SIGNAL PARAMETER ESTIMATION AND CLASSIFICATION USING MIXED SUPERVISED AND UNSUPERVISED MACHINE LEARNING APPROACHES

Signal Parameter Estimation and Classification Using Mixed Supervised and Unsupervised Machine Learning Approaches

The increasing use of modern power electronics raises the issue of harmonics in power systems which ultimately deteriorate its optimal performance in terms of: increased power loss, breaker failure and mal-operation of equipment.It has been found that the most severe harmonics in the system are odd ones due to their unsymmetrical nature.This work p

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Integration of Site Effects into Probabilistic Seismic Hazard Assessment (PSHA): A Comparison between Two Fully Probabilistic Methods on the Euroseistest Site

The integration of site effects into Probabilistic Seismic Hazard Assessment (PSHA) is still an open issue within the seismic hazard community.Several approaches have been proposed varying from deterministic to fully probabilistic, through hybrid (probabilistic-deterministic) approaches.The present study compares the hazard curves that have been ob

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Znet: Deep Learning Approach for 2D MRI Brain Tumor Segmentation

Background: Detection and segmentation of brain tumors using MR images are challenging and valuable tasks in the medical field.Early diagnosing and localizing of brain tumors can save lives and provide timely options for physicians to select efficient treatment plans.Deep learning approaches have attracted researchers in medical imaging due to thei

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