1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.LG2021
Fed-NILM: A Federated Learning-based Non-Intrusive Load Monitoring Method for Privacy-Protection
Haijin Wang, Caomingzhe Si, Junhua Zhao +2
Non-intrusive load monitoring (NILM) is essential for understanding customer's power consumption patterns and may find wide applications like carbon emission reduction and energy c…
eess.SP2021
A Federated Learning Framework for Non-Intrusive Load Monitoring
Haijin Wang, Caomingzhe Si, Junhua Zhao
Non-intrusive load monitoring (NILM) aims at decomposing the total reading of the household power consumption into appliance-wise ones, which is beneficial for consumer behavior an…
cs.CV2019★ 1 cited
Rethinking Convolutional Features in Correlation Filter Based Tracking
Fang Liang, Wenjun Peng, Qinghao Liu +1
Both accuracy and efficiency are of significant importance to the task of visual object tracking. In recent years, as the surge of deep learning, Deep Convolutional NeuralNetwork (…