Sürdürülebilir Kalkınma Amaçları
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- MakaleAçık Erişim2024Atomic Comagnetometer Gyroscopes for Inertial Navigation Systems: A Review
Karabi̇Naoğlu, Murat Salim; Çakir, Bekir; Başoğlu, Mustafa Engin
In recent years, developments in quantum sensing, laser, and atomic sensor technologies have also enabled advancement in the field of quantum navigation. Atomic-based gyroscopes have emerged as one of the most critical atomic sensors in this respect. In this review, a brief technology statement of spin exchange relaxation free (SERF) and nuclear magnetic resonance (NMR) type atomic comagnetometer gyroscope (CG) is presented. Related studies in the literature have been gathered, and the fundamental compositions of CGs with technical basics are presented. A comparison of SERF and NMR CGs is provided. A basic simulation of SERF CG was carried out because of its high theoretic bias stability limit. Besides, some highly critical challenges for CGs were examined and reported with compensation methods. The objective of this review is to offer a guide for researchers to develop high-precision atomic gyroscopes and to encourage further research.
- MakaleAçık Erişim2022Comparison of deep learning and regression-based MPPT algorithms in PV systems
Karabi̇Naoğlu, Murat Salim; Çakir, Bekir; Başoğlu, Mustafa Engin; Kazdaloğlu, Abdülvehhab; Güneroğlu, Aziz
Solar energy systems (SES) and photovoltaic (PV) modules should be operated at the maximum power point (MPP) to achieve the highest efficiency in the energy generation processes. Maximum power point tracking (MPPT) applications using conventional methods may not be able to follow the global MPP (GMPP) of the PV system under changing atmospheric conditions and they could oscillate around the local MPP. In this study, a machine learning and deep learning (DL) based long short-term memory (LSTM) model is proposed as an innovative solution for MPPT. Contrary to the traditional MPPT applications using current and voltage sensors, the output resistance of the PV module estimation was made by using environmental parameters (such as temperature and radiation) and artificial intelligence algorithms in this study.The LSTM model was compared with artificial neural networks (ANN) and regression methods regarding mean square error (MSE), root mean square error(RMSE) and mean absolute error (MAE) parameters. It has been determined that the LSTM model has a better performance and could more successfully follow MPP compared to the other methods. Finally, after the comparison with the ANN method, it is proved that LSTM gives 37%, 21%, and 31% more successful MSE, RMSE, and MAE results, respectively.