Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Nanying Liang"'
Autor:
Jing Kai Sim, Kaichao William Xu, Yuyang Jin, Zhi Yu Lee, Yi Jie Teo, Pallavi Mohan, Lihui Huang, Yuan Xie, Siyi Li, Nanying Liang, Qi Cao, Simon See, Ingrid Winkler, Yiyu Cai
Publikováno v:
Applied Sciences, Vol 14, Iss 6, p 2559 (2024)
An up-and-coming concept that seeks to transform how students learn about and study complex systems, as well as how industrial workers are trained, metaverse technology is characterized in this context by its use in virtual simulation and analysis. I
Externí odkaz:
https://doaj.org/article/640017b28b9c4d33bc90345f3ffdd415
Publikováno v:
Robotics, Vol 13, Iss 2, p 22 (2024)
Accurate and complete 3D point clouds are essential in creating as-built building information modeling (BIM) models, although there are challenges in automating the process for 3D point cloud creation in complex environments. In this paper, an autono
Externí odkaz:
https://doaj.org/article/4bc766a4b64a4225a1d7c21924a4b08f
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 9475-9485 (2021)
The rapid development of remote sensing technology has brought abundant data support for deep learning based temperature forecasting research. However, recently proposed methods usually focus on the temporal relationship among temperature observation
Externí odkaz:
https://doaj.org/article/7ce6b59e3e174b2fbacf106049a8914f
Publikováno v:
Neurocomputing. 273:634-642
In this work, we propose a novel deep neural network referred to as Multi-Target Deep Neural Network (MT-DNN). We theoretically prove that different stable target models with shared learning paths are stable and can achieve optimal solutions respecti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::234ed71a384125c8ab6b93877ad70ca1
https://doi.org/10.1016/j.neucom.2017.08.044
https://doi.org/10.1016/j.neucom.2017.08.044
Publikováno v:
IFMBE Proceedings ISBN: 9783319129662
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::de20126ec8f6c38e36bb74d216a5c474
https://doi.org/10.1007/978-3-319-12967-9_20
https://doi.org/10.1007/978-3-319-12967-9_20
Publikováno v:
IEEE Transactions on Neural Networks. 17:1411-1423
In this paper, we develop an online sequential learning algorithm for single hidden layer feedforward networks (SLFNs) with additive or radial basis function (RBF) hidden nodes in a unified framework. The algorithm is referred to as online sequential
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d8b3ef3477fc9f2c87539473f1aa87ad
https://doi.org/10.1109/tnn.2006.880583
https://doi.org/10.1109/tnn.2006.880583
Autor:
Laurent Bougrain, Nanying Liang
Publikováno v:
Frontiers in Neuroscience
Frontiers in Neuroscience, 2012, 6 (91), ⟨10.3389/fnins.2012.00091⟩
Frontiers in Neuroscience, Frontiers, 2012, 6 (91), ⟨10.3389/fnins.2012.00091⟩
Frontiers in Neuroscience, Vol 6 (2012)
Frontiers in Neuroscience, 2012, 6 (91), ⟨10.3389/fnins.2012.00091⟩
Frontiers in Neuroscience, Frontiers, 2012, 6 (91), ⟨10.3389/fnins.2012.00091⟩
Frontiers in Neuroscience, Vol 6 (2012)
International audience; This article presents the method that won the brain-computer interface (BCI) competition IV addressed to the prediction of the finger flexion from electrocorticogram (ECoG) signals. ECoG-based BCIs have recently drawn the atte
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ad06eb8b1278c8983ff4d0212816da87
Autor:
Nanying Liang
A novel sequential learning algorihtm for training Single Hidden Layer Feedforward Neural Network (SLFN), Online Sequential Extreme Learning Machine (OS-ELM) is proposed. OS-ELM is based on the combination of Extreme Learning Machine (ELM) and the re
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ae4f42b01d3983c6cb086900e720f691
https://hdl.handle.net/10356/4601
https://hdl.handle.net/10356/4601
Publikováno v:
International journal of neural systems. 16(1)
In this paper, a recently developed machine learning algorithm referred to as Extreme Learning Machine (ELM) is used to classify five mental tasks from different subjects using electroencephalogram (EEG) signals available from a well-known database.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e5f8e15503ffd9cf8272bc51be1994f5
https://pubmed.ncbi.nlm.nih.gov/16496436
https://pubmed.ncbi.nlm.nih.gov/16496436
Autor:
Nanying Liang, Bougrain, Laurent
Publikováno v:
Frontiers in Neuroscience; Jun2012, Vol. 6, p1-6, 6p