Elektrik Elektronik Mühendisliği Bölümü, Makale Koleksiyonu

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https://hdl.handle.net/11492/3319

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A machine learning-based real-time remaining useful life estimation and fair pricing strategy for electric vehicle battery swapping stations
(İeee-Inst Electrical Electronics Engineers İnc, 2025) Çeltek, Seyit Alperen; Kul, Seda; Polat, A. Ozgur; Zeinoddini-Meymand, Hamed; Shahnia, Farhad
The increasing adoption of electric vehicles (EVs) has led to the widespread implementation of battery swapping stations. However, ensuring fairness in battery pricing remains a significant challenge since variations in battery health and performance among swapped batteries can result in user dissatisfaction and operational inefficiencies. This paper introduces a novel approach to enhance fairness in battery swapping by integrating a machine learning-based real-time prediction model with a pricing strategy based on remaining useful life (RUL) estimation to address this issue. The proposed solution comprises a real-time RUL estimation system and a dynamic pricing mechanism that ensures fair pricing based on battery health and performance. This integrated approach aims to improve user satisfaction and the operational efficiency of swapping stations. The paper evaluates various machine learning algorithms for real-time RUL estimation regarding accuracy, computation time, and memory usage. The results suggest that XGBoost provides the most suitable balance between accuracy and efficiency, making it an effective solution for real-world applications. Comparative analysis shows that the XGBoost model outperforms the second-best method (Random Forest) with a lower error (3.50 vs 3.79) while maintaining competitive computational efficiency (9.75 vs 8.52 seconds) and memory usage (2.12 vs 2.32 MB) when solving a typical numerical case study problem. The proposed approach has the potential to accelerate the adoption of electric vehicles and contribute to sustainability goals by promoting efficient battery utilization and fair pricing mechanisms.
Hexadecimal permutation and 2D cumulative diffusion image encryption using hyperchaotic sinusoidal exponential memristive system
(Springer, 2025) Şimsek, Cemaleddin; Erkan, Uğur; Toktaş, Abdurrahim; Lai, Qiang; Gao, Suo
The performance of chaos-based image encryption (IE) highly depends upon chaotic system's complexity and diversity; and IE algorithm's permutation and diffusion strategies. Existing chaotic systems often face limitations in achieving sufficient complexity and dynamical richness, limiting their effectiveness in high unpredictability. To overcome these limitations, a novel hyperchaotic 2D sinusoidal exponential memristive system (2D-SEMS) is designed and validated through a hardware circuit. Additionally, a novel hexadecimal permutation and two dimensional (2D) cumulative diffusion IE (Hp2DCd-IE) is contrived using the 2D-SEMS. The 2D-SEMS is built upon two introduced designs of simplified exponential discrete memristors (SEDMs), forming the basis of its dynamic and chaotic framework. The 2D-SEMS validated by comparison with existing maps through an evaluation in terms of Lyapunov exponents (LE1, LE2), sample entropy (SE), correlation dimension (CD), and Kolmogorov entropy, and (KE), which are measured on average as 4.2889, 0.0250, 1.3204, 1.7599, and 1.6428. The Hp2DCd-IE is corroborated across wide range of cryptanalysis by comparing with the existing IE algorithms. The results demonstrate that the Hp2DCd-IE has high shuffling and manipulating performance thanks to complexity and diversity of the 2D-SEMS. Image Encryption;Chaotic System; Hyperchaotic Systems; Memristive System; Exponential Memristor
Fault analysis in power transformers with finite element analysis and deep learning: a study on flux distributions
(Institute of Electrical and Electronics Engineers Inc., 2024) Sinay, Merve; Aslan, Büşra; Balcı, Selami; Kayabaşı, Ahmet; Aslan, Mehmet Fatih
- In this study, a 2000 kVA power transformer with34.5/0.4 kV voltage, 50 Hz frequency, and Delta-Star connection is modeled under nonlinear loading conditions using Finite Element Analysis (FEA). Various fault types (short circuit, voltage imbalance, and harmonic distortions) that may occur under operating conditions of the transformer are analyzed through FEA simulations. For each fault type and for a healthy transformer, magnetic flux distributions are obtained. These magnetic flux distributions reflect the distinctive characteristics of each fault type, illustrating points of divergence in the transformer's internal magnetic behavior. The obtained flux distribution images are used as a dataset for the automatic classification of fault types. For the classification process, deep learning-based Artificial Intelligence (AI) models, namely ResNet18, ResNet50, VGG16, GoogleNet, and MobileNetv2, are utilized. These models are distinguished by their ability to differentiate between various fault conditions based on flux distributions. Results demonstrate that faults can be predicted from flux distribution images with accuracies up to 100%. The conducted classification studies provide an effective method for the early detection of faults in power transformers, thereby enhancing system reliability. In conclusion, this study presents a comprehensive analysis of the use of AI techniques for identifying and classifying different fault conditions in power transformers based on flux distributions. This method has the potential to optimize transformer maintenance and repair processes, thereby improving the reliability of energy systems. ©2024 IEEE.
