Zobrazeno 1 - 10
of 1 152
pro vyhledávání: '"R. Bhushan"'
Autor:
Jiangong Zhu, Yixiu Wang, Yuan Huang, R. Bhushan Gopaluni, Yankai Cao, Michael Heere, Martin J. Mühlbauer, Liuda Mereacre, Haifeng Dai, Xinhua Liu, Anatoliy Senyshyn, Xuezhe Wei, Michael Knapp, Helmut Ehrenberg
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-10 (2022)
Accurate capacity estimation is crucial for lithium-ion batteries' reliable and safe operation. Here, the authors propose an approach exploiting features from the relaxation voltage curve for battery capacity estimation without requiring other previo
Externí odkaz:
https://doaj.org/article/0d2de2d9a30c498789b5e17adb2f089b
Autor:
Wang, Shuyuan, Duan, Jingliang, Lawrence, Nathan P., Loewen, Philip D., Forbes, Michael G., Gopaluni, R. Bhushan, Zhang, Lixian
Model-free reinforcement learning (RL) is inherently a reactive method, operating under the assumption that it starts with no prior knowledge of the system and entirely depends on trial-and-error for learning. This approach faces several challenges,
Externí odkaz:
http://arxiv.org/abs/2410.16821
Autor:
Lawrence, Nathan P., Loewen, Philip D., Wang, Shuyuan, Forbes, Michael G., Gopaluni, R. Bhushan
Willems' fundamental lemma enables a trajectory-based characterization of linear systems through data-based Hankel matrices. However, in the presence of measurement noise, we ask: Is this noisy Hankel-based model expressive enough to re-identify itse
Externí odkaz:
http://arxiv.org/abs/2404.15512
Publikováno v:
Climate of the Past, Vol 14, Pp 1331-1343 (2018)
Somali upwelling history has been reconstructed for the last 18.5 ka BP based on biogenic silica fluxes estimated from a sediment core retrieved from the western Arabian Sea. Surface winds along the east African coast during the southwest mons
Externí odkaz:
https://doaj.org/article/30019c5bd82c43fab0768d78169adeb9
Autor:
Lawrence, Nathan P., Damarla, Seshu Kumar, Kim, Jong Woo, Tulsyan, Aditya, Amjad, Faraz, Wang, Kai, Chachuat, Benoit, Lee, Jong Min, Huang, Biao, Gopaluni, R. Bhushan
Publikováno v:
Control Engineering Practice 2024
With the rise of deep learning, there has been renewed interest within the process industries to utilize data on large-scale nonlinear sensing and control problems. We identify key statistical and machine learning techniques that have seen practical
Externí odkaz:
http://arxiv.org/abs/2401.13836
Autor:
Lawrence, Nathan P., Loewen, Philip D., Wang, Shuyuan, Forbes, Michael G., Gopaluni, R. Bhushan
Publikováno v:
Automatica 2024
We propose a framework for the design of feedback controllers that combines the optimization-driven and model-free advantages of deep reinforcement learning with the stability guarantees provided by using the Youla-Kucera parameterization to define t
Externí odkaz:
http://arxiv.org/abs/2310.14098
Autor:
Wang, Shuyuan, Loewen, Philip D., Lawrence, Nathan P., Forbes, Michael G., Gopaluni, R. Bhushan
Publikováno v:
IFAC-PapersOnLine 2023
We adapt reinforcement learning (RL) methods for continuous control to bridge the gap between complete ignorance and perfect knowledge of the environment. Our method, Partial Knowledge Least Squares Policy Iteration (PLSPI), takes inspiration from bo
Externí odkaz:
http://arxiv.org/abs/2304.13223
Autor:
Lawrence, Nathan P., Loewen, Philip D., Wang, Shuyuan, Forbes, Michael G., Gopaluni, R. Bhushan
Publikováno v:
IFAC-PapersOnLine 2023
We propose a framework for the design of feedback controllers that combines the optimization-driven and model-free advantages of deep reinforcement learning with the stability guarantees provided by using the Youla-Kucera parameterization to define t
Externí odkaz:
http://arxiv.org/abs/2304.03422
Publikováno v:
Applied Energy, Volume 333, 1 March 2023, 120633
The carbon-capturing process with the aid of CO2 removal technology (CDRT) has been recognised as an alternative and a prominent approach to deep decarbonisation. However, the main hindrance is the enormous energy demand and the economic implication
Externí odkaz:
http://arxiv.org/abs/2301.07768
A significant portion of the effort involved in advanced process control, process analytics, and machine learning involves acquiring and preparing data. Literature often emphasizes increasingly complex modelling techniques with incremental performanc
Externí odkaz:
http://arxiv.org/abs/2211.06440