Main Teachers Inchin Alexander

Inchin Alexander


Associate professor
The Department of «Smart technologies in engineering»

Candidate of technical Sciences


Professional experience

Academic:
Work in this organization
From 2014 to present: Position and place of work in this organization
From 2014 to present: Artificial intelligence system
From 2014 to present: Full time employment
Previous places of work in educational organizations:
Non-academic:

1966-2006: Laboratory assistant - head of the laboratory of the Institute of Mathematics and Mechanics of the Academy of Sciences of the Republic of Kazakhstan
2006-2008: Head of department - JSC "NC "Kazakhstan GaryshSapary"
2008: Deputy Director - JSC "National Center for Space Research and Technology".

Education

1968 - 1973 Kazakh State University named after S.M. Kirova. Mathematician.

1975 - 1997 European University ADCOGITANTUMETAGENDIUMHOMONATUSEST.. Mathematician.

Scientific interests

  1. 2009-2011 Responsible executor of the project "Develop scientific and methodological support and information processing technologies for a space system for scientific purposes"
  2. 2012-2014 Responsible executor of the project "Develop scientific, methodological and technological support for the creation, testing and operation of the target equipment of the scientific and technological spacecraft"
  3. 2015-2017 Responsible executor of the project "Develop software and mathematical support for an experimental sample of the nanosatellite onboard control complex"
  4. 2015-2017 Head of the grant project "Create an electromagnetic measurement system for lightning direction finding and research of atmospheric-lithospheric relations"


Publications

1. A.Lozbin, M.Shpadi, A.Inchin, P.Inchin, Yu.Shpadi, G.Ayazbayev, L.Mailibayeva About the possibility of lithosphere-ionosphere electromagnetic coupling research with DIAS Software. The 32nd Progress in Electromagnetics Research Symposium in Moscow, Russia. August 19-23, 2012, p1.
2. A. S. Inchin, Yu. R. Shpadi, A. Yu. Lozbin, M. Yu. Shpadi, P. A. Inchin, G. M. Ayazbaev, R. Zh. Experimental sample and payload software for scientific and technological nanosatellite. Bulletin of NAEN No. 4, 2014 10p.
3. A.S. Inchin, Yu.R. Shpadi, M.Yu. Shpadi, A.Yu. Lozbin, P.A., Inchin, G.M. Ayazbaev, R.Zh. Bykaev, L.I.Mailibaeva. An experimental sample of the target equipment of the scientific and technological nanosatellite”. // Research of solar-terrestrial relations: Proceedings of the scientific session of the Section of solar-terrestrial relations of the Space Council of the Russian Academy of Sciences / Ed. A. A. Petrukovich. Moscow: IKI RAN, 2015, pp. 119–127.
4. AnatoliyLozbin, Alexander Inchin, Yuri Shpadi, PavelInchin, Maxim Shpadi, GalymzhanAyazbayev, RakhimBykayev, Lyudmila Mailibayeva Scientific Lightning Detection Network for Kazakhstan AGO Fall meeting, San Francisco 2015.
5. A. S. Inchin, A. Yu. Lozbin, Yu. R. Shpadi, P. A. Inchin, M. Yu. Shpadi, G. M. Ayazbaev, R. Zh. Detection Network for Kazakhstan and its possible application for Electric Power Industry 2-nd international conference "Information technologies in science and industry 2016" P. -1512

Disciplines taught


loT in industry: technology and security

The course deals with the problems of implementing IoT technology, data transfer to IoT, and interaction with Internet things. As a result of the training, the doctoral student can understand existing It technologies, design It systems, cloud platforms, create their own industrial IoT application that can ensure the functioning of many production cells, as well as perform work analysis.

BIG-DATA (Big Data Analytics)

The purpose of the discipline is to form students' practical skills of "mining", processing, analyzing large arrays of structured and unstructured data using statistical analysis and mathematical modeling methods, finding patterns and making forecasts for making effective management and business decisions, as well as conducting scientific research. As a result of the training, students will be able to work with rapidly incoming data in very large volumes, understand the basic concepts of big data.

Timetable of classes

Opening lessons