Any growing country's business development relies on the availability of electricity. Recent years have seen an increase in the development and marketing of electric vehicles (EVs) and hybrid electric cars due to environmental concerns and increasing oil costs (HEV). Electric vehicles are becoming more popular, and their accompanying equipment has become a necessary part of the equation. One module where this has been tried is the charging station. As part of the proposed work, ANN-based MPPT is utilized to monitor the solar panel's maximum power. This approach has the benefit of being more precise. ANN uses the Bayesian Regularization technique for maximum power point tracking. The neural network accustomed to manage the DC-DC converter's duty cycle is trained using this approach. Solar PV, energy storage, and the grid are all included in this electric car charging station. In case of an emergency, the PV and storage systems may draw electricity from the grid. The proposed method's efficiency is simulated and tested using the MATLAB/Simulink programme.
Heterogeneous multi-cloud environments make use of a collection of varied performance rich cloud resources, linked with huge-speed, performs varied applications which are of computational nature. Applications require distinct computational features for processing. Heterogeneous multi-cloud domain well suits to satisfy the computational need of very big diverse nature of collection of tasks. Mapping problem provides an optimal solution in scheduling tasks to distributed heterogeneous clouds is termed NP-complete, which leads to the ultimate establishment of heuristic problem solving technique. Identifying the heuristic which is appropriate and best still exists as a complicated problem. In this paper, to address scheduling collection of ‘n’ tasks in two groups among a set of 'm' clouds, we propose three heuristics PTL (Pair-Task Threshold Limit), PTMax-Min, and PTMin-Max. Firstly to determine the tasks scheduling order, proposed heuristics based on the tasks attributes calculate tasks threshold value. Tasks sorted in descending value of threshold. Group G1 comprises tasks ordered in descending value of threshold. Group G2 comprises remaining tasks ordered in ascending value of threshold. Secondly, tasks form Group 1 are scheduled first based on minimum completion time, and then tasks in Group 2 are scheduled. The proposed heuristicsare compared with existing heuristics, namely MCT, MET, Min-Min using benchmark dataset. Heuristics PTL, PTMax-Min, and PTMin-Max bring out reduced makespan compared to MCT, MET, and Min-min.
Many individuals throughout the globe need to be constantly monitored for health reasons, including diabetes patients and individuals with other chronic diseases, the elderly, and the disabled. At any time, these individuals may be at a higher risk of suffering life-threatening falls or experiencing fainting. HAR (Human Activity Recognition) model using machine learning techniques play an important role in observing the activities of the people. The existing methods of activity monitoring lacks accuracy. Hence, the proposed method focuses to improve the prediction accuracy using accelerometer and gyroscope data. The research work analysis accelerometer and gyroscope data using various decomposition techniques such as EMD(Empirical Mode Decomposition), DWT (Discrete Wavelet Transform), FFT (Fast Fourier Transform) to process non-linear data and to split series of signal data into set of IMF(Intrinsic Mode Function), PCA(Principal Component Analysis) was performed for selecting optimal features. Then human activities are recognized by using multi-class classification techniques. The proposed EMD method achieves better performance with 98.4% accuracy, 100% Precision, 100% Recall and 100% F-measure.
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