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Analyzing complex fractional order systems physical phenomena in IOPI controller design
(John Wiley and Sons Inc, 2024) Şenol, Bilal; Demiroğlu, Uğur
The Fractional Complex Order Plant model, which has lately gained popularity in applied physics and control systems, is the main subject of this study. The major contribution of this study to the literature is the discussion of the physical phenomena of complex plant models and how they affect the stability and robustness of the systems. Because the Fractional Complex Order Plant model is the most general mathematical form, other plant models covering the Integer Order Plant and the Fractional Order Plant can be easily created with this benefit. The proposed approach using the classical Proportional Integral controller which is recalled as the Integer-Order PI controller in this paper gives the calculation equations of the physical alterations of plants having integer, fractional, and complex orders. Along with the visuals and with the aid of simulations, the consequences of the parameters on the system are described. Additionally, the advantages and disadvantages of the proposed controller designs for each of the three plant species are discussed.
Simulation and forecasting of power by energy harvesting method in photovoltaic panels using artificial neural network
(Elsevier Ltd, 2024) Demir, Hasan
Heat is an important efficiency reducing factor in photovoltaic systems. Although many studies in the literature are related to the removal of waste heat from photovoltaic panels, the disadvantages such as installation and maintenance of cooling systems should not be forgotten. A study has shown that with a new approach, energy can be produced with the method of energy harvesting from waste heat. The results of the study were limited to a geographical region and climatic conditions in Aksaray, Turkey. In this article, the results of the study, which was limited to a region, were extended using the finite element method analysis and the artificial neural network model. Ten different cases were determined and temperature gradient was found by finite element analysis. The forecasting algorithm was developed with artificial neural network and estimates the harvestable power based on the temperature gradient. The accuracy of the algorithm was tested with the MSE and nRMSE statistical metrics which were calculated as 59.1423 mW and 13.6189 %, respectively. The training data accuracy of the network was 0.93987 and the combined accuracy was 0.94364. The results of this study are important to be a reference for researchers who want to establish a photovoltaic panel energy harvesting system.
Investigating the Effect of Albedo in Simulation-Based Floating Photovoltaic System: 1 MW Bifacial Floating Photovoltaic System Design
(Multidisciplinary Digital Publishing Institute (MDPI), 2024) Coşgun, Atıl Emre; Demir, Hasan
Photovoltaic (PV) modules have emerged as a promising technology in the realm of sustainable energy solutions, specifically in the harnessing of solar energy. Photovoltaic modules, which use solar energy to generate electricity, are often used on terrestrial platforms. In recent years, there has been an increasing inclination towards the installation of photovoltaic (PV) modules over water surfaces, including lakes, reservoirs, and even oceans. The novel methodology introduces distinct benefits and complexities, specifically pertaining to the thermal characteristics of the modules. In order to accomplish this objective, a photovoltaic (PV) module system with a capacity of 1 MW was developed as a scenario in the PVsyst Program. The scenario simulation was conducted on the Mamasın Dam, situated in the Gökçe village within the Aksaray province. To conduct the efficiency analysis, a comparative evaluation was conducted between bifacial and monofacial modules, which were installed from above the water at 1 m. The comparison was made considering two different types of modules. Additionally, the albedo effect, water saving amount, and CO2 emissions of the system were also investigated. Albedo measurements were made in summer when the PV power plant will operate most efficiently. As a result of the simulations, it was found that bifacial modules produce 12.4% more energy annually than monofacial modules due to the albedo effect. It is estimated that PV power plant installation will save 19,562.695 and 17,253.475 tons of CO2 emissions in bifacial and monofacial systems, respectively.
Agrivoltaic systems for sustainable energy and agriculture integration in Turkey
(Elsevier Ltd, 2024) Coşgun, Atıl Emre; Endiz, M.S.; Demir, H.; Özcan, M.
