A Model-Data Hybrid Driven Diagnosis Method for Open-Switch Faults in Three-Phase T-Type Grid-Connected Converters
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Journal of Emerging and Selected Topics in Power Electronics"
DOI: 10.1109/jestpe.2024.3402751
Abstract: With the widespread application of three-level T-type converters (3LT2Cs), fault diagnosis has become increasingly important. Existing diagnostic methods can be divided into two types: model-driven and data-driven. Model-driven diagnosis is fast and accurate, but defining… read more here.
Keywords: diagnosis method; diagnosis; model data; driven diagnosis ... See more keywords
A New Data-Driven Diagnosis Method for Mixed Eccentricity in External Rotor Permanent Magnet Motors
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Industrial Electronics"
DOI: 10.1109/tie.2022.3229391
Abstract: In this article, a new data-driven diagnosis method is proposed for mixed eccentricity (ME) in the external rotor permanent magnet motors (ERPMMs). Differently from the previous articles, the proposed method has the advantages of large… read more here.
Keywords: driven diagnosis; diagnosis; new data; data driven ... See more keywords
Online Attribute Matching Based Few-Sample Data-Driven Diagnosis of Electrical Faults in PMSM Drive
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Transactions on Power Electronics"
DOI: 10.1109/tpel.2023.3335268
Abstract: In this article, an online attribute matching based few-sample data-driven diagnosis method for electrical faults in permanent magnet synchronous motor drive is proposed to improve the diagnosis precision with fewer training data and lower computational… read more here.
Keywords: driven diagnosis; attribute matching; online attribute; data driven ... See more keywords
Enhancing AI-Driven Diagnosis of Invasive Ductal Carcinoma with Morphologically Guided and Interpretable Deep Learning
Sign Up to like & getrecommendations! Published in 2025 at "Applied Sciences"
DOI: 10.3390/app15126883
Abstract: Artificial intelligence is increasingly shaping the landscape of computer-aided diagnosis of breast cancer. Despite incrementally improved accuracy, pathologist supervision remains essential for verified interpretation. While prior research focused on devising deep model architecture, this study… read more here.
Keywords: enhancing driven; invasive ductal; ductal carcinoma; driven diagnosis ... See more keywords