A 3D memristive cubic map with dual discrete memristors: Design, implementation, and application in image encryption
(Institute of Electrical and Electronics Engineers Inc., 2025) Gao, Suo; Iu, Herbert Ho-Ching; Erkan, Uğur
Discrete chaotic systems based on memristors exhibit excellent dynamical properties and are more straightforward to implement in hardware, making them highly suitable for generating cryptographic keystreams. However, most existing memristor-based chaotic systems rely on a single memristor. This paper introduces a novel discrete chaotic system employing dual memristors, named the 3D memristive cubic map with dual discrete memristors (3D-MCM). The 3D-MCM system demonstrates richer and more intricate dynamical behaviors compared to its single-memristor counterparts, as verified through bifurcation diagrams, Lyapunov exponent spectra, and complexity analyses. Notably, the system exhibits coexisting attractors, substantially enhancing its dynamical complexity. Hardware implementation of the 3D-MCM attractors confirms its feasibility for industrial applications. To illustrate the system's potential in encryption tasks, this study integrates the quaternary-based permutation and dynamic emanating diffusion (QPDED-IE) scheme for image encryption with the 3D-MCM.Experimental results demonstrate that the QPDED-IE scheme based on the 3D-MCM exhibits strong diffusion and confusion properties, effectively resisting cryptanalytic attacks.
A new diagnosing method for psoriasis from exhaled breath
(Institute of Electrical and Electronics Engineers Inc., 2025) Tozlu, Bilge Han; Faruk Akmeşe, Ömer; Şimşek, Cemaleddin; Senel, Engin
Psoriasis is a chronic inflammatory skin disease with a high global prevalence. A skin biopsy is still required to diagnose the disease; no non-invasive diagnosis method has been found. It has become a popular approach for physicians as a support system, as it classifies biological data collected without human intervention in various ways with machine learning methods. Numerous studies have been conducted using machine learning methods to increase the accuracy, performance, speed, and reliability of diagnosing various diseases. This study aims to predict whether a group of patients admitted to Hitit University Erol Olçok Training and Research Hospital have psoriasis based on exhaled breath measurements using an electronic nose system which was produced for this study by the authors. In total, 263 clinical records were examined; 120 (45.6%) were obtained from healthy individuals, while 143 (54.4%) belonged to psoriasis patients. In order to distinguish data from those of psoriasis patients and those of healthy individuals, six different machine learning algorithms were used on the breath data set. The best classification result was provided by the ExtraTreesClassifier algorithm, with an accuracy rate of 96.1%, while other algorithms have rates between 66.6% and 94.2%. The most important outcome of this study is that the model determined to distinguish psoriasis patients from healthy ones can also help in the early diagnosis of psoriasis. © 2025 IEEE.
Parameter estimation and validation of cascaded DC-DC boost converters for renewable energy systems using the IGWO optimization algorithm
(Elsevıer Science Sa, 2025) Çeltik, Seyit Alperen; Kül, Seda; Balcı, Selami; Dik, Abdullah
The voltage amplitude generated by renewable energy sources is often unstable, necessitating the use of power electronic circuits for effective grid integration. Among these, DC-DC converters play a critical role in maintaining a constant DC link voltage, typically 400 V or 800 V, at the input of inverter circuits that supply power to the load or the grid. The study focuses on the voltage gain behavior of a high-gain dual cascaded DC-DC boost converter designed for PV (photovoltaic) power systems. Using ANSYS Electronics software with its parametric solver, a comprehensive dataset was generated based on key parameters such as input voltage, power switch duty ratio, and switching frequency. The Improved Grey Wolf Optimizer (IGWO) algorithm was employed to estimate mathematical models for this dataset using linear and quadratic equations. The accuracy of the proposed models was validated across six test scenarios, demonstrating superior performance compared to traditional optimization algorithms, including Harmony Search (HS), Particle Swarm Optimization (PSO), Differential Evolution (DE), and the standard Grey Wolf Optimizer (GWO). Experimental validations yielded output voltages of 23.5 V and 36.1 V for input voltages of 4.8 V and 6.2 V, respectively, closely aligning with simulation results of 23.113 V and 36.447 V. The findings, supported by detailed simulations and graphical analyses, highlight the IGWO algorithm's precision and reliability in predicting converter output voltages under variable input conditions. This work advances renewable energy systems integration by enhancing the modeling and performance of cascaded DC-DC boost converters.