In recent years, the use of solar photovoltaic (PV) energy, which is one of the leading renewable energy sources, has become increasingly widespread around the world due to its numerous advantages. However, PV-based electricity generation necessitates a large amount of land. Agrivoltaic (AV) systems, an innovative approach to combining agricultural and electricity production in the same area through solar modules positioned several meters above the surface of the ground, are growing rapidly in renewable energy and farming communities. This study explores Turkey's solar power generation and agricultural activities, combining crop cultivation and electricity generation for sustainable development on the same land. Furthermore, the AV potential for the most agriculture ten cities in different climate zones in Turkey is investigated using the PVsyst program. A list of the most commonly grown crops in the ten selected cities and the types of AV systems that can be employed with these crops is provided. The results show that AV systems present a great opportunity for the optimal integration of solar power generation with food production, especially for the cities of Konya, Kayseri, and Manisa, with the most ideal conditions for agricultural and solar power production. By combining the solar power potential of the country with the production capacity of arable lands, the increasing energy needs can be met and more efficient agricultural production can be provided. This study is expected to demonstrate that in specific regions of Turkey, AV farming will be suitable for certain
Smart and digital world: The technologies needed for digital twins and human digital twins
(American Society of Mechanical Engineers (ASME), 2024) Coşgun, Atıl Emre
The transition to Industry 5.0 begins with the integration of the human aspect into Industry 4.0 technologies. Industry 5.0 is a human-centric design approach that aims to overcome the issues raised by Industry 4.0 and involves collaborating both with humans and robots in a shared working environment. The new idea demonstrates a great connection between technology and people, or “soft” sectors. At this point, the idea of a digital twin (DT), a novel technological innovation, appears. The digital twin is a newly developed technology that is essential for digital transformation and intelligent updates. The fundamental basis of this concept involves the amalgamation of artificial intelligence (AI) with the notion of digital twins, which refer to virtual renditions of tangible entities, systems, or procedures. Therefore, this article focuses on digital twins and the innovative concept of human digital twins (HDTs), with particular emphasis on the technological tools of AI in the usage of mentioned technology. Also, this article conducts a comprehensive political (P), economic (E), social (S), technological (T), legal (L), and environmental (E) (PESTLE) analysis of Industry 5.0, while specifically delving into the concepts of digital twin and human digital twin.
Automatically Finding the Biggest Fold Value for More Accurate Classification and Diagnosis in Machine Learning Algorithms
(Springer, 2024) Avuçlu, Emre
Correct diagnosis in medicine is of great importance as it is one of the most important issues in medicine. Today, researchers have embarked on many new searches to make an accurate medical diagnosis. In order for any disease to be cured, it is necessary to define it precisely early and accurately. In this study, a new method was proposed to make a more accurate medical diagnosis. This method is based on automatically selecting the fold with the best accuracy rate after k-fold crossvalidation is performed in any database. In this way, scientific studies that lead to more accurate results will be carried out by using the fold with the highest accuracy in both classification and medical diagnosis procedures. This method has been applied on two different databases, Ecoli and Wisconsin Breast Cancer Diagnostic (WBCD) databases, which are used in scientific studies by many researchers in the literature. The statistical measurements of each fold values of both databases used have been examined in detail. Diagnostics for these databases were carried out using 7 different Machine Learning Algorithms (MLA), (k nearest neighbor (k-NN), Decision Tree (DT), Random Forest (RF), Multinominal Logistic Regression (MLR), Naive Bayes (NB), Support Vector Machine (SVM), Minumum (Mean) Distance Classifier (MMDC)). In the test procedures for Ecoli dataset, the following accuracy values were obtained for k-NN, DT, RF, MLR, NB, SVM, MMDC, respectively; 0.8485, 0.8358, 0.9848, 0.8182, 0.6667, 0.8636, 0.7424. For the WBCD database, the following accuracy values were obtained for k-NN, DT, RF, MLR, NB, SVM, MMDC, respectively; 0.9856, 0.9568, 0.9784, 0.9856, 0.9856, 0.9856, 0.9784. Other results were given in detail in the experimental studies section. It is of great importance to choose the most accurate MLAs to be used in medical diagnosis for human life. Thus, in the studies to be done with MLAs in medicine or any field in the literature, how the best score that can be obtained from MLAs will be introduced to the literature. In this study, an original study was conducted on how to make the correct medical diagnosis, which is one of the most important issues for human life.
