no code implementations • 13 Jan 2023 • Shuan Dong, Bin Wang, Jin Tan, Cameron J. Kruse, Brad W. Rockwell, Anderson Hoke
This letter discusses the 18-20 Hz oscillation event at 05:30 am on November 21, 2021, in Kaua`i's power system following the trip of an oil power plant.
1 code implementation • ICCV 2023 • Wenxuan Zeng, Meng Li, Wenjie Xiong, Tong Tong, Wen-jie Lu, Jin Tan, Runsheng Wang, Ru Huang
Secure multi-party computation (MPC) enables computation directly on encrypted data and protects both data and model privacy in deep learning inference.
no code implementations • 20 Sep 2022 • Shuan Dong, Xin Fang, Jin Tan, Ningchao Gao, Xiaofan Cui, Anderson Hoke
The simulation results in the IEEE 39-bus system with different types of FFR demonstrate that the proposed method provides an accurate and fast prediction of the frequency nadir under various disturbances.
no code implementations • 20 Aug 2020 • Chaochao Chen, Jun Zhou, Li Wang, Xibin Wu, Wenjing Fang, Jin Tan, Lei Wang, Alex X. Liu, Hao Wang, Cheng Hong
In this paper, we first present CAESAR, which combines HE and SS to build secure large-scale sparse logistic regression model and achieves both efficiency and security.
no code implementations • 18 May 2020 • Wenjing Fang, Derun Zhao, Jin Tan, Chaochao Chen, Chaofan Yu, Li Wang, Lei Wang, Jun Zhou, Benyu Zhang
Privacy-preserving machine learning has drawn increasingly attention recently, especially with kinds of privacy regulations come into force.