Fault diagnosis in thermal images of transformer and asynchronous motor through semantic segmentation and different CNN models
(2025) Aslan, Büşra; Balcı, Selami; Kayabaşı, Ahmet
Transformers are crucial power equipment that play an important role in changing voltage levels to meet consumer needs and in transmitting electricity. A fault in a transformer can cause significant economic losses and social problems. Similarly, asynchronous motors are widely used in industry, and faults in these motors can have substantial negative effects on both the economy and human life. Early detection of faults in both types of power equipment can save time and costs, as well as allow for remedial measures to prevent the failure of the entire system. Traditional fault diagnosis methods, which integrate various monitoring and measurement equipment into power systems, are not sufficient for early fault detection. Therefore, modern solutions have evolved towards more reliable and risk-free artificial intelligence (AI)-based automatic fault diagnosis methods. In our application, we aim to determine faults based on AI in thermal images of asynchronous motors and transformers in operation. Specifically, we propose a semantic segmentation application that highlights fault areas on thermal images, setting other pixels as background. This approach allows the region where the fault occurred to be taken as a reference for later fault diagnosis. As a result of semantic segmentation, the winding of the transformer and the stator region of the asynchronous motor are automatically segmented. Data augmentation techniques are then applied to these segmented images. Augmented and segmented motor and transformer images are classified using seven different Convolutional Neural Network (CNN) models. The results show that CNN models provide fault classification with accuracy reaching 100% for transformers and 96.49% for asynchronous motors.
Influence of core window height on thermal characteristics of dry-type transformers
(Elsevier Ltd, 2025) Dawood, Kamran; Kül, Seda
Elevated temperatures in transformer windings and cores pose a significant risk of damage to power transformers. The objective of this work is to analyze the influence of core window dimensions on the thermal efficiency of power transformers. Analytical approaches are limited in their ability to consider the impact of core window dimensions on the transformer's thermal behavior. Conversely, experimental methods are both expensive and time-consuming. To overcome these constraints, this work assesses and optimizes the temperature distribution in dry-type power transformers using finite element models, specifically examining the impact of the core window. The thermal model treats core and winding losses as sources of heat generation. Four different transformers, with varying heights of the transformer core window, have been modeled to assess the impact of window height on the thermal conditions of the transformers. The simulation findings indicate that variations in core window height have a significant impact on the transformer's thermal properties. By comparing the model's predictions of short-circuit impedance with experimental data, this study demonstrates the model's capability to reliably estimate parameters influenced by core window variations, thereby validating its usefulness. © 2025 The Author(s)
Artificial Intelligence Techniques in Crop Yield Estimation Based on Sentinel-2 Data: A Comprehensive Survey
(Mdpi, 2024) Aslan, Muhammet Fatih; Sabancı, Kadir; Aslan, Büşra
This review explores the integration of Artificial Intelligence (AI) with Sentinel-2 satellite data in the context of precision agriculture, specifically for crop yield estimation. The rapid advancements in remote sensing technology, particularly through Sentinel-2's high-resolution multispectral imagery, have transformed agricultural monitoring by providing critical data on plant health, soil moisture, and growth patterns. By leveraging Vegetation Indices (VIs) derived from these images, AI algorithms, including Machine Learning (ML) and Deep Learning (DL) models, can now predict crop yields with high accuracy. This paper reviews studies from the past five years that utilize Sentinel-2 and AI techniques to estimate yields for crops like wheat, maize, rice, and others. Various AI approaches are discussed, including Random Forests, Support Vector Machines (SVM), Convolutional Neural Networks (CNNs), and ensemble methods, all contributing to refined yield forecasts. The review identifies a notable gap in the standardization of methodologies, with researchers using different VIs and AI techniques for similar crops, leading to varied results. As such, this study emphasizes the need for comprehensive comparisons and more consistent methodologies in future research. The work underscores the significant role of Sentinel-2 and AI in advancing precision agriculture, offering valuable insights for future studies that aim to enhance sustainability and efficiency in crop management through advanced predictive models.
