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Weiwei Yin on Muck Rack

Weiwei Yin

As seen in: Nature, Wiley Online Library, Frontiers in Public Health, MDPI, Cell Press, Cell Reports Medicine, EBioMedicine
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Articles

Transmission line fault detection and diagnosis based on fuzzy logic and artificial intelligence

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By Rui Li, Yongsheng Cheng, Weiwei Yin
| Nature Verified
Abstract Long-distance power transmission lines are prone to physical faults due to voltage, natural, or other factors. Detecting such faults is automated using modern algorithms with computer-aided features. A Variability-dependent Fault Detection Module (VFDM) is introduced for long-distance grid transmission lines. This module identifies the flow variations and impedance between the transmitting and receiving terminals based on input/output voltage measurements.
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Correction: Fetal health state detection method based on parameters efficient ensembling of deep learning

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By Weiwei Yin, Zhengyuan Shen, Zhenbo Cheng, Chun Feng, Guoquan Sun
| Frontiers in Public Health Verified
A Correction on Fetal health state detection method based on parameters efficient ensembling of deep learning By Yin, W., Shen, Z., Cheng, Z., Feng, C., and Sun, G. (2026). Front. Public Health 14:1808284. doi: 10.3389/fpubh.2026.1808284 The Data availability statement was erroneously given as “The datasets analyzed for this study can be found in the UC Irvin Machine Learning Repository.
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Fetal health state detection method based on parameters efficient ensembling of deep learning

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By Weiwei Yin, Zhengyuan Shen, Zhenbo Cheng, Chun Feng, Guoquan Sun
| Frontiers in Public Health Verified
Abstract Background: The classification of cardiotocography (CTG) can assist obstetricians in assessing the health status of the fetus. However, traditional fetal heart rate monitoring data has the problem of strong subjectivity in manual interpretation, and deep learning models have poor representation ability on tabular data. Methods: This study proposed PLE-TabM, a tabular deep learning model combined piecewise linear encoding (PLE) and efficient weight integration.
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