Abstract: This paper presents a comprehensive fault classification framework for three-phase Induction Motors (IMs) using a novel Grey Wolf Optimization-enhanced Support Vector Machine (GWO-SVM) ...
The goal of a machine learning binary classification problem is to predict a variable that has exactly two possible values. For example, you might want to predict the sex of a company employee (male = ...
ABSTRACT: This paper presents a method for detecting, classifying, and locating short-circuit faults in meshed electrical networks using Artificial Neural Networks (ANNs). The proposed approach is ...
Market.us Scoop, we strive to bring you the most accurate and up-to-date information by utilizing a variety of resources, including paid and free sources, primary research, and phone interviews. Learn ...
Researchers from the Institute of Modern Physics (CAS-IMP) have introduced an innovative ML model for classifying faults occurring in SRF cavities during accelerator operation. Deployed at the CAFE2 ...
This project investigates the application of Deep Neural Networks (DNNs) for automated fault classification and fault location in power transmission lines. Using data generated from a simulated 4-bus ...
Binary classification of Diabetic Retinopathy using SVM in MATLAB with fundus image features. This repository compares the performance of Adaline, Logistic Regression, and Perceptron models on binary ...
Abstract: This paper presents a fault classification technique based on Haar Wavelet Transform (WT) and Artificial Neural Network (ANN) for six phase transmission line against phase to phase faults.
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