Regression Model Extractions of a T-Equivalent Circuit Modeling for Medium-Length Transmission Line Based-on the Parametric Simulation Approach
(Gazi Univ, 2024) Balcı, Selami; Akkaya, Mustafa
In medium-length power transmission line models, the difference between the end-of-line and head-of-line voltage can be calculated with classical mathematical expressions. However, since the line parameters are not linear, these calculations can be approximated according to certain assumptions. The parametric data analysis approach proposed in this study obtained a data set for different variations by changing the line length and line parameters (transmission line-specific parameters such as resistance, inductance, and capacitance) with certain steps. Then, using this data set, a classification is made with machine learning. In addition, data analysis is carried out with the end-of-line voltage value graphs obtained with different line parameters, and the proposed approach is verified by constructing a test simulation circuit of a three-phase 200 km length with a 154 kV line voltage value. Thus, a parametric simulation study has been presented, especially in electrical engineering education. In addition, Support Vector Regression (SVR) and Decision Tree Regression (DTR) models in the field of machine learning were used to measure the consistency of the data set created for 5 pF, 8 pF, and 10 pF capacity values. With the figures and numerical data presented comparatively, it is seen that the Long Short-Term Memory (LSTM) algorithm produces more successful scores in all three categories. In this context, the prediction accuracy was between 97% and 98% with DTR, while the accuracy was between 81% and 85% with SVR. Thus, prediction results in the of 98%- 99% were obtained in the LSTM model.
Design, Dynamical Analysis, and Hardware Implementation of a Novel Memcapacitive Hyperchaotic Logistic Map
(IEEE-Inst Electrical Electronics Engineers Inc, 2024) Gao, Suo; Ho-Ching Iu, Herbert; Erkan, Uğur; Şimşek, Cemaleddin; Mou, Jun; Toktaş, Abdurrahim; Wu, Rui
Currently, discrete memristors are a focal point in the study of chaotic maps. Similar to memristors, memcapacitors-another type of memory circuit component-have not received widespread attention in the design of chaotic maps. In this article, we propose a 4-D memcapacitive hyperchaotic logistic map (4D-MHLM) by integrating memcapacitors with the logistic map. The dynamical behavior of the 4D-MHLM is analyzed using Lyapunov exponent analysis, and the impact of different parameters on system performance is discussed. The complexity of generating pseudo-random sequences with the 4D-MHLM is investigated through complexity analysis, including spectral entropy complexity and C0 complexity. Notably, attractor analysis reveals a unique phenomenon of infinite coexisting attractors within the 4D-MHLM. Finally, the chaotic attractor generated by the 4D-MHLM is successfully implemented on a hardware platform. Theoretical analysis and digital circuit implementation results indicate that the 4D-MHLM exhibits rich dynamical behavior and higher complexity, offering significant value for practical applications.
Power quality enhancement of rectifiers of water electrolysis for the green hydrogen
(IEEE, 2024) Kul, Seda; Balcı, Selami; Çeltek, S. Alperen; Polat, A. Özgür
Electrolysis, a clean and efficient method, utilizes renewable alternating current (AC) for water decomposition into hydrogen. This study addresses power quality improvement in rectifiers converting renewable AC to direct current (DC) for electrolysis. This study focuses on improving the power quality of rectifiers used to convert AC from renewable energy sources into a DC source for hydrogen production in water electrolysis systems. For this purpose, the 12-pulse topology, using two interconnected 6-pulse rectifiers, delivers a smoother DC output with reduced ripple and improved mains current quality. This significantly minimizes harmonics, achieving a waveform closer to a pure sine wave without additional filtering. This approach offers a promising solution to mitigate power quality issues in renewable hydrogen production systems.
