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- Classification (66)
- Deep learning (61)
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- Genetic algorithm (57)
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- Particle swarm optimization (49)
- Artificial neural network (34)
- Support vector machine (34)
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- Power quality (27)
- Artificial neural networks (26)
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- Induction motor (24)
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- Neural network (17)
- Reliability (17)
- Sliding mode control (17)
- Electric vehicle (16)
- Natural language processing (16)
- Renewable energy (16)
- Artificial bee colony (15)
Articles 481 - 510 of 3020
Full-Text Articles in Physical Sciences and Mathematics
Classification Of P300 Based Brain Computer Interface Systems Using Longshort-Term Memory (Lstm) Neural Networks With Feature Fusion, Ali̇ Osman Selvi̇, Abdullah Feri̇koğlu, Derya Güzel
Classification Of P300 Based Brain Computer Interface Systems Using Longshort-Term Memory (Lstm) Neural Networks With Feature Fusion, Ali̇ Osman Selvi̇, Abdullah Feri̇koğlu, Derya Güzel
Turkish Journal of Electrical Engineering and Computer Sciences
Enabling to obtain brain activation signs, electroencephalography is currently used in many applications as a medical diagnostic method. Brain-computer interface (BCI) applications are developed to facilitate the lives of individuals who have not lost their brain functions yet have lost their motor and communication abilities. In this study, a BCI system is proposed to make classification using Bi-directional long short term memory (Bi-LSTM) neural networks. In the designed system, spectral entropy method including instantaneous frequency change of signal is used as feature fusion. In the study, electroencephalography (EEG) data of 10 participants are collected with Emotiv EPOC+ device using 2x2 …
Employing Deep Learning Architectures For Image-Based Automatic Cataractdiagnosis, Emrullah Acar, Ömer Türk, Ömer Faruk Ertuğrul, Erdoğan Aldemi̇r
Employing Deep Learning Architectures For Image-Based Automatic Cataractdiagnosis, Emrullah Acar, Ömer Türk, Ömer Faruk Ertuğrul, Erdoğan Aldemi̇r
Turkish Journal of Electrical Engineering and Computer Sciences
Various eye diseases affect the quality of human life severely and ultimately may result in complete vision loss. Ocular diseases manifest themselves through mostly visual indicators in the early or mature stages of the disease by showing abnormalities in optics disc, fovea, or other descriptive anatomical structures of the eye. Cataract is among the most harmful diseases that affects millions of people and the leading cause of public vision impairment. It shows major visual symptoms that can be employed for early detection before the hypermature stage. Automatic diagnosis systems intend to assist ophthalmological experts by mitigating the burden of manual …
Mri Based Genomic Analysis Of Glioma Using Three Pathway Deep Convolutionalneural Network For Idh Classification, Sonal Gore, Jayant Jagtap
Mri Based Genomic Analysis Of Glioma Using Three Pathway Deep Convolutionalneural Network For Idh Classification, Sonal Gore, Jayant Jagtap
Turkish Journal of Electrical Engineering and Computer Sciences
As per 2016 updates by World Health Organization (WHO) on cancer disease, gliomas are categorized and further treated based on genomic mutations. The imaging modalities support a complimentary but immediate noninvasive diagnosis of cancer based on genetic mutations. Our aim is to train a deep convolutional neural network for isocitrate dehydrogenase (IDH) genotyping of glioma by auto-extracting the most discriminative features from magnetic resonance imaging (MRI) volumes. MR imaging data of total 217 patients were obtained from The Cancer Imaging Archives (TCIA) of high and low-grade gliomas. A 3-pathway convolutional neural network was trained for IDH classification. The multipath neural …
Medical Image Fusion With Convolutional Neural Network In Multiscaletransform Domain, Asan Abas, Hasan Erdi̇nç Koçer, Nurdan Baykan
Medical Image Fusion With Convolutional Neural Network In Multiscaletransform Domain, Asan Abas, Hasan Erdi̇nç Koçer, Nurdan Baykan
Turkish Journal of Electrical Engineering and Computer Sciences
