Elektrik-Elektronik Mühendisliği Bölümü / Department of Electrical and Electronics Engineering
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- PublicationRestrictedDecentral Smart Grid Control System Stability Analysis Using Machine Learning(Institute of Electrical and Electronics Engineers Inc., 2022 [Date of Conference: 15-16 July 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.
- PublicationRestrictedGlobal Impact of the Pandemic on Education: A Study of Natural Language Processing(Institute of Electrical and Electronics Engineers Inc., 2022 [Date of Conference: 07-09 September 2022]) AYAZ, TEOMAN BERKAY; USLU, MUHAMMED SAFA; AĞCABAY, İBRAHİM; AHMED, FARUK; KORKMAZ, ÖMER FARUK; KÜREKSİZ, MESUT; ULUÇAM, EMRE; YILDIRIM, ELİF; KOCAÇINAR, BÜŞRA; AKBULUT, FATMA PATLARSchool closures due to the Covid-19 pandemic have changed education forever and we have witnessed the rise of online learning platforms. The education units of the countries made great efforts to adapt to this new order. The expanding, quick spread of the virus and careful steps have prompted the quest for reasonable choices for continuing education to guarantee students get appropriate education and are not impacted logically or mentally. Different methods were attempted to understand how students were affected by this big change. In addition to the significance of traditional surveys and consulting services, the utilization of social media analysis is used as a supportive approach. This paper analyzes the feedback of students on social media via tweets. Deep sentiment analysis is employed to identify embedded emotions such as negative, neutral, and positive. We also aimed to classify irrelevant tweets as the fourth category. Our experiments showed that the tweets are mostly biased toward negative emotions. © 2022 IEEE.
- PublicationRestrictedCapacity 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.
- PublicationRestrictedHeterofonik Türk Makam Müziginde İşitsel Melodi Kestirimi(Enstitute of Electrical and Electronics Engineers Inc., 2021, [Date of Conference: 09-11 June 2021]) ŞİMŞEK, BERRAK ÖZTÜRKIn this study, the Improved Variable Mode Decomposition Method (IVMD) is proposed for the estimation of the audio melody in heterophonic works that constitute the general texture of Turkish maqam music. In our study, the fundamental frequencies of the records belonging to huzzam, kurdilihicazkar, ussak, and rast maqams were estimated by using the IVMD method. Since the basis of the heterophonic texture is that the same melody is performed by more than one instrument, the estimated fundamental frequencies are more than one for each time window. After the multiple frequency estimation, in order to obtain the audio melody of the music recording and therefore a single frequency line, the selection of the frequencies belonging to the audio melody line from the fundamental frequencies was made. The study has been compared with the methods widely used in the analysis of polyphonic music works such as YIN and MELODIA. When the comparisons were evaluated on the basis of maqam and mixture according to the MIREX criteria, successful results were obtained with the IVMD method. © 2021 IEEE.
- PublicationRestrictedDesign and Realization of an Automatic Optical Inspection System for PCB Solder Joints(Institute of Electrical and Electronics Engineers Inc., 2021, [Date of Conference: 25-27 August 2021]) ÇALIŞKAN, AYHAN; Gürkan, GürayRecent developments in electronics has led to an increase in fabrication and assembly speeds of printed circuit boards (PCBs). In addition, the size of manufactured PCBs and electronic components (e.g. resistors, capacitors, transistors etc.) are becoming much smaller. By increased demand and production speed, the reliability and thus the inspection of manufactured PCB assemblies became an important issue. In the assembly PCB production process, detection of surface mount technology (SMT) solder defects is made with automatic optical inspection (AOI) devices using image processing methods. Besides these expensive device methods, the method by which the controls are made by the operators visually is a low-cost solution used by most of the companies that manufacture printed circuit board assembly. In addition, circuit defects and solder defects cannot be tested and controlled with 100% accuracy due to human error. This paper proposes to detect solder joint defects with machine learning methods using YOLO algorithm to speed up time and increase accuracy in assembly PCB production line. Approximately 40000 images were obtained from the real production line before training with the YOLOv4 algorithm for high accuracy rate. Detection of solder defects of SMT circuit elements in approximately 5K (4056x3040) images resolution can be achieved with 97% accuracy in around 4 seconds. As a result of the use of the system, this proposed method has been proven with the reports received from the production line and precision-recall curves. Thus, it has been observed that the production speed and accuracy rate are increased. © 2021 IEEE.