Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Zhaobin Mo"'
Publikováno v:
Algorithms, Vol 16, Iss 6, p 305 (2023)
For its robust predictive power (compared to pure physics-based models) and sample-efficient training (compared to pure deep learning models), physics-informed deep learning (PIDL), a paradigm hybridizing physics-based models and deep neural networks
Externí odkaz:
https://doaj.org/article/7d79cf8e00a2417c98c286cb90c0faae
Autor:
Rolando Bautista-Montesano, Renato Galluzzi, Zhaobin Mo, Yongjie Fu, Rogelio Bustamante-Bello, Xuan Di
Publikováno v:
Applied Sciences, Vol 13, Iss 8, p 5089 (2023)
The development of more sustainable urban transportation is prompting the need for better energy management techniques. Connected electric vehicles can take advantage of environmental information regarding the status of traffic lights. In this contex
Externí odkaz:
https://doaj.org/article/a2893375625548b2831241fd59d98c58
Publikováno v:
Games, Vol 14, Iss 1, p 13 (2023)
How to sample training/validation data is an important question for machine learning models, especially when the dataset is heterogeneous and skewed. In this paper, we propose a data sampling method that robustly selects training/validation data. We
Externí odkaz:
https://doaj.org/article/ed72d7f32ba54d9cb1642353b1b0af4b
Publikováno v:
IEEE Transactions on Intelligent Transportation Systems. 23:11688-11698
Traffic state estimation (TSE) bifurcates into two categories, model-driven and data-driven (e.g., machine learning, ML), while each suffers from either deficient physics or small data. To mitigate these limitations, recent studies introduced a hybri
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a21c50f76817cdac59197c730cc58816
https://doi.org/10.1109/tits.2021.3106259
https://doi.org/10.1109/tits.2021.3106259
Publikováno v:
Algorithms; Volume 16; Issue 6; Pages: 305
For its robust predictive power (compared to pure physics-based models) and sample-efficient training (compared to pure deep learning models), physics-informed deep learning (PIDL), a paradigm hybridizing physics-based models and deep neural networks
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=multidiscipl::68fe31c26215f573e97a56288503acbf
Publikováno v:
Machine Learning and Knowledge Discovery in Databases ISBN: 9783031264085
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f8d83e38e0387dba595410dd8a5a565d
https://doi.org/10.1007/978-3-031-26409-2_20
https://doi.org/10.1007/978-3-031-26409-2_20
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 35:540-547
Traffic state estimation (TSE) reconstructs the traffic variables (e.g., density or average velocity) on road segments using partially observed data, which is important for traffic managements. Traditional TSE approaches mainly bifurcate into two cat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::782ffd9bf3d81ae85f99a35324321a85
https://doi.org/10.1609/aaai.v35i1.16132
https://doi.org/10.1609/aaai.v35i1.16132
Autor:
Xuan Di, Yiqiao Yin, Yongjie Fu, Zhaobin Mo, Shaw-Hwa Lo, Carolyn DiGuiseppi, David W. Eby, Linda Hill, Thelma J. Mielenz, David Strogatz, Minjae Kim, Guohua Li
Publikováno v:
Artificial Intelligence in Medicine. 138:102510
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a869a850cd2d78960a89747d3aa70ef7
https://doi.org/10.1016/j.artmed.2023.102510
https://doi.org/10.1016/j.artmed.2023.102510
Autor:
Zhe Li, Anhao Zuo, Zhaobin Mo, Mu Lin, Chengyu Wang, Jianbo Zhang, Markus H. Hofmann, Andreas Jossen
Publikováno v:
SSRN Electronic Journal.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4a98249f3931209d0317a689a06bfec4
https://doi.org/10.2139/ssrn.4201713
https://doi.org/10.2139/ssrn.4201713
Autor:
Zhe Li, Anhao Zuo, Zhaobin Mo, Mu Lin, Chengyu Wang, Jianbo Zhang, Markus H. Hofmann, Andreas Jossen
Publikováno v:
Cell Reports Physical Science. 3:101154
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7ee7d0ebbff89f1e5f242d1a372ab030
https://doi.org/10.1016/j.xcrp.2022.101154
https://doi.org/10.1016/j.xcrp.2022.101154