Yazar "Demir, Batikan Erdem" seçeneğine göre listele

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    Coding, robotics and computational thinking in preschool education: the design of magne-board
    (2021) Demir, Batikan Erdem; Demir, Funda
    The 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.
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    Comparison of Metaheuristic Optimization Algorithms for Quadrotor PID Controllers
    (Univ Osijek, Tech Fac, 2023) Demir, Batikan Erdem; Demir, Funda
    In 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.
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    Computer assisted glass mosaic tiling automation
    (Pergamon-Elsevier Science Ltd, 2012) Cayiroglu, Ibrahim; Demir, Batikan Erdem
    Artistic mosaic tiling applications have been used in all periods of time since the oldest ages. Nowadays the mosaics, mostly called glass mosaics, are manufactured in square forms and applied by the factories. Tiling of the mosaics on a grid base is done manually in factories to reflect a predetermined design. In this study a fully-automated system has been developed for tiling the mosaics without human intervention. All the phases, starting from a digital photo, through to the tiling of mosaics, up to the packaging of panels in the production, are performed without any manual intervention in a continuous flow. The developed system has a modular structure, so the number of modules may be increased according to the desired speed and the number of colors. A software package has been developed in order to process the image to be tiled, to convert the colors of the image to the colors of mosaics, to perform simulation before the actual production and to control the various stepper motors, relays and sensors on the machine during manufacturing. (C) 2012 Elsevier Ltd. All rights reserved.
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    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, Funda
    Solar 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.
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    Development of a deep learning-based surveillance system for forest fire detection and monitoring using UAV
    (Public Library Science, 2024) Shamta, Ibrahim; Demir, Batikan Erdem
    This study presents a surveillance system developed for early detection of forest fires. Deep learning is utilized for aerial detection of fires using images obtained from a camera mounted on a designed four-rotor Unmanned Aerial Vehicle (UAV). The object detection performance of YOLOv8 and YOLOv5 was examined for identifying forest fires, and a CNN-RCNN network was constructed to classify images as containing fire or not. Additionally, this classification approach was compared with the YOLOv8 classification. Onboard NVIDIA Jetson Nano, an embedded artificial intelligence computer, is used as hardware for real-time forest fire detection. Also, a ground station interface was developed to receive and display fire-related data. Thus, access to fire images and coordinate information was provided for targeted intervention in case of a fire. The UAV autonomously monitored the designated area and captured images continuously. Embedded deep learning algorithms on the Nano board enable the UAV to detect forest fires within its operational area. The detection methods produced the following results: 96% accuracy for YOLOv8 classification, 89% accuracy for YOLOv8n object detection, 96% accuracy for CNN-RCNN classification, and 89% accuracy for YOLOv5n object detection.
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    Development of an experiment set for embedded system education and analyzing its contribution
    (2021) Demir, Batikan Erdem; Demir, Funda
    In 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.
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    A New Low-Cost Internet of Things-Based Monitoring System Design for Stand-Alone Solar Photovoltaic Plant and Power Estimation
    (Mdpi, 2023) Demir, Batikan Erdem
    The increasing demand for solar photovoltaic systems that generate electricity from sunlight stems from their clean and renewable nature. These systems are often deployed in remote areas far from urban centers, making the remote monitoring and early prediction of potential issues in these systems significant areas of research. The objective here is to identify maintenance requirements early and predict potential problems within the system. In this study, a cost-effective Internet of Things-based remote monitoring system for solar photovoltaic energy systems is presented, along with a machine learning-based photovoltaic power estimator. An Internet of Things-compatible data logger developed for this system gathers critical data from the photovoltaic system and transmits them to a server. Real-time visualization of these data is facilitated through web and mobile monitoring interfaces. The measured data encompass current, voltage, and temperature information originating from the photovoltaic generator and battery, alongside environmental parameters such as temperature, radiation, humidity, and pressure. Subsequently, these acquired data are employed for photovoltaic power estimation using machine learning techniques. This enables the estimation of potential issues within the photovoltaic system. In the event of a problem occurring within the photovoltaic system, users are alerted through a mobile application. Early detection and intervention assist in preventing power loss and damage to system components. When evaluating the results according to performance assessment criteria, it was observed that the random forests algorithm yielded the best results with an accuracy rate of 87% among the machine learning methods such as linear regression, support vector machine, decision trees, random forests, and k-nearest neighbor. When prediction models using other algorithms were ranked in terms of success, decision trees exhibited an accuracy rate of 81%, k-nearest neighbor achieved 79%, support vector machine reached 67%, and linear regression achieved 64% accuracy. In conclusion, the developed monitoring and estimation system, when integrated with web and mobile interfaces, has been demonstrated to be suitable for large-scale photovoltaic energy systems.