in performance of cloud resources, these algorithms have challenges in these environments. In this paper, we focus on the problem of fault-tolerant scheduling of task graphs in the presence of performance fluctuations of computational resources. With the aim of reducing the adverse impacts of both soft errors and resource performance degradations, we propose an opportunistic task replication scheme …
Space-Track Modulation And Coding For High Density Aerial Vehicle Downlinknetworks With Free Space Optical And Visible Light Communications, Burhan Gülbahar
Space-Track Modulation And Coding For High Density Aerial Vehicle Downlinknetworks With Free Space Optical And Visible Light Communications, Burhan Gülbahar
Turkish Journal of Electrical Engineering and Computer Sciences
Aerial vehicles (AVs) have challenges in terms of realizing low complexity and wide coverage area wireless communications architectures, especially for crowded or high density groups of AVs with state-of-the-art free space optical (FSO) and radio frequency (RF) based system designs. FSO architectures generally target point-to-point high gain directional links while requiring strict acquisition and tracking due to the narrow beam width of laser transmitters with challenges of vibration, turbulence, misalignment, atmospheric absorption, scattering, and fading. In this article, a novel multiuser free space optical system design of modulation and coding denoted by space-track modulation and coding (STMC) is proposed for …
Design Of Inverted-F Antenna For Long-Term Evolution-Based Wireless Handhelddevices, Paulkani Iyampalam, Indumathi Ganesan
Design Of Inverted-F Antenna For Long-Term Evolution-Based Wireless Handhelddevices, Paulkani Iyampalam, Indumathi Ganesan
Turkish Journal of Electrical Engineering and Computer Sciences
In this article, a compact triple-band inverted-F antenna for long-term evolution (LTE)-based wireless handheld devices is presented. The proposed antenna comprises three arms, namely the radiating arm, shorting arm, and feeding arm. The structure of the designed antenna looks like an inverted F with folded L-shaped strip antenna. It has three resonant frequencies of 795, 2050, and 3405 MHz with a reflection coefficient of --11.7 dB, --15.1 dB, and --30.5 dB, respectively. The size of the suggested antenna is 10 $\times$ 35 $\times$ 0.8 mm$^3$ and it is built on the FR-4 substrate which is more compact and easy to …
Empirical Single Frequency Network Threshold For Dvb-T2 Based On Laboratory Experiments, Bundit Ruckveratham, Sathaporn Promwong
Empirical Single Frequency Network Threshold For Dvb-T2 Based On Laboratory Experiments, Bundit Ruckveratham, Sathaporn Promwong
Turkish Journal of Electrical Engineering and Computer Sciences
DVB-T2 broadcasting with a single frequency network (SFN) allows an efficient management of frequency utilization and extends the coverage area, which will enable more people to view a broadcast. The SFN mode also increases the concentration of the signal in overlap areas. However, some difference of overlap areas in actual use of SFN networks may have some degradation of the received signal due to the effect of the SFN. In this research, we analyze SFN broadcasting in SISO mode. This paper represents the effects of delays on the SFN signal over different delay times within the guard interval (GI) by …
Extracting Accent Information From Urdu Speech For Forensic Speaker Recognition, Falak Tahir, Sajid Saleem, Ayaz Ahmad
Extracting Accent Information From Urdu Speech For Forensic Speaker Recognition, Falak Tahir, Sajid Saleem, Ayaz Ahmad
Turkish Journal of Electrical Engineering and Computer Sciences
This paper presents a new method for extraction of accent information from Urdu speech signals. Accent is used in speaker recognition system especially in forensic cases and plays a vital role in discriminating people of different groups, communities and origins due to their different speaking styles. The proposed method is based on Gaussian mixture model-universal background model (GMM-UBM), mel-frequency cepstral coefficients (MFCC), and a data augmentation (DA) process. The DA process appends features to base MFCC features and improves the accent extraction and forensic speaker recognition performances of GMM-UBM. Experiments are performed on an Urdu forensic speaker corpus. The experimental …
