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Öğe Coding, robotics and computational thinking in preschool education: the design of magne-board(2021) Demir, Batikan Erdem; Demir, FundaThe coding education given within the scope of STEM (Science, Technology, Engineering, and Mathematics) education gives children computational thinking skills. Computational thinking involves a set of problem-solving, algorithmically thinking, analytical thinking and critical thinking skills. When the coding education is given to children by an ER (Educational Robotics), the content of the education becomes more tangible and fun. In addition, ER helps develop motor skills and hand-eye coordination. It supports children's social development by directing them to collaboration and teamwork. In this study, an educational coding robot with magnetic board that makes the coding education suitable for preschool children was designed. This platform has attractive visual design, audible and illuminated warnings. In addition, it is computer-independent, easily portable and can be operated wirelessly. The educational robot was introduced for use by 40 children aged 4-5 years old. The interaction of the children with the robot was observed by 10 people in total, consisting of pre-school teachers and academicians. An evaluation form containing open-ended questions has been created to evaluate whether the prepared educational robot is a useful material for teaching pre-school children. Answers and suggestions from users were recorded and interpreted according to content analysis. It was determined that the educational coding robot with magnetic platform developed according to the obtained data is suitable for the pedagogical properties of the target group. In addition, it is concluded that there is an educational material that can be used for the expected purpose.Öğe Comparison of Metaheuristic Optimization Algorithms for Quadrotor PID Controllers(Univ Osijek, Tech Fac, 2023) Demir, Batikan Erdem; Demir, FundaIn the present study, different solution methods are discussed in order to control the quadrotor with the most optimal PID parameters for the determined purposes. One of these methods is to make use of meta-heuristic algorithms in control systems. There are some limitations of using a PID controller as a classical construct. However, it is thought that more successful results will be obtained by optimizing its parameters through meta-heuristic algorithms. Initially, the mathematical model of the vehicle was created in MATLAB/Simulink. Then, genetic algorithms (GA), artificial bee colony (ABC), particle swarm optimization (PSO) and firefly algorithms (FA) were determined respectively as optimization methods. And these optimization methods used to determine the PID control parameters are applied to the developed mathematical model in the MATLAB/Simulink environment. In addition, the performances of the optimization methods are evaluated according to the comparison criteria. As a result of the comparison carried out according to ITAE (Integral Time Absolute Error) fitness criteria, ABC (1.2% -4.4%) in terms of altitude, FA (4% -13%) in terms of roll angle, GA (13% -%21) in terms of pitch angle, and PSO (4% -%15) in terms of yaw angle has been more successful than other methods.Öğe Deep learning based fault detection and diagnosis in photovoltaic system using thermal images acquired by UAV(Gazi Univ, 2024) Kayci, Baris; Demir, Batikan Erdem; Demir, FundaSolar power is one of the largest renewable energy sources in the world. With photovoltaic systems, electrical energy can be generated wherever the sun is located. To prevent efficiency losses in photovoltaic systems, these systems should be tested at regular intervals. In this study, it is discussed to detect cell, module and panel faults in panels using thermal images obtained from solar panels. Within the scope of the study, a four-rotor unmanned aerial vehicle (drone) was designed and a thermal camera was placed on the vehicle. Thus, thermal images of the solar panels on the roof of Karabuk University buildings were taken. A thermal data set with cell fault, module fault and panel fault were created using the resulting thermal images. The YOLOv3 deep learning-based convolutional neural network was trained with the created dataset. This training was conducted on Nvidia Jetson TX2, an embedded AI (Artificial Intelligence) computing device. After the completion of the training of the YOLOv3 network, it was concluded that the faults mentioned in the tests were successfully detected.