Multimodal medical image fusion approaches have been commonly used to diagnose diseases and involve merging multiple images of different modes to achieve superior image quality and to reduce uncertainty and redundancy in order to increase the clinical applicability. In this paper, we proposed a new medical image fusion algorithm based on a convolutional neural network (CNN) to obtain a weight map for multiscale transform (curvelet/ non-subsampled shearlet transform) domains that enhance the textual and edge property. The aim of the method is achieving the best visualization and highest details in a single fused image without losing spectral and anatomical details. …
New Normal: Cooperative Paradigm For Covid-19 Timely Detection Andcontainment Using Internet Of Things And Deep Learning, Farooque Hassan Kumbhar, Ali Hassan Syed, Soo Young Shin
New Normal: Cooperative Paradigm For Covid-19 Timely Detection Andcontainment Using Internet Of Things And Deep Learning, Farooque Hassan Kumbhar, Ali Hassan Syed, Soo Young Shin
Turkish Journal of Electrical Engineering and Computer Sciences
The spread of the novel coronavirus (COVID-19) has caused trillions of dollars of damages to the governments and health authorities by affecting the global economies. It is essential to identify, track and trace COVID-19 spread at its earliest detection. Timely action can not only reduce further spread but also help in providing an efficient medical response. Existing schemes rely on volunteer participation, and/or mobile traceability, which leads to delays in containing the spread. There is a need for an autonomous, connected, and centralized paradigm that can identify, trace and inform connected personals. We propose a novel connected Internet of Things …
A Transfer Learning-Based Deep Learning Approach For Automated Covid-19diagnosis With Audio Data, Devri̇m Akgün, Abdullah Talha Kabakuş, Zehra Karapinar Şentürk, Arafat Şentürk, Enver Küçükkülahli
A Transfer Learning-Based Deep Learning Approach For Automated Covid-19diagnosis With Audio Data, Devri̇m Akgün, Abdullah Talha Kabakuş, Zehra Karapinar Şentürk, Arafat Şentürk, Enver Küçükkülahli
Turkish Journal of Electrical Engineering and Computer Sciences
The COVID-19 pandemic has caused millions of deaths and changed daily life globally. Countries have declared a half or full lockdown to prevent the spread of COVID-19. According to medical doctors, as many people as possible should be tested to identify their status, and corresponding actions then should be taken for COVID-19 positive cases. Despite the clear necessity of these medical tests, many countries are still struggling to acquire them. This fact clearly indicates the necessity of a large-scale, cheap, fast, and accurate alternative prescreening tool that can be used for the diagnosis of COVID-19 while waiting for the medical …
Deep Hyperparameter Transfer Learning For Diabetic Retinopathy Classification, Mahesh Patil, Satyadhyan Chickerur, Yeshwanth Kumar V S, Vijayalakshmi Bakale, Shantala Giraddi, Vivekanand Roodagi, Yashaswini Kulkarni
Deep Hyperparameter Transfer Learning For Diabetic Retinopathy Classification, Mahesh Patil, Satyadhyan Chickerur, Yeshwanth Kumar V S, Vijayalakshmi Bakale, Shantala Giraddi, Vivekanand Roodagi, Yashaswini Kulkarni
Turkish Journal of Electrical Engineering and Computer Sciences
The detection of diabetic retinopathy (DR) in millions of diabetic patients across the globe is a challenging problem. Diagnosis of retinopathy is a lengthy and tedious process, requiring a medical professional to assess the individual fundus images of a patient's retina. This process can be automated by applying deep learning (DL) technology given a huge dataset. The problems associated with DL are the unavailability of a large dataset and their higher training time. The DL model's best performance is achieved using set of optimal hyperparameters (OHPs) obtained by performing costly iterations of hyperparameter optimization (HPO). These problems can be addressed …
Improved Cell Segmentation Using Deep Learning In Label-Free Optical Microscopyimages, Aydin Ayanzadeh, Özden Yalçin Özuysal, Devri̇m Pesen Okvur, Sevgi̇ Önal, Behçet Uğur Töreyi̇n, Devri̇m Ünay
Improved Cell Segmentation Using Deep Learning In Label-Free Optical Microscopyimages, Aydin Ayanzadeh, Özden Yalçin Özuysal, Devri̇m Pesen Okvur, Sevgi̇ Önal, Behçet Uğur Töreyi̇n, Devri̇m Ünay
Turkish Journal of Electrical Engineering and Computer Sciences
