The intermittent and uncertain behavior of renewable energy sources; moreover, the increase in penetration of these resources causes some drawbacks in power grids, particularly in low-inertia microgrids. In addition to supply-side challenges, changing load rate has a considerable negative effect on small-size microgrids. Therefore, according to these challenges, the lack of balance between generation and demand is a vital issue in microgrids. One way to face these challenges would be using a suitable control strategy. In this research, an adaptive fuzzy model predictive control has been proposed as a novel control approach and has been compared with conventional controllers such as an optimal PI, and an adaptive optimal model predictive control. It is important to mention that, various types of load changing and time-varying parameters have been considered for a new model of small size microgrids in this study. The comparison and simulation results obviously indicate the effectiveness of the proposed control strategy.
The lungs are COVID-19’s most important focus, as it induces inflammatory changes in the lungs that can lead to respiratory insufficiency. Reducing the supply of oxygen to human cells negatively impacts humans, and multiorgan failure with a high mortality rate may, in certain circumstances, occur. Radiological pulmonary evaluation is a vital part of patient therapy for the critically ill patient with COVID-19. The evaluation of radiological imagery is a specialized activity that requires a radiologist. Artificial intelligence to display radiological images is one of the essential topics. Using a deep machine learning technique to identify morphological differences in the lungs of COVID-19-infected patients could yield promising results on digital images of chest X-rays. Minor differences in digital images that are not detectable or apparent to the human eye may be detected using computer vision algorithms. This paper uses machine learning methods to diagnose COVID-19 on chest X-rays, and the findings have been very promising. The dataset includes COVID-19-enhanced X-ray images for disease detection using chest X-ray images. The data were gathered from two publicly accessible datasets. The feature extractions are done using the gray level co-occurrence matrix methods. K-nearest neighbor, support vector machine, linear discrimination analysis, naïve Bayes, and convolutional neural network methods are used for the classification of patients. According to the findings, convolutional neural networks’ efficiency linked to imaging modalities with fewer human involvements outperforms other traditional machine learning approaches.
To determine how the anti-lock braking system is implemented, it is necessary to examine how the wheels can be prevented from locking. Depending on how the car's transmission system is designed in braking conditions, if one of the car's wheels has a higher or lower rotational speed than the other wheels, that wheel may be locked. So the first step is to check the condition of each wheel in braking mode. For this purpose, automotive engineers turn to electronics and study the conditions of each wheel using sensors mounted on each wheel. In the next step, the engineers take the wheel out of the critical state by changing the force from the brake. That is, if a wheel spins at a slower speed than other car wheels in braking mode, by reducing the braking force on this wheel, its rotational speed will increase, and the wheel will exit the critical state. In the other case, if a wheel spins faster than other car wheels in braking mode, the rotational speed can be reduced by increasing the braking force on that wheel to get the wheel out of the critical state. The anti-lock braking system (ABS) improves vehicle control during abrupt braking, especially on slippery road surfaces. The purpose of such control is to increase the tensile force of the wheel in the desired direction and, at the same time, the appropriate stability and strength of the vehicle and also to reduce the stopping distance of the vehicle. In this paper, an optimal fuzzy controller is presented. The functional purpose of the anti-lock braking system is to optimally maintain the wheel slip to achieve maximum wheel traction and maximum vehicle deceleration.
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