An intelligence Indirect Vector Control (IVC) of a Doubly Fed Induction Generator (DFIG) by using a five-level inverter with space vector modulation (5L-SVM) strategy is proposed in this article. The novelty of the proposed intelligence strategy is an Artificial Neural Network (ANN), which is utilised for the IVC command of the DFIG. In this research, ANN has been replaced with the hysteresis comparators of the 5L-SVM technique. The backpropagation process is used for the training process of the ANN. By using the ANN outputs, the SVM-ANN strategy develops command pulses for the five-level inverter. Subsequently, the proposed intelligence strategy is implemented in the MATLAB/Simulink platform and the effectiveness is analysed by comparing different tests. The simulation and comparison results have demonstrated the superiority of the proposed approach and confirmed the potential of the proposed system for solving the problem.

Introduction: Indirect calorimetry (IC) is the optimal tool for the assessment of the resting energy expenditure, as well as the reference standard for energy expenditure measurement. During the outbreak of coronavirus, performing respiratory tests such as IC has become challenging. The Current guidance was designed to prevent the virus spread at calorimetry centers. Methods: This protocol was devised in accordance with the latest recommendations of the World Health Organization (WHO) and Centers for Disease Control (CDC), as well as the related articles. Conclusion: Due to the specific features of COVID-19, unnecessary ICs should be avoided in the hotspots of the disease. If these tests must be performed, adherence to the validated infection Control recommendations is critical.

This research presents a new sliding-mode adaptive technique for stabilization of the output voltage of a single-phase Switched Inductor Z-source Inverter (SIZSI) as an interface in renewable energy sources. The proposed method is based on a sliding mode Controller modified by an online adaptation of uncertain inverter parameters. The sliding mode Controller improves the system's robustness in the face of external disturbances and preserves the system's output for any load, such as linear, nonlinear, and even changing loads. The proposed approach has been simulated in MATLAB/Simulink software package to show the Controller's performance. The comparison results with the traditional sliding mode Control have been conducted to validate the proposed method's superiority in resolving problems such as adaptive and robust against instantaneous deviations of input voltage and output Current. The presented sliding-mode adaptive Control technique shows a more efficient dynamic response to the system and less Total Harmonic Distortion (THD) than traditional Controllers.