Assessing the potential of solar power generation in Turkey: A PESTLE analysis and comparative study of promising regions using PVsyst software
(Elsevier Ltd, 2023) Endiz, Mustafa Sacid; Coşgun, Atıl Emre
Renewable energy sources have a tremendous amount of potential in Turkey. In the previous year, 43.2% of the country's electricity was generated from renewable energy sources. Solar photovoltaic (PV) energy accounted for 4.7% of the electricity generation and the installed capacity was 9.425 GW with 9353 solar power plants of various types. This paper provides an overview of the current state of solar PV potential in Turkey, evaluates its capacity to meet the country's energy demand, and discusses its future prospects. The PESTLE analysis is performed based on political (P), economic (E), social (S), technological (T), legal (L), and environmental (E) factors to study the sustainable development of solar energy potential in Turkey. Furthermore, a comparative analysis of the solar energy potential between the Konya and Diyarbakır regions which are recognized as promising areas for harnessing the country's abundant solar resources has been conducted using the PVsyst software. The electricity consumption in Turkey will be 380.2 TWh in 2025, 455.3 TWh in 2030, and 510.5 TWh in 2035, according to the Turkey National Energy Plan. Hence, it is essential to maximize the use of solar energy capacity in the production of electricity to meet the increased energy demand. The main objective of this study is to help strategic and systematic evaluation of the solar energy resource potential affecting large and small-scale solar power projects in Turkey.
A fused electrocardiography arrhythmia detection method
(Springer, 2024) Demiroğlu, Uğur; Şenol, Bilal; Matuš?, Radek
Recently, Electrocardiography (ECG) signals are commonly used in diagnosing the cardiac arrhythmia that shows up with the loss of the regular movement of the heart. Approximately 5% of the world population have cardio motor disorders. Therefore, usage of the ECG signals in biomedical signal processing algorithms and machine learning methods for automated diagnosis of this widespread health problem is a popular research topic. In this paper, the Particle Swarm Optimization (PSO) technique is implemented to tune the parameters of Tunable Q-Factor Wavelet Transform (TQWT) and the new generation feature generator Hamsi Hash Function (Hamsi-Pat) is used to obtain the characteristics of the signal. Sub-signals of 10 s obtained from the original ECG signal are divided into their sub-bands of 25 levels with PSO and TQWT. Each of these low pass filters generates 536 dimensional features by applying Hamsi-Pat and statistical methods. Then, all these features are combined and 536 × 25 = 13400-dimensional feature set is obtained. The features in the set are reduced and the best of them are selected by using the Iterative Neighborhood Component Analysis (INCA) method. Finally, the k-Nearest Neighbors (kNN) classification method is applied to the best features according to the City Block measurement criterion. All studies cited to compare the results in this paper also use the MIT-BIH Arrhythmia ECG database. Hence, the difference could be observed in the used techniques. In contrast to the existing studies, this study shows its superior performance by classifying all 17 classes simultaneously by applying a “fused” approach. The method in the paper reached 98.5% classification accuracy on the 17 classes of the MIT-BIH Arrhythmia ECG database. The results indicate that the proposed method showed better rates from the existing studies related to arrhythmia diagnosis using ECG signals in the literature.
Regions of robust relative stability for PI controllers and LTI plants with unstructured multiplicative uncertainty: A second-order-based example
(Elsevier Ltd, 2023) Matuš?, Radek; Şenol, Bilal; Peka?, Libor
his example-oriented article addresses the computation of regions of all robustly relatively stabilizing Proportional-Integral (PI) controllers under various robust stability margins ? for Linear Time-Invariant (LTI) plants with unstructured multiplicative uncertainty, where the plant model with multiplicative uncertainty is built on the basis of the second-order plant with three uncertain parameters. The applied graphical method, adopted from the authors’ previous work, is grounded in finding the contour that is linked to the pairs of P–I coefficients marginally fulfilling the condition of robust relative stability expressed using the H? norm. The illustrative example in the current article emphasizes that the technique itself for plotting the boundary contour of robust relative stability needs to be combined with the precondition of the nominally stable feedback control system and with the line for which the integral parameter equals zero in order to get the final robust relative stability regions. The calculations of the robust relative stability regions for various robust stability margins ? are followed by the demonstration of the control behavior for two selected controllers applied to a set of members from the family of plants.
