Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Ruben Glatt"'
Autor:
Guilherme Vieira Hollweg, Van-Hai Bui, Felipe Leno Da Silva, Ruben Glatt, Shivam Chaturvedi, Wencong Su
Publikováno v:
IEEE Open Access Journal of Power and Energy, Vol 10, Pp 629-642 (2023)
This paper presents a novel approach for grid-injected current control of DC-AC converters using a robust model reference adaptive controller (RMRAC) with deep symbolic optimization (DSO). Grid voltages are known to be time-varying and can contain di
Externí odkaz:
https://doaj.org/article/75de0279ee0b46df9751339c52bd5c38
Autor:
Van-Hai Bui, Fangyuan Chang, Wencong Su, Mengqi Wang, Yi Lu Murphey, Felipe Leno Da Silva, Can Huang, Lingxiao Xue, Ruben Glatt
Publikováno v:
IEEE Access, Vol 10, Pp 78702-78712 (2022)
The optimal design of power converters often requires a huge number of simulations and numeric analyses to determine the optimal parameters. This process is time-consuming and results in a high computational cost. Therefore, this paper proposes a dee
Externí odkaz:
https://doaj.org/article/759f5922d36046dcb2cc1286ea4c99c0
Autor:
Ruben Glatt, Felipe Leno da Silva, Reinaldo Augusto da Costa Bianchi, Anna Helena Reali Costa
Publikováno v:
Federated and Transfer Learning ISBN: 9783031117473
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::981553f667e0de90bb9fdff8d87d14f4
https://doi.org/10.1007/978-3-031-11748-0_14
https://doi.org/10.1007/978-3-031-11748-0_14
Autor:
Edward Rusu, William A. Dawson, Braden Soper, Ruben Glatt, Felipe Leno da Silva, Ryan Goldhahn
Publikováno v:
BuildSys@SenSys
The ongoing industrialization and rising technology adoption around the world are leading to ever higher energy consumption. The benefits of electrification are enormous, but the growing demand also comes with challenges with respect to associated gr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d1a56cb9ecb149ebac377c298e1dd6ae
https://doi.org/10.1145/3486611.3488732
https://doi.org/10.1145/3486611.3488732
Publikováno v:
LatinX in AI at International Conference on Machine Learning 2021.
Although Machine Learning algorithms are solving tasks of ever-increasing complexity, gathering data and building training sets remains an error prone, costly, and difficult problem. However, reusing knowledge from related previously-solved tasks ena
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7ba775d36d3b4304950fd7deabe8bd46
https://doi.org/10.52591/lxai202107242
https://doi.org/10.52591/lxai202107242
Publikováno v:
Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual)
Universidade de São Paulo (USP)
instacron:USP
Universidade de São Paulo (USP)
instacron:USP
Having the ability to solve increasingly complex problems using Reinforcement Learning (RL) has prompted researchers to start developing a greater interest in systematic approaches to retain and reuse knowledge over a variety of tasks. With Case-base
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3136489438dafac861f4d977b77d0f9a
Autor:
Ruben Glatt
Publikováno v:
Biblioteca Digital de Teses e Dissertações da USP
Universidade de São Paulo (USP)
instacron:USP
Universidade de São Paulo (USP)
instacron:USP
With the rise of Deep Learning the field of Artificial Intelligence (AI) Research has entered a new era. Together with an increasing amount of data and vastly improved computing capabilities, Machine Learning builds the backbone of AI, providing many
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4187075fd67a420e6cb3335503b8c280
https://doi.org/10.11606/t.3.2019.tde-18092019-074805
https://doi.org/10.11606/t.3.2019.tde-18092019-074805
Autor:
Ruben Glatt, Anna Helena Reali Costa, Rodrigo Cesar Bonini, Felipe Leno Da Silva, Edison Spina
Publikováno v:
BRACIS
Reinforcement Learning is a successful yet slow technique to train autonomous agents. Option-based solutions can be used to accelerate learning and to transfer learned behaviors across tasks by encapsulating a partial policy. However, commonly these
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6634c8a079fc8ad3e17adb66b2d79253
https://doi.org/10.1109/bracis.2018.00027
https://doi.org/10.1109/bracis.2018.00027
Publikováno v:
Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual)
Universidade de São Paulo (USP)
instacron:USP
Universidade de São Paulo (USP)
instacron:USP
Reinforcement learning (RL) is a widely known technique to enable autonomous learning. Even though RL methods achieved successes in increasingly large and complex problems, scaling solutions remains a challenge. One way to simplify (and consequently
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::02c12f9527d97f9aba4cc2ac64975d26
https://pubmed.ncbi.nlm.nih.gov/29990289
https://pubmed.ncbi.nlm.nih.gov/29990289