Zobrazeno 1 - 10
of 183
pro vyhledávání: '"kernel-based learning"'
Autor:
Ye, Qi
Publikováno v:
In Engineering Analysis with Boundary Elements 1 December 2024 169 Part A
Publikováno v:
IEEE Access, Vol 8, Pp 82131-82141 (2020)
Demand response (DR) aims at improving the reliability and efficiency of the power grids by shaping the power demand over time. Given that building energy consumption constitutes a significant portion of the overall grid load, building energy managem
Externí odkaz:
https://doaj.org/article/50bdcc1d083a45a2a5e38ae8257134b8
Autor:
Viviana Gómez-Orozco, Iván De La Pava, Andrés Álvarez-Meza, Mauricio A. Álvarez, Álvaro Orozco-Gutiérrez
Publikováno v:
Revista Facultad de Ingeniería Universidad de Antioquia, Iss 95 (2020)
Adjusting the stimulation parameters is a challenge in deep brain stimulation (DBS) therapy due to the vast number of different configurations available. As a result, systems based on the visualization of the volume of tissue activated (VTA) produced
Externí odkaz:
https://doaj.org/article/a739bd6819884ed881cd16680797189c
Autor:
Álvaro Orozco-Gutiérrez, Mauricio A. Álvarez-López, Andrés Marino Álvarez-Meza, Viviana Gómez-Orozco, Iván De La Pava Panche
Publikováno v:
Gómez-orozco, V, Pava, I D L, Álvarez-meza, A, Álvarez, M A & Orozco-Gutiérrez, Á 2020, ' A machine learning approach to support deep brain stimulation programming ', Revista Facultad de Ingenieria, no. 95, pp. 20-33 . https://doi.org/10.17533/udea.redin.20190729
Revista Facultad de Ingeniería, Iss 95, Pp 20-33 (2019)
Revista Facultad de Ingeniería, Iss 95, Pp 20-33 (2019)
Adjusting the stimulation parameters is a challenge in deep brain stimulation (DBS) therapy due to the vast number of different configurations available. As a result, systems based on the visualization of the volume of tissue activated (VTA) produced
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5370b6c85595d4d9efa8f4c046ffe7fd
https://doi.org/10.17533/udea.redin.20190729
https://doi.org/10.17533/udea.redin.20190729
Publikováno v:
International Journal of Control. 95:1668-1681
In many engineering applications, continuous-time models are preferred to discrete-time ones, in that they provide good physical insight and can be derived also from non-uniformly sampled data. How...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::95e67d23c1aa6d662f7426732a709aa9
https://doi.org/10.1080/00207179.2020.1868580
https://doi.org/10.1080/00207179.2020.1868580
Akademický článek
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Akademický článek
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Akademický článek
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Publikováno v:
IEEE Access, Vol 8, Pp 82131-82141 (2020)
Demand response (DR) aims at improving the reliability and efficiency of the power grids by shaping the power demand over time. Given that building energy consumption constitutes a significant portion of the overall grid load, building energy managem
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::55ad0e9ca96cbac23829902411cdf115
https://ieeexplore.ieee.org/document/9079854/
https://ieeexplore.ieee.org/document/9079854/
Autor:
Fabio Massimo Zanzotto, Antonella Spila, Piero Barbanti, Gabriella Egeo, Patrizia Ferroni, Alessandro Rullo, Raffaele Palmirotta, Noemi Scarpato, Luisa Fofi, Fiorella Guadagni
Publikováno v:
Computational and Structural Biotechnology Journal, Vol 18, Iss, Pp 1487-1496 (2020)
Computational and Structural Biotechnology Journal
Computational and Structural Biotechnology Journal
Graphical abstract
Highlights • Medication overuse is related to chronicization and medication-overuse headache. • Prediction of medication overuse (MO) is a challenge in the management of migraine. • Machine learning and random optimizati
Highlights • Medication overuse is related to chronicization and medication-overuse headache. • Prediction of medication overuse (MO) is a challenge in the management of migraine. • Machine learning and random optimizati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8417bad483ef751fb083731a2f1c4de8
https://doi.org/10.1016/j.csbj.2020.06.006
https://doi.org/10.1016/j.csbj.2020.06.006