Grey wolf-based heuristic methods for accurate parameter extraction to optimize the performance of pv modules
(Inst Engineering Technology-Iet, 2024) Çeltek, Seyit Alperen; Kül, Seda; Singla, Manish Kumar; Gupta, Jyoti; Safaraliev, Murodbek; Zeinoddini-Meymand, Hamed
Parameterprediction for PV solar cells plays a crucial role in controlling andoptimizing the performance of PV modules. In this study, the parameter prediction of a four-diode PV model wascarried out using the Improved Grey Wolf Optimization (IGWO) algorithm, whichbuilds upon the Grey Wolf Optimization (GWO) algorithm. The parameters requiredfor the four-diode PV model were optimized based on a predefined objectivefunction. Subsequently, the obtained data were compared with the data from RTCFrance Solar Cell to validate the accuracy and reliability of the optimizationresults. The evaluation of the optimization results revealed that the SumSquare Error (SSE) values for PSOGWO, AGWOCS, GWOCS, and GWO were 3.96E-05, while the MSE value for IGWO was 3.6309E-05. These findings clearly demonstratethat the proposed IGWO algorithm outperforms the other algorithms used in thestudy, based on the minimized SSE values. This study emphasizes the importanceof parameter prediction in optimizing PV performance, and it contributes to thefield by introducing the novel IGWO algorithm for the four-diode PV model. Thealgorithm's superior performance, as demonstrated through extensive testing andcomparison with existing algorithms, validates its efficacy in accuratelypredicting the parameters for the PV solar cell model. This paper presents the parameter prediction of a four-diode PV model using the improved grey wolf optimization (IGWO) algorithm, which builds upon the grey wolf optimization (GWO) algorithm. Initially, the proposed algorithm underwent rigorous testing across ten benchmark test functions. The results of this comprehensive evaluation demonstrated the superiority of the proposed IGWO algorithm over the other algorithms used for comparison. image
Machine learning and computer vision technology to analyze and discriminate soil samples
(Nature Portfolio, 2024) Kaplan, Sema; Ropelewska, Ewa; Günaydın, Seda; Sabancı, Kadir; Çetin, Necati
Soil texture is one of the most important elements to consider before planting and tillage. These features affect the product selection and regulate its water permeability. Discrimination of soils by determining soil texture features requires an intense workload and is time-consuming. Therefore, having a powerful tool and knowledge for texture-based soil discrimination could enable rapid and accurate discrimination of soils. This study focuses on presenting new models for 6 different soil sample groups (Soil_1 to Soil_6) based on 12 different machine learning algorithms that can be utilized for various problems. As a result, overall accuracy values were determined as greater than 99.2% (Trilayered Neural Network). The greatest accuracy value was found in Bayes Net (99.83%) and followed by Subspace Discriminant (99.80%). In the Bayes Net algorithm, MCC (Matthews Correlation Coefficient) and F-measure values were obtained as 0.994 and 0.995 for Soil_4 and Soil_6 sample groups while these values were 1.000 for other soil groups. Soil types can visually vary based on their texture, mineral composition, and moisture levels. The variability of this can be influenced by fertilization, precipitation levels, and soil cultivation. It is important to capture the images in soil conditions that are more stable. In conclusion, the present study has proven the feasibility of rapid, non-destructive, and accurate discrimination of soils by image processing-based machine learning.
Consensus-based virtual leader tracking swarm algorithm with GDRRT*-PSO for path-planning of multiple-UAVs
(Elsevier, 2024) Yıldız, Berat; Aslan, Muhammet Fatih; Durdu, Akif; Kayabaşı, Ahmet
UAV technology is rapidly advancing and widely utilized, particularly in social and military domains, due to its extensive motion and maneuverability. Coordinating multiple UAVs enables more rapid and efficient task execution compared to a single UAV. The proliferation of UAVs across various sectors, including entertainment, transportation, delivery, and social domains, as well as military applications such as surveillance, tracking, and attack, has spurred research in swarm systems. In this study, a new swarm topology is presented by combining the Consensus-Based Virtual Leader Tracking Swarm Algorithm (CBVLTSA), which provides formation control in swarm systems, with the Goal Distance-based Rapidly-Exploring Random Tree with Particle Swarm Optimization (GDRRT*-PSO) route planning algorithm. Recently proposed, GDRRT* is notable for its efficient operation in expansive environments and rapid convergence to the goal. Within this framework, the path generated by GDRRT* is optimized using PSO to yield the shortest current route. CBVLTSA employs a potential push and pull function to facilitate cooperative, coordinated flight among swarm members. While applying pushing force to avoid collisions with each other and obstacles, members also exert pulling force to maintain flight formation while navigating to target points. This ensures controlled flight formation and collision-free traversal along the GDRRT*-PSO route. Consequently, unlike the others, the proposed algorithm achieves faster target reach with pre-planned routes, demonstrating a robust and flexible swarm topology with CBVLTSA. Moreover, we anticipate the significant utility of this algorithm across various swarm applications, including target detection, observation, tracking, trade and transportation logistics, and collective defense and attack strategies.
