Multi-output ensemble deep learning: A framework for simultaneous prediction of multiple electrode material properties
Hanqing Yu(Beihang University), Xinhua Liu(Beihang University), Mengzheng Ouyang(Imperial College London), Bin Ma(Queensland University of Technology), Wentao Wang(Liaoning Cancer Hospital & Institute), Lisheng Zhang(Beihang University), Kaiyi Yang(Beihang University), Junfu Li(Harbin Institute of Technology), Shichun Yang(Beihang University)
Cited by 23
Related Papers
Fungal diversity notes 929–1035: taxonomic and phylogenetic contributions on genera and species of fungi
|Fungal Diversity|2019|344
Remaining useful life and state of health prediction for lithium batteries based on differential thermal voltammetry and a deep-learning model
|Journal of Power Sources|2022|107
A Review on the Fault and Defect Diagnosis of Lithium-Ion Battery for Electric Vehicles
|Energies|2023|92
Identifying nitrate sources and transformation in groundwater in a large subtropical basin under a framework of groundwater flow systems
|Journal of Hydrology|2022|62
Electric vehicle lifecycle carbon emission reduction: A review
|Carbon Neutralization|2023|62