Öğe Development of an experiment set for embedded system education and analyzing its contribution(2021) Demir, Batikan Erdem; Demir, FundaIn this study, a teaching material developed to provide application support to the theoretical expression of the embedded systems course in undergraduate and graduate education of engineering faculties is presented. The modular experiment set consists of STM32F4 Discovery microcontroller board and digital output, digital input, analog input, relay control, DC motor control, stepper motor control, alphanumeric LCD display, seven segment display and power distribution circuit boards connected to the board. The control software of the experimental set was developed using Waijung block sets in MATLAB / Simulink environment. The Waijung block set, which can be added to the MATLAB / Simulink library, allows the card to be programmed quickly and easily. At the same time, the program codes written by the user can be included in the developed model. With this experiment set, basic and some advanced embedded system applications can be performed. To research the availability of the experiment set in education, a group of undergraduate and graduate students was given the opportunity to use this set. Students were asked several questions about the experiment set and content analysis was performed on the answers obtained. In line with the data obtained, it was concluded that the experimental set developed eliminated a significant lack of material needed in the training of embedded systems.Öğe Increasing PEM fuel cell performance via fuzzy-logic controlled cascaded DC-DC boost converter(Pergamon-Elsevier Science Ltd, 2024) Kart, Sude; Demir, Funda; Kocaarslan, Ilhan; Genc, NaciThe voltage level produced by Proton Exchange Membrane (PEM) fuel cell (FC), which is one of the clean energy sources, is low for many industrial applications. For this reason, for many industrial applications such as electric vehicles, it is necessary to use a boost type DC-DC power electronic circuit at the output of PEM FC. In this case, in addition to the PEM FC performance, the dynamic performance and efficiency of the DC-DC converter to be used affect the performance and efficiency of the total system. In this study, a singleswitch cascaded DC-DC boost converter, which has both higher voltage gain and more efficiency compared to conventional cascade converter and other boost type converters, is proposed for PEM FC. Since the proposed single-switch cascaded DC-DC boost converter includes only one controlled-switch, it is efficient as well as smaller in size and less costly compared to the other types. In addition, the proposed single-switch cascaded DC-DC boost converter is tested with the traditional PID and fuzzy-logic control methods to observe the dynamic performance of PEM FC. When the fuzzy logic controller is compared with the PID controller, it is clearly seen that it gives 21% more successful results as per rise time, 71% settling time, 97% overshoot, and 26% RMSE (Root Mean Square Error) error rate. As a result, when the proposed single-switch cascaded DC-DC boost converter is regulated via fuzzy control method, a more efficient PEM FC system which has better dynamic performance can be designed. (c) 2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.Öğe Optimum Operating Conditions of (PbxX1-x)( ZryTizY1-y-z) Piezoelectric Transducer for Vibrational Energy Harvesting Applications(Hindawi Ltd, 2016) Demir, Funda; Anutgan, MustafaThe electrical energy production capability of bimorph (PbxX1-x)( ZryTizY1-y-z) fiber composite piezoelectric transducer has been investigated for energy harvesting applications. The material has been analyzed under different frequencies, bending amounts, and temperatures. The operating conditions for maximum electrical energy outcome have been determined. The natural frequencies of oscillations in the macro dimensions have been found to be inversely proportional to the length of the material. On the other hand, the voltage output with respect to the oscillation frequency exhibits an interesting behavior such that the characteristic curve shifts to higher frequencies as the bending radius is decreased. This behavior has been interpreted as a result of possible overtone transitions of the oscillations to a stiffer mode. The increasing temperature has been observed to have a negative effect on the piezoelectric energy harvesting property. When the determined optimum conditions were utilized, the amount of electrical energy stored in 6300 s by an energy harvester circuitry has been found to be 0.8 J.