Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Ming, Yurui"'
Autor:
Ming, Yurui, Liang, Yuanyuan
We report the possibility of using a simple neural network for effortless restoration of low-light images inspired by the retina model, which mimics the neurophysiological principles and dynamics of various types of optical neurons. The proposed neur
Externí odkaz:
http://arxiv.org/abs/2210.01806
Autoencoder can give rise to an appropriate latent representation of the input data, however, the representation which is solely based on the intrinsic property of the input data, is usually inferior to express some semantic information. A typical ca
Externí odkaz:
http://arxiv.org/abs/2205.15592
Autor:
Ming, Yurui
The auto feature extraction capability of deep neural networks (DNN) endows them the potentiality for analysing complicated electroencephalogram (EEG) data captured from brain functionality research. This work investigates the potential coherent corr
Externí odkaz:
http://arxiv.org/abs/2102.10994
Autor:
Ming, Yurui
In this paper we craft a cascaded fuzzy controlling system for the traditional Truck-and-Trailer Backer-Upper problem, which is a benchmarking for testing various intelligent controlling systems. Inspired by the most inclination of human operations,
Externí odkaz:
http://arxiv.org/abs/2010.04884
Technologies of the Internet of Things (IoT) facilitate digital contents such as images being acquired in a massive way. However, consideration from the privacy or legislation perspective still demands the need for intellectual content protection. In
Externí odkaz:
http://arxiv.org/abs/2003.12428
Fatigue is the most vital factor of road fatalities and one manifestation of fatigue during driving is drowsiness. In this paper, we propose using deep Q-learning to analyze an electroencephalogram (EEG) dataset captured during a simulated endurance
Externí odkaz:
http://arxiv.org/abs/2001.02399
Publikováno v:
npj Computational Materials volume 5, Article number: 88 (2019)
Topologically ordered materials may serve as a platform for new quantum technologies such as fault-tolerant quantum computers. To fulfil this promise, efficient and general methods are needed to discover and classify new topological phases of matter.
Externí odkaz:
http://arxiv.org/abs/1811.12630
Publikováno v:
In Neurocomputing 27 November 2021 466:128-138
Autor:
Ming, Yurui, Ding, Weiping, Pelusi, Danilo, Wu, Dongrui, Wang, Yu-Kai, Prasad, Mukesh, Lin, Chin-Teng
Publikováno v:
In Applied Soft Computing Journal November 2019 84
Autor:
Ming, Yurui1 (AUTHOR), Pelusi, Danilo2 (AUTHOR), Fang, Chieh-Ning1 (AUTHOR), Prasad, Mukesh1 (AUTHOR), Wang, Yu-Kai1 (AUTHOR), Wu, Dongrui3 (AUTHOR), Lin, Chin-Teng1,4 (AUTHOR) Chin-Teng.Lin@uts.edu.au
Publikováno v:
Neural Computing & Applications. Jun2020, Vol. 32 Issue 12, p7611-7621. 11p.