FAULT DIAGNOSIS FOR VARIABLE FREQUENCY DRIVE-FED INDUCTION MOTORS USING WAVELET PACKET DECOMPOSITION AND GREEDY-GRADIENT MAX-CUT LEARNING

Fault Diagnosis for Variable Frequency Drive-Fed Induction Motors Using Wavelet Packet Decomposition and Greedy-Gradient Max-Cut Learning

In this paper, a novel fault diagnosis method for variable frequency drive (VFD)-fed induction motors is proposed using Wavelet Packet Decomposition (WPD) and greedy-gradient max-cut (GGMC) learning algorithm.The proposed method is developed using experimental stator current data in the lab for two 0.25 HP induction motors Bumper D-Ring fed by a VF

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Fidelity of human ovarian cancer patient-derived xenografts in a partially humanized mouse model for preclinical testing of immunotherapies

Background Immune checkpoint blockers (ICBs) have been approved by the Food and Drug Administration to be used alone in front-line therapies or in combination with other regimens for certain advanced cancers.Since ICB only works in a subset of patients and has limited efficacy in treating ovarian cancer (OVC), developing preclinical models that hel

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Phospholipids are A Potentially Important Source of Tissue Biomarkers for Hepatocellular Carcinoma: Results of a Pilot Study Involving Targeted Metabolomics

Background: Hepatocellular carcinoma (HCC) pathogenesis involves the alteration of multiple liver-specific metabolic pathways.We systematically profiled cancer- and liver-related classes of metabolites in HCC and adjacent liver tissues and applied supervised machine learning to compare their potential yield for HCC biomarkers.Methods: Tumor and cor

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