Abstract: The growing complexity of modern power networks requires intelligent fault classification techniques capable of handling multiple fault conditions with high accuracy. Classifying faults in ...

The risk of bias was evaluated using the Quality In Prognosis Studies tool and the Prediction model Risk Of Bias Assessment Tool by 2 investigators (MMS and MAS) independently. A narrative synthesis ...

Outdated targeting data may have resulted in a mistaken missile strike, according to the ongoing military investigation, which undercuts President Trump’s assertion that Iran could be to blame. By ...

The proposed algorithm enhances the traditional conventional convolutional neural network (CNN) algorithm by introducing a domain category judgment module and an inter-domain conditional probability ...

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) ...

Code, configuration templates, and documentation for the PSCC 2026 paper Comparison of Deep Learning Methods for Fault Analysis in Power System Protection. An end-to-end machine learning pipeline for ...

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As the core equipment in industrial production, rotating machinery bearings play a critical role. However, traditional feature extraction algorithms for vibration signals are susceptible to noise ...

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 = ...