This paper considers a new Double Integral transform called Double Sumudu-Elzaki transform DSET. The combining of the DSET with a semi-analytical method, namely the variational iteration method DSETVIM, to arrive numerical solution of nonlinear PDEs of Fractional Order derivatives. The proposed dual method property decreases the number of calculations required, so combining these two methods leads to calculating the solution's speed. The suggested technique is tested on four problems. The results demonstrated that solving these types of equations using the DSETVIM was more advantageous and efficient.

Abstract

Currently, renewable energy sources are a well-liked alternative to traditional electrical energy sources that governments are even planning to increase the quantity of electricity generated from renewable energy sources. The rapid growth of investments in renewable energy has drawn the attention of experts who are working to improve its effectiveness. This really is due to a surge in electrical energy consumption brought on by the quick development of technology, population expansion, and high fuel prices utilized in traditional electrical energy generation. Solar energy has gotten a lot of attention from researchers and scientists because it is the most important source of sustainable energy for electric power generation. However, it has a problem that is exacerbated by two factors: changes in solar irradiation intensity besides temperature, which causes their electrical features-to become non-linear, lowering its efficiency. With respect to efficiency issue solving, the researchers devised a number of methods for tracking the MPP point and harvesting the maximum energy from solar panels under a variety of measuring scenarios. In solar energy systems, MPPT technology is the most extensively employed. In this paper, MPPT technology based on the FLC algorithm developed utilizing MATLAB/Simulink is simulated aimed at a purpose of power maximization plus controlling the DC-DC buck converter duty cycle. Through the simulation results, an efficiency of 97.35% is achieved under standard test conditions and 96.41% under volatile weather conditions which gives the superiority of FLC in performance and efficiency of the FLC algorithm.

Abstract

Drowsy driving is a major cause of road accidents worldwide, necessitating the development of effective drowsiness detection systems. Each year, there are more accidents and fatalities than ever before for a variety of causes. For instance, there were 22,952 fatalities and 79,545 injuries as a result of nearly 66,500 vehicle accidents in the last 10 years. In this paper, we propose a novel approach for detecting drowsiness based on behavioral cues captured by a digital camera and utilizing the multi-task cascaded convolutional neural network (MTCNN) deep learning algorithm. A high-resolution camera records visual indications like closed or open eye movement to base the technique on the driver's behavior. In order to measure a car user's weariness in the present frame of reference, eyes landmarks are evaluated, which results in the identification of a fresh constraint known as "eyes aspect ratio." A picture with a frame rate of 60 frames per second (f/s) and a resolution of 4,320 eyeballs was used. The accuracy of sleepiness detection was more than 99.9% in excellent lighting and higher than 99.8% in poor lighting, according to testing data. The current study did better in terms of sleepiness detection accuracy than a lot of earlier investigations.