no code implementations • 4 Aug 2023 • Jinyu Long, Jetic Gū, Binhao Bai, Zhibo Yang, Ping Wei, Junli Li
Speech enhancement is a demanding task in automated speech processing pipelines, focusing on separating clean speech from noisy channels.
no code implementations • 13 May 2023 • Chengpei Wu, Yang Lou, Lin Wang, Junli Li, Xiang Li, Guanrong Chen
This paper addresses the robustness of a network to sustain its connectivity and controllability against malicious attacks.
no code implementations • 25 Aug 2022 • Chengpei Wu, Yang Lou, Ruizi Wu, Wenwen Liu, Junli Li
In this paper, we investigate the performance of CNN-based approaches for connectivity and controllability robustness prediction, when partial network information is missing, namely the adjacency matrix is incomplete.
no code implementations • 20 Mar 2022 • Yang Lou, Ruizi Wu, Junli Li, Lin Wang, Xiang Li, Guanrong Chen
Extensive experimental studies on both synthetic and real-world networks, both directed and undirected, demonstrate that 1) the proposed LFR-CNN performs better than other two state-of-the-art prediction methods, with significantly lower prediction errors; 2) LFR-CNN is insensitive to the variation of the network size, which significantly extends its applicability; 3) although LFR-CNN needs more time to perform feature learning, it can achieve accurate prediction faster than attack simulations; 4) LFR-CNN not only can accurately predict network robustness, but also provides a good indicator for connectivity robustness, better than the classical spectral measures.
no code implementations • 27 Oct 2020 • Jingjun Wen, Daowei Dou, Jinfu Zhu, Zhi Zeng, Tao Xue, Jianmin Li, Junli Li, Yinong Liu
This paper describes a FADC Readout system developed for the tap water $\alpha/\beta$ dose monitoring system which is based on EJ444 phoswich scintillation detector and wavelength shifting fiber readout.
Instrumentation and Detectors
no code implementations • 24 Oct 2020 • Wenhan Dai, Zhi Zeng, Daowei Dou, Hao Ma, Jianping Chen, Junli Li, HUI ZHANG
We apply multilayer perceptron (MLP) to analyze the 662 keV full energy peak of Cs-137 in the seawater spectrum.