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Articles by Junshuai Yan
Sustainability | Free Full-Text | A Novel Convolutional Neural Net Architecture Based on Incorporating Meteorological Variable Inputs into Ultra-Short-Term Photovoltaic Power Forecasting
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Wind Turbine Gearbox Condition Monitoring Using Hybrid Attentions and Spatio-Temporal BiConvLSTM Network
5.5. Condition Monitoring When model offline training is completed, the well-trained HA-BiConvLSTM normal behavior model can be employed to implement online condition monitoring for a wind turbine gearbox. Generally, when a wind turbine operates normally, the NBMs prediction residuals would be small with stable fluctuations. Nevertheless, when a wind turbine gearbox suffers from condition deteriorations, the NBMs prediction residuals would be larger with violent fluctuations.
Wind Turbine Condition Monitoring Using the SSA-Optimized Self-Attention BiLSTM Network and Changepoint Detection Algorithm
Abstract : Condition-monitoring and anomaly-detection methods used for the assessment of wind turbines are key to reducing operation and maintenance (O&M) cost and improving their reliability.
Energies | Free Full-Text | An Early Fault Detection Method for Wind Turbine Main Bearings Based on Self-Attention GRU Network and Binary Segmentation Changepoint Detection Algorithm
Abstract : The condition monitoring and potential anomaly detection of wind turbines have gained significant attention because of the benefits of reducing the operating and maintenance costs and enhancing the reliability of wind turbines. However, the complex and dynamic operation states of wind turbines still pose tremendous challenges for reliable and timely fault detection.
Human serum albumin gradient in serous ovarian cancer cryosections measured by fluorescence lifetime
F. R. Lin, P. Das, Y. H. Zhao, B. L. Shen, R. Hu, F. F. Zhou, L. W. Liu, and J. L. Qu, “Monitoring the endocytosis of bovine serum albumin based on the fluorescence lifetime of small squaraine dye in living cells,” Biomed. Opt. Express 11(1), 149–159 (2020). [Crossref] B. L. Shen, J. S. Yan, S. Q. Wang, F. F. Zhou, Y. H. Zhao, R. Hu, J. L. Qu, and L. W.
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