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Öğe Assessing the Spreading Behavior of the Covid-19 Epidemic: A Case Study of Turkey(Institute of Electrical and Electronics Engineers Inc., 2022) Demir, E.; Canitez, M.N.; Elazab, M.; Hameed, A.A.; Jamil, A.; Al-Dulaimi, A.A.Coronavirus (Covid-19) disease is a rapidly spreading type of virus that was discovered in Wuhan, China, and emerged towards the end of 2019. During this period, various studies were conducted, and intensive studies are continued in different fields regarding coronavirus, especially in the field of medicine. The virus continues to spread and is yet to be controlled fully. Machine learning is a well-explored field in the domain of computer science that can learn patterns based on existing data and make predictions on new data. This study focused on using various machine learning approaches for predicting the spreading behavior of the COVID-19 virus. The models that were considered include SARIMAX, Extreme Gradient Boosting (XGBoost), Linear Regression (LR), Decision Tree (DT), Gradient Boosting (GB), and Artificial Neural Network (ANN). The models were trained and then predictions were made by applying these models to the daily updated data provided by the Turkish Ministry of Health. Experiments on the test data showed that both XGBoost and Decision Tree models outperformed other models. © 2022 IEEE.Öğe Investigation of Thermal Modeling for Underground Cable Ampacity under Different Conditions of Distances and Depths(Institute of Electrical and Electronics Engineers Inc., 2021) Al-Dulaimi, A.A.; Guneser, M.T.; Hameed, A.A.Underground cables are affected by a many different factors, including the temperature distribution surrounding the cable, the depth of the cable, distances between the cables, the thermal resistivity of the soil, and the material the cable is backfilled. The study and analysis of these factors are exploited as much as possible to carry the maximum possible current through the power transmission cable. Calculations were made for single power cables with a flat configuration at a burial depth (0.8 and 1) meters, (0.8 and 1) km/w soil resistivity, (0.2 and 0.4 (meters)) distances between the cables, and two types of backfill materials: cement-sand mixture backfill (CSB) and thermal backfill for the Aluminum conductor. The proposed model can determine the temperature distribution in the soil, thermal backfill, and around cables. The results essentially show that appropriate thermal backfill, and spatial geometric characteristics are useful for reducing conductor temperature and securing a specific cost metric while being of exceptional importance to take full advantage of cable ampacity. © 2021 IEEE.Öğe Photovoltaics Cell Anomaly Detection Using Deep Learning Techniques(Springer Science and Business Media Deutschland GmbH, 2024) Al-Dulaimi, A.A.; Hameed, A.A.; Guneser, M.T.; Jamil, A.Photovoltaic cells play a crucial role in converting sunlight into electrical energy. However, defects can occur during the manufacturing process, negatively impacting these cells’ efficiency and overall performance. Electroluminescence (EL) imaging has emerged as a viable method for defect detection in photovoltaic cells. Developing an accurate and automated detection model capable of identifying and classifying defects in EL images holds significant importance in photovoltaics. This paper introduces a state-of-the-art defect detection model based on the Yolo v.7 architecture designed explicitly for photovoltaic cell electroluminescence images. The model is trained to recognize and categorize five common defect classes, namely black core (Bc), crack (Ck), finger (Fr), star crack (Sc), and thick line (Tl). The proposed model exhibits remarkable performance through experimentation with an average precision of 80%, recall of 87%, and an mAP@.5 score of 86% across all defect classes. Furthermore, a comparative analysis is conducted to evaluate the model’s performance against two recently proposed models. The results affirm the excellent performance of the proposed model, highlighting its superiority in defect detection within the context of photovoltaic cell electroluminescence images. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.Öğe The Thermal Modeling for Underground Cable Based on ANN Prediction(Springer Science and Business Media Deutschland GmbH, 2022) Al-Dulaimi, A.A.; Guneser, M.T.; Hameed, A.A.Many factors affect the ampacity of the underground cable (UC) to carry current, such as the backfill material (classical, thermal, or a combination thereof) and the depth at which it is buried. Moreover, the thermal of the UC is an effective element in the performance and effectiveness of the UC. However, it is difficult to find thermal modeling and prediction in the UC under the influence of many parameters such as soil resistivity (?soil), insulator resistivity (?insulator), and ambient temperature. In this paper, the calculation of the UC steady-state rating current is the most important part of the cable installation design. This paper also applied an artificial neural network (ANN) to develop and predict for 33 kV UC rating models. The proposed system was built by using the MATLAB package. The ANN-based UC rating is achieves the best performance and prediction for the UC rating current. The performance of the proposed model is superior to other models. The experiment was conducted with 200 epochs. The proposed model achieved high performance with low MSE (0.137) and the regression curve gives an excellent performance (0.99). © 2022, Springer Nature Switzerland AG.