Detection of moisture of flowing grain with a novel deep learning structure using 2D spectrogram data
(Elsevier Sci Ltd, 2024) Yiğit, Enes; Aksoy, Abdullah; Duysak, Hüseyin; Işıker, Hakan
In order to preserve the stored grain for a long time without deterioration, the moisture content must be accurately known. In this study, the moisture content of flowing grain was determined using radar spectrogram data and CNN structure. A free-space measurement technique-based experimental environment was established for the purpose of collecting the necessary signals to measure the moisture content. The measurement plant is composed of two horn antennas, a vector network analyzer (VNA), and a flowing grain mechanism. In the experimental environment, the S11 and S21 parameter values are recorded from the VNA. The short-time Fourier transforms (STFT) of the recorded signals were then taken, and 2D spectrogram images were created. The dataset, comprising 22,780 images, was split into 80 % for training and 20 % for testing; then, they were subjected to regression analysis using CNNs. First, the mean absolute error (MAE) values of the 27 pre-trained CNN architectures in a single epoch are calculated. Subsequently, the architecture with the best four regression results is automatically selected. The training and testing steps are performed independently for each selected architecture, and the results are recorded. The MAE, root mean square error (RMSE), mean square error (MSE) and mean absolute percentage error (MAPE) metrics are employed to assess the efficacy of the CNN architectures. Among these, ResNet50 is the architecture that yields the most favorable results. Subsequently, a subsequent architecture with fewer parameters and a more expeditious processing time is proposed. The novel deep-learning CNN architecture demonstrated superior performance compared to the pre-trained architectures. The results are as follows: MAE = 0.0411, MSE = 0.0149, RMSE = 0.122, and MAPE = 0.0397. When comparing the time spent on training and testing, the least time-consuming architecture required approximately 72 min, whereas this study was completed in approximately 325 s. The pronounced disparity is readily apparent. The results demonstrate that when the CNN is appropriately modeled and trained, the combination of CNN and appropriate signal processing can effectively determine the moisture content of grains.
Multi-detection of seratonin and dopamine based on an electrochemical aptasensor
(Springer Int Publ Ag, 2024) Cuhadar, Sare Nur; Durmaz, Habibe; Yildirim-Tirgil, Nimet
Chemical stimuli that enable neurons to communicate with each other are called neurotransmitters. The task of these stimulants is to transmit the messages of neurons to target cells. In this work, a multi-detection mode electrochemical aptasensor system was developed for the neurotransmitters dopamine (3,4-dihydroxyphenylethylamine, DA) and serotonin (5 hydroxytryptamine, SER), which are associated with motivation-based learning, motor control, addiction, activation, arousal, immunity, central nervous systems. These neurotransmitters are related to many diseases, such as depression, primary pulmonary hypertension, kidney disease, Alzheimer's, Huntington's, and Parkinson's. This study used aptamer, a nucleic acid-based biorecognition agent, to capture the target molecules from complex matrix samples. After the capturing procedures, an electrochemical method (differential pulse voltammetry) was performed to observe the specific oxidation profiles of DA and SER. The developed multi-detection mode biosensor system had a linear response from 0.1 to 4 mu M with detection limits of 0.06 mu M for DA and 0.12 mu M for SER. In addition, real serum analyses were performed in the prepared direct electrochemical aptasensor system, and 86-92% of recovery values were observed. As a general conclusion, the main drawback of the multi-detection mode-based methods, sensitivity, can be solved by the aptamer binding process as implemented in this work.
