Related references
Note: Only part of the references are listed.Secure and Efficient Federated Learning for Smart Grid With Edge-Cloud Collaboration
Zhou Su et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2022)
Gaussian Distribution Based Oversampling for Imbalanced Data Classification
Yuxi Xie et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2022)
Incentive edge-based federated learning for false data injection attack detection on power grid state estimation: A novel mechanism design approach
Wen-Ting Lin et al.
APPLIED ENERGY (2022)
A Novel Deep Class-Imbalanced Semisupervised Model for Wind Turbine Blade Icing Detection
Xu Cheng et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2022)
Design and performance analysis of an airborne wind turbine for high-altitude energy harvesting
Qazi Shahzad Ali et al.
ENERGY (2021)
A Multilevel Convolutional Recurrent Neural Network for Blade Icing Detection of Wind Turbine
Weiwei Tian et al.
IEEE SENSORS JOURNAL (2021)
Electricity Consumer Characteristics Identification: A Federated Learning Approach
Yi Wang et al.
IEEE TRANSACTIONS ON SMART GRID (2021)
A review on ice detection technology and ice elimination technology for wind turbine
Kexiang Wei et al.
WIND ENERGY (2020)
Experimental study on wake interactions and performance of the turbines with different rotor-diameters in adjacent area of large-scale wind farm
Zhao Fei et al.
ENERGY (2020)
Model-Based Synthetic Sampling for Imbalanced Data
Chien-Liang Liu et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2020)
Efficient and Privacy-Enhanced Federated Learning for Industrial Artificial Intelligence
Meng Hao et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)
A Novel Densely Connected Convolutional Neural Network for Sea-State Estimation Using Ship Motion Data
Xu Cheng et al.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2020)
Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data
Felix Sattler et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2020)
Linear and nonlinear features and machine learning for wind turbine blade ice detection and diagnosis
Alfredo Arcos Jimenez et al.
RENEWABLE ENERGY (2019)
Highly transparent and robust slippery lubricant-infused porous surfaces with anti-icing and anti-fouling performances
Meiling Zhang et al.
JOURNAL OF ALLOYS AND COMPOUNDS (2019)
Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0
Alberto Diez-Olivan et al.
INFORMATION FUSION (2019)
Intelligent wind turbine blade icing detection using supervisory control and data acquisition data and ensemble deep learning
Yao Liu et al.
ENERGY SCIENCE & ENGINEERING (2019)
Numerical and field experimental investigation of wind turbine dynamic de-icing process
Lichun Shu et al.
JOURNAL OF WIND ENGINEERING AND INDUSTRIAL AERODYNAMICS (2018)
Development and application of a simulator for offshore wind turbine blades installation
Zhengru Ren et al.
OCEAN ENGINEERING (2018)
Research on experiment and numerical simulation of ultrasonic de-icing for wind turbine blades
Jing Zeng et al.
RENEWABLE ENERGY (2017)
Numerical simulation and experimental validation of ultrasonic de-icing system for wind turbine blade
Congbo Yin et al.
APPLIED ACOUSTICS (2016)
Ice detection using thermal infrared radiometry on wind turbine blades
Carlos Quiterio Gomez Munoz et al.
MEASUREMENT (2016)
Optimizing the Layout of Heaters for Distributed Active De-icing of Wind Turbine Blades
Shervin Shajiee et al.
WIND ENGINEERING (2014)
Preparation and anti-icing of superhydrophobic PVDF coating on a wind turbine blade
Chaoyi Peng et al.
APPLIED SURFACE SCIENCE (2012)
A review of surface engineering issues critical to wind turbine performance
N. Dalili et al.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2009)