The recently popular deep neural networks (DNNs) have a significant effect on the improvement of segmentation accuracy from various perspectives, including robustness and completeness in comparison to conventional methods. We determined that the naive U-Net has some lacks in specific perspectives and there is high potential for further enhancements on the model. Therefore, we employed some modifications in different folds of the U-Net to overcome this problem. Based on the probable opportunity for improvement, we develop a novel architecture by using an alternative feature extractor in the encoder of U-Net and replacing the plain blocks with residual blocks in the …
Optimal Design Of A Flux Reversal Permanent Magnet Machine As A Wind Turbinegenerator, Majid Ghasemian, Farzad Tahami, Zahra Nasiri-Gheidari
Optimal Design Of A Flux Reversal Permanent Magnet Machine As A Wind Turbinegenerator, Majid Ghasemian, Farzad Tahami, Zahra Nasiri-Gheidari
Turkish Journal of Electrical Engineering and Computer Sciences
Flux reversal permanent magnet generators are well suited for use as wind turbine generators owing to their high torque generation ability and magnetic gear. However, they suffer from poor voltage regulation due to their high winding inductance. In this paper, a design optimization method is proposed for flux reversal generators in wind turbine applications. The proposed method includes a new multiobjective function. Cost, volume of the generator, and mass of the permanent magnet are considered in it independently and simultaneously. Besides the new objective function, the main superiority of this paper compared with published papers is considering winding inductance in …
A Spreadsheet-Based Decision Support System For Examination Timetabling, Mehmet Güray Güler, Ebru Geçi̇ci̇
A Spreadsheet-Based Decision Support System For Examination Timetabling, Mehmet Güray Güler, Ebru Geçi̇ci̇
Turkish Journal of Electrical Engineering and Computer Sciences
Examination timetabling is an inevitable problem of educational institutions. Each institution has its own particular limitations; however, the main structure is the same: assigning exams to time slots and classrooms. Several institutions solve the problem manually, but it becomes more difficult every year with increasing numbers of students and limited resources. There are many studies in the literature addressing the examination timetabling problem (ETP) and providing high quality solutions within reasonable amounts of time. Nevertheless, almost none of them can be used in practice since they are not converted into a decision support system (DSS). Commercial DSSs, on the other …
Adaptive Modified Artificial Bee Colony Algorithms (Amabc) For Optimization Ofcomplex Systems, Rabi̇a Korkmaz Tan, Şebnem Bora
Adaptive Modified Artificial Bee Colony Algorithms (Amabc) For Optimization Ofcomplex Systems, Rabi̇a Korkmaz Tan, Şebnem Bora
Turkish Journal of Electrical Engineering and Computer Sciences
Complex systems are large scale and involve numerous uncertainties, which means that such systems tend to be expensive to operate. Further, it is difficult to analyze systems of this kind in a real environment, and for this reason agent-based modeling and simulation techniques are used instead. Based on estimation methods, modeling and simulation techniques establish an output set against the existing input set. However, as the data set in a given complex systems becomes very large, it becomes impossible to use estimation methods to create the output set desired. Therefore, a new mechanism is needed to optimize data sets in …
Mutant Selection By Using Fourier Expansion, Savaş Takan, Tolga Ayav
Mutant Selection By Using Fourier Expansion, Savaş Takan, Tolga Ayav
Turkish Journal of Electrical Engineering and Computer Sciences
Mutation analysis is a widely used technique to evaluate the effectiveness of test cases in both hardware and software testing. The original model is mutated systematically under certain fault assumptions and test cases are checked against the mutants created to see whether the test cases can detect the faults or not. Mutation analysis is usually a computationally intensive task, particularly in finite state machine (FSM) testing due to a possibly huge amount of mutants. Random selection could be a practical reduction method under the assumption that each mutant is identical in terms of the probability of occurrence of its associating …
Variable Gain High Order Sliding Mode Control Approaches For Pmsg Basedvariable Speed Wind Energy Conversion System, Ameen Ullah, Laiq Khan, Qudrat Khan, Saghir Ahmad
Variable Gain High Order Sliding Mode Control Approaches For Pmsg Basedvariable Speed Wind Energy Conversion System, Ameen Ullah, Laiq Khan, Qudrat Khan, Saghir Ahmad
Turkish Journal of Electrical Engineering and Computer Sciences