An approach to determine pathological breast tissue samples with free-space measurement method at 24 GHz
(Wiley, 2024) Toprak, Rabia; Gültekin, Seyfettin Sinan; Kayabaşı, Ahmet; Çelik, Zeliha Esin; Tekin, Fatma Hicret; Uzer, Dilek
Pathology is an important branch of science in the diagnosis and treatment of several diseases. In cancer diseases, serious investigations have been made about the course of the diseases. A report that is essential for both the patient and the doctor is prepared by the pathologists as a result of a detailed cellular examination. These reports contain information about the disease. Access duration to these reports, which affects the form and duration of the treatment, is extremely important today. It is possible to shorten this period with systems using antenna technologies. The pathological breast tissue samples have been examined by using horn antenna structures with high gain in this study. Dual identical horn antennas have been placed opposite each other as receivers and transmitters in the measurement setup at 24 GHz. Measurements of normal and cancerous breast tissues have been made, and the normalization process has been applied to the measured scattering parameters. The different values between normal and cancerous breast tissues have been shown with this process. The normalized values are compared with other analyzed values. According to the results obtained, the percentage of normalized values for transmission is much more effective and meaningful than other results.
Parameter extraction of PV solar cell using metaheuristic methods
(2023) Celtek, Seyit Alperen; Kul, Seda
Due to the increasing crises in energy and environmental factors, the importance of renewable energy is increasing. However, it is gaining importance in developing photovoltaic energy systems. Therefore, great efforts are made to maximize success in accurately modeling PV parameters. Parameter estimation is a complex problem and requires advanced design tools such as optimization techniques because the current voltage (I–V) characteristics of PVs are nonlinear. This study investigates the best technique for the most accurate estimation of the parameters obtained in single-diode and double-diode cases. The Gray Wolf Optimization (GWO), Improved Gray Wolf Optimization (IGWO), Sine Cosine Algorithm (SCA), Whale Optimization Algorithm (WOA), and Multi-Verse Optimizer (MVO) are the algorithms used in this paper. Apart from the literature, this study considers that the PV parameter extraction problem is not just an offline optimization problem but also a real-time optimization issue. The performance of all methods has been compared with experimental data. The lowest error on minimum iteration and highest convergence accuracy have been achieved for offline optimization by using IGWO. The results clearly state that the IGWO is not usable in real-time applications even though IGWO is the best optimizer in offline optimization.
Kapasitif Enkoderler için Sağlam bir Algılayıcı Mekaniği
(2020) Yavsan, Emrehan; Kara, Muhammet Rojhat; Karalı, Mehmet; Erismis, Mehmet Akif
Kapasitif enkoder teknolojisi manyetik ve optik enkoder teknolojilerine kıyasla daha güncel ve günümüzde halen geliştirilmekte olan bir açısal pozisyon algılayıcı teknolojisidir. Çeşitli özelliklerde ve farklı bileşenlere sahip kapasitif enkoderler bulunmaktadır. Yenilikçi ve yüksek performanslı kapasitif bir enkoderin geliştirilebilmesi için mevcut kapasitif enkoderlerin ayrıntılı bir şekilde irdenlenmesi ve sınıflandırılması gerektiğinden bu çalışmada genel bir kapasitif enkoder mimarisi tanımlanarak kapasitif enkoderler için detaylı bir sınıflandırma verilmiştir. Sunulan mimari; sinyal işleme ön devresi, algılayıcı mekaniği ve sinyal işleme son devresinden oluşmaktadır. Sinyal işleme ön devresi kapasitif enkoderin tetikleme sinyal devresini, sinyal işleme son devresi kapasitif enkoderin demodülasyon devresini içermektedir. Burada sağlam bir algılayıcı mekaniğin belirlenebilmesi için kapasitif enkoderler detaylı bir şekilde sınıflandırılmıştır. Sınıflandırma işlemi kapasitif enkoderlerin plaka sayılarına, plakalar üzerine yerleştirilen elektrot dizilişlerine ve rotor plakaların malzemelerine göre yapılmıştır. Sınıflandırma sonucunda sağlam bir algılayıcı mekaniğine karar verilmiştir. Algılayıcı mekaniğindeki bileşenler çeşitli analizlerle belirlenip bilgisayar destekli tasarım programlarıyla tasarlanmıştır. Tasarlanan mekanik bileşenler üretildikten sonra sunulan algılayıcı mekaniği kurulmuştur. Kurulan algılayıcı mekaniği için de bir ön tasarım süreci işletilmiştir. Algılayıcı mekaniği kurulduktan sonra uygun fiyatlı bir test düzeneğinde test edilmiştir. Bu çalışmayla, geliştirilmekte olan yenilikçi ve yüksek performanslı kapasitif enkodere sağlam bir algılayıcı mekaniği kazandırılmıştır.

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