Classification of computerized tomography images to diagnose non-small cell lung cancer using a hybrid model
(Springer, 2023) Demiroğlu, Uğur; Şenol, Bilal; Yıldırım, Muhammed; Eroğlu, Yeşim
Lung cancer arises from the abnormal and uncontrolled reproduction of parenchymal cells. Among all cancer cases, lung cancer is one of the prevailing types. Prevalence and death rates of the cancer increases day by day. From this point of view, early diagnosis and treatment of this cancer increases survival times and rates. The main idea in the development of the method presented in this publication is to increase the rate of early diagnosis. Computerized Tomography (CT) is the major screening method when encountered with suspicious symptoms. The cancer can be determined with CT and besides subtyping can be done. Diagnosing the disease with the human eye can sometimes lead to the emergence of deficiencies. This is one of the problems faced today. In this direction, the study in this paper presents a hybrid method to predict and diagnose the lung cancer from CT images to minimize potential human errors. Using the method, feature maps of the CT images of the dataset are obtained using the previously trained DarkNet-53 and DenseNet-201 deep model architectures. DarkNet-53 and DenseNet-201 architectures were chosen because they gave the best feature extraction results among 7 different architectures. The purpose of using the two architectures is to combine two high-performance models to create a hybrid classification method with high accuracy. Feature concatenation is applied to increase the diagnosis accuracy. To optimize the performance and computation cost of the proposed method, the Neighborhood Component Analysis (NCA) optimization method is used in determining and analysis of the features with more information. Therefore, features with less contribution in the accuracy are eliminated. Next, new feature maps are achieved by grading all features upon their weights and applying an elimination using a threshold value. The new feature maps are classified using Classical Machine Learning (CML) classifiers. Classification accuracies on DarkNet-53 architecture were calculated as 69.11% with SoftMax and 96.25% with Ensemble Classifiers and Nearest Neighbor Classifiers respectively. Similarly, accuracies on DenseNet-201 architecture were calculated as 68.29% with SoftMax and 97.39% with Ensemble Classifiers and Nearest Neighbor Classifiers respectively. With the proposed hybrid model, the Ensemble Classifiers reached the accuracy of 98.69% and the highest accuracy is achieved by using k-Nearest Neighbor Classifier (kNN) with the value of 98.86%. The results are supported with detailed illustrations.
An examination of complex fractional order physical phenomena in IOPD controller design
(John Wiley and Sons Ltd, 2023) Demiroğlu, Uğur; Şenol, Bilal; Matuš?, Radek
This research focuses on the fractional complex order plant (FCOP). The significant contribution is the role of complex plant models in system stability and robustness and associated physical phenomena. A general transfer function is studied in the paper. Other plant models may be built with this structure since the FCOP is a general mathematical form covering integer order plant (IOP) and fractional order plant (FOP). Using the equations produced with the proposed technique and the recommended integer order proportional derivative (IOPD controller, physical changes in integer, fractional and complex coefficients, and orders are observed within this paper. Analysis of the plant controlled with an IOPD controller is done by applying an integrator to reveal the differences. The effects of the parameters are discussed together with the visuals, supported by simulations. The aim is to tune the controller parameters to achieve the phase and specifications as the researcher desired. It is observed that the integrator greatly takes part in reducing the steady-state error. The IOP with the integrator showed the lowest steady-state error, and also, the settling and overshoot time were enhanced. Increase in the phase margin also caused an increase in the phase crossover frequency. It is also observed that the fractional order affected the phase crossover frequency comparing with the IOP, and the complex order modification also had an effect comparing to the fractional order version. The complex order of the system is considered with its conjugate components in the imaginary part thus, the results are found separately for each case.