This research article proposes two different variants of variable gain higher-order sliding mode control (HOSMC) strategy for a variable-speed wind energy conversion system (WECS) based on a permanent magnet synchronous generator (PMSG). The main objective is to extract the maximum wind power with reduced chattering and mechanical stress. The main flaw of the classical sliding mode control (SMC) is the high-frequency switching, called chattering, which is alleviated by employing HOSMC strategies. The control law design is based on a super-twisting algorithm (STA) and a real-twisting algorithm (RTA) with variable gains. The proposed control techniques inherit the property of robustness and …
Energy Efficiency In Cmos Power Amplifier Designs For Ultralow Power Mobile Wireless Communication Systems, Selvakumar Mariappan, Jagadheswaran Rajendran, Norlaili Mohd Noh, Harikrishnan Ramiah, Asrulnizam Abd Manaf
Energy Efficiency In Cmos Power Amplifier Designs For Ultralow Power Mobile Wireless Communication Systems, Selvakumar Mariappan, Jagadheswaran Rajendran, Norlaili Mohd Noh, Harikrishnan Ramiah, Asrulnizam Abd Manaf
Turkish Journal of Electrical Engineering and Computer Sciences
Wireless communication standards keep evolving so that the requirement for high data rate operation can be fulfilled. This leads to the efforts in designing high linearity and low power consumption radio frequency power amplifier (RFPA) to support high data rate signal transmission and preserving battery life. The percentage of the DC power of the transceiver utilized by the power amplifier (PA) depends on the efficiency of the PA, user data rate, propagation conditions, signal modulations, and communication protocols. For example, the PA of a WLAN transceiver consumes 49 % of the overall efficiency from the transmitter. Hence, operating the PA …
A Hybrid Model Based On The Convolutional Neural Network Model And Artificial Bee Colony Or Particle Swarm Optimization-Based Iterative Thresholding For The Detection Of Bruised Apples, Mahmut Heki̇m, Onur Cömert, Kemal Adem
A Hybrid Model Based On The Convolutional Neural Network Model And Artificial Bee Colony Or Particle Swarm Optimization-Based Iterative Thresholding For The Detection Of Bruised Apples, Mahmut Heki̇m, Onur Cömert, Kemal Adem
Turkish Journal of Electrical Engineering and Computer Sciences
In this study, apple images taken with near-infrared (NIR) cameras were classified as bruised and healthy objects using iterative thresholding approaches based on artificial bee colony (ABC) and particle swarm optimization (PSO) algorithms supported by a convolutional neural network (CNN) deep learning model. The proposed model includes the following stages: image acquisition, image preprocessing, the segmentation of anatomical regions (stem-calyx regions) to be discarded, the detection of bruised areas on the apple images, and their classification. For this aim, by using the image acquisition platform with a NIR camera, a total of 1200 images at 6 different angles were taken …
Time Series Forecasting On Multivariate Solar Radiation Data Using Deep Learning (Lstm), Murat Ci̇han Sorkun, Özlem Durmaz İncel, Christophe Paoli
Time Series Forecasting On Multivariate Solar Radiation Data Using Deep Learning (Lstm), Murat Ci̇han Sorkun, Özlem Durmaz İncel, Christophe Paoli
Turkish Journal of Electrical Engineering and Computer Sciences
Energy management is an emerging problem nowadays and utilization of renewable energy sources is an efficient solution. Solar radiation is an important source for electricity generation. For effective utilization, it is important to know precisely the amount from different sources and at different horizons: minutes, hours, and days. Depending on the horizon, two main classes of methods can be used to forecast the solar radiation: statistical time series forecasting methods for short to midterm horizons and numerical weather prediction methods for medium- to long-term horizons. Although statistical time series forecasting methods are utilized in the literature, there are a limited …
A New Biometric Identity Recognition System Based On A Combination Of Superior Features In Finger Knuckle Print Images, Hadis Heidari, Abdolah Chalechale
A New Biometric Identity Recognition System Based On A Combination Of Superior Features In Finger Knuckle Print Images, Hadis Heidari, Abdolah Chalechale
Turkish Journal of Electrical Engineering and Computer Sciences
