Zobrazeno 1 - 10
of 63
pro vyhledávání: '"Quang P. Ha"'
Publikováno v:
Ecological Informatics, Vol 82, Iss , Pp 102750- (2024)
Sustainable development in cities requires advanced technologies for monitoring and estimating air pollution emissions, which directly affect the health of local inhabitants and residents in the neighborhoods. For this, low-cost sensors and informati
Externí odkaz:
https://doaj.org/article/e29442549c92470a86f6b4b978bafa63
Autor:
Huynh A. D. Nguyen, Quang P. Ha, Hiep Duc, Merched Azzi, Ningbo Jiang, Xavier Barthelemy, Matthew Riley
Publikováno v:
IEEE Access, Vol 11, Pp 35710-35725 (2023)
This paper presents a data fusion framework to enhance the accuracy of air-pollutant forecast in the state of New South Wales (NSW), Australia using deep learning (DL) as a core model. Here, we propose a long short-term memory Bayesian neural network
Externí odkaz:
https://doaj.org/article/af5a3ebfd83648f8a81b61403fcc50a1
Autor:
Huynh A. D. Nguyen, Quang P. Ha
Publikováno v:
IEEE Access, Vol 10, Pp 40051-40062 (2022)
This paper presents an Internet of Things-enabled low-cost wireless sensor network with newly-developed dependable schemes to improve reliability for monitoring air quality in suburban areas. The system features sensing units for router communication
Externí odkaz:
https://doaj.org/article/613a8a81afc84ab98ecf325e49732838
Autor:
Quang, Minh Ha
This work presents an explicit description of the Fisher-Rao Riemannian metric on the Hilbert manifold of equivalent centered Gaussian measures on an infinite-dimensional Hilbert space. We show that the corresponding quantities from the finite-dimens
Externí odkaz:
http://arxiv.org/abs/2310.10182
Publikováno v:
SAGE Open, Vol 14 (2024)
Nowadays, both academic scholars and decision makers are raising much attentions to underscore the meanings of social sustainability practices to the success of firms, particularly in the context of emerging countries. Under the stakeholder resource-
Externí odkaz:
https://doaj.org/article/0c3a8b5ed6a64d21b7e2781fca07f7c1
Autor:
Quang, Minh Ha
In this work, we present formulations for regularized Kullback-Leibler and R\'enyi divergences via the Alpha Log-Determinant (Log-Det) divergences between positive Hilbert-Schmidt operators on Hilbert spaces in two different settings, namely (i) cova
Externí odkaz:
http://arxiv.org/abs/2207.08406
Autor:
Quang, Minh Ha
This work studies finite sample approximations of the exact and entropic regularized Wasserstein distances between centered Gaussian processes and, more generally, covariance operators of functional random processes. We first show that these distance
Externí odkaz:
http://arxiv.org/abs/2104.12368
Autor:
Quang, Minh Ha
This work studies the convergence and finite sample approximations of entropic regularized Wasserstein distances in the Hilbert space setting. Our first main result is that for Gaussian measures on an infinite-dimensional Hilbert space, convergence i
Externí odkaz:
http://arxiv.org/abs/2101.01429
Autor:
Quang, Minh Ha
This work studies the entropic regularization formulation of the 2-Wasserstein distance on an infinite-dimensional Hilbert space, in particular for the Gaussian setting. We first present the Minimum Mutual Information property, namely the joint measu
Externí odkaz:
http://arxiv.org/abs/2011.07489
Autor:
Quang, Minh Ha
This work presents a parametrized family of distances, namely the Alpha Procrustes distances, on the set of symmetric, positive definite (SPD) matrices. The Alpha Procrustes distances provide a unified formulation encompassing both the Bures-Wasserst
Externí odkaz:
http://arxiv.org/abs/1908.09275