Öğe The performance comparison of machine learning methods for solar PV power prediction(Emerald Group Publishing Ltd, 2024) Demir, FundaPurposeThe energy generation process through photovoltaic (PV) panels is contingent upon uncontrollable variables such as wind patterns, cloud cover, temperatures, solar irradiance intensity and duration of exposure. Fluctuations in these variables can lead to interruptions in power generation and losses in output. This study aims to establish a measurement setup that enables monitoring, tracking and prediction of the generated energy in a PV energy system to ensure overall system security and stability. Toward this goal, data pertaining to the PV energy system is measured and recorded in real-time independently of location. Subsequently, the recorded data is used for power prediction.Design/methodology/approachData obtained from the experimental setup include voltage and current values of the PV panel, battery and load; temperature readings of the solar panel surface, environment and the battery; and measurements of humidity, pressure and radiation values in the panel's environment. These data were monitored and recorded in real-time through a computer interface and mobile interface enabling remote access. For prediction purposes, machine learning methods, including the gradient boosting regressor (GBR), support vector machine (SVM) and k-nearest neighbors (k-NN) algorithms, have been selected. The resulting outputs have been interpreted through graphical representations. For the numerical interpretation of the obtained predictive data, performance measurement criteria such as mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE) and R-squared (R2) have been used.FindingsIt has been determined that the most successful prediction model is k-NN, whereas the prediction model with the lowest performance is SVM. According to the accuracy performance comparison conducted on the test data, k-NN exhibits the highest accuracy rate of 82%, whereas the accuracy rate for the GBR algorithm is 80%, and the accuracy rate for the SVM algorithm is 72%.Originality/valueThe experimental setup used in this study, including the measurement and monitoring apparatus, has been specifically designed for this research. The system is capable of remote monitoring both through a computer interface and a custom-developed mobile application. Measurements were conducted on the Karab & uuml;k University campus, thereby revealing the energy potential of the Karab & uuml;k province. This system serves as an exemplary study and can be deployed to any desired location for remote monitoring. Numerous methods and techniques exist for power prediction. In this study, contemporary machine learning techniques, which are pertinent to power prediction, have been used, and their performances are presented comparatively.Öğe Piezoelektrik malzeme ile rüzgardan enerji hasatı(Karabük Üniversitesi, 2017) Demir, Funda; Anutgan, MustafaBu çalışmada ilk olarak, (PbxX1-x) (ZryTizY1-y-z) O3 kimyasal yapısına sahip bimorf piezoelektrik (PZT) dönüştürücünün enerji üretim koşulları incelenmiştir. Bu amaç doğrultusunda malzeme, farklı frekans, eğilme miktarı ve sıcaklıklarda denenerek maksimum enerji üretebileceği koşullar belirlenmiştir. Elde edilen elektriksel sinyal salınımlarının doğal frekansı materyalin boyu ile ters orantılıdır. Diğer taraftan, malzemeye uygulanan eğilme miktarı artıkça belirlenen doğal frekansda yüksek değerlere doğru bir kayma görülmektedir. Bu davranış, meydana gelen salınımların daha sert bir moda overton geçişiyle açıklanabilir. Yükselen sıcaklığın, piezoelektrik özelliği olumsuz bir şekilde etkilediği gösterilmiştir. PZT dönüştürücü için optimum koşullar belirlendikten sonra üretilen elektrik sinyali, depolama düzeneğine aktarılmıştır. 