Biometric methods are among the safest and most secure solutions for identity recognition and verification. One of the biometric features with sufficient uniqueness for identity recognition is the finger knuckle print (FKP). This paper presents a new method of identity recognition and verification based on FKP features, where feature extraction is combined with an entropy-based pattern histogram and a set of statistical texture features. The genetic algorithm (GA) is then used to find the superior features among those extracted. After extracting superior features, a support vector machine-based feedback scheme is used to improve the performance of the biometric system. Two …
Reliability Comparisons Of Mobile Network Operators: An Experimental Case Study From A Crowdsourced Dataset, Engi̇n Zeydan, Ahmet Yildirim
Reliability Comparisons Of Mobile Network Operators: An Experimental Case Study From A Crowdsourced Dataset, Engi̇n Zeydan, Ahmet Yildirim
Turkish Journal of Electrical Engineering and Computer Sciences
It is of great interest for Mobile Network Operators (MNOs) to know how well their network infrastructure performance behaves in different geographical regions of their operating country compared to their horizontal competitors. However, traditional network monitoring and measurement methods of network infrastructure use limited numbers of measurement points that are insufficient for detailed analysis and expensive to scale using an internal workforce. On the other hand, the abundance of crowdsourced content can engender various unforeseen opportunities for MNOs to cope with this scaling problem. This paper investigates end-to-end reliability and packet loss (PL) performance comparisons of MNOs using a previously …
Pulse Width Modulation Control Of Fifteen-Switch Inverter For Four Ac Loads, Gaurav Goyal, Mohan Aware
Pulse Width Modulation Control Of Fifteen-Switch Inverter For Four Ac Loads, Gaurav Goyal, Mohan Aware
Turkish Journal of Electrical Engineering and Computer Sciences
In many studies in the literature, various topologies with reduced switch count are proposed. With the use of these topologies, a lower number of semiconductor switches are required to produce a desired set of voltage. This in turn reduces the size and cost of the inverter. This paper proposes a new reduced switch count topology named "fifteen-switch inverter (FSI)" which is experimentally verified. The FSI has five switches in one leg and have three legs for three phases. It is capable of controlling four three-phase ac loads. In this proposed inverter topology, fifteen switches are used against the twenty four …
Performance Improvement Of Induction Motor Drives With Model-Based Predictive Torque Control, Fati̇h Korkmaz
Performance Improvement Of Induction Motor Drives With Model-Based Predictive Torque Control, Fati̇h Korkmaz
Turkish Journal of Electrical Engineering and Computer Sciences
One of the most important advantages of using modeling and simulation software in design and control engineering is the ability to predict system behavior within specified conditions. This paper presents a novel error vector-based control algorithm that aims to reduce torque ripples predicting flux and torque errors in a conventional vector-controlled induction motor. For this purpose, a new control model has been developed that envisages flux change by applying probabilistic space vectors' torque and flux control. In the proposed predictive control algorithm, flux and torque errors are calculated for each candidate voltage vector. Thus, the optimal output voltage vector that …
A Preliminary Survey On Software Testing Practices In Khyber Pakhtunkhwa Region Of Pakistan, Bushra Latif, Tauseef Rana
A Preliminary Survey On Software Testing Practices In Khyber Pakhtunkhwa Region Of Pakistan, Bushra Latif, Tauseef Rana
Turkish Journal of Electrical Engineering and Computer Sciences
Conducted to ensure the quality of software products, the software testing process has a great significance in the software development and is the vital step of the verification and validation process. For conforming a software feature to the end user requirements, organizations rely on extensive testing procedures. Despite being the key factor, many of the software development industries/companies do not define/follow a systematic testing process. In this paper, we analyze/learn from the conducted surveys in the past and formulate a questionnaire for a survey in the northern region of Pakistan. To the best of our knowledge, no such survey has …
Adaptive Blind Equalization For A Mimo Chaotic Communication System, Gökçen Çeti̇nel, Cabi̇r Vural