Fractional order PD controller design for third order plants ıncluding time delay
(Pamukkale Üniversitesi, 2023) Demiroğlu, Uğur; Şenol, Bilal; Matuš?, Radek
Due to the lack of integral operator, proportional derivative controllers have difficulties in providing stability and robustness. This difficulty is especially felt in higher order systems. In this publication, analytical design method of fractional proportional derivative controllers is presented to ensure the stability of third order systems with time delay. In this method, it is aimed to achieve the frequency characteristics of a standard control system to ensure stability. It is aimed to provide the desired gain crossover frequency, phase crossover frequency and phase margin properties of the system. In this way, the stability and robustness of the system can be obtained by choosing the appropriate values. The reason for choosing a fractional order controller is that the controller parameters to provide these features can be tuned more accurately. In order for the obtained stability to be robust to unexpected external effects, it is aimed to flatten the system phase. In the literature, phase flattening is performed by setting the phase derivative to zero at a specified frequency value. This can lead to mathematical complexity. In this publication, the phase flattening process is provided graphically by correctly selecting the frequency characteristics given above. Thus, an accurate and reliable controller design method is presented, avoiding mathematical complexity. The effectiveness of the proposed method has been demonstrated on three different models selected from the literature. The positive contribution of the method to the system robustness has been proven by changing the system gain at certain rates.
Designing a dynamic system to control building components and computers with voice commands for disabled individuals
(Springer, 2023) Avuçlu, Emre; Özçifçi, Ayhan; Elen, Abdullah
Nowadays, with the development of technology, there have been many innovations in human life. Technology has made many things easier in human life depending on these innovations. In this study, an application has been developed to more easily solve the needs of people in their daily lives. The developed application is designed to control the internal units of the house with voice commands. The doors, windows, lamps, etc. that we use daily in our house can be controlled by voice commands through wireless headphones. Voice commands can be sent from inside or outside the house. In addition to these external units, all applications on the personal computer are controlled by voice commands without using a keyboard and mouse. The application developed in C # programming language, provides control of newly installed programs to the computer without coding. To do so, it is enough to add that program and voice command to the interface. Moreover, the application provides a lot of convenience for disabled-aged citizens and some dangerous situations in the industrial environment.
A new hybrid model for classification of corn using morphological properties
(Springer Science and Business Media Deutschland GmbH, 2023) Avuçlu, Emre; Taşdemir, Şakir; Köklü, Murat
Automated classification of corn is important for corn sorting in intelligent agriculture. Corn classification process is a necessary and accurate process in many places in the world today. Correct corn classification is important to identify product quality and to distinguish good from bad. In this study, a hybrid model was proposed to classify the 3 corn species belonging to the Zea mays family. In the hybrid model, 12 different morphological features of corn were obtained. These morphological features were used for the classification process in the hybrid model created using machine learning (ML) algorithms. When morphological features were given as input to ML algorithms for normal classification, the test score was 96.66% for Decision Tree (DT), 97.32% for Random Forest (RF) and 96.66% for Naive Bayes (NB). With the proposed hybrid model, this rate has reached 100% test score in all three algorithms. Test processes were measured by statistical models. While Accuracy was 97.67% as a result of normal classification, this rate was 100% in hybrid model. The experimental results demonstrated the effectiveness of the proposed corn classification system.
Application of thermal energy harvesting from photovoltaic panels
(MDPI, 2022) Demir, Hasan
This paper describes a newly developed system for harvesting thermoelectric energy from photovoltaic panels. This system helps to power monitoring systems for photovoltaic panels (PVs) in locations where there is no energy source using waste thermal energy from PVs exposed to the sun’s rays. In the study described here, the thermal energy from a PV panel was captured and transferred to a thermoelectric generator (TEG). A temperature gradient was created by reducing the temperature using an aluminium heat sink in ambient weather conditions. This temperature gradient was used to generate electricity via two TEGs. In field tests carried out in April, in Aksaray province in central Turkey, the maximum temperature gradient due to solar radiation was measured as 21.08 °C. The harvested energy was increased to a usable level of 4.1 V using a DC-to-DC converter and stored in a li-ion rechargeable battery. The maximum charge current level of the battery was 147 µA. The maximum harvested energy was 458.64 mW, and a stable level of around 350 mW was achieved. The experimental operation of the prototype system was carried out in stable weather conditions; however, weather and climatic conditions greatly affect levels of energy harvested as a result of changing temperature gradients. The energy obtained with the prototype may reduce the battery maintenance costs of PV monitoring systems and lead to the development of new such systems which cannot presently be used due to a lack of energy.
