Ievgen Zaitsev Editor at IgMin Research
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Biography
Ievgen Zaitsev born in Kiev, Ukraine, 1983. Received the B.Tech. and M.Tech degrees in measurement engineering from National Technical University of Ukraine "Kyiv Polytechnic Institute", Kyiv, Ukraine, in 2005 and 2007, respectively. The Ph. D. degree in devices and methods of measurement electric and magnetic quantities from Institute of Electrodynamics, National Academy of Sciences of Ukraine, Kyiv, Ukraine, in 2012.
The Doctor of science degree in computer systems and components from Institute of Electrodynamics, NAS of Ukraine, Kyiv, Ukraine, in 2020. He is currently head of the department of theoretical electrical engineering and diagnostics of electrical equipment, Institute of Electrodynamics, National Academy of Sciences of Ukraine, Kyiv, Ukraine. He holds several patents, is the author of over 200 journals, conference papers, books and book chapters.
Research Interest
Sensor design and integration for power equipment fault diagnosing, renewable energy sources, distributed generation, smart grid, optoelectronics, device modelling and fabrication
Editor
Work Details
Department Head
Institute of Electrodynamics of the National Academy of Sciences of Ukraine
Theoretical electrical engineering and diagnostics of electrical equipment
Ukraine
0000-0003-3303-471X Contribution by Topic Area
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Which articles are now trending?
Research Articles
- What is the Hybrid Lognormal Distribution?
- Synergistic Assessment of Supplementation of Ascorbic Acid and Massularia acuminata Extracts on Serum Electrolyte and Lipid Profile Indices of Dyslipidemia in Adult Wistar Rats Exposed to Aluminum Chloride Toxicity
- Scientific Productivity of the Internet of Things (IoT) in 2011-2025: A Bibliometric Analysis
- Prevalence of Diabetic Retinopathy among Self-reported Newly Diagnosed Diabetics
- Gaussian-Transform for the Dirac Wave Function and its Application to the Multicenter Molecular Integral Over Dirac Wave Functions for Solving the Molecular Matrix Dirac Equation
- Enhancing Material Property Predictions through Optimized KNN Imputation and Deep Neural Network Modeling