Adaptive Blind Equalization For A Mimo Chaotic Communication System, Gökçen Çeti̇nel, Cabi̇r Vural
Turkish Journal of Electrical Engineering and Computer Sciences
There exist few blind solutions for chaotic MIMO channel equalization. In this work, a chaotic MIMO channel equalization framework is proposed. The objective function to be minimized in the proposed solution is obtained by adopting the objective function developed for chaotic SISO channel equalization. Furthermore, an optimum filter that minimizes the proposed cost function is designed to recover chaotic input signals assuming that the channel is known. The stationary point of the adaptive solution is equal to the optimal filter if the adaptive filter coefficients change sufficiently slowly. The adaptive solution is contrasted with the optimum filter in terms of …
Towards Human Activity Recognition For Ubiquitous Health Care Using Data From Awaist-Mounted Smartphone, Umar Zia, Wajeeha Khalil, Salabat Khan, Iftikhar Ahmad, Naeem Khatak
Towards Human Activity Recognition For Ubiquitous Health Care Using Data From Awaist-Mounted Smartphone, Umar Zia, Wajeeha Khalil, Salabat Khan, Iftikhar Ahmad, Naeem Khatak
Turkish Journal of Electrical Engineering and Computer Sciences
Understanding human activities is a newly emerging paradigm that is greatly involved in developing ubiquitous health care (u-Health) systems. The aim of these systems is to seamlessly gather knowledge about the patient?s health and, after collecting knowledge, make suggestions to the patient according to his/her health profile. For this purpose, one of the most important ubiquitous communication trends is the smartphone, which has drawn the attention of both professionals and caregivers for monitoring the aging population, childcare, fall detection, and cognitive impairment. Recognizing human actions in a ubiquitous environment is very challenging and researchers have extensively investigated different methods to …
A Multibeam Subarrayed Time-Modulated Linear Array, Uğur Yeşi̇lyurt, İhsan Kanbaz, Ertuğrul Aksoy
A Multibeam Subarrayed Time-Modulated Linear Array, Uğur Yeşi̇lyurt, İhsan Kanbaz, Ertuğrul Aksoy
Turkish Journal of Electrical Engineering and Computer Sciences
In conventional time-modulated arrays (TMAs), because of the usage of the RF-switch, harmonics are generated at multiples of the modulation frequency. In this study, the synthesis of time-modulated arrays has been analyzed for cognitive radio (CR) systems, in which these harmonics are suitably exploited for more efficient utilization of the spectrum. In order to accomplish the desired pattern at requested harmonic frequencies, a new excitation strategy with sinusoidal signals is proposed. The use of sinusoidal waveforms creates independent beams, allowing independent steering capability. Moreover, by utilizing the subarray structure, it is possible to have a smaller number of excitation functions …
Robust Optimal Operation Of Smart Distribution Grids With Renewable Basedgenerators, Omid Zare, Sadjad Galvani, Murtaza Farsadi
Robust Optimal Operation Of Smart Distribution Grids With Renewable Basedgenerators, Omid Zare, Sadjad Galvani, Murtaza Farsadi
Turkish Journal of Electrical Engineering and Computer Sciences
Modern distribution systems are equipped with various distributed energy resources (DERs) because of the importance of local generation. These distribution systems encounter more and more uncertainties because of the ever-increasing use of renewable energies. Other sources of uncertainty, such as load variation and system components? failure, will intensify the unpredictable nature of modern distribution systems. Integrating energy storage systems into distribution grids can play a role as a flexible bidirectional source to accommodate issues from constantly varying loads and renewable resources. The overall functionality of these modern distribution systems is enhanced using communication and computational abilities in smart grid frameworks. …
Dynamic Software Rejuvenation In Web Services: A Whale Optimizationalgorithm-Based Approach, Kimia Rezaei Kalantari, Ali Ebrahimnejad, Homayun Motameni
Dynamic Software Rejuvenation In Web Services: A Whale Optimizationalgorithm-Based Approach, Kimia Rezaei Kalantari, Ali Ebrahimnejad, Homayun Motameni
