no code implementations • 23 Jan 2023 • Kunlong Chen, Liu Yang, Yitian Chen, Kunjin Chen, Yidan Xu, Lujun Li
It is of great significance to estimate the performance of a given model architecture without training in the application of Neural Architecture Search (NAS) as it may take a lot of time to evaluate the performance of an architecture.
1 code implementation • 14 Aug 2021 • Yitian Chen, Kunlong Chen, Kunjin Chen, Lin Wang
From our perspective, the major challenge of this competition is how to extend the classical DQN framework to traffic signals control in real-world complex road network and traffic flow situation.
no code implementations • 1 Nov 2020 • Kunjin Chen, Tomáš Vantuch, Yu Zhang, Jun Hu, Jinliang He
The detection and characterization of partial discharge (PD) are crucial for the insulation diagnosis of overhead lines with covered conductors.
no code implementations • 17 Nov 2019 • Kunjin Chen, Yu Zhang, Qin Wang, Jun Hu, Hang Fan, Jinliang He
Non-intrusive load monitoring addresses the challenging task of decomposing the aggregate signal of a household's electricity consumption into appliance-level data without installing dedicated meters.
1 code implementation • 22 Dec 2018 • Kunjin Chen, Jun Hu, Yu Zhang, Zhanqing Yu, Jinliang He
This paper develops a novel graph convolutional network (GCN) framework for fault location in power distribution networks.
no code implementations • 6 Jun 2018 • Kunjin Chen, Qin Wang, Ziyu He, Kunlong Chen, Jun Hu, Jinliang He
A convolutional sequence to sequence non-intrusive load monitoring model is proposed in this paper.
1 code implementation • 30 May 2018 • Kunjin Chen, Kunlong Chen, Qin Wang, Ziyu He, Jun Hu, Jinliang He
We present in this paper a model for forecasting short-term power loads based on deep residual networks.