Aleksei Zhukov
Junior research fellow:
Baikal School of BRICS,
Industrial Mathematics Lab (since 2020)
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Education
Education, degrees
2011
Radiophysics and microelectronics, Irkutsk State University
Internships
2012
Metamodelling in Embedded systems, Real-Time and Embedded Systems group of Christian-AlbrechtsUniversitat
2016
Applications of Machine learning models for Wind Energy, Wind Energy department in University College Cork
Awards
URSI Young scientist award (2018)
Russian Foundation for Basic Research Grant for Young scientists (2018)
Languages
English
Professional Interests
Publications
A. V. Zhukov, Y. V. Yasyukevich, and A. E. Bykov, Gimli: Global ionospheric total electron content model based on machine learning, GPS Solutions, vol. 25, no. 1, pp. 1-9, 2021
D. Sidorov, A. Zhukov, D. Panasetsky, et al., Toward zero-emission hybrid ac/dc power systems with renewable energy sources and storages: A case study from lake baikal region, Energies, vol. 13, no. 5, p. 1226, 2020.
N. I. Voropai, N. V. Tomin, A. V. Zhukov, et al., A suite of intelligent tools for early detection and prevention of blackouts in power interconnections, Automation and Remote Control, vol. 79, no. 10, pp. 1741-1755, 2018.
A. Zhukov, N. Tomin, V. Kurbatsky, D. Sidorov, D. Panasetsky, and A. Foley, Ensemble methods of classification for power systems security assessment, Applied Computing and Informatics, 2017.
A. Zhukov and D. Sidorov, Modification of random forest based approach for streaming data with concept drift, Vestnik Yuzhno-Uarlskogo Universiteta, vol. 9, no. 4, pp. 86-95, 2016.
N. Tomin, A. Zhukov, D. Sidorov, V. Kurbatsky, D. Panasetsky, and V. Spiryaev, Random forest based model for preventing large-scale emergencies in power systems, International Journal of Artificial Intelligence, vol. 13, no. 1, pp. 211-228, 2015.
D. Sidorov, A. Zhukov, D. Panasetsky, et al., Toward zero-emission hybrid ac/dc power systems with renewable energy sources and storages: A case study from lake baikal region, Energies, vol. 13, no. 5, p. 1226, 2020.
N. I. Voropai, N. V. Tomin, A. V. Zhukov, et al., A suite of intelligent tools for early detection and prevention of blackouts in power interconnections, Automation and Remote Control, vol. 79, no. 10, pp. 1741-1755, 2018.
A. Zhukov, N. Tomin, V. Kurbatsky, D. Sidorov, D. Panasetsky, and A. Foley, Ensemble methods of classification for power systems security assessment, Applied Computing and Informatics, 2017.
A. Zhukov and D. Sidorov, Modification of random forest based approach for streaming data with concept drift, Vestnik Yuzhno-Uarlskogo Universiteta, vol. 9, no. 4, pp. 86-95, 2016.
N. Tomin, A. Zhukov, D. Sidorov, V. Kurbatsky, D. Panasetsky, and V. Spiryaev, Random forest based model for preventing large-scale emergencies in power systems, International Journal of Artificial Intelligence, vol. 13, no. 1, pp. 211-228, 2015.
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