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, we suggest a method for determining the restarting time for web services to increase availability, known as rejuvenation. We consider different parameters such as number of users, maximum service request number, response time, and throughput of a web service to determine its restarting time. Software rejuvenation is an effective technique to counteract software aging in continuously running applications such as web service-based systems. In these systems, web services are allocated based on the needs of the receivers and facilities of servers. One of the challenges while assigning web services is selecting the appropriate server to reduce faults. …
An Automated Eye Disease Recognition System From Visual Content Of Facial Imagesusing Machine Learning Techniques, Ashrafi Akram, Rameswar Debnath
An Automated Eye Disease Recognition System From Visual Content Of Facial Imagesusing Machine Learning Techniques, Ashrafi Akram, Rameswar Debnath
Turkish Journal of Electrical Engineering and Computer Sciences
Many eye diseases like cataracts, trachoma, or corneal ulcer can cause vision problems. Progression of these eye diseases can only be prevented if they are recognized accurately at the early stage. Visually observable symptoms differ a lot among these eye diseases. However, a wide variety of symptoms is necessary to be analyzed for the accurate detection of eye diseases. In this paper, we propose a novel approach to provide an automated eye disease recognition system using visually observable symptoms applying digital image processing techniques and machine learning techniques such as deep convolution neural network (DCNN) and support vector machine (SVM). …
Correlation Coefficients Of Pythagorean Hesitant Fuzzy Sets And Their Applicationto Radar Lpi Performance Evaluation, Cheng Xiu Yang, Qianzhe Wang, Weidong Peng, Shaoting Pei
Correlation Coefficients Of Pythagorean Hesitant Fuzzy Sets And Their Applicationto Radar Lpi Performance Evaluation, Cheng Xiu Yang, Qianzhe Wang, Weidong Peng, Shaoting Pei
Turkish Journal of Electrical Engineering and Computer Sciences
Evaluating low probability of intercept (LPI) performance is the first step to design parameters and arrange radar resources. In the evaluation process it is hard to rely on the intercept receiver?s working scenarios and operating parameters. On the other hand, indicators that affect the LPI performance of radiating side are difficult to consider comprehensively. Thus, building an effective evaluation system is crucial. This research considers the natural parameters of radar extracted from a radiating scenario. Subsequently, a number of criteria are selected, including spatial, time, frequency domain, polarization status, energy status, and waveform features. A multidomain radar LPI performance evaluation …
Piezoresistive Disposable Weight Sensor With Increased Sensitivity, Kuter Erdi̇l, Tuğçe Ayraç, Ömer Gökalp Akcan, Yi̇ği̇t Dağhan Gökdel
Piezoresistive Disposable Weight Sensor With Increased Sensitivity, Kuter Erdi̇l, Tuğçe Ayraç, Ömer Gökalp Akcan, Yi̇ği̇t Dağhan Gökdel
Turkish Journal of Electrical Engineering and Computer Sciences
This study presents the design, simulation, implementation, and experimental characterization of a paperbased perforated disposable weight sensor system with a double piezoresistive layer. The demonstrated system is designed to achieve highly sensitive weight sensing operations with low-cost materials. For that purpose, the main fabrication material of the proposed disposable sensor is selected as a 289 $\mu$m thick Strathmore 400 series Bristol paper. Approximately 48 $\mu$m thick piezoresistive graphite paste is coated onto both sides of the paper-based cantilever beam with the aim of acquiring more sensitive weight-sensing capability. Additionally, the proposed paper-based structure has rows of closely spaced perforations at …
Prediction Of Railway Switch Point Failures By Artificial Intelligence Methods, Burak Arslan, Hasan Ti̇ryaki̇
Prediction Of Railway Switch Point Failures By Artificial Intelligence Methods, Burak Arslan, Hasan Ti̇ryaki̇
Turkish Journal of Electrical Engineering and Computer Sciences
In recent years, railway transport has been preferred intensively in local and intercity freight and passenger transport. For this reason, it is of utmost importance that railway lines are operated in an uninterrupted and safe manner. In order to carry out continuous operation, all systems must continue to operate with maximum availability. In this study, data were collected from switch motors, which are the important equipment of railways, and the related equipment and these data were evaluated with sector experience and the results related to the failure status of the switch points were revealed. The obtained results were processed with …