Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Mohammad Razeghi"'
Autor:
Farnaz Farmani, Neda Soleimani, Mohammad Razeghi, Amir Zamani, Sahand Mohammadzadeh, Davoud Soleimani
Publikováno v:
Clinical Case Reports, Vol 11, Iss 7, Pp n/a-n/a (2023)
Key Clinical Message In contrast to intestinal balantidiasis, which is widespread throughout the world, urinary balantidiasis is uncommon. It often affects people with underlying diseases, and acute infections may be fatal. Even though urine is not t
Externí odkaz:
https://doaj.org/article/e7ff04c9d82f4cdca6ead731174da7dd
Autor:
Xuyang Yan, Mohammad Razeghi-Jahromi, Abdollah Homaifar, Berat A. Erol, Abenezer Girma, Edward Tunstel
Publikováno v:
IEEE Access, Vol 7, Pp 184985-185000 (2019)
As an unsupervised learning technique, clustering can effectively capture the patterns in a data stream based on similarities among the data. Traditional data stream clustering algorithms either heavily depend on some prior knowledge or predefined pa
Externí odkaz:
https://doaj.org/article/c0bc17ab9e0446309b2128394b36c8a8
Publikováno v:
2023 IEEE PES Grid Edge Technologies Conference & Exposition (Grid Edge).
Publikováno v:
2022 IEEE Power & Energy Society General Meeting (PESGM).
Publikováno v:
2021 IEEE Conference on Control Technology and Applications (CCTA).
Publikováno v:
IEEE/CAA Journal of Automatica Sinica. 7:39-47
In this paper, the car-like robot kinematic model trajectory tracking and control problem is revisited by exploring an optimal analytical solution which guarantees the global exponential stability of the tracking error. The problem is formulated in t
Publikováno v:
2020 IEEE International Conference on Power Systems Technology (POWERCON).
An algorithm for distributed optimal voltage regulation of distribution networks with distributed generators (DGs) at the grid edge is proposed in the paper. We first introduce a distributed recursive algorithm to estimate the sensitivity parameters
Autor:
Abdollah Homaifar, Xuyang Yan, Mohammad Razeghi-Jahromi, Berat A. Erol, Edward Tunstel, Abenezer Girma
Publikováno v:
IEEE Access, Vol 7, Pp 184985-185000 (2019)
As an unsupervised learning technique, clustering can effectively capture the patterns in a data stream based on similarities among the data. Traditional data stream clustering algorithms either heavily depend on some prior knowledge or predefined pa
Publikováno v:
Complex Systems. 27:63-84