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All over the world, photovoltaic (PV) systems have now become a hopeful way to obtain transition to sustainable energy. Under different operating conditions, the voltage and maximum power generated by PV arrays can differ. The present study aims to employ a PV experimental model to propose a technique for tracking the maximum power point using a new control method for DC-DC converters. The Perturb and Observe (P&O) and Incremental Conductance Method (ICM) were combined to obtain a new combination method. The test system comprises a PV model, a DC-DC converter, a battery, an inverter, and a load. Simulation and experimental results that are totally compatible with each other demonstrate that the devised method can be used as a platform for the PV maximum power point tracking (MPPT) systems.

This paper aims to enhance the accuracy of human gait prediction using machine learning algorithms. Three classifiers are used in this paper: XGBoost, Random Forest, and SVM. A predefined dataset is used for feature extraction and classification. Gait prediction is determined during several locomotion activities: sitting (S), level walking (LW), ramp ascend (RA), ramp descend (RD), stair ascend (SA), stair descend (SD), and standing (ST). The results are gained for steady-state (SS) and overall (full) gait cycle. Two sets of sensors are used. The first set uses inertial measurement units only. The second set uses inertial measurement units, electromyography, and electro-goniometers. The comparison is based on prediction accuracy and prediction time. In addition, a comparison between the prediction times of XGBoost with CPU and GPU is introduced due to the easiness of using XGBoost with GPU. The results of this paper can help to choose a classifier for gait prediction that can obtain acceptable accuracy with fewer types of sensors.