no code implementations • 4 Mar 2024 • P. Bilha Githinji, Xi Yuan, Zhenglin Chen, Ijaz Gul, Dingqi Shang, Wen Liang, Jianming Deng, Dan Zeng, Dongmei Yu, Chenggang Yan, Peiwu Qin
Realizing sufficient separability between the distributions of healthy and pathological samples is a critical obstacle for pathology detection convolutional models.
1 code implementation • 20 Mar 2023 • Liang Zhang, Jianming Deng
To address these challenges, we propose: (1) a cyclical offline dataset (COD), designed based on common real-world scenarios to facilitate easy collection; (2) an offline RL model called DataLight, capable of learning satisfactory control strategies from the COD; and (3) a method called Arbitrary To Cyclical (ATC), which can transform most RL-based methods into cyclical signal control.
no code implementations • 7 Apr 2022 • Liang Zhang, Shubin Xie, Jianming Deng
We would like to withdraw this article for the following reasons: 1 this article is not satisfactory for limited language and theoretical description; 2 we have enriched and revised this article with the help of other authors; 3 we must update the author contribution information.
2 code implementations • 30 Dec 2021 • Liang Zhang, Shubin Xie, Jianming Deng
We propose two new methods: (1) Max Queue-Length (M-QL), an optimization-based traditional method designed based on the property of queue length; and (2) AttentionLight, an RL model that employs the self-attention mechanism to capture the signal phase correlation without requiring human knowledge of phase relationships.