Person: AYRANCI, AHMET AYTUĞ
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AYRANCI
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AHMET AYTUĞ
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Publication Restricted Capacity Loss Analysis Using Machine Learning Regression Algorithms(IEEE, 2022) Atay, Sergen; AYRANCI, AHMET AYTUĞ; Erkmen, BurcuIn this study, time dependent measurements of the power capacitor, which is the main equipment of a compensation unit, are given. The power capacitor is actively working in an industrial facility. Six months of the data from this capacitor were recorded and tests were carried out using Machine Learning (ML) algorithms for its remaining useful life. ML algorithms were selected from the algorithms that used for regression problems. In the study, Support Vector Machine (SVM), Linear Regression (LR) and Regression Trees (RT) algorithms were used. The rated powers of the analyzed capacitor are 50kVAR and 25kVAR from the active plant. The data set was created by running the capacitor continuously for 6 months and the capacity loss was examined with using ML algorithms. The algorithm that gives the best result in the regression analyzes is the LR algorithm. With the results obtained, it is possible to analyze how long the useful life of capacitors with the same characteristics have under the same stress.Publication Open Access Speaker Accent Recognition Using MFCC Feature Extraction and Machine Learning Algorithms(Marmara Üniversitesi, Fen Bilimleri Enstitüsü, 2021) AYRANCI, AHMET AYTUĞ; Atay, Sergen; Yıldırım, TülaySpeech and speaker recognition systems aim to analyze parametric information contained in the human voice and recognize it at the highest possible rate. One of the most important features in the audio signal for the speaker to be recognized successfully by the system is the speaker's accent. Speaker accent recognition systems are based on the analysis of patterns such as the way the speaker speaks and the word choice he uses while speaking. In this study, the data obtained by the MFCC feature extraction technique from voice signals of 367 speakers with 7 different accents were used. The data of 330 speakers in the data set were taken from the "Speaker Accent Recognition" data set in the UC Irvine Machine Learning (ML) open data source. The data of the other 37 speakers were obtained by converting the voice recordings in the "Speaker Accent Archive" data set created by George Mason University into data using the MFCC feature extraction technique. 9 ML classification algorithms were used for the designed speaker accent recognition system. Also, the k-fold cross-validation technique was used to test the data set independently. In this way, the performance of ML algorithms is shown when the data set is divided into a k number of parts. Information about the classification algorithms used in the designed system and the hyperparameter optimizations made in these algorithms are also given. The success performances of the classification algorithms are shown with performance metrics.Publication Restricted Decentral Smart Grid Control System Stability Analysis Using Machine Learning(Institute of Electrical and Electronics Engineers Inc., 2022) AYRANCI, AHMET AYTUĞ; İlhan, HacıElectrical Grid Systems transmit power produced from various facilities to end-users. Supply and demand must be in balance to achieve secure and stable use in the power grid. To ensure this stability, the amount of electricity fed into the system must always be the same as the amount of demand. High demand makes electrical grid systems' stability more important than ever. Current electrical infrastructures are hard to adapt to these needs. A smart grid system enables two-way electricity flow according to the demand from end-users. Digital communication in smart grid systems enables the system to detect demands, problems, and changes. Also collects information to ensure stability in the system. This study is using the Electrical Grid Stability data set shared at UC Irvine (UCI) Machine Learning repository. Multi-Layer Perceptron (MLP), Radial Basis Function (RBF) Network, K-Nearest Neighbors (K-NN), and Naïve Bayes (NB) Machine Learning (ML) algorithms were used to examine the stability performance of the Smart Grid system. Acquired performance metrics compared using Accuracy, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and F-Score. According to the results obtained, the system and its performance are interpreted. © 2022 IEEE.Publication Restricted Edge Computing and Robotic Applications in Modern Agriculture(IEEE-Inst Electrical Electronics Engineers Inc., 2024) AYRANCI, AHMET AYTUĞ; Erkmen, BurcuThe modernization of agricultural practices prominently features robotics as a key technology. Efforts are concentrated on achieving automation and enhancing efficiency in agriculture through advancements in robotic applications. The widespread integration of remote sensing systems into agricultural areas facilitates real-time information acquisition, enabling drones and robots to operate with enhanced efficiency and effectiveness. Robotics plays a crucial role in the evolution of agriculture 4.0 and agriculture 5.0 strategies, marking significant strides in agricultural technology. Specifically designed robots for agricultural use are currently employed in tasks like planting, fertilizing, irrigating, pest controlling, and harvesting, proving a certain level of effectiveness. Edge computing is crucial in enhancing efficiency and sustainability within modern agricultural practices. Edge computing can instantly process data from numerous devices, mitigating network congestion effectively. In modern agricultural applications, it is possible to perform multiple tasks in a coordinated and effective manner by using Unmanned Aerial Vehicle (UAV) and Unmanned Ground Vehicle (UGV) together. These devices can serve as both data collection and edge devices in the network. Multiple agricultural robot applications and benefits of these applications are explained in the study. © 2024 IEEE.Publication Open Access IoT-Based Fire Detection: A Comparative Study of Machine Learning Techniques(Niğde Ömer Halisdemir Üniversitesi, 2024) AYRANCI, AHMET AYTUĞ; Erkmen, BurcuFires that cannot be detected quickly become uncontrollable. The fires that start to spread uncontrollably pose a significant danger to humans and natural life. Especially in public and crowded areas, fires can lead to possible loss of life and massive property damage. Because of this, it is necessary to detect fires as accurately and quickly as possible. Smoke detectors used with Internet of Things (IoT) technology can exchange data with each other. In this study, data collected from two different types of IoT-based smoke detectors were processed using machine learning algorithms. The k-Nearest Neighbor (k-NN), Multilayer Perceptron (MLP), Radial Basis Function (RBF) Network, Naïve Bayes (NB), Decision Tree (DT), Random Forest (RF), and Logistic Model Tree (LMT) algorithms were used. The data obtained from the smoke detectors were processed using machine learning algorithms to create a highly successful model design. The aim of the study is to design an artificial intelligence-based system that enables the early detection of fires occurring both indoors and outdoors.