Application Of Multiscale Fuzzy Entropy Features For Multilevel Subject-Dependent Emotion Recognition, Hamzah Lotfalinezhad, Ali Maleki
Application Of Multiscale Fuzzy Entropy Features For Multilevel Subject-Dependent Emotion Recognition, Hamzah Lotfalinezhad, Ali Maleki
Turkish Journal of Electrical Engineering and Computer Sciences
Emotion recognition can be used in clinical and nonclinical situations. Despite previous works which mostly used time and frequency features of electroencephalogram (EEG) signals in subject-dependent emotion recognition issues, we used multiscale fuzzy entropy as a nonlinear dynamic feature. The EEG signals of the well-known Database for Emotion Analysis Using Physiological signals dataset was used for classification of two and three levels of emotions in arousal and valence space. The compound feature selection with a cost of average accuracy of support vector machine classifier was used to reduce feature dimensions. For subject-dependent systems, the proposed method is superior in comparison …
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 …
Empirical Model Development For The Estimation Of Clearness Index Using Meteorological Parameters, Fakhar Alam, Saif Ur Rehman, Shafiqur Rehman, Muhammad Jahangir, Muhammad Shoaib, Imran Siddiqui, Intikhab Ulfat
Empirical Model Development For The Estimation Of Clearness Index Using Meteorological Parameters, Fakhar Alam, Saif Ur Rehman, Shafiqur Rehman, Muhammad Jahangir, Muhammad Shoaib, Imran Siddiqui, Intikhab Ulfat
Turkish Journal of Electrical Engineering and Computer Sciences
The clearness index is an indispensable parameter required for the design and analysis of solar energy systems. In the absence of measured values for a specific location, the clearness index can be estimated from other measured meteorological variables. In this study three meteorological parameters, sunshine hours, monthly mean values of the temperature difference ($\Delta$T), and cloudiness, are used to develop empirical models for the estimation of clearness index. The empirical models are developed for five major cities in Pakistan (Karachi, Multan, Lahore, Islamabad, and Quetta). For empirical model development, long-term data (1991 to 2010) of monthly average clearness index, sunshine …
Computation Of Stability Regions For Load Frequency Control Systems Including Incommensurate Time Delays, Şahi̇n Sönmez
Computation Of Stability Regions For Load Frequency Control Systems Including Incommensurate Time Delays, Şahi̇n Sönmez
Turkish Journal of Electrical Engineering and Computer Sciences
This article studies the impact of incommensurate communication time delays on stability regions defined in proportional-integral (PI) controller parameter space for a two-area load frequency control (LFC) system. Distributed power generations and large power plants increase the complexity and control issues of interconnected power systems. In interconnected power systems, LFC systems need to have complex communication networks to exchange data between control center and geographically dispersed generations. The receiving/transmitting of remote measuring data through communication infrastructures causes inevitable time delays, which adversely affect controller performance and stability of the LFC system. Time delays introducing feedback control loops of a multiarea …
Predicting Co And Nox Emissions From Gas Turbines: Novel Data And A Benchmark Pems, Heysem Kaya, Pinar Tüfekci̇, Erdi̇nç Uzun
Predicting Co And Nox Emissions From Gas Turbines: Novel Data And A Benchmark Pems, Heysem Kaya, Pinar Tüfekci̇, Erdi̇nç Uzun
Turkish Journal of Electrical Engineering and Computer Sciences
Predictive emission monitoring systems (PEMS) are important tools for validation and backing up of costly continuous emission monitoring systems used in gas-turbine-based power plants. Their implementation relies on the availability of appropriate and ecologically valid data. In this paper, we introduce a novel PEMS dataset collected over five years from a gas turbine for the predictive modeling of the CO and NOx emissions. We analyze the data using a recent machine learning paradigm, and present useful insights about emission predictions. Furthermore, we present a benchmark experimental procedure for comparability of future works on the data