3B yazıcılarda kullanılan farklı tip ekstüderlerin ANSYS programı ile termal analizlerinin gerçekleştirilmesi
(Düzce Üniversitesi Fen Bilimleri Enstitüsü, 2022) Demir, Hasan; Coşgun , Atıl Emre
Bu çalışmanın amacı, 3B yazıcılarda ticari olarak kullanılan J-Head ekstrüder ve Volcano ekstrüderlerin ANSYS sonlu elemanlar yazılımı ile modellerin kararlı hal termal analizlerini gerçekleştirmek, modellerin eksiklikleri ve birbirlerine göre üstünlüklerini belirlemektir. Modellerin tasarımsal farklılıkları ve geometrik özellikleri farklı termal davranışlar göstermesine neden olmaktadır. Başlangıç koşulları ve sınır şartları her iki model için aynı olması sağlanmış, böylelikle diğer değişkenlerin sabit olmasına bağlı olarak tasarımların termal analizleri ön plana çıkarılmıştır. Termal analiz ile modellerin tasarımlarının baskı malzemesinin füzyonu üzerindeki etkileri incelenmiştir. Analiz sonuçları, modellerin avantaj ve dezavantajlarını ortaya koymuş ve gelecekte yapılabilecek yeni tasarımlar için bilgi kaynağı oluşturmuştur.
Impact of activation functions and number of layers on detection of exudates using circular Hough transform and convolutional neural networks
(Elsevier, 2022) Adem, Kemal
Convolutional neural networks (CNN), which are used for object detection, are widely used in image recognition and segmentation fields due to their good performance. In this study, a method is proposed for the detection of exudate from DR lesions based on image processing and CNN model with different activation functions and layer numbers. Activation functions are one of the main reasons why the CNN model is complicated in hierarchical structure. CNN models have real artificial intelligence capabilities, especially thanks to non-linear activation functions. Although ReLU is the most commonly used activation function in CNN models, it has some shortcomings in practice. The most important shortcoming is that if the input value is negative, the learning process will slow down due to the inability to take the derivative. In order to solve this problem, the effect of the activation function in the CNN model on a real world problem and its image was investigated in this study. By applying circular Hough transform, one of the image processing methods, the OD region, which has a similar structure to the exuding regions, was determined and removed from the image, and then the detection of the exuding regions was carried out using the CNN model. Exudate detection was performed using ReLU, ELU, Leaky ReLU, Softplus and Swish activation functions in the CNN model and the results were compared. Experimental results in DiaretDB0 and DiaretDB1 databases show that the Swish activation function has a better performance than the others.
The effect on energy efficiency of Yttria-Stabilized Zirconia on brass, copper and hardened steel nozzle in additive manufacturing
(MDPI, 2022) Demir, Hasan; Coşgun, Atıl Emre
This study aimed to investigate if a thermal barrier coating (TBC) affected the energy efficiency of 3D printers. In accordance with this purpose, the used TBC technique is clearly explained and adapted to a nozzle in a simulation environment. Brass, copper, and hardened steel were selected to be the materials for the nozzles. The reason for the usage of a thermal barrier coating method is that the materials are made with low thermal conductivity, which reduces the thermal conductivity and energy losses. Yttria-stabilized zirconia was used to coat material on brass, copper, and hardened steel. To prevent temperature fluctuations, yttria-stabilized zirconia together with a NiCRAl bond layer was used and, thus, heat loss was prevented. Additionally, the paper addressed the effects of the coating on the average heat flux density and the average temperature of the nozzles. In addition, by means of the finite element method, steady-state thermal analyses of the coated and uncoated nozzles were compared, and the results show that the thermal barrier coating method dramatically reduced energy loss through the nozzle. It was found that the average heat flux was reduced by 89.4223% in the brass nozzle, 91.6678% in the copper nozzle, and 79.1361% in the hardened steel nozzle.

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