Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Givaki, Kamyar"'
Autor:
Hojabr, Reza, Givaki, Kamyar, Pourahmadi, Kossar, Nooralinejad, Parsa, Khonsari, Ahmad, Rahmati, Dara, Najafi, M. Hassan
Publikováno v:
2020 IEEE International Symposium on Circuits and Systems (ISCAS), 2020, pp. 1-5
Emerging intelligent embedded devices rely on Deep Neural Networks (DNNs) to be able to interact with the real-world environment. This interaction comes with the ability to retrain DNNs, since environmental conditions change continuously in time. Sto
Externí odkaz:
http://arxiv.org/abs/2010.05197
Autor:
Givaki, Kamyar, Salami, Behzad, Hojabr, Reza, Tayaranian, S. M. Reza, Khonsari, Ahmad, Rahmati, Dara, Gorgin, Saeid, Cristal, Adrian, Unsal, Osman S.
Deep Neural Networks (DNNs) are inherently computation-intensive and also power-hungry. Hardware accelerators such as Field Programmable Gate Arrays (FPGAs) are a promising solution that can satisfy these requirements for both embedded and High-Perfo
Externí odkaz:
http://arxiv.org/abs/2001.00053
Akademický článek
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Akademický článek
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Publikováno v:
8th International Conference on Renewable Power Generation
A passive machine learning based technique to estimate the impedance of the power grid at the point of common coupling of a converter interfaced distributed generation source is proposed. The proposed method is based on supervised learning and provid
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=core_ac_uk__::55ca20bcd50f46f8b9d96ff8113bdd11
https://strathprints.strath.ac.uk/69622/1/Givaki_etal_RPG2019_Machine_learning_based_impedance_estimation_in_power_system.pdf
https://strathprints.strath.ac.uk/69622/1/Givaki_etal_RPG2019_Machine_learning_based_impedance_estimation_in_power_system.pdf
Autor:
Hojabr, Reza, Givaki, Kamyar, Reza Tayaranian, S. M., Esfahanian, Parsa, Khonsari, Ahmad, Rahmati, Dara, Najafi, M. Hassan
Publikováno v:
DAC: Annual ACM/IEEE Design Automation Conference; 2019, Issue 56, p1021-1026, 6p