6300 sn'de depolanan enerji 0,8 J olarak hesaplanmıştır. Enerji depolama ölçümleri sırasında sabit motora bağlı pervanenin çarpma etkisi ile harekete geçirilmiş olan PZT dönüştürücü, rüzgar enerjisi ile aktif edilerek malzemenin bir çeşit doğal enerji kaynağı olarak kullanılabilirliği incelenmiştir. Dönüştürücünün rüzgara karşı konumlandırılmasını doğru bir şekilde gerçekleştirmek için dönüştürücü, metal çerçeveye farklı eksenlerde ve eğilme miktarını artıracak farklı ek parçalarla birlikte bağlanmıştır. Elde edilen farklı bağlantı durumları 4-20 m/s rüzgar hız aralıklarında denenerek üretilen voltaj çıkışları karşılaştırılmıştır. Farklı bağlantı konumlarının birbirlerine göre avantaj ve dezavantajları açıklanmıştır. Elde edilen maksimum voltaj değeri 13 V ve hesaplanan güç değeri ise 338 ?W'dır. Çalışmada kullanılan bimorf yapıdaki PZT dönüştürücü, unimorf yapıya göre daha yüksek enerji çıkışına sahiptir ancak esnekliği çok daha azdır. Bu nedenle dönüştürücüden maksimum enerji elde edebilmek için gerekli rüzgar hızı yüksek şiddetli rüzgar olarak adlandırılan 16-20 m/s aralığıdır. Çalışmada incelenen ikinci malzeme Makro fiber kompozit (MFC) dönüştürücüdür. Dönüştürücüyü aktif etmek için yine rüzgar hızından yararlanılmıştır. Ancak uygulamayı dış mekandan bağımsız kılmak için minyatür bir rüzgar tüneli tasarlanmış ve 2-8 m/s aralığındaki rüzgar hızı elektrikli fanlar yardımıyla MFC dönüştürüye aktarılmıştır. PZT dönüştürücüde yapılan konum belirleme ölçümleri MFC dönüştürücü için de gerçekleştirilmiştir. Uygun konumda malzemeden elde edilen maksimum gerilim değeri 10,3 V ve hesaplanan güç değeri ise 108 ?W'tır. MFC dönüştürücü için uygun rüzgar hızı meltem olarak adlandırılan 6,5 m/s'dir. Bu rüzgar hızı, dış mekanda gerçekleştirilebilecek herhangi bir piezoelektrik rüzgar gülü ya da piezoağaç çalışması için uygundur. Anahtar Kelimeler : Yenilenebilir enerji, PZT dönüştürücü, minyatür rüzgar tüneli, elektrik enerjisi hasatı. Bilim Kodu : 905.1.035Öğe Sayısal işaret işlemciler için gömülü sistem deney seti tasarımı(Karabük Üniversitesi, 2011) Demir, Funda; Bayır, Raif; Duran, FecirSayısal işaret işlemciler (Sİİ) günümüzde, askeri elektronik, tıp elektroniği, telekomünikasyon, enstrümantasyon, denetim sistemleri, ses ve görüntü işleme alanlarında yaygın olarak kullanılmaktadır. Yüksek hızda ve doğrulukta işlem yapabilmeleri ve karmaşık denetim yöntemlerinin uygulanmasında başarılı olduklarından tercih edilmektedirler. Bu çalışmada, teknik ve mühendislik eğitiminde 32 bit sayısal işaret işlemcilerin kullanımını ve yaygınlaşmasını sağlamak için prototip deney seti gerçekleştirilmiştir. Bu deney seti sayesinde, sayısal işaret işlemcilerin eğitiminde gösteri ve uygulama yöntemlerinin kullanımı etkin hale getirilmektedir. Bu sayede etkin ve kalıcı bir öğrenme olanağı sağlanmaktadır.Deney setinde Texas Instrument firmasının TMS320F2812 sayısal işaret işlemcisi kullanılmaktadır. Gerçekleştirilen deney setinde kullanıcılar, denetim algoritmalarını ve sistem modellerini Matlab/Simulink ortamında kurarak, benzetimlerini ve uygulamalarını gerçekleştirmektedir. Sayısal işaret işlemci deneyleri için Matlab'ın Embedded Target for TC2000 DSP ve Real Time Workshop (RTW) araçları kullanılarak, yazılıma ihtiyaç duyulmadan, kodlarının üretimi otomatik olarak yapılmaktadır. Otomatik üretilen kodlar Texas Instrument firmasının Code Composer Studio 2000 yazılım geliştirme ortamında derlenerek F2812 eZdsp kartına yüklenmektedir. Kullanıcılar, F2812 eZdsp için geliştirilen deney seti ile analog ve sayısal elektronik deneylerini gerçekleştirebilmektedir. Bu deney seti fakülte ve yüksekokullarda sayısal işaret işleme eğitiminde rahatlıkla kullanılabilir.Öğe Solar Photovoltaic Power Estimation Using Meta-Optimized Neural Networks(Mdpi, 2022) Gumar, Ali Kamil; Demir, FundaSolar photovoltaic technology is spreading extremely rapidly and is becoming an aiding tool in grid networks. The power of solar photovoltaics is not static all the time; it changes due to many variables. This paper presents a full implementation and comparison between three optimization methods-genetic algorithm, particle swarm optimization, and artificial bee colony-to optimize artificial neural network weights for predicting solar power. The built artificial neural network was used to predict photovoltaic power depending on the measured features. The data were collected and stored as structured data (Excel file). The results from using the three methods have shown that the optimization is very effective. The results showed that particle swarm optimization outperformed the genetic